esa-sceva/satcom-chunk-collection huggingface dataset:
# Earthquake-Tolerant Energy-Aware Algorithm for WDM Backbone Network
## 2 Materials and Methods
### Energy Contribution and Seismic Measurements
#### 2.1.1 Energy Contribution  
The main sources of power consumption in IP over WDM optical networks are IP router ports, WDM transponders, and EDFAs [29]. In detail, the energy consumption of each component is outlined below.  
IP router port: an IP router port consumes about 1000 W [30] and is the most energy-consuming component of an optical network, processing all incoming and outgoing information. WDM transponder: a WDM transponder consumes about 73 W and is associated with each wavelength for data transmission and OEO conversion [31]. Erbium-doped fiber amplifier (EDFA): An EDFA consumes about 8 W and is deployed every 80 km of optical fiber installation [32]. In addition, there are always two EDFAs at the two ends of each link, to amplify the incoming and outgoing signals. The EDFAs have been chosen because they outperform other options in longer transmission distances, as can be seen in [33], and they can be effectively integrated into optical networks [34].  
The total energy consumption can be estimated (in Watts) using the following Formula (1) [35]:  
\[\text{total energy consumption}=\ \sum_{i\in N}E_{r}\times\left(\Delta_{i}+ \sum_{j\in N,\ i\
eq j}C_{ij}\right)+\sum_{m\in N}\sum_{n\in N_{m}}E_{t}\times W _{mn}+\sum_{m\in N}\sum_{n\in N_{m}}E_{e}\times A_{mn}\times f_{mn} \tag{1}\]  
The \(m\) and \(n\) symbols are the indexes of the nodes in the physical topology. The \(N_{m}\) symbol resembles a set of neighboring nodes of node \(m\) on the physical topology. The symbols \(i\) and \(j\) refer to indexes of the nodes in the virtual topology. The \(C_{ij}\) parameter  
Figure 2: Backup lightpaths.  
refers to the number of wavelengths between nodes \(i\) and \(j\). The \(W_{mn}\) symbol refers to the number of wavelengths between node \(m\) and node \(n\). The parameter \(Amm\) means the number of EDFAs used on each physical link between nodes \(m\) and \(n\). The \(f_{mn}\) symbol resembles the number of optical fibers between nodes \(m\) and \(n\). The parameter \(E_{r}\) refers to the energy consumption at the IP layer, which accounts for the energy consumption of the IP router ports, while the parameters \(E_{t}\) and \(E_{e}\) refer to the power consumption of the WDM transponders and EDFAs, respectively. The \(L_{mn}\) parameter resembles the physical distance between nodes \(m\) and \(n\). Furthermore, the parameter \(\Delta_{i}\) is calculated as [(\(\Delta d\)\(\in\)\(N\)\(\lambda\)\(id\))/\(B\)], where \(B\) is the transmission capacity of each wavelength (40 Gbps), and \(A_{mn}\) is calculated as [\(L_{mn}/S-1\)] + 2, where \(S\) is the distance between two adjacent optical amplifiers.
