esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Attention Mechanism  
In geophysics studies, the temporal attention mechanism is chosen to analyze the temporal characteristics of well logging data. Well logging data typically consist of measurements at multiple time steps, reflecting the physical properties of subsurface layers, such as resistivity, gamma-ray, and density, which change with depth. The model needs to extract important information related to the formation characteristics and fluid content from these continuous measurement sequences. This approach significantly enhances the accuracy of identifying physical features and deepens the analysis of subsurface properties.  
The core idea of the attention mechanism is to assign varying weights to elements at different positions in the input sequence, thereby enabling the model to prioritize important ones. The following is a simplified formula representation for the attention mechanism.  
Figure 1: The structures of the different Inception versions. (**a**) The naive version, (**b**) the dimensional reduction version.  
Figure 2: The operating mechanism of the dimensionality reduction version of the Inception module.  
Suppose the input sequence is:  
\[\text{X}=(\text{x}_{1},\text{x}_{2},\dots,\text{x}_{T}) \tag{1}\]  
where \(T\) is the sequence length.  
For each time step \(t\), calculate the corresponding attention weight \(a_{t}\) and then perform a weighted sum of the input sequence to obtain the output.  
The formula for calculating the attention weight is:  
\[\begin{array}{l}e_{t}=\text{score}(h_{t-1},\text{x}_{t})\\ \alpha_{t}=\frac{\text{exp}(e_{t})}{\sum_{t=1}^{T}\text{exp}(\alpha_{t})}\end{array} \tag{2}\]  
where \(h_{t}\) represents the hidden state or output vector at a specific time step \(t\), the score is a scoring function that measures the relationship between the context \(h_{t-1}\), the input \(x_{t}\), \(e_{t}\) is the score, and \(\alpha_{t}\) is the normalized attention weight.  
The formula for weighted summation is:  
\[c_{t}=\sum_{k=1}^{T}\alpha_{k}x_{k} \tag{3}\]  
where \(c_{t}\) is the result of a weighted sum of the input sequence with attention.  
The attention mechanism calculates the correlation between query and key, identifies the most relevant value based on this correlation, and then assigns attention weights to the value to generate the final output result. This process entails deriving attention weights from the query-key correlation and applying them to process relevant values for computing the output. The attention mechanism process is shown in Figure 3.
# Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems
## 4 Analysis Results
### Stripline with Differential Signal
#### 4.2.3 Eye Diagram and \(V_{p2p}\) Reduction in Time Domain  
A PAM-4 signal with 1.4 V \(V_{p2p}\) at a 10-GHz data rate is applied to perform the eye diagram simulation. The eye diagram against the width (W) and space ratio (\(K_{S}\)) variation is shown in Tables 6 and 7, respectively. In addition, the eye diagram of the optimal structure at minimum allowable \(V_{p2p}\) is shown in Figure 11.  
The simulation results show the independence of the EH on the signal propagation modes and the distance. In this stripline structure, the channel requires a large width to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**W = 85 \(\upmu\)m**} & \multicolumn{4}{c}{\(K_{S}\)} \\ \cline{2-5}  & **1.0** & **2.0** & **3.0** & **4.0** \\ \hline \(EH_{normal}\) & 142.4 & 136.4 & 149.5 & 144.6 \\ \hline \(EH_{addmode}\) & 143.8 & 134.6 & 149.4 & 144.6 \\ \hline \(EH_{evenmode}\) & 138.4 & 136.2 & 149.5 & 144.7 \\ \hline \(EH_{invertscase}\) & 138.4 & 136.2 & 149.4 & 144.6 \\ \hline \(V_{P2p}\) for min EH & 1.01 & 1.03 & 0.94 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Eye diagram simulation against \(K_{S}\) in the stripline structure.  
Figure 10: S-parameter prediction for the optimal structure W = 85 \(\upmu\)m, \(K_{D}\) = 4.  
achieve \(Z_{0}\) of 100 \(\Omega\). In addition, the spacing between adjacent channels is higher than its width which resulted in a negligible FEXT coefficient. Hence, the EH is more dependent on the channel width due to trivial far-end crosstalk noise. While the ANN model suggests the optimal design with W = 85 \(\upmu\)m and \(K_{S}\) = 4, the simulation results show that the structure of W = 85 \(\upmu\)m, \(K_{S}\) = 3 and W = 90 \(\upmu\)m, \(K_{S}\) = 4 provide the better eye diagram. However, these structures are already excluded in the frequency domain because the return loss is out of the constraint. So, the one suggested by the ANN model is the optimal structure that satisfies the 100-mV EH requirement with a 0.43-V equivalent to 30.7% \(V_{P2P}\) reduction leading to the minimum power consumption.
