esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimal Location-Based Beamforming  
A location-based beamformer can be expressed as [22]  
\[\mathbf{w}(\psi)=\frac{1}{\sqrt{M}}\left[1,e^{-j\text{rcos}(\psi)},...,e^{-j(M -1)\text{rcos}(\psi)}\right]^{T} \tag{36}\]  
where \(\psi\in[0,\pi]\) is the beamforming direction. The optimal beamformers can be denoted as \(\mathbf{w}_{b}^{*}=\mathbf{w}\big{(}\psi_{b}^{*}\big{)}\) and \(\mathbf{w}_{\text{AN}}^{*}=\mathbf{w}\big{(}\psi_{\text{AN}}^{*}\big{)}\) where \(\psi_{b}^{*}\) and \(\psi_{\text{AN}}^{*}\) are the optimal beam directions for useful signal and AN, respectively.  
In this paper, the beamforming scheme is designed based on the QoS of Bob and Eve. Because ECC has been widely used in wireless communication systems, the redundancy of code allows the receiver to correct a limited number of error bits. Hence, the receiver can successfully decode the message when the minimum SINR requirement is satisfied. On the other hand, AN needs to be strong enough at the eavesdropper side to make the BLER of the eavesdropper close to 1. When the scale of the antenna array is not sufficiently large at Alice, it is difficult to differentiate the performance between Bob and Eve nearby. In order to minimize the area near Bob with eavesdropping risk, it is necessary to increase the AN at Eve as much as possible without affecting the BLER of Bob. Hence, the beamforming design formulation ensures a minimum SINR requirement of Bob, denoted as \(\hat{\gamma}_{b}\), while minimizing the SINR of Eve. In this case, the BLER of Eve is maximized.  
We note that for each \((\psi_{b},\psi_{\text{AN}})\) pair, the resulted \(\mathbb{E}[\text{SINR}_{b}]\) monotonically increases with \(g_{b}\). Hence, when there exists values of \((\psi_{b},\psi_{\text{AN}},g_{b})\) such that the corresponding \(\mathbb{E}[\text{SINR}_{b}]\geq\hat{\gamma}_{b}\), the values of \((\psi_{b}^{*},\psi_{\text{AN}}^{*},g_{b}^{*})\) can be approached through Algorithm 1.
# Modeling Activity-Time to Build Realistic Plannings in Population Synthesis in a Suburban Area
## 6 Algorithm for Time-Choices Setting
### End-Time Range Components
#### 6.1.2 Start Time Minus Total Travel Time and Durations \(ST\_TTD_{A\to z}\)  
In this section, the calculation of \(ST\_TTD\) is detailed. \(ST\_TTD_{x\to y}\) represents the difference between (1) the total travel time from \(x\) to \(y\) and the total of duration of all intermediate activities \(I_{x\to y}\) and (2) the latest possible start-time of activity \(y\).  
Figure 13: Local latest time: \(LLT\) is equals the minimum of (1) \(LST\) from simulation, (2) \(e^{*}\) of the current activity, and (3) \(ST\_TTD\) the latest possible time to complete the activity-plan (next activities), by satisfying the duration and end-time constraints (spending the typical duration in each activity and finishing each activity before the closing-time).  
First, the start-time \(ST_{x}\) of the activity \(x\) is defined as equal to the difference between (1) the typical \(duration_{x}\) and (2) the end-time \(e_{x}\) (see Equation (4)).  
\[ST_{x}=e_{x}-duration_{x} \tag{4}\]  
Then, the latest possible start-time \(lpST_{x}\) of activity \(x\) is calculated. It refers to the difference between \(duration_{x}\) and \(e_{x}^{*}\) (see Equation (5)).  
\[lpST_{x}=e_{x}^{*}-duration_{x} \tag{5}\]  
\(I_{x\to y}\) represents the set of activities visited (intermediates) along on the path from \(x\) to \(y\). It refers to all activities from \(x\) to \(y\) exclude \(x\) and \(y\) (see Equation (6)).  