# Prediction of Transient Temperature Distributions for Laser Welding of Dissimilar Metals
## 4 Results
### Temperature Probes
The maximum temperature reached in the z = 0 surface is more than the z = -2.5 surface, which is very normal for any inward heat flux of the welding process. Figure 11: Temperature distribution of temperature probes of two different domains having the same Y = 20 mm. Figure 12: Edge temperature probe along the direction of welding: (**a**) z = 0 and (**b**) z = -2.5. Edge probes of the two edges parallel to the weld line (x = 0 and x = 20) are considered for temperature distribution as well, as shown in Figure 14. The difference in the thermal properties of AISI 316L and Ti6 Al4V makes a visible difference in the maximum temperature distributions along those edges (Figure 15). Initially, the difference is more in favor of steel for a sudden temperature increase. The difference decreases with time. The difference can be as high as 100 K and as low as 20 K.The fluctuations are reduced in radial directions because of the high resistance to heat by the parent materials. The fluctuation value comes within a range of 50-100 K for the same domain within a few millimeters, which keeps the melt zone less. The inclination of the curve is greater initially when the heat source started heating the materials. The width of the samples is 10 mm only, and from Figure 15, it is clear that the extreme edge line temperatures (parallel to the heat source) can reach up to 1200 K for both materials. This indicates that recrystallization will occur in most of the areas within the samples. For a larger width sample, the base metal area can be observed even during the heat process, but it not possible for this particular geometry. It increases flatly as the process progresses when the heating and cooling process began to occur simultaneously. [PERSON] et al. (2018) predicted the temperature from the residual stresses measured from XRD analysis. The reverse process agrees with the nature of the temperature distribution modeled here. They measured the temperature in some particular nodes and tried to explain the distribution by considering other dimensions of the material behaviors and results. Figure 14: Edge temperature probes (**a**) x = 0, (**b**) x = 20, and on the z = 0 surface. Figure 13: Maximum temperature (computational) on the edge temperature probes along weld line at \(Z=0\) (green) and \(Z=-2.5\) (blue). A comparison between the experiment and the simulation result generally shows the slight differences in the shape that could be due to the fact that the beam profile used in the experiments was slightly modified, which was not considered during modeling. There was a minimal difference in penetration depth and weld width between the model and simulation. The reason could be that as the temperature of the material increases there was a change in the absorptivity of the laser that was not taken into account in the model [40; 41; 42; 43].
## 4 Experiments and Analysis
### Dataset
When using Resnet50 for feature extraction, its time complexity and memory occupation are generally smaller than VGG16; however, its accuracy is also reduced to a certain extent. Due to \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Train Time/ms** & **Test Time/ms** & **Flops/M** & **Paras/M** & **Accuracy/\%** \\ \hline VGG16 & 6 & 3 & 781 & 65.1 & 90.7 \\ Rene50 & 8 & 2 & 549 & 45.7 & 80.7 \\ This Paper & 2 & 0.7 & 23 & 1.9 & 90.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification performance of single-channel classification network with different feature extraction modules. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Shape** & **Simulated Images** & **Real Images** \\ \hline sphere & 1200 & 24 \\ cylinder & 2410 & 18 \\ truncated cone & 2410 & 27 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of experimental data set. that, it is not suitable for the underwater object classification task. The model with feature extraction network designed in this paper reduces the classification time to some extent on average, compared with the other two networks, in achieving object classification. That is, the classification accuracy is only 0.6% worse than VGG16. Meanwhile, it has the least computational cost and the lightest model structure, which is more suitable for the object classification task.
# Correction Compensation and Adaptive Cost Aggregation for Deep Laparoscopic Stereo Matching
## 3 Materials and Methods
### Adaptive Cost Aggregation Module
#### 3.3.2 Interleaved Group Convolution Structure  
Because the cost distribution information is explicitly incorporated into the disparity aggregation network, there is a possibility of parameter redundancy. To address this problem, we replace the standard convolution layers with group convolution layers. Meanwhile, we design an interleaved group convolution structure (IGCS) to make full use of the information learned through the network.  
The IGCS, as depicted in Figure 4, incorporates two additional operations compared with the conventional group convolution structure (GCS). This includes an interleaving operation before the convolution and a rollback operation after the convolution. When the interleaved group convolution is utilized alone, there is no significant difference from the conventional group convolution. However, when used in conjunction with conventional group convolution, it facilitates information interaction between features in different groups, enabling the network to capture more correlation information in channels than pure conventional group convolution. Remarkably, the interleaving and rollback operations in interleaved group convolution do not require floating-point arithmetic. This means that the interleaved group convolution can enhance the learning ability of the network with minimal additional computational burden.  
