esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Computational Methodology
#### 3.3.2 PSU-WOPWOP  
The noise generated by an isolated rotor in a quiescent field can be computed based on the rotor's surface pressure distribution and the rotor's rotation rate. PSU-WOPWOP solves the Ffowcs-Williams Hawking (FWH) equation, using Farassat formulation 1A [54; 55; 56]. The thickness and loading acoustic pressure components are listed below as shown in [56]:  
\[4\pi p_{T}^{\prime}(x,t)=\int_{f=0}\biggl{[}\frac{\rho_{0}(\dot{v}_{n}+v_{n})} {r(1-M_{r})^{2}}\biggr{]}_{\text{ret}}dS+\int_{f=0}\biggl{[}\frac{\rho_{0}v_{n }(r\dot{M}_{r}+cM_{r}-cM^{2})}{r^{2}|1-M_{r}|^{3}}\biggr{]}_{\text{ret}}dS, \tag{1}\]  
\[\begin{split} 4\pi p_{L}^{\prime}(x,t)=\frac{1}{c}\int_{f=0} \biggl{[}\frac{\dot{l}_{r}}{r|1-M_{r}|^{2}}\biggr{]}_{\text{ret}}dS+\int_{f=0} \biggl{[}\frac{l_{r}-l_{M}}{r|1-M_{r}|^{2}}\biggr{]}_{\text{ret}}dS+\\ \frac{1}{c}\int_{f=0}\biggl{[}\frac{l_{r}(rM_{r}+cM_{r}-cM^{2})}{ r^{2}|1-M_{r}|^{3}}\biggr{]}_{\text{ret}}dS.\end{split} \tag{2}\]  
Here, \(p_{T}^{\prime}\) is the acoustic thickness pressure, \(p_{L}^{\prime}\) is the acoustic loading pressure, \(c\) is the speed of sound in the fluid medium, \(\rho_{0}\) is the fluid density, \(v_{n}\) is the body's normal velocity component, \(\dot{r}_{i}\) is the radiation vector, \(r\) is the distance between the source and observer, \(l_{i}=\Delta P_{ij}\dot{n}_{j}\) is the surface loading vector, \(M_{i}\) is the vectorized Mach number, and \(M_{r}\) is the radiation Mach number. The terms \(l_{r}\) and \(l_{M}\) are the dot product of the loading vector with the radiation vector and vectorized Mach number, respectively. Although this formulation is used for both impermeable and permeable surface calculations, it should be noted that the Mach number terms are zero for the non-moving permeable surface, reducing the acoustic equation to three integrals.  
The integrals are computed over the surfaces represented by \(f=0\) at the discrete elements of the computational mesh. The blade surface pressure and position were stored over the last five rotor revolutions for impermeable surface calculation. For the permeable surface, point probes were extracted from Helios' off-body solver SAMCART to recreate the topology of a cylinder with radius 1.125R and length of 0.25R encapsulating the rotor. The bottom surface of the cylinder, in the rotor wake, was excluded to avoid hydrodynamic waves from the vortices contaminating the signal. A surface spacing of \(0.025c\) was chosen to match the off-body fixed refinement region and minimize numerical interpolation errors. The coupling of the CFD and CAA methodology in this paper will be referred to as Helios/PSU-WOPWOP.
# Sustainability-Based Analysis of Conventional to High-Speed Machining of Al 6061-T6 Alloy
## 2 Materials and Methods
### Surface Finish  
The surface finish data obtained for the CSM experiments were investigated using ANOVA to determine the significance of cutting variables as shown in Table 6. It can be seen from ANOVA results that feed is the most significant factor for surface finish, followed by depth of cut. Cutting speed has been observed to be insignificant for surface finish. In terms of CR (%), feed has the highest contribution ratio, followed by depth of cut. The high significance of feed on the surface finish is an expected outcome and is well aligned with the published literature. A better insight into the impact of machining variables on surface properties can be obtained by developing a main effect plot as shown in Figure 5. The main effect plot shows that feed should be kept at a minimum level to attain the best surface finish. This is because a high feed increases heat generation, which can increase  
Figure 4: Main effect plot showing the influence of cutting parameters on SCE in CSM.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **DOF** & **Seq. SS** & **Adj. MS** & **F-Value** & \(p\)**-Value** & **CR (\%)** \\ \hline \(v\) (m/min) & 3 & 0.040946 & 0.013648667 & 24.77 & 0.000 & 10.74\% \\ \(f\) (mm/rev) & 3 & 0.267851 & 0.089283667 & 162.06 & 0.000 & 72.75\% \\ \(d\) (mm) & 3 & 0.036177 & 0.012059 & 21.89 & 0.000 & 9.44\% \\ Error & 38 & 0.020936 & 0.000550947 & 24.77 & & 7.08\% \\ Total & 47 & 0.36591 & & & & 100.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analysis of variance and percentage contribution results for SCE.  