\[I_{x\to y}=C_{x\to y}-\{x,y\} \tag{6}\]  
\(pTT_{A-z}\) represents the predicted travel time of the whole path from \(A\) to \(z\), passing by all the intermediate activities (\(I_{A\to z}\)) (see Equation (7)).  
\[pTT_{A-z}=\sum_{i=0}^{|C|-1}pTT_{C_{i}\to C_{+1}} \tag{7}\]  
\(pTT_{x\to y}\) is the predicted travel time between \(x\) and \(y\). It is calculated based on the Euclidean distance \(d_{x\to y}\) and the travel-speed from \(x\) to \(y\) (see Equation (8)).  
\[pTT_{x\to y}=\frac{d_{x\to y}}{travelSpeed_{mode_{x\to y}}} \tag{8}\]  
\(TD_{l}\) is the total typical duration of all activities that belong to the intermediate set \(l\) (see Equation (9)).  
\[TD_{I_{A\to z}}=\sum_{y\in I_{A\to z}}duration_{y} \tag{9}\]  
Finally, \(ST_{-}TTD_{A\to z}\) can be defined as in Equation (10).  
\[ST_{-}TTD_{A\to z}=lpST_{z}-(pTT_{A\to z}+TD_{I_{A\to z}}) \tag{10}\]  
The different calculated parameters are detailed in Figures 14 and 15.  
Figure 14: Latest possible start-time for next an activity \(x\).
## 2 Methodology
### Exergo-Economic Analysis
#### 2.4.2 Cost Balance  
The SPECO method applied in this research is based on three main stages [51]. In the initial stage, the exergy of each flow is determined, and a detail flow diagram of the system is developed presenting the exergy value of the inputs and outputs stream as a result of the exergy balance. In the second stage, the exergy performance of each component is studied as a function of the different flow interacting in the boundary of the device. Subsequently, in the last stage, the cost balances equations are proposed, as shown for this case in Table 4. The exergy cost of the k-device is the addition of the capital investment (\(\dot{Z}_{\text{k}}^{\text{CI}}\)) and the O&M costs (\(\dot{Z}_{\text{k}}^{\text{OM}}\)), as shown in Equation (16).  
\[\dot{Z}_{\text{K}}=\dot{Z}_{\text{K}}^{\text{CI}}+\dot{Z}_{\text{k}}^{\text{OM}} \tag{16}\]The exergy costs balance applied to open systems under a steady-state operation condition follow the structure presented in Equation (17). [54].  
\[\sum_{\text{j=1}}^{\text{n}}\dot{\text{C}}_{\text{j,k,in}}+\dot{Z}_{\text{K}}= \sum_{\text{j=1}}^{\text{m}}\dot{\text{C}}_{\text{j,k,out}} \tag{17}\]  
The exergetic balances equations for the devices of the SORC systems employing Equation (17) are presented in detail in Table 4.  
The total exergy cost flow can be defined as shown in Equation (18).  
\[\dot{\text{C}}_{\text{j}}=\text{ c}_{\text{j}}\cdot\dot{\text{E}}_{\text{j}}. \tag{18}\]  
The term \(\text{c}_{\text{j}}\) is the levelized cost per unit of exergy. In the ITC1 and ITC 3 of the SORC system, the costs per unit of exergy of state 11 and state 1A are null, because they have negligible acquisition and selling costs.