Figure 3: An illustration of the relationship between the disparity distribution and the unimodal distribution coefficient \(U\). In comparison, the distribution in (**a**) exhibits a multimodal state with a lower degree of unimodality and a larger value of \(U\). Conversely, the distribution in (**b**) is unimodal, characterized by a higher degree of unimodality and a smaller value of \(U\). That is, the lower the degree of unimodal distribution, the higher the value of \(U\). This indicates more inaccurate registration and the corresponding cost information ought to be strengthened during the aggregation process. In this figure, \(d\) represents the candidate disparity, \(\hat{d}\) denotes the predicted disparity, \(c\) represents the cost, and \(c\) is a softmax operation.
## 3 Joint Optimization Algorithm Design
### MAHPPO Algorithm
#### 3.1.2 State Space  
The state space is denoted as the global state \(\mathcal{S}_{k}\)\(=\)\(\left\{\kappa_{k},c_{k}^{\text{buoy}}\),\(E_{k}^{\text{buoy}}\),\(\chi_{k},y_{k},c_{k}^{\text{uav}}\),\(E_{k}^{\text{uav}}\),\(\chi_{k},\chi_{k}^{\text{new}}\),\(B_{k}\right\}\), where the meanings of each variable are as follows:  
* \(\kappa_{k}=\left\{\kappa_{u,k},\forall u\in\mathcal{U}\right\}\) is defined as the associated state of the buoy-UAV and UAV-OBS, where \(\kappa_{u,k}=i,\forall i\in\mathcal{I}\) represents the number of buoys or OBS associated with UAV \(u\). If UAV \(u\) is not associated with any buoy or OBS, it yields that \(\kappa_{u,k}=-1\).
* \(c_{k}^{\text{buoy}}\)\(=\)\(\left\{c_{m,k}^{\text{buoy}},\forall m\in\mathcal{M}\right\}\) and \(c_{k}^{\text{uav}}\)\(=\)\(\left\{c_{u,k}^{\text{uav}},\forall u\in\mathcal{U}\right\}\) represent the set of remaining data volume for the buoys and UAVs, respectively.
* \(E_{k}^{\text{buoy}}\)\(=\)\(\left\{E_{m,k}^{\text{buoy}},\forall m\in\mathcal{M}\right\}\) and \(E_{k}^{\text{uav}}\)\(=\)\(\left\{E_{u,k}^{\text{uav}},\forall u\in\mathcal{U}\right\}\) represent the set of remaining energy for the buoys and UAVs, respectively.
* \(x_{k}\)\(=\)\(\left\{x_{u,k},\forall u\in\mathcal{U}\right\}\) and \(y_{k}\)\(=\)\(\left\{y_{u,k},\forall u\in\mathcal{U}\right\}\) represent the set of horizontal coordinates of the UAVs.
* In MAHPPO, the agent action selection is not limited by the constraints, such as the SNR threshold, energy consumption, and so on. Therefore, it is necessary to make a series of constraint judgments on the action choices of the agents. If the selected action of the agent violates the constraint, the action will be changed to meet the constraint conditions. Therefore, we define \(\chi_{k}^{\text{new}}\)\(=\)\(\left\{\chi_{u,k}^{\text{new}},\forall u\in\mathcal{U}\right\}\) as the changed mission mode selection of the UAVs and \(\chi_{u,k}^{\text{new}}\)\(=\)\(\left\{\chi_{u,k},2\right\}\), where \(\chi_{u,k}^{\text{new}}\)\(=\)\(2\) means that UAV \(u\) neither collects nor offloads data.
* \(B_{k}\)\(=\)\(\left\{B_{u,k},\forall u\in\mathcal{U}\right\}\), where \(B_{u,k}\) indicates the bandwidth occupied by UAV \(u\) at time slot \(k\).
