esa-sceva/satcom-chunk-collection huggingface dataset:
## 12 Simulation and Results  
This section illustrates the effectiveness of the proposed system model through two case studies: adaptive and opportunistic algorithm modes. The proposed multi-objective algorithm is simulated in NS-3 along with the support of the AquaSim framework. The results of the proposed method are analyzed and compared with the baseline schemes, e.g., RSMB, EEPDBR, UDB, 3 DUL, and UL, respectively. The rationale for choosing these schemes lies in their similar nature of localization, which facilitates the scheduling and appropriate positioning of nodes. The extensive experimental setup connects 700 nodes that start from 50 until it reaches its limits, like 700 nodes.  
In many UASN simulation models, nodes are randomly distributed within a 3D space (with defined boundaries for the X, Y, and Z coordinates). This approach is often used for simplicity and generalization, as it does not require any prior knowledge of the underwater environment. A 3D environment with 1600 \(\times\) 1000 \(\times\) 1000 dimensional area is used with acoustic link 1500 m/s. All of the other important parameters like data packet size, data rates, transmission range, initial energy, and beacon message size are listed in Table 2.
# High Frequency Transformers for Solid-State Transformer Applications
## 4 Simulation Results  
### Introduction  
The application under study consists in supplying a fast charger of an electric vehicle from the electrical network provided by the energy distributors. Furthermore, it is assumed that there is power bidirectionality. Figure 10 shows the generic block diagram that summarizes the various elements inherent to the application.  
The present study focuses only on the \"Module\", which consists of the two converters and the HFT, as represented generically in Figure 7. The main considerations assumed are as follows:  
* \"Three-phase bridge rectifiers\", fed from the conventional three-phase grid (400 V 50 Hz), composed of fully controlled semiconductors. For simulation purposes, consider an ideal DC voltage source of equal effective value;
* To simplify the analysis, the power requested by the \"Charger/Battery\" will be considered as a pure resistive load with equal power value. Consider that the charger should be powered with 1000 V DC.  
Based on the flowchart presented in Figure 4 and the above considerations, Tables 2 and 3 show the main dimensioned parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Value** \\ \hline \(S\) & Apparent power & 50 kVA \\ \hline \(V_{P}\) & Effective voltage applied to the primary & 530 V \\ \hline \(V_{S}\) & Effective voltage applied to the secondary & 1 kV \\ \hline \(f\) & Operating frequency & 1 kHz \\ \hline \(\Delta T\) & Admissible temperature variation & 35.0 \({}^{\circ}\)C \\ \hline \multicolumn{3}{c}{Magnetic core: core type, amorphous} \\ \multicolumn{3}{c}{material, manufacturer Transmart, model} \\ \multicolumn{3}{c}{CACC-3604} \\ \hline \(B_{Sat}\) & Saturation flux & 1.56 T \\ \hline \(B_{Opt}\) & Optimum saturation flux & 0.59 T \\ \hline \(A_{P}\) & Value concerning the model CACC-3604 & 3563.5 cm\({}^{4}\) \\ \hline \(k_{f}\) & Stacking factor & 0.9 \\ \hline \(N_{P}\) & Number of primary coils & 33 \\ \hline \(N_{s}\) & Number of secondary windings & 61 \\ \hline \(J_{0}\) & Current density & 0.81 A/mm\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main dimension parameters.  
Figure 10: Generic application diagram.  
For the constitution of the windings, we used the characteristics of the conductors provided by the manufacturer New England Wire Technologies. The size of the windings must be compatible with the size of the magnetic core chosen; Table 3 summarizes this.  
For the previous data, we obtain a value of theoretical power losses in the iron of 98.5W and in the copper of 526.5W, for calculation purposes until the 13 th harmonic. This results in a theoretical efficiency of 98.8%.  
The parameters of the HFT, which respond to the equivalent scheme presented in Figure 6, are shown in Table 4. The need to add a capacitor in parallel with the load and an inductance in series with the transformer were verified to improve the behavior/performance of the controller and consequently of the system to be modeled.