## 5 Results and Discussion
### Hyper-Parameters  
In this section, the results produced by hyper-parameters are examined. The settings for hyper-parameters used in this research study are shown in Table 4. The most important parameters of the proposed architecture are learning rate, hidden states, Stacked BiLSTM, and dropout rate. We have used a learning rate from 2.5 \(\times\) 10\({}^{-5}\) to 0.00005 as cited in Figure 5 to optimize the model. It indicates that the model produces poor results on a learning rate of 0.00005. Therefore, it is evident that the model is unable to converge on high learning rates. In contrast, the model produced joint goal accuracy of more than 87.20% and 93.70% on  
Furthermore, we used the different numbers of layers of Stacked BiLSTM, i.e., 2, 3, 4, 5, and 6 to find the best suitable number of layers for BLA. We noticed that when the neural network increased in depth, it reduced the results of the model as cited in Figure 6. When the number of layers is more than two, the results for joint goal accuracy are produced at less than 93.73% for WoZ-2.0 dataset and less than 87.31% for DSTC-2. Similarly, the performance of the model is decreased on turn request accuracy for the DSTC-2 dataset. However, the performance on the WoZ-2.0 dataset increased by 0.02% for turn request accuracy after setting up the number of layers to 4 and 6. Thus, it is perceived from the experiments that keeping the number of layers smaller is sufficient. We attained better results after setting up the two layers for the Stacked BiLSTM layers module of the model.  
We used the dropout layer as cited in Figure 7 to analyze the effect on neural network regularization. We observed that the model significantly improved the results between 0.2 and 0.4. On the other hand, when the dropout rate is first decreased and then increased, the model performed poorly among the joint goal accuracies and turn request accuracies on both datasets. The model produced the best results when the dropout is set up to 0.25. We picked values that are beneficial for the model to produce better results upon joint goal accuracy. It demonstrated that Stacked BiLSTM layers and learning rate have a significant impact on the model during the learning of features. We also observed that when the learning rate is larger, i.e., 0.001, 0.01, and 0.1, the model was unable to produce joint goal accuracy and remained at 0%. Meanwhile, the turn request accuracies are less than 70% for both datasets on a large learning rate.  
Figure 6: Impact of Stacked BiLSTM.
## 3 Vibration Signal Preprocessing and Fault Feature Extraction
### GWO-VMD-Based Vibration Signal Decomposition
#### 3.1.2 Deficiencies of VMD  
Compared with EMD, VMD has better decomposition capabilities in processing intermittent and noise-containing signals. When VMD parameters are properly set, the mode mixing phenomenon could be better suppressed than with EMD [37].  
However, VMD also has deficiencies--its two important parameters, the mode number \(K\) for decomposition and the penalty factor \(\alpha\) for frequency bandwidth adjustment, need to be manually determined with experience, and the decomposition result would be affected when the two parameters are not properly set. According to the principle of VMD, in cases in which the value of \(\alpha\) is fixed, if \(K\) is too large, the signal will be over-decomposed; this causes a same frequency band to be decomposed into multi-mode. While if \(K\) is too small, the signal is under-decomposed, and the central frequencies of different bands appear on the same mode. In another case, when the value of \(K\) is fixed, a too big \(\alpha\) results in a bandwidth between modes that is very small, resulting in the loss of useful feature information. If the value of \(\alpha\) is too small, the bandwidth of each mode becomes wide, resulting in more noise being retained. This will further lead to low fault classification accuracy. Therefore, it is important to optimize the two parameters to get more reasonable decomposition results.
## 3 Results  
### Long-Range Imaging Experiment  
Figure 8 shows 4 example profiles from the long-range experiment. The blue trace corresponds to the profile extracted using the matrix inversion technique and the red trace corresponds to the profile from the optimizer. The black diamonds correspond to the \(C_{n}^{2}\) outputs from the 4 anemometers. Gaps in the blue trace are due to negative values of \(C_{n}^{2}\), which sometimes appear in the matrix inversion method. The oscillations in the blue trace are due to noise from the measurement and from the profile extraction method. The constrained optimization technique performs better in presence of noise. Overall, there is good agreement between the optimizer profiles and the anemometer derived \(C_{n}^{2}\) values. They follow the same trend, and the values are reasonably close. It is important to note here that due to a power issue, one of the 4 anemometers stopped recording during some data collections. It is apparent from Figure 8 that the present technique fails to profile turbulence beyond 370 m from the cameras. As mentioned before, the weighting functions start looking very similar beyond this point.  