# Propagation of Non-Linear Lamb Waves in Adhesive Joint with Micro-Cracks Distributing Randomly
## 5 Conclusions  
In this research, the propagation of nonlinear Lamb waves in an adhesive joint with micro-cracks distributing in a random way is systematically investigated by using the numerical simulation method. Based on the dispersion curves, a tri-layer adhesive structure model with randomly distributed micro-cracks is examined, and the Lamb wave mode pair satisfying phase velocity matching is chosen to investigate the interactions of the micro-cracks and the Lamb waves. The study demonstrates that the higher harmonics will be generated because of the bi-linear stiffness response at the area with micro-cracks under the action of tensile and compressive wave stress. The results show that:  
1. The ANPs increase with the propagating distance of the Lamb wave in the region of micro-cracks. Particularly, the magnitudes of the generated second harmonics in the \(z\)-direction are bigger than those in the \(x\)-direction, which indicates that the nonlinear effect in the \(z\)-direction is stronger.
2. The ANPs increase linearly with the micro-crack density. Specially, the ANP is more sensitive to the change of the micro crack length.
3. The ANPs are slightly related to the friction coefficient between the surfaces of micro-cracks.  
This investigation numerically demonstrates that the nonlinear Lamb waves can be used effectively in characterizing the micro-crack-induced damages within the adhesive layer of an adhesively bonded joint.  
[PERSON], [PERSON] and [PERSON] conceived and designed the simulations; [PERSON] performed the simulations; [PERSON] and [PERSON] wrote the paper; [PERSON], [PERSON] and [PERSON] revised the paper. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (No. 11472039, No. 11604033) and the Fundamental Research Funds for the Central Universities of China (No. 2019 CDXYHK0001).  
The authors declare no conflict of interest.  
Figure 16: ANP versus friction coefficient (\(c=0.002\)).
## 2 Methods  
### Conventional Median Filter  
Conventional median filters are widely used in VSP wavefield separation. We set the amplitude of geophone \(i\) at time \(t\) as \(\mathrm{d}(t_{i})\) and half the length of the median filtering window as \(k\). The median filter is then derived as follows [26]:  
\[\mathrm{M}(t_{i})=\arg\min\{\mathrm{d}(t_{i-k}\ ),\ldots,\mathrm{d}(t_{i+k})\},k \leq\frac{\max(i)}{2}, \tag{1}\]Before use of the median filter, the target wavefield needs to be aligned by time shifting, which is applied through the first arrival:  
\[\Delta t(i)=t(i)-t(1), \tag{2}\]  
where \(t(i)\) is the time of the first arrival at geophone \(i\) by the manual picking or automatic picking algorithm. However, there are four kinds of wavefields in the VSP wavefield, namely, the downgoing P-wave, downgoing S-wave, upgoing P-wave, and upgoing S-wave. Normally, the downgoing P-wave is easy to pick at first arrival, but the other three kinds of wavefields are difficult to pick because they are covered by the strong energy of the downward P-waves. Furthermore, the events of the same wavefield may come from different reflection layers and the reflection coefficient may be different, which will lead to changes in the slowness, and the events of the same wavefield will not be parallel as shown in Figure 1a. Even though the first arrival is picked accurately and aligned, the non-parallel events of the wavefield cannot be aligned through the same time shifting as shown in Figure 1b, which leads to a result in which the median filtering is incorrect. As shown in Figure 1c, there is an event (the black dashed arrow) that is missed in the result. Meanwhile, in the case of a low signal-to-noise ratio, the picking of the downgoing P-wave is affected by the noise, which leads to a result in which the events of the wavefield cannot be aligned, and this will reduce the accuracy of the results of the median filter. In order to obtain accurate time shifting and align all the events of the target wavefield, we calculated time shifting through the linear Radon transform as described in the following section.
## 4 Evaluation
### Subjective Evaluation  
An informal listening test was conducted to evaluate the subjective quality of reproduced speech signals using the Mean Opinion Score (MOS) test. In this test, 20 listeners with normal hearing ability participated and were asked to subjectively score the quality of the reproduced speech signals in terms of intelligibility and naturalness. The quality rating scale for each factor is Excellent = 5/Good = 4/Fair = 3/Poor = 2/Bad = 1. The data set for evaluation was composed of a randomly selected 20 pairs of utterances. Quality evaluation was carried out on the speech signals reproduced by the eight schemes presented in Table 4. Note that in the MLP-based methods with the PD and HE, \(w_{s}\) and \(N_{k}\) were chosen so that the average PESQ was maximized according to the objective results. The order of the stimuli synthesized by each method was randomly permuted. Each subject was allowed to listen to the stimuli as many times as needed before decision. The test stimuli were presented through a headphone in a quiet room.  