## 2 Materials and Methods
### Feature Selection
The \(|v|\) parameter quantifies whether the scattering mechanism of the target corresponds to odd-bounce or even-bounce scattering. Additionally, the \(\gamma\) parameter distinguishes between polarized and non-polarized targets based on their depolarization \begin{table}
\begin{tabular}{l l l} \hline \hline
**Alternatives** & **Feature Vectors** & **Accuracies** \\ \hline ReliefF & \([|\tau_{i}-\tau_{s}|,|\tau_{i}+\tau_{s}|,|v|,\gamma,\gamma,K_{e},K_{h}]\) & 91.6\% \\ LASSO & \([|\tau_{i}-\tau_{s}|,|\tau_{i}+\tau_{s}|,|v|,\gamma,K_{e},K_{h},H,\gamma]\) & 89.3\% \\ RFFI & \([K_{h\
u}|\tau_{i}-\tau_{s}|,|\gamma_{i}+\tau_{s}|,H,|v|,K_{o}]\) & 89.4\% \\ PCA & \([|\tau_{i}-\tau_{s}|,|\tau_{i}+\tau_{s}|,|v|,\gamma,K_{o}]\) & 84.9\% \\ MRMR & \([K_{h\
u}|v|,H,|\tau_{i}+\tau_{s}|,|\tau_{i}-\tau_{s}|,K_{o}d]\) & 89.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of alternatives. Figure 7: Quantitative classification metrics based on the feature vectors obtained by alternative feature selection methods. properties, \(K_{e}\) and \(K_{h}\) respectively represent the contributions of the even-order scattering and helical scattering mechanisms to the overall scattering mechanism, \(K_{e}\) represents the contributions of even-order scattering to the overall scattering mechanism, and \(K_{h}\) represents the target's rotational symmetry. For targets that exhibit a high degree of symmetry and smooth surfaces, the scattering properties are predominantly influenced by even-order reflections. The targets analyzed in this paper possess exact symmetry and rotational symmetry. The physical meaning of the selected features can correspond to the structure of the space target. This feature vector is subsequently utilized for interpreting space target structures.
# Mutual Information, the Linear Prediction Model, and CELP Voice Codes
## 4 Entropy Power/Entropy Rate Power  
Given a random variable \(X\) with probability density function \(p(x)\), we can write the differential entropy \(h(X)=-\int_{-\infty}^{\infty}p(x)\log p(x)dx\), where the variance \(var(X)=\sigma^{2}\). Since the Gaussian distribution has the maximum differential entropy of any distribution with mean zero and variance \(\sigma^{2}\)[16],  
\[h(X)\leq\frac{1}{2}\log 2\pi e\sigma^{2}, \tag{5}\]  
from which we obtain  
\[Q=\frac{1}{(2\pi e)}\exp 2h(X)\leq\sigma^{2}\, \tag{6}\]  
where \(Q\) was defined by [PERSON] to be the entropy power associated with the differential entropy of the original random variable [7]. In addition to defining entropy power, this equation shows that the entropy power is the minimum variance that can be associated with the not-necessarily-Gaussian differential entropy \(h(X)\).
# Trellis Tone Modulation Multiple-Access for Peer Discovery in D2D Networks
## 3 Trellis Tone Modulation Multiple-Access  
A new modulation scheme based on single-tone transmission is used for the new proposed multiple access scheme. We first consider a trellis composed of a number of states, where each state corresponds to a choice of a subcarrier, a tone. The trellis tone modulation is performed based on the tone index change between two consecutive OFDM symbols according to the discovery message, so that we can find its resemblance to the differential modulations. Each UE transmits its separately modulated signal, while signals from multiple UEs are superposed at the receiver. This section proposes trellis tone modulation multiple-access for peer discovery.
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 4 Solutions and Algorithms
### Solution Development
#### 4.1.1 Lyapunov Function  
First, we define \(L(t)\) as the Lyapunov function to describe the square of all modeled queues in time slot \(t\), i.e.,  
\[L(t)=\frac{1}{2}\sum_{i=1}^{M}\left[\mathcal{D}_{i}^{2}(t)+(E_{i}(t)-\mathcal{ B})^{2}\right], \tag{12}\]  
where \((E_{i}(t)-\mathcal{B})\) denotes the remaining capacity of the battery.