tool wear and hence surface roughness [47]. With regard to cutting speed, surface finish has been found to be best at 250 m/min. Since the impact of the cutting speed on Ra was seen to be negligible in conventional speed machining, its value can be set at any level that is appropriate from the viewpoint of the machine tool and cutting tool, etc.
# At the Nexus of Blockchain Technology, the Circular Economy, and Product Deletion
## 3 Framework and Propositions  
The product deletion, circular economy and blockchain technology nexus conceptualization is presented in Figure 1. We offer two general propositions from the previous background discussion in Section 2. Additionally, they set the stage for more a detailed analysis and evaluation in Section 4. These propositions are generic and serve the secondary function as research questions. Figure 1 shows the interrelationships between blockchain as both a direct and indirect influencer with both product deletion and the circular economy. We have shown and made this argument in a number of examples. The primary arguments made thus far have shown a number of dyadic relationships between the three subjects. The complexities involve multiple relationships, including two-way interactions and moderating relationships. Thus, we initially posit three general propositions. **Proposition 1**. : _Product deletion is interrelated with circular economy practices. Product deletion impacts circular economy practices and circular economy practices support product deletion management concerns; the relationships aid improved product deletion decision making processes and reduces product deletion risks._  
**Proposition 2**. : _Blockchain technology is an enabler that can moderate the interrelationships between product deletion and circular economy. Blockchain technology activates and upgrades the inter- and intra-organization  
Figure 1: A Conceptual Framework of Product deletion, Circular Economy and Blockchain Relationships. information management systems that facilitate product deletion decision making and advances circular economy development and operations._  
**Proposition 3**. : _Although not explicitly shown in Figure 1, we posit that these relationships can occur at multiple circular economy levels. Micro, Meso, and Macro level influences and relationships exist amongst the three subject areas._  
Some of the practical and theoretical foundations of this framework and discussions are further elicited in Section 4. **4. Blockchain Enabled Product Deletion Decision Making in a Circular Economy**  
This section introduces how blockchain-based information management enables and facilitates the product deletion relationships within a circular economy (Table 1). The analyses are conducted at three levels, the macro (institutional) level, the meso (networks and supply chains) level and the micro (organizational and consumer) level. At each level, the discussions are organized by circular economy initiatives, followed by a short discussion on how blockchain technology can contribute to the circular economy initiative and additional short discussion concerning blockchain, product deletion and circular economy synergies.
# Power Optimization Model for Energy Sustainability in 6G Wireless Networks
This study aimed to reduce the total energy overhead, but these methods also find applications in energy-efficient internet-of-everything (IoE) and IoT networks. In this paper, a power optimization model is proposed for 6G-enabled massive IoT network which aims to maximize system performance, thereby offering energy-saving capabilities. The power overhead, owing to the huge number of connected devices, is reduced through the optimal management of power resources. The proposed network is evaluated for the maximum power allocated and the spectral efficiency for different network operations with different precoding schemes.
## 3 OLSR-Based Protocols
### D-Olsr  
The Directional Optimized Link State Routing Protocol (D-OLSR) [26] is a routing protocol for UAVs with directional antennas. This protocol's major emphasis is the MPR execution, making it its most significant component. The D-OLSR protocol tries to limit the amount of MPR in the network in order to reduce latency and route overhead [26]. Figure 3 depicts a flowchart for the D-OLSR routing protocol.  
Figure 2: Format of messages for standard OLSR protocols [11]. (**a**)“Hello” message. (**b**) Topology control message.  