The results in Figure 8 show the average MOSs for each method. Similar to the results of PESQ, the highest MOS was obtained by the approach using both MSE and PD with HE (MLP + PD + HE). The maximum MOS scores were obtained by the proposed method (MMP + PD + HE) both in terms of intelligibility and naturalness. Only the MLP + PD + HE method yielded an average MOS score greater than 3.0. The listeners also indicated that both intelligibility and naturalness were superior to the other methods. In terms of  
Figure 7: Example of spectra. (**Top**): Original speech. (**Middle**): Predicted using baseline method (\(w_{s}=0\), \(N_{k}=0\)). (**Bottom**): Predicted using PD and He (\(w_{s}=0.25\), \(N_{k}=3\)).  
naturalness, the relatively low MOS scores of the three linear estimation-based methods (LR, MLR, and GMM) are mainly due to the averaging effects of the estimation scheme, which resulted in ambiguous and unclear voices. The quality improvements were achieved in part, by applying the NN-based methods. Applying the PD and HE further improved the quality of the reproduced speech signals. The listeners indicated the speech signals synthesized by the MLP + PD + HE method were clearer and more natural than other methods. A possible explanation for such MOS improvements over the conventional DNN-based methods is that a certain degree of harmonic structure is maintained in the voiced regions of the synthesized speech signals.  
The listeners also indicated that the proposed SSI method yielded noisy speech, although the overall quality was improved over the baseline method (in case of \(w_{s}=0\), \(N_{k}=0\)). This was due mainly to the usage of a random phase in the synthesizing procedure. But the reproduced speech signals continued to sound noisy when a least square-based phase estimation method [43] was adopted. Accordingly, it is highly desirable to apply a more reliable phase estimation scheme to construct the prediction rules for speech parameters.
# Failure Identification Method and LMI-Based Control System for Small Spacecraft
## 3 Attitude Controller
### Uncertainties in the \(B_{u}\) Matrix  
The reaction wheels' failure is considered as uncertainty in the matrix \(B_{u}\). First, the actuator health indicator (AHI) matrix is defined as  
\[AHI=\Delta=\begin{bmatrix}\delta_{1}&0&0&0\\ 0&\delta_{2}&0&0\\ 0&0&\delta_{3}&0\\ 0&0&0&\delta_{4}\end{bmatrix}\]  
where \(\delta_{i}\) indicates the status of the i-th reaction wheel. It is a scalar value between zero and one, where zero represents a failed reaction wheel and one represents a healthy one. It is possible to rewrite the spacecraft attitude dynamics equation to include the AHI  
\[J\dot{\omega}=u_{ext}-Z\Delta\tau-\omega\times(J\omega+Zh_{med}^{\omega})\]  
In the nominal case, when all of the reaction wheels are working, it is verified that  
\[Z\Delta\tau=Z\tau\]In the case of failure, the ratio between the component of \(Z\Delta\tau\) and \(Z\tau\) is not equal to one, and it is used to represent the \(B_{u}\) uncertainties. Defining  
\[b_{i}=\frac{(Z\Delta\tau)_{i}}{(Z\tau)_{i}}\]  
where \(b\in\mathbb{R}^{3}\) is the actuators' uncertainty vector, and \(B_{u}\) is corrected as  
\[(B_{u})_{un}=\begin{bmatrix}0&0&0\\ 0&0&0\\ 0&0&0\\ b_{1}/J_{x}&0&0\\ 0&b_{2}/J_{y}&0\\ 0&0&b_{3}/J_{z}\end{bmatrix}\]  
The operation used for the \(A\) matrix uncertainties is applied to each failure case. All of the \(M_{i}\) matrices are included to generate a single state-feedback controller \(K\)  
\[\begin{array}{c}M_{1}<0\\ M_{2}<0\\ \vdots\\ M_{5m}<0\\ Q>0\end{array}\quad\rightarrow\quad K=Y_{sol}Q_{sol}^{T} \tag{6}\]  
The new design process for \(K\) allows the controller to guarantee system robustness to failure.