# A Fundamental Study on an SAP Mixed Asphalt Mixture for Reducing the Urban Heat Island Effect
## 2 Materials and Methodology
### Evaluation of Thermal Characteristics  
#### 2.4.1 An Indoor Experiment  
Spraying tests were conducted to evaluate the temperature-reducing effect of the SAP asphalt mixtures. The most important goal in spraying is to maximize the cooling effect on the pavement relative to the total amount of water sprayed onto the pavement. When spraying water on road pavements, the temperature reduction effect is short-term because the water flows onto the surface and flows into the drain or evaporates. Therefore, an SAP can be mixed into asphalt mixtures to absorb water quickly so that the amount of water used in spraying can be utilized most efficiently and the temperature reduction effect can be maintained for a longer period of time. In this study, laboratory experiments were conducted to understand the thermal behavior of SAP asphalt mixtures that absorb water and delay the temperature recovery time of the asphalt surface, thereby reducing the surface temperature.  
To measure the indoor thermal properties of asphalt mixtures containing an SAP, asphalt mixture specimens measuring 30 \(\times\) 30 \(\times\) 5 cm were prepared using different types and contents of SAPs. Two types of SAPs (SAP-500 and SAP-3500) were utilized. The contents of the SAPs were applied as 0%, 3%, 5%, and 7% of the asphalt mixture weight. The experimental process and setup are shown in Figure 4. The specimen surface was heated to approximately 70 \({}^{\circ}\)C, which is the highest temperature of the asphalt pavement surface in summer, using a 250 W heat lamp. The temperature change after water spraying was measured at 1 min intervals using a temperature logger (Tokyo Sokki Kenkyujo, TDS-540, Tokyo, Japan). An automatic sprayer (Kyeyang Industry, HY-76, Chungju, Republic of Korea) was used to spray 400 mL of water for 1 min.  
Figure 4: Indoor experiment appearance.
## 6 Benefits and Deployment Considerations of GLI-Split
### Incentives for Early Adopters  
GLI-nodes of early adopters usually communicate with the classic Internet which reduces the set of advantages provided by GLI-Split. However, it still has appealing benefits. Multihoming is still possible. GLI-domains can change providers without renumbering, but global GLI-addresses communicated to external nodes need to be changed. Traffic engineering for outbound and inbound traffic can still be performed.
## 3 Proposed Algorithm
### Proposed Algorithm Description
```
1:Randomly initialize the parameters, import the WSNs environment, \(episode_{1}=1\)
2:for episode = 1 to \(M\)do
3:Initialize the current state \(s\), time slot number \(T\), \(episode_{2}=1\)
4:while\(episode_{2}<T\),do
5:Action \(a\) is determined according to the exploration and development strategy, and it is added to the transmission waiting queue \(L\)
6:Add the remaining theoretically reachable data \((t\geq h\) ) without conflicting data transmissions, \(w=100\), \(a_{1}=-1\)
7:if\(L^{\prime}\) is not empty do
8:Take data \(i\) in turn from \(L^{\prime}\) and calculates \(h_{i}/(t_{i}\cdot(t_{i}-h_{i}))\)
9:if\(h_{i}/(t_{i}\cdot(t_{i}-h_{i}))<w\) and \(t_{i}\geq h_{i}\),do
10:\(a_{1}=i\), \(w=h_{i}/(t_{i}\cdot(t_{i}-h_{i}))\)
11:else return to step 7
12:endif
13:if\(a_{1}>0\)do
14:Add \(a_{1}\) to queue \(L\), and return to Step 6
15:else Determine the transmission queue L
16:endif
17:Perform the action \(a\), calculate the reward \(R\), and move to the next state \(s^{\prime}\)
18:Put \((s,a,r,s^{\prime})\) into the experience pool
19:if The experience pool is full do
20:Enter the learning process and calculate \(q_{eval}\), \(q_{tgt}\), loss
21:if Current loss is minimum do
22:Update loss and store the network parameter model
23:endif
24:\(s=s^{\prime}\), \(episode_{2}+=1\)
25:endif
26:endif
27:if Loss meets the accuracy requirements do
28:break
29:endif
30:\(episode_{1}+=1\)
31:endfor
32:The training is completed and the final SAE network parameter model is obtained