The UAV will employ D-OLSR with a directional antenna if the distance between the UAV point and the destination UAV is greater than _Dmax2_(the maximum distance that the directional antenna can cover). Nonetheless, if the distance is less than _Dmax2_, the UAV will switch to the standard OLSR if an omni-directional antenna is available; otherwise, it will revert to the D-OLSR [26].
# A Lightweight Reconstruction Model via a Neural Network for a Video Super-Resolution Model
## 2 Materials and Methods
### Depth-Separable Convolution  
The DSC has been popularized by notable models like the Extreme version of Inception (Xception) [30] and MobileNet [31]. These two models developed by Google are landmark achievements that feature DSC prominently. As shown in Figure 1, the DSC is comprised of two elements: Depthwise Convolution and Pointwise Convolution.  
The operation of Depthwise Convolution is relatively simple. It employs a distinct convolution kernel for each channel of the input feature map. The outcomes of all convolution kernels are subsequently concatenated to yield the final output. This procedure is illustrated in the Depthwise Convolution section of Figure 2.  
DSC is a specialized convolution operation within CNNs. It offers a more lightweight approach to convolution, boasting fewer parameters and reduced computational requirements than traditional convolution operations.  
DSC initiates the process by performing feature extraction on the input images, executing convolution operations independently for each input channel. Subsequently, Pointwise Convolution is employed to amalgamate the output results from Depthwise Convolution. This process weights and sums the results from each channel to produce the final output results.  
The primary advantage of DSC over traditional convolution operations is its significant reduction in the number of parameters and computational demands. This efficiency allows DSC to maintain model accuracy while significantly improving the model's speed. DSC is particularly suited for scenarios with limited computational resources, including mobile and embedded devices. This makes it a popular choice in the design of CNNs.  
An essential characteristic of Depthwise Convolution is that the quantity of output channels is equal to the number of convolution kernels. Given that each channel in Depthwise Convolution leverages a single convolution kernel, the number of output channels posts the convolution operation is also 1. Hence, if the count of channels in the input feature map is N (as illustrated in Figure 2), executing discrete operations using a single convolution kernel for N channels would produce one feature map per N channels. Concatenating these N feature maps culminates in an output feature map consisting of N channels.
## 2 Principle
### Propagation Characteristics of Elastic Stress Wave in Cylindrical Bar
#### 2.1.3 Relationship Between Normalized Frequency and Propagation Time  
For a stress wave of a certain frequency, the time period is  
\[T=1/f=L/C \tag{4}\]  
Define the time for the elementary theoretical elastic wave to propagate from the center to the edge of the pressure bar as  
\[T_{a}=a/C_{0} \tag{5}\]  
Divide Equation (5) by Equation (4) to get  
\[T_{a}/T=\frac{fa}{C_{0}}=\frac{C}{L}\frac{a}{C_{0}}=\frac{(D/L)(C/C_{0})}{2} \tag{6}\]  
The energy of the stress wave propagates forward at the group velocity \(C_{g}\) in the pressure bar, so the ratio of the time \(t^{\prime}\) when the wave energy propagates to a certain position \(z\) in the bar and the time \(0.5T_{0}\) that the elementary theoretical wave propagates to the same position is the normalized propagation time  
\[\frac{t^{\prime}}{0.5T_{0}}=\frac{z/C_{g}}{z/C_{0}}=\frac{C_{0}}{C_{g}} \tag{7}\]  
Both of the normalized frequency parameters \(fa/C_{0}\) described by Equation (6) and the normalized propagation time parameters \(t^{\prime}/0.5T_{0}\) described by Equation (7) can be obtained by simple calculation of the phase velocity curve (Figure 3) and group velocity curve (Figure 4) and their independent variable \(D/L\). Figure 5 is the first to fourth orders of normalized frequency and propagation time curve when Poisson's ratio is 0.287. Assuming that there are no dispersion effects of various orders in the bar, a vertical line should be shown in Figure 5 and the abscissa should be 1, that is, each frequency component in the bar propagates at the same velocity and reaches any point in the pressure bar at the same time. However, considering the dispersion effect, each order curve is not a simple vertical line, but presents a more complicated pattern.