## 2 Case Study: Energy Harvesting Floor
### Testing Piezoelectric Transducers
#### 2.2.2 Lead Zirconate Titanate Disk  
The piezoelectric disk presented better performance regarding the tested MFC transducers. This is because the structure of the piezo-material polarity is highly distributed along the vertical axis, making the PZT polarity structure close to having two dipoles, being the more effective applied force. The small size enabled a greater number of transducers on the platform. The tests performed on all the transducers highlight the importance of selecting the right piezoelectric type, which is analyzed in Section 3.
## 4 Analysis of the \"Overall-Local\" Coal Burst Type of Isolated Working Face
### Overall Stability of Isolated Working Face
#### 4.1.2 Total Bearing Strength of Isolated Working Face  
The total bearing strength R of the isolated working face is obtained by piecewise integration of Equation (7) over the interval L, that is,  
\[\begin{split}\text{R}=&\int_{0}^{\text{L}}R_{\text{x}} \text{dx}=\int_{0}^{\text{x}_{\text{p}}}\frac{\sigma_{\text{c}}}{\text{x}_{ \text{p}}}\text{dx}\text{dx}+\int_{\text{x}_{\text{p}}}^{\text{x}_{\text{p}}+ \text{x}_{\text{e}}}\sigma_{\text{c}}+\frac{\sigma_{\text{c}}(\text{k}-1)( \text{x}-\text{x}_{\text{p}})}{\text{x}_{\text{e}}}\text{dx}+\int_{\text{x}_{ \text{p}}+\text{x}_{\text{e}}}^{\text{L}-\text{x}_{\text{p}}-\text{x}_{\text{e }}}\text{k}\sigma_{\text{c}}\text{dx}\\ &\qquad\qquad+\int_{\text{L}-\text{x}_{\text{p}}-\text{x}_{\text {e}}}^{\text{L}-\text{x}_{\text{p}}}\sigma_{\text{c}}+\frac{\sigma_{\text{c}}( \text{k}-1)(\text{L}-\text{x}-\text{x}_{\text{p}})}{\text{x}_{\text{e}}}\text{ dx}+\int_{\text{L}-\text{x}_{\text{p}}}^{\text{L}}\frac{\sigma_{\text{c}}}{\text{x}_{ \text{p}}}(\text{L}-\text{x})\text{dx}\\ &=\left[\text{k}\left(\text{L}-3\text{x}_{\text{p}}-\text{x}_{ \text{e}}\right)+2\text{x}_{\text{p}}+\text{x}_{\text{e}}\right]\sigma_{\text{c }}\end{split} \tag{9}\]  
The variation in the total bearing strength \(R\) of the isolated working face, calculated through piecewise integration, directly affects the stress distribution and overall stability of the working face. During mining, factors such as surrounding rock pressure, coal seam strength, and support systems interact to change the bearing capacity of the coal and surrounding rock, thus influencing stability. When the total bearing strength is low, stress concentrates in the coal and surrounding rock, especially near the excavation face. As mining progresses, the supporting capacity of the surrounding rock decreases, and stress concentrates on the working face, which may lead to instability. A lower total bearing strength reduces the overall stability of the working face and increases the risk of coal bursts. Therefore, maintaining appropriate bearing strength and optimizing support design is crucial.  
The total bearing strength \(R\) obtained from piecewise integration helps more accurately assess the stability of the working face and develop suitable support strategies based on different mining conditions.
# AR in the Architecture Domain: State of the Art
## 4 AR Virtual Content
### Extension of 3D Models to AR  
Reality-based or virtual-based 3D models may require an additional step to be translated into data suitable for AR (Figure 9). The more straightforward passage regardsdirect uplo to an AR application, in which the automatic process of model adaptation is carried out. This case happens when a 3D model is ready for AR visualization without requiring post-production. Moreover, some modeling platforms foresee specific modules/plugins supporting editing process, i.e., AR-media (3 DS, Cinema4D); Kubity (Google SketchUp), Mindesk (Rhinoceros), Marui (Maya), and Revit Live (Revit). The third, and more complex, solution regards the necessity of managing geometric, material, illumination, or animation modifications. In this situation, the modeling activity diverges from AR data preparation, adopting game engine platforms. These programs support complex 3D model management, interoperability of file formats, rendering, animation, and data interaction. On the one hand, they are limited by the complexity of the platforms--a significant obstacle for inexperienced programmers. On the other hand, the passage between different platforms requires verification of the transition of the 3D model's geometric and material characteristics. For example, in model generation, particular attention must be paid to the creation of shapes. The use of solid or mesh models, for different reasons, allows better directional control of the surface normals--a fundamental feature for the correct visualization of the model in real-time rendering.  