# Experimental Study on Phase Scintillation of Optical Transmission in Atmospheric Turbulence
## 3 Experimental Measurements
Under light rain (Figure 5c), heavy rain (Figure 5d), dust storm (Figure 5e), and snowy (Figure 5f) conditions, the slope vectors exhibit significant disturbances, indicating a large number of high-order aberrations and complex distortions. Overall, the wavefront distortion is minimal under sunny (Figure 5a) and cloudy (Figure 5b) conditions, with defocus aberration being the primary type. The curvature is smooth, and the slope vectors are regular, indicating minimal high-order aberrations, which have the least impact on optical wireless communication. The wavefront distortion is significant  
Figure 6: Wavefront phase (left) and slope (right) in link 2 under different weather conditions. Figure 7: Wavefront phase (left) and slope (right) in link 3 under different weather conditions. under light rain (Figure 5c), heavy rain (Figure 5d), dust storm (Figure 5e), and snowy (Figure 5f) conditions, with irregular curvature. The slope vectors show obvious disturbances and irregularities. Particularly under heavy rain (Figure 5d) and snowy (Figure 5f) conditions, the high-order aberrations increase significantly. In link 2, as shown in Figure 6, the wavefront phase and slope diagrams are similar to those in link 1, with the wavefront phase diagrams under different weather conditions showing a convex parabolic shape. The slope vectors generally radiate outward from the center. In contrast, in link 3, shown in Figure 7a,b, the wavefront phase diagrams exhibit a concave parabolic shape. The reasons for this phenomenon are as follows. As the transmission distance increases, the effect of atmospheric turbulence on the wavefront becomes more significant. Phase distortions caused by turbulence accumulate with an increasing propagation distance, leading to more complex and severe phase variations. For a transmission distance of 10.2 km, the wavefront may experience stronger atmospheric turbulence, resulting in more pronounced concave distortions. Additionally, as the transmission distance increases, the beam gradually diffuses, reducing the coherence of the beam and degrading the quality of the wavefront. This diffusion effect can cause the wavefront phase diagram to exhibit concave or other complex forms of distortion. Changes in terrain can also cause variations in local airflows, particularly in long-distance transmission, where the wavefront may be affected by the turbulence formed by different terrains. In summary, the concave shape is mainly due to the cumulative effects of atmospheric turbulence, beam diffusion, and terrain conditions. At a transmission distance of 10.2 km, these factors become more pronounced, leading to the appearance of concave distortions in the wavefront.
## 3 Results
### Compressive Behaviour  
#### 3.2.1 Elastic Modulus and Yield Strength  
The specific elastic moduli and yield strengths (Figure 14) were determined from the stress-strain data for compression in the x (Figure 15a,b) and y directions (Figure 15c,d). Orientations for compression testing are given in Figure 6. The yield strength was determined using the 0.2% offset method. During compression in the y direction, all the samples except sample 3 exhibited sudden collapse by shearing of the layer either near top or bottom of the sample. Sample 3 did not reach its yield point within the load limit of the machine (100 kN), and, therefore, no yield strength data point is provided for sample 3 in Figure 14.  
Figure 12: Outer-edge porosity of struts.  
Figure 13: Dross formation on down skin surfaces of lattices.  
There is minimal variation in Ex (0.89 \(\pm\) 0.02 GPa-1.91 \(\pm\) 0.05 GPa, a range of 1.02 \(\pm\) 0.07 GPa) across the different auxetic structures, whereas Ey varies considerably (from 0.72 \(\pm\) 0.02 GPa-5.2 \(\pm\) 0.1 GPa, a range of 4.4 \(\pm\) 0.1 GPa). There is considerable variation in both ox and ox, each varying similarly with structure type.  
Buckling of the auxetic structures is observed in Figure 15, and this is the common mode of collapse, and failure, of an auxetic structure [43], who were able to provide a model the buckling mode for three cell edges for conventional hexagonal honeycombs of uniform thickness edges under uniaxial compression, concluding that buckling became more likely as density was reduced. Their model was extended by [PERSON] and [PERSON] to other cell geometries, including square, triangular, Kagome and diamond [44]. Recent attempts have been made to make auxetic structures more resilient to buckling collapse, using reinforced structures such as the rhombic reinforced normal re-entrant hexagonal honeycomb (NRHH), which displayed improved buckling strength over the unreinforced NRHH structure [45].