33:The node scheduling strategy was obtained by importing WSNs environment and SAE network parameter model
```  
**Algorithm 1** RS-DQL algorithm
# Screen-Based Sports Simulation Using Acoustic Source Localization +
## 5 Experimental Results  
### System Implementation  
As shown in Figure 5, our system places 49 microphones, in a \(7\times 7\) rectangular array, with 25 mm between two adjacent microphones. An impulse sound is generated at least 1 m away from the microphone array. The distance of 25 mm is close enough to assume that the sound wavefront is planar. Although only 13 MEMS microphones (seven microphones for each axis, sharing the microphone positioned in the center) are required to estimate the direction-based beamforming algorithm in our system, all of the 49 microphones are used to compare the accuracy of the location-based beamforming technique used in [16].  
All the signals of the MEMS microphones (Knowles SPH1642 HT5H-1 which has dimensions of width 2.65 mm \(\times\) length 3.5 mm \(\times\) height 1 mm, the highest signal-to-noise ratio in the current market, and an analog top type) are inputted to 25 two-channel A/D converters (Cirrus Logic Inc. CS5351). The audio signals are sampled at a rate of 192 kHz for each microphone with a 24-bit resolution. We designed an A/D conversion board by referencing the development kit offered by the manufacturer.  
Figure 5 shows the hardware-based implementation of the main controller that processes the DSBF algorithm and manages every device in our system such as 49 MEMS microphones, A/D converters, power management, DDR memory, camera, USB, and others. For a quick and robust  
Figure 4: Overview of the proposed evaluation architecture.  
Figure 5: Implementation of the two-dimensional (2D) microelectromechanical system (MEMS) microphone array and the processor: (**a**) Arrangement of the MEMS microphone array, (**b**) A/D conversion modules, and (**c**) field programmable gate array (FPGA-) based controller for impulsive acoustic source localization.  
hardware implementation, the circuitry related to the FPGA was implemented by utilizing Trenz Electronic's TE0713-01-200-2C, which is one of the most cost-efficient modules for implementing the proposed method.  
Figure 6 shows a block diagram of the proposed FPGA-based design for processing both position and direction DSBF algorithms. To reduce the resources in the FPGA and boost the performance, the MCU in Figure 4 calculates the time delay between the microphone array and the candidate directions and positions of acoustic source in advance. This design scheme makes it easy to adjust the position and direction resolutions. Furthermore, the proposed design processes all DSBF algorithms in parallel as the DSBF processes for a single microphone are summed independently. The size of each frame, \(L\), is set to 256 as the main target sound is mostly impulsive and lasts only milliseconds.
## 1 Introduction
Through the analysis and comparison of simulation results from PID control, it is identified that the H\({}_{\infty}\) controller can improve the stability of power frequency under external load disturbance and the HESS can convert the output power demand signal from the H\({}_{\infty}\) controller into the corresponding power flow effectively. This paper is organized as follows: in Section 2, a new topology diagram of four-area interconnected power system with the HESS is presented and concepts of the LFC and the HESS are introduced. Section 3 presents a detailed mathematical model of \(i\)th control area within the four-area interconnected power system in the form of state space equation. H\({}_{\infty}\) state feedback controller is designed for power system with the HESS in the \(i\)th control area in Section 4, based on the mathematical model established in Section 3. Further simulation verification and results analysis of the method proposed in this paper are presented in Section 5. Finally, the conclusion is given in Section 6.