Figure 8: Comparison of time-lapse derived estimates with sonic anemometer measurements. (**a**) At 15:50 local (UTC—4), 27 June 2019; (**b**) At 11:30 local (UTC—4), 28 June 2019; (**c**) At 12:15 local (UTC—4), 28 June 2019 and (**d**) At 20: 45 local (UTC—4), 25 July 2019.  
Figure 7: Weighting functions corresponding to 3 sensing geometries and 81 separations on the target board used in the short-range imaging experiment.  
In strong turbulence conditions, the spots with the smallest separation merged, making it impossible to track their motion. This resulted in only 7 usable separations, and hence 21 weighting functions that could be used for extraction of profile in the strong turbulence cases. The reduced set of weighting functions for the strong turbulence case makes the technique fail even earlier. Thus, it is not possible to compare the \(C_{n}^{2}\) value obtained from the anemometer at 400 m with a corresponding estimate derived from time-lapse measurements. The profiles from time-lapse measurements (red trace) in Figure 8b,c have \(C_{n}^{2}\) values lower than those from the anemometers. Both these profiles correspond to times when the turbulence was strong and hence the assumption that the phase structure function is approximately equal to the wave structure function does not hold good. This assumption used in the derivation of the weighting functions probably caused the underestimation of \(C_{n}^{2}\) in these cases. However, the differences are not significant. Although effects due to any common mode disturbances were eliminated by using a differential tilt signal, any difference in the motion of the two cameras could have affected the results. During strong turbulence situations, it was difficult to get the cameras focused. This defocus could have been an additional source of noise.
## 4 Failure Characteristics and Mechanisms
### Evolution of Crack Closure Stress and Damage Stress Under Dynamic Cyclic Loading-Unloading  
In the progressive failure of coal, crack closure stress (\(\sigma_{\rm cc}\)) and crack damage stress (\(\sigma_{\rm cd}\)) play critical roles. \(\sigma_{\rm cc}\) represents the stress at which pre-existing cracks within the material fully close, while \(\sigma_{\rm cd}\) denotes the onset of unstable crack propagation and corresponds to the long-term strength threshold of the material [39]. Figure 10 illustrates the determination methodologies for \(\sigma_{\rm cc}\) and \(\sigma_{\rm cd}\) under quasi-static and dynamic loading conditions.  
\[\varepsilon_{\rm p}=\varepsilon_{1}+\varepsilon_{2}+\varepsilon_{3} \tag{1}\]  
Figure 8: Evolution of acoustic emission signals during quasi-static loading.  
\[\varepsilon_{vc}=\varepsilon_{v}-\frac{(1-2\mu)(\sigma_{1}-\sigma_{3})}{E} \tag{2}\]  
where \(\varepsilon_{1}\), \(\varepsilon_{2}\), and \(\varepsilon_{3}\) are the principal strains; \(E\) and \(\mu\) denote [PERSON]'s modulus and [PERSON]'s ratio, respectively; and (\(\sigma_{1}\)-\(\sigma_{3}\)) is the deviatoric stress.  
For dynamic loading condition, lateral strains (\(\varepsilon_{2}\), \(\varepsilon_{3}\)) are experimentally inaccessible. Following [PERSON] et al. (2018), \(\sigma_{\rm cc}\) and \(\sigma_{\rm cd}\) were determined using axial stress-strain data, with elastic volumetric strain (\(\varepsilon_{\rm ve}\)) calculated via the tangent modulus at half of the peak stress (Figure 10b).  
Figure 11 shows that under quasi-static loading, \(\sigma_{\rm cc}\) initially decreased and then increased with N. This trend arose because \(\sigma_{\rm cc}\) reflects the minimum stress required to close internal cracks. Early cycles generate new cracks, reducing \(\sigma_{\rm cc}\). However, at higher N and confining pressures, crack closure is mechanically constrained, elevating the scale of \(\sigma_{\rm cc}\)[PERSON] et al. (2018). In contrast, under dynamic loading, \(\sigma_{\rm cc}\) first increased (due to confinement-restricted crack closure) and then decreased (post-critical damage overwhelmed the confinement effects).  