## 4 Methodology
### Fuzzy TOPSIS
\right\}\,i=1,\ 2,\ \ldots,\ n \tag{4}\]  
Step 2: The normalized fuzzy weighted decision matrix is calculated by multiplying the weights of the criteria \(\widetilde{w}_{j}\) by the fuzzified normalized scores \(\widetilde{\times}_{ij}\) as  
\[\widetilde{V}=\left[\widetilde{\times}_{ij}\right]_{n\times p}\] (5a) where \[\widetilde{\times}_{ij}=\widetilde{\times}_{ij}.\widetilde{w}_{j} \tag{5b}\] \[\widetilde{\times}_{ij}=\left(l_{ij}\times l_{j},\ m_{ij}\times m_{j},\ u_{ij} \times u_{j}\right)\]  
Step 3: The fuzzy positive ideal solution (FPIs: the best condition, \(A^{+}\)) and the fuzzy negative ideal solution (FNIS: the worst condition, \(A^{-}\)) are defined using the following equations:  
\[A^{+}=\left\{\widetilde{v}_{1}^{+},\widetilde{v}_{2}^{+},\ldots,\widetilde{v} _{p}^{+}\right\} \tag{6a}\] \[A^{-}=\left\{\widetilde{v}_{1}^{-},\widetilde{v}_{2}^{-},\ldots,\widetilde{v} _{p}^{-}\right\} \tag{6b}\]  
where \(\widetilde{v}_{j}^{+}\) and \(\widetilde{v}_{j}^{-}\) correspond to'very high' and'very low' on the linguistical scale. Step 4: The distance \(s_{i}^{+}\) and \(s_{i}^{-}\) of each alternative from \(\widetilde{v}_{j}^{+}\) and \(\widetilde{v}_{j}^{-}\) can now be calculated as follows:  
\[s_{i}^{+}=\sqrt{\sum_{j=1}^{p}\left(\widetilde{v}_{ij}-\widetilde{v}_{j}^{+} \right)^{2}},\;\;i=1,\,2,\,\ldots,\,n \tag{7a}\]  
\[s_{i}^{-}=\sqrt{\sum_{j=1}^{p}\left(\widetilde{v}_{ij}-\widetilde{v}_{j}^{-} \right)^{2}},\;\;i=1,\,2,\,\ldots,\,n \tag{7b}\]  
This step indicates how near or far the route is to the most and least accessible conditions. Step 5: The relative closeness coefficient \(\widetilde{C}_{i}\) of each alternative is calculated as follows:  
\[\widetilde{C}_{i}=\frac{s_{i}^{+}}{s_{i}^{+}+s_{i}^{-}}\,i=1,\,2,\,\ldots,\,n \tag{8}\]  
Step 6: The calculated fuzzy relative closeness coefficient \(\widetilde{C}_{i}\) is defuzzified by the following Equation:  
\[P\left(\widetilde{M}\right)=M=\frac{l+4m+u}{6} \tag{9}\]  
Step 7: Finally, the \(ACI\) for each route is calculated as follows:  
\[ACI=1-M \tag{10}\]  
As the \(ACI\) of a route approaches 1, it moves away from the FNIS \((A^{-})\) and toward the FPIS \((A^{+})\). Thus, a higher \(ACI\) value is the best case, so the index is referred to as a level of accessibility. The \(ACI\) will used as a cost value in calculating the cost of accessible routes for disabled persons vehicle users. The pseudo-code of fuzzy TOPSIS algorithms is attached as Appendix A.
## 4 Bibliometric Analysis Results
### Predictive Maintenance
#### 4.1.2 Period 2000-2008  
First, research fronts and lead documents are extracted analogous to the way described in Section 4.1.1 (cf. Table A1). Some research front labels from the previous period can be reused, while new clusters are presented in the following. The topic of _predictive models_ (1) bundles general approaches to model and improve maintenance approaches, including even basic ideas (e.g., corporations between firms for data exchange). In the previous period, the keyword analysis recognized _monitoring_ (3) as being important, but no further cluster were identified for monitoring. In turn, during the second period, there exists a cluster with a focus on visualization. Third, when considering maintenance, costs constitute an important factor as well. The cluster of _cost-based models_ (4) emphasizes the costs for exchanging and maintaining machine parts. This cluster can be seen in contrast to the approach of _reliability-centered maintenance_. Concerning abrasion and the condition of cables, the cluster _cable diagnostic_ (8) discusses solutions. With growing complexity of maintenance systems, the _management_ (9) aspect is evolving accordingly. In this cluster, the literature stresses the organization of maintenance strategies as well as the evaluation of their performance. Next, research on _motor fault prediction_ (10) grew forming a cluster. A widely used method in this context is the _root cause analysis_. Moreover, a new cluster denoted as _generic approach_ (11) was found. Thereby, in most papers data-mining or machine-learning methods are utilized to enable adaptive maintenance systems. Finally, the cluster _sensor data_ (13) deals with the integration of sensors into maintenance systems. A visualization of the research fronts corresponding to Table A1 is illustrated in Figure 10. Articles from the same cluster are plotted with the same shape and color, whereas their proximity indicates a thematic similarity. Please note that a high article density is marked with dark blue. Figure 10 depicts all 574 papers from the second period. When regarding their connections, however, only the strongest 242 out of 4826 are shown. The research front _managing pdm_ is the central cluster, as it is an indirect part of many other research fields. By contrast, the _software prediction_ cluster is outside of the center as it is not thematically connected to most other clusters.