# Ensemble-Based Knowledge Distillation for Video Anomaly Detection
## 4 Experimental Evaluation
### Discussion on Results
Study [30] utilizes an enhanced context encoding method for handling high-level semantic and temporal variations and a unique noise simulation strategy based on weak supervision for handling noises in video samples. Another study [32] deploys a transformer-based convolutional structure to improve the encoding process of high-level features from video samples by using a two-stage multi-sequence-based training method. Finally, the study [33] again makes use of transformer-based glance and focus sub-modules to effectively capture global and local level information details from video samples by using a special amplification mechanism for obtaining an improved feature representation. The crucial point can be noticed when we examine the disadvantages of these recent studies compared to our approaches. Firstly, the models in these studies are trained and tested in fixed-size datasets. These studies do not include a scenario where there is a dataset continuously growing in time and the model has to constantly adapt itself for learning incoming samples while also preserving the old patterns learned from previous samples. Secondly, depending on this scenario, these studies do not integrate an additional adaptation mechanism designed to prepare the model for this scenario. With respect to this case, even though these studies outperform the baseline model trained with our proposed adaptation mechanism in Table 18, we conclude that their performance will be dramatically reduced in the aforementioned scenario compared to our adaptation mechanism. Another possible explanation for the performance limitation in Table 18 is that since our proposed mechanisms require baseline teacher and student models for the adaptation process, the structures of baseline networks utilized for our proposed mechanisms (in our case, GCN and AR-Net networks) can inherently limit the performance of the student model, because of the specific baseline network's individual restricted performance related to its designed structure. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline ([PERSON] et al., 2021) [30] & - More Advanced Temporal and Semantic Context Encoding & - Trained and Tested \\  & - More Advanced Weak & - On Fixed-Size Datasets \\  & Supervision Strategy & Mechanism \\ \hline \multirow{4}{*}{([PERSON] et al., 2022) [32]} & - Transformer Based Convolutional & - Trained and Tested \\  & Network Structure & on Fixed-Size Datasets \\  & - Two Stage Self & - No Separate Adaptation \\  & Training Strategy & Mechanism \\ \hline \multirow{4}{*}{([PERSON] et al., 2023) [33]} & - Transformer Based Glance & - Trained and Tested \\  & and Focus Modules & on Fixed-Size Datasets \\ \cline{1-1}  & - Feature Amplification Mechanism & - No Separate Adaptation \\ \cline{1-1}  & Mechanism \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Qualitative Comparison with State-of-the-Art (SOTA) Studies.
# Computational Evaluation of Shock Wave Interaction with a Cylindrical Water Column
## 3 Results  
### Shock Tube Flow  
The computational setup described in Section 2.1, without the presence of the water column, practically corresponded to [PERSON]'s shock tube problem [29], which admits an exact solution and has become a standard test case for compressible flow solvers. Therefore, while demonstrating the air flow in which the water column was immersed, the initial simulation of the shock tube device also represented a preliminary verification and validation test which was satisfactorily completed, as was done in [8], for example.  
The robustness of the present CFD model and the accuracy of the transient numerical solution of the shock tube problem were assessed through comparison with the analytical discontinuous solution [19]. Here, the resolved flow was illustrated by looking at the profiles across the shock tube of some flow field variables at the moment, say \(t=t_{0}\), when the incident shock impacted the column. The pressure, density, temperature, and streamwise velocity profiles along the tube are reported in Figure 2, for the three different CFD solutions with varying resolution. Note that, as happens in real flows, theoretical discontinuities were smoothed out by viscous effects.  
In Table 4, the results in terms of the traveling shock wave and contact surface velocities are tabulated. By inspection of these results, grid convergence was practically demonstrated, since the matching between numerical and analytical solutions improved with increasing mesh resolution. In Figure 3, a close-up view of the FV mesh in the space region occupied by the water body was shown for the intermediate resolution case, that is CFD II. In the following section, the computational evaluation of the shock wave interaction with the cylindrical water column is presented and discussed for the two cases CFD II and CFD III.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Solution** & **\# of FV Cells** & \(h_{min}/d_{0}\) & \(V_{S}\) (m/s) & \(V_{2}\) (m/s) \\ \hline CFD I & \(2.0\times 10^{5}\) & \(1.7\times 10^{-2}\) & 593.0 & 329.0 \\ CFD II & \(3.2\times 10^{5}\) & \(4.7\times 10^{-3}\) & 593.3 & 329.2 \\ CFD III & \(5.0\times 10^{5}\) & \(2.3\times 10^{-3}\) & 593.5 & 329.3 \\ Analytical & – & – & 593.7 & 329.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Shock tube flow: traveling velocities for different CFD solutions.