Figure 10: Schematic diagram for calculating \(\sigma_{\rm cc}\) and \(\sigma_{\rm cd}\) under quasi-static and dynamic loading.  
Figure 9: Surface crack evolution during dynamic loading.  
Figure 12 shows that \(\sigma_{\text{cd}}\) increased with N under both loading regimes. Confining pressure suppresses new crack initiation, necessitating higher deviatoric stresses to propagate cracks (quasi-static) [42]. Under dynamic loading, cyclic impacts elevate the crack damage stress threshold [43]. However, Figure 12b shows a slight \(\sigma_{\text{cd}}\) reduction in the final cycle, which was attributed to severe cumulative damage impairing the load-bearing capacity.
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 5 Aviation Big Data Multi-Layer Security Sharing Framework  
According to the ATM data-sharing scenario and sharing requirements, the framework protects ATM data layer by layer through different protection strategies, achieving comprehensive security from data storage to sharing. This security architecture employs technologies such as blockchain, federated learning, attribute-based encryption, and multi-objective optimization to achieve secure data transmission, storage, and sharing in the ATM system. It provides a feasible solution for aviation big data security sharing, and its system architecture is shown in Figure 2.  
Figure 2: Multi-layer security sharing architecture for ATM data.  
A trusted data-sharing mechanism is established using blockchain, leveraging its tamper-proof, traceable, decentralized, and single-point-failure-resistant characteristics to protect data security. By adopting a clustering-based grouped PBFT consensus mechanism, the consensus efficiency and throughput among ATM data participants are improved, ensuring efficient data sharing. Through a multi-objective optimization-based block deployment strategy, the storage occupancy ratio of participants is significantly reduced. The optimized block deployment and efficient consensus strategy address blockchain's storage and latency issues, laying the foundation for subsequent data sharing and model sharing.  
In terms of data sharing, hash algorithms and encryption algorithms are used to ensure data integrity and confidentiality. To tackle the challenges of massive data retrieval and the over-/under-authorization issues in traditional access control, a data attribute-based retrieval method and a fine-grained attribute-based access control method are proposed.  
To address privacy leakage risks in sharing, an ATM-oriented model-sharing method is introduced, leveraging blockchain's decentralization to provide a secure and reliable storage environment for federated learning. The Shapley value contribution assessment method is used to identify low-quality data and incentivize user sharing.  
The combination of data sharing and model sharing covers the multi-level data sharing needs of current ATM participants.
## 2 Coded Aperture Compressive Temporal Imaging (CACTI)
### Improved Reconstruction Algorithm Using Multiple Regularization  
To utilize inter-frame redundant information and sparsity in multiple transform domains, the proposed reconstruction algorithm adopts multi-regularization constraints to reconstruct the video. Reconstructing the images with multiple regularizations is equivalent to the following constraint problem:  
\[\underset{\omega}{\mathrm{min}}\lambda_{1}\|\omega\|_{\ell_{2,1}^{2}}+\underset {x}{\mathrm{min}}\lambda_{2}\|\mathrm{TV}(x)\|,\,\,\,\,  
where \(\mathbf{H}\) is the Transformation matrix of Total Variation, and:  
\[y^{(t)}=y^{(t-1)}+\Big{(}y-\mathbf{\Phi}\theta^{(t-1)}\Big{)},\forall t\geq 1 \tag{31}\]  
Equation (29) is further developed into the following sub-problems:  
\[\theta_{\Gamma_{k}}^{(t)}=\chi_{\Gamma_{k}}^{(t)}\text{max}\Bigg{\{}1-\frac{ \lambda^{(t)}}{\left\|\chi_{\Gamma_{k}}^{(t)}\right\|_{2}^{2}},0\Bigg{\}} \tag{32}\]  
It has been proved that both grouping and weights play an important role for enhancing the reconstruction accuracy in GAP with weighted \(\ell_{2,1}\) norm, which demonstrates the importance of exploiting the inherent structural sparsity of natural images. The groups seem to have yielded greater improvements than the weights, probably because the former capture the structural sparsity of solvers in a more direct way. To simplify the implementation of the multi-regularization algorithm, we only use the group when comparing with GAP using the weighted \(\ell_{2,1}\) norm.