# A Heuristic Approach for a Real-World Electric Vehicle Routing Problem
## 4 Solution Approach
### Removal Heuristics
#### 4.3.4 Historical Knowledge Node Removal Heuristic  
The historical knowledge node removal heuristic is derived from the one used in [PERSON] et al. (2016). It records the position cost of every customer \(i\), defined as the sum of the distances between its preceding and following nodes, and computed as \(d_{i}=d_{i-1,i}+d_{i},i_{i+1}\). At each iteration, the best position cost \(d_{i}^{*}\) is updated to be the minimum of all \(d_{i}\) found so far. An array \(L\) containing all visited customers from solution \(s\) not in \(D\) is constructed. This array is sorted according to descending values of the maximum deviation between current and best position cost (i.e., \(d_{i}-d_{i}^{*}\)). Then, a random number \(y\) is chosen from the interval [0, 1) and customer \(L[y^{\text{phistorical}}\mid L\mid]\) is removed and put into \(D\). This process is repeated until \(q\) customers are removed from solution \(s\).
# Research on the Reliability of Bridge Structure Construction Process System Based on Copula Theory
## 3 Engineering Applications
### Calculation of System Reliability Indicators
#### 3.2.4 Identification of Optimal Copula Functions  
In this paper, we used the AIC and the BIC for the selection of the optimal Copula function. According to Equations (11)-(13), the AIC and BIC values were calculated for each combination of different failure modes when fitted with each of the four Copula functions, and the results are shown in the following Table 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(g_{i}g_{j}\) & \(\tau\) & \(g_{i}g_{j}\) & \(\tau\) & \(g_{i}g_{j}\) & \(\tau\) \\ \hline \(g_{1}g_{2}\) & 0.00983 & \(g_{2}g_{4}\) & 0.00386 & \(g_{3}g_{7}\) & 0.97839 \\ \(g_{1}g_{3}\) & \(-0.01303\) & \(g_{2}g_{5}\) & 0.01034 & \(g_{4}g_{5}\) & 0.94616 \\ \(g_{1}g_{4}\) & \(-0.01323\) & \(g_{2}g_{6}\) & 0.00915 & \(g_{4}g_{6}\) & 0.94947 \\ \(g_{1}g_{5}\) & \(-0.01242\) & \(g_{2}g_{7}\) & 0.01015 & \(g_{4}g_{7}\) & 0.95101 \\ \(g_{1}g_{6}\) & \(-0.01259\) & \(g_{3}g_{4}\) & 0.94389 & \(g_{5}g_{6}\) & 0.98746 \\ \(g_{1}g_{7}\) & \(-0.01345\) & \(g_{3}g_{5}\) & 0.97422 & \(g_{5}g_{7}\) & 0.99070 \\ \(g_{2}g_{3}\) & 0.00960 & \(g_{3}g_{6}\) & 0.98442 & \(g_{6}g_{7}\) & 0.98955 \\ \hline \end{tabular}
\end{table}
Table 4: Calculations of Kendall rank correlation coefficients for each combination of failure modes.  
According to the calculation results, it can be seen that the failure mode combinations _8334_, _8485_, _8486_, and _8487_ should use the Gumbel Copula function to establish the binary joint distribution, and the failure mode combinations _8385_, _8386_, _8387_, _8586_, _8587_, and _8687_ should use the Gaussian Copula function to establish the binary joint distribution.
# Predictive Power Management for Wind Powered Wireless Sensor Node
## 2 Motivation and Related Work
### Joint Optimization Design  
Based on the above related works, some researchers continue to work on the strategy of combining different techniques together to jointly optimize the performance of EH-WSN: wireless energy harvesting, wake-up radio scheme, and error control coding are investigated as enabling solutions to enhance the performance of WSN while reducing its carbon footprint in reference [21], it formulates the data-utility lifetime trade-off problem by taking an approximated lifetime function as well as the energy harvesting, wake up radio duty cycling, and retransmissions into the utility function. [PERSON] et al. propose a global power management approach for EH-nodes. It is based on a joint duty-cycle optimization and transmission power control. By simultaneously adapting both parameters, the node can maximize the number of transmitted packets while respecting the limited and time-varying amount of available energy [22]. [PERSON] et al. propose a strategy, named wake-up and harvesting-based energy predictive forwarding, leverages the combination of prediction-based techniques and Markov Decision Processes to allow each node in the network to take pro-active forwarding and energy allocation decisions [23], results show that this approach delivers up to 72% more packets, 1.6 times faster, and consuming 58% less energy than EHWA. Especially reference [24] presents a power management technique for improving the efficiency of harvesting energy from air-flows in WSN application. The architecture consists of a two-stage energy conversion circuit: an AC-DC converter followed by a DC-DC buck-boost regulator with MPPT capability. The key feature is the adaptive hybrid voltage rectifier, which exploits both passive and active topologies combined with power prediction algorithms.  