# Genetic Algorithms Optimized Adaptive Wireless Network Deployment
## 6 Experimental Results and Discussion
### Experiments on Training Scenarios
#### 6.2.2 GANet-1 hop vs. ATRI  
Figure 10 shows networks deployed using ATRI and \(Best_{1h}\) on the second (\(T_{r2}\)) and third (\(T_{r3}\)) training scenarios. In \(T_{r2}\), which has two user clusters, ATRI's performance is 28.2% lower than \(Best_{1h}\)'s. A similar pattern can be seen for \(T_{r3}\) as well. ATRI-deployed UAVs are more uniformly distributed across the AOI, whereas, with GANet, UAVs have learned parameter values that enable them to cluster around users while maintaining connectivity to the command center. This increases the number of AUs (active UAVs) in the network and thus increases the fitness and reduces the per active UAV data rate to users. Note that, on \(T_{r1}\), where users are more uniformly distributed, ATRI's performance is comparable to GANet. However, in the remaining scenarios, where the users' distribution is non-uniform, GANet performed better, pointing out the weakness of the ATRI technique over non-uniform users' distributions.
## 2 Experimental Details
### Preparation of Si/C Nanocomite
#### Fabrication of Si/C Nanocomite  
Citric acid (C\({}_{6}\)H\({}_{8}\)O\({}_{7}\), Daejung chemical Co., Ltd., Siheung, Gyonggi-do, Korea) and propylene glycol (CH\({}_{3}\)CH(OH)CH\({}_{2}\)OH, Samchun pure Chemical Co., Ltd., Pyeongtaek, Gyonggi-do, Korea) were used to prepare the carbon solution. A typical carbon solution was formed by mixing of 50 wt % of citric acid and 50 wt % propylene glycol using the magnetic stirring for 7 h. Subsequently, the carbon solution was added to the produced silicon powders in the weight ratio (Carbon solution:Si) of 10:20. After that, the resulting mixture was heat-treated at 900 \({}^{\circ}\)C in a horizontal quartz tube furnace for 1 h in the Argon ambient for the carbonization to form the Si/C composite. The ramp rate was 10 \({}^{\circ}\)C/min, and Ar gas flow rate was maintained at 2.5 L/min. The carbon coated silicon powders were again milled for 8 h under the same milling conditions. Thus, the final Si/C nanocomposite anode materials were obtained.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
In addition, with the development of intelligent algorithms in recent years, more and more intelligent algorithms are being introduced into routing protocol design, especially in the applications which have high performance requirements. Intelligent algorithm-based routing protocols can achieve better performance than traditional routing algorithms, and have been widely used in the terrestrial environment, so even though an overview of intelligent algorithm-based routing protocols in the terrestrial environment is not the main scope of this paper, in order to provide an complete background for readers, we will introduce these routing protocols briefly. The contributions of this paper are as follows:  
* As the cross-layer design method has become more and more important in recent years, we classify the routing protocols based on the cross-layer design method and non-cross-layer design method. To the best of our knowledge, our survey is the first paper that provides a detailed classification based on cross-layer design methods. * Considering that the intelligent algorithms can effectively improve the routing performance, we also review the intelligent algorithm-based routing protocols which can provide a wide range of concepts for routing protocol design. * To give researchers clear and direct insights for the development of underwater acoustic sensor network routing protocols, in this paper, we investigate the development trends in UASN routing protocol design in recent years. The classification of the UASN routing protocols in this paper is shown in Figure 1. The rest of this paper is arranged as follows: in Section 2, we will introduce the background knowledge of routing protocol design, including the principle of cross-layer design and different kinds of intelligent algorithms; in Section 3, we will introduce the knowledge of underwater acoustic sensor network briefly; in Section 4, we will review the traditional non-cross-layer design routing protocols; in Section 5, the cross-layer design routing protocols which include the traditional cross-layer design routing protocols and the intelligent algorithm based routing protocols will be investigated; in Section 6, we conclude the challenges and open issues of UASN routing protocol design, and discuss the developing trends of UASN routing protocols; finally, in Appendix, we compare different aspects of the routing protocols.