## 3 Materials and Methods
### Reward Function
#### 3.3.4 QoE Function  
In this section, we highlight the QoE function. As previously delineated, QoE relies on three key parameters: the quality of each segment, the perceptual change in quality, and rebuffering or playback interruptions. The agent receives a reward for each decision, leading to the following function:  
\[r_{t}=f(q_{t}\,,q_{t-1}), \tag{4}\]  
Consequently, the QoE can be articulated as:  
\[QoE_{max}=\sum_{k=1}^{K}(r_{k})-\sum_{k=1}^{K}|\mu Rb_{k}|-\sum_{k=1}^{K-1}| \lambda(\Delta\text{fact}_{k})|, \tag{5}\]  
where \(\mu\) and \(\lambda\) represent the penalties attributed to rebuffering and changes in quality, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Quality** & **High Quality** & **Medium Quality** & **Poor Quality** \\ \hline High quality & 0 & \(-1\) & \(-2\) \\ Medium quality & 1 & 0 & \(-1\) \\ Poor quality & 2 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distance factor values between quality levels.  
We normalize the \(QoE_{value}\) to align it with the state of the art [17; 24; 29; 38], resulting in the following expression:  
\[QoE=\frac{QoE_{max}}{N}\;*\;100, \tag{6}\]  
Here, N denotes the total count of video segments.
## 3 Numerical Examples
### Arches without Buttresses
Finally, the arch lost its stability and collapsed due to significant deformation. The dominant four hinges are denoted as hinge I, II, III and IV in Figure 5d. The seismic capacity factor \(\lambda^{*}\) of the investigated arches are presented in Table 2, in which \(e\) is the relative difference between the simulation results and the results based on limit analysis. Clearly, the results from the FDEM simulation agree very well with the results in [PERSON] and Tornabene [17], especially with the analytical results based on limit analysis. However, additional results on the circular and basket-handle arches with \(\beta=120^{\circ}\) show that the relative errors are higher, suggesting the limitation of the proposed model. The failure modes of circular and basket-handle arches are shown in Figures 6 and 7, respectively. It is observed that the failure modes predicted from the FDEM simulations are in excellent agreement with the results predicted by the UDEC [17], especially for the hinges and their locations. Figure 5: Failure process of the circular arch with \(\beta=180^{\circ}\): (**a**) t = 0.5 s; (**b**) t = 0.65 s; (**c**) t = 0.85 s; (**d**) t = 0.95 s. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Embrace Angle \(\beta\) (\({}^{\circ}\))** & \multicolumn{3}{c}{\(\lambda\)\({}^{\star}\)} \\ \cline{3-5}  & **FDEM/\(e\) (\%)** & **Limit Analysis [17]** & **UDEC [17]/\(e\) (\%)** \\ \hline \multirow{3}{*}{Circular} & 120 & 1.14/10.2 & 1.27 & 1.28/0.79 \\  & 150 & 0.618/0.32 & 0.62 & 0.64/3.23 \\  & 180 & 0.294/1.38 & 0.29 & 0.31/6.90 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Basket- \\ handle \\ \end{tabular} } & 120 & 2.10/10.6 & 2.35 & 2.38/1.28 \\  & 150 & 1.067/0.66 & 1.06 & 1.10/3.77 \\  & 180 & 0.539/0.19 & 0.54 & 0.56/3.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(\lambda\)\({}^{\star}\) of circular and basket-handle arches without buttresses. Figure 6: Failure modes of circular arches. Figure 7: Failure modes of basket-handle arches.