However, all these studies do not consider both the energy harvesting and consuming together as a whole, so in order to maximize the entire energy efficiency of EH-WSN system while keeping the network service acceptable, joint optimization of MPPT parameters, predictive energy allocation algorithm and TPC settings are definitely essential and extremely important.
## 2 Material and Method
### Molecular Docking
#### 2.5.2 Preparation of the Receptors  
Three receptors were selected (Tyrosine-tRNA ligase (TyRS)), DNA gyrase and Dihydrofolate reductase (DHFR) to perform the docking study based on their well-known role in the bacterial growth and survival. Each receptor's Protein Data Bank (PDB) file was obtained from the protein data bank website. The receptors' X-ray crystal structures were selected for their completeness, resolution, and compatibility with our study goal. Details of the selected receptors are described in Table 1. Before performing the analysis, Discovery Studio Visualizer v 19.1.0 (BIOVIA, San Diego, United states (windows software)) was used to begin the preparation of the receptors by removing water molecules and heteroatoms. Later, AutoDock Tools were used to add Gasteiger charges and polar hydrogen atoms to the structure. For the final preparation for molecular docking, the final file was converted to the PDBQT format.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Receptor** & **PID** & **Resolution (Å)** & **Classification** \\ \hline TyRS & 1 jj & 3.20 Å & Ligase \\ DNA gyrase & 1 KZN & 2.30 Å & Isomerase \\ DHFR & 3 fyv & 2.20 Å & Oxidoreductase \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the studied receptors.  
Figure 2: Major compounds of _Centaurea bruguieriana_ from chloroform extract.
# Evaluation of Malolactic Starters in White and Rose Winemaking of Moschofilero Wines
## 3 Results  
Alcoholic fermentation, of both the white and rose wines, was completed within 15 days. Then, malolactic fermentation was effectively driven by the starter culture that was added in each experimental case. Degradation of malic acid started 48 h after inoculation with the \(O\). _oeni_ strains and could be considered as completed (malic acid \(<0.1\) g/L) after 15 days. On the contrary, during spontaneously driven MLF, malic acid degradation started after 5 days and complete degradation was not achieved, even upon prolongation of the MLF duration to 19 days.
# Single-Shot Object Detection with Split and Combine Blocks
## 1 Introduction
In this work, we construct a novel and valid unit to implement better feature fusion, and ultimately improve the quality of feature representation, designated as a Split and Combine (SC) block. The SC block evenly splits the input feature information into several parts and processes them in parallel by branches with different depths, and then implements the feature fusion between these branches. This mechanism guides the network to process feature information with different emphasis, and then continuously transmit the information that is more critical to the task to the subsequent layers. The structure of a SC block can be seen in Figure 1. We employ the conv block (e.g., a convolution, a normalization, and a nonlinear activation) and identity mapping to process information. First of all, a conv module maps the input to the feature maps \(X^{\prime}\in C^{\prime}\times H\times W\), where the channel dimension is reduced to depress computational demand. The feature maps \(X^{\prime}\) and then goes through a split operation, which equally divides the feature maps into \(w\) parts across their channel dimension. These sub-feature maps with spatial dimensions of \(C^{\prime}/w\times H\times W\) are then transmitted to network branches, and the output of each branch is passed to the next branch, from a shallower one to a deeper one. Finally, concatenate the output of the last branch with the feature maps \(X^{\prime}\) to further promote feature fusion. The purpose of this architecture is to direct the network to assign an appropriate number of learnable parameters to each part of the feature information based on its contribution to the final task, thus allowing the network to focus on the more critical features and improve the quality of feature representation. The architecture of our SC block is simple, clear, and extendable. Block variants can be easily obtained by modifying the number and depth of branches within the SC block. Performance can be further improved by assembling the proposed SC block with other existing excellent architectures, such as ResNet, DenseNet, and Deformable ConvNets [12; 13]. The SC block does not use any bells and whistles and causes only a slight increase in network complexity and inference latency. To verify the effectivity of the proposed block, we utilize SC blocks to build a backbone network and an FPN, and design a one-stage detector based on them. Our detector is evaluated on a highly competitive MS COCO [14] dataset and exhibits good detection accuracy while maintaining high detection speed. The best model achieves an AP of 38.9% with 40 FPS on a COCO test-dev set.