In the mathematical surfaces, different directions of generative curves and main lines imply different distributions of normals. This aspect must necessarily be controlled a priori to reduce any subsequent post-processing work [18]. The most popular game engines are Unity 3D (Unity Reflect in the architecture domain), OpenSceneGraph, Unreal Engine, and CryEngine. Unity 3D is perhaps the most accessible platform that supports fully cross-platform development, but it does not allow real-time modeling. OpenSceneGraph is widely used for visual simulation, modeling, games, and mobile applications [83], but it is more oriented towards desktop and web-based applications. Unreal Engine 4 includes outstanding graphics features and is arguably the best tool for achieving accurate results. It enables the development and deployment of projects across all platforms and devices. CryEngine requires experienced developers.
## 5 Performance Evaluation
### Simulation Environment  
In this experiment, we install a Linux virtual machine program on the Windows 11 system, and then conduct simulations in the Mininet emulation environment under the Linux system. Mininet is a network emulator that creates a network of virtual hosts, switches, controllers, and links. It runs real network applications, using the Linux kernel and network stack, enabling easy transition of developed code to real systems for deployment, and its switches support OpenFlow for highly flexible custom routing and SDN. The virtual machine is allocated 8 CPU cores and 8 GB RAM. The selected controller is Ryu, which is based on Python, and the network topology used in the simulation experiment is a 6-pod fat-tree topology, as shown in Figure 7. It consists of edge layer switches that connect hosts, aggregation layer switches that consolidate traffic from the edge layer, and core layer switches responsible for routing traffic between different aggregation layers. Each Pod includes edge and aggregation layer switches and their interconnections, ensuring high scalability and redundancy of the network. The controller, located on the control plane, is not shown here but is connected to the network. Excluding the hosts, there are a total of 45 nodes, and the size and complexity of the topology are sufficient to meet the requirements of the experiment. The experimental traffic in the network is generated by _iPerf3_, and the types of flows mainly include bandwidth-demanding and latency-sensitive types. Moreover, for convenience of the experiment, the number of backup paths calculated and stored in the backup path generation module (introduced in Section 4.3.2) is set to be 3.
possible by structural load reduction [1]. Although several successful field trials have been reported (as discussed in Section 2.3), LAC is generally still limited to research activities; there remain a variety of obstacles preventing the widespread use of LAC in the wind industry. The objective of this paper is to provide a summary of how lidar systems can be optimized specifically for control applications in order to overcome the barriers preventing the widespread deployment of LAC.  
The results presented in this paper are based on the outcome of the IEA Wind Task 32 workshop \"Optimizing Lidar Design for Wind Turbine Control Applications\" held in Boston, MA in July 2016. IEA Wind Task 32: \"Wind Lidar Systems for Wind Energy Deployment\" is an international open platform with the objective of bringing together experts from the academic and industrial communities to identify and mitigate barriers to the use of lidar for wind energy applications. During the workshop, participants from academia, national research laboratories, as well as the lidar and wind turbine industries discussed the barriers preventing the widespread use of lidars for wind turbine control, strategies for overcoming those barriers, and ideas for maximizing the effectiveness of lidars for control applications. In this paper, both practical considerations for overcoming the obstacles to the use of lidars for control and theoretical approaches for optimizing lidar scan patterns are discussed.  
The remainder of this paper is organized as follows. A review of lidar technology, LAC, and previous work on lidar optimization for control purposes is provided in Section 2. Sections 3 and 4 discuss the identified practical barriers to the widespread use of lidars for control and strategies for overcoming the barriers, respectively. Theoretical approaches to optimizing lidar scan patterns by minimizing either (a) the error between the lidar measurements and the rotor effective wind speed or (b) the deviation from the intended setpoint of a control variable of interest (e.g., rotor speed) with the use of LAC are described in Section 5. Section 6 outlines how a frequency domain model can be used to calculate measurement or controller setpoint error for different scan configurations. Optimal scan parameters are provided for a variety of scan scenarios. In Section 7, design choices required to maximize the utility of lidar measurements in a feedforward pitch control scenario are illustrated. Time domain simulations show that the rotor speed regulation (or equivalently, generator speed regulation) achieved by an optimized control scenario matches the performance predicted by the frequency domain model. Next, a technology outlook, discussing potential future directions of lidars for control purposes, is provided in Section 8. Finally, Section 9 concludes the paper.