esa-sceva/satcom-chunk-collection huggingface dataset:
The basics of the traditional service life evaluation principles are provided by the application of damage accumulation hypotheses, such as those according to [PERSON] and [PERSON] [7; 8]. However, experimental investigations have shown that, especially in the case of additional corrosive (chemical) loading, the assumption of linear accumulation damage being related to stresses and strains applied is only valid to a limited extent. Again, safety factors are the current solution to cover the resulting inaccuracies and uncertainties [9]. Short-time evaluation procedures (STEP) developed in recent years need to be mentioned in this context, including the StrainLife method to be considered here, which can reduce the number of fatigue tests, as well as the time and cost required by more than 90%. Many of those STEP methods are based on generalized Morrow and Basquin equations, e.g., in a combination of strain (SIT) or load (LIT) increase tests and stress or strain-controlled constant amplitude tests (CAT), which enable the evaluation of S-N curves for the materials investigated. The validation of the STEP has already been successfully performed on various light metal alloys and steels in different heat treatment conditions, including the determination of scatter bands for different fracture probabilities [10; 11; 12; 13; 14; 15]. In addition to conventional mechanical stress-strain measurements, STEP for determining S-N curves can also take advantage of NDT-based techniques and parameters, which may rely on temperature and electrical resistance, but also on magnetism or ultrasound. These methods may, therefore, provide a potential for detecting and tracing the damage evolution in a non-invasive way. Electrochemical parameters based on the corrosion potential [16; 17; 18; 19] or the corrosion current density [18; 20] can also be used for damage characterization in corrosion fatigue tests. Microstructural changes, such as the formation of slip bands or the crack initiation/propagation on the material's surface, can be correlated with these measured variables [20]. This has been successfully shown for different materials allowing the progression of corrosion fatigue damage to be described [21; 22]. The STEP mentioned above was extended in \"MiBaLeb\" to include aspects, such as strain control, thermo-mechanical loading and corrosion, which are important aspects for the design of piping systems in nuclear facilities, and this in view of allowing a transition between component behavior as well as material properties and vice versa.
## 3 Principals of Routing Protocols and Their Breakdowns
### Data-Based Routing Protocols
#### 3.4.4 OMR [68]  
A novel routing protocol called optimal multi-modal routing (OMR) is developed for underwater networks of multi-modal nodes to optimize the quantity of information conveyed across all technologies accessible to each node. For forwarding a packet toward its destination, OMR makes distributed choices about the flow of each connection, avoids bottlenecks, and allocates resources equally to various nodes. To prevent excessive retransmission, routes with relay and bias are picked in the route to the sink. The nodes must select how many bits to broadcast through each accessible node.  
* Comparison  
Here, most protocols use a void avoidance system; hence, the end-to-end delay ranges from medium to low for all the protocols. From Table 17 we can deduce that most of these protocols have low delivery rates apart from EBOR, and the network cost varies according to the protocols but all of them have low energy consumption.  
* Flood-Based Routing Protocol: The flooded routing protocol was one of the first to be suggested. In the flooding protocol, the node that receives the data information broadcasts the packet to its neighbors until it reaches the destination node or exceeds a predetermined number of modifications. Most flood routing protocols are straightforward and efficient routing methods that do not necessitate network topology or computational routing maintenance. Flooding protocols offer the advantages of being simple to build and having a high level of fault tolerance. However, there are issues such as internal message disclosure and resource waste. The following are a few examples of flood-based protocols.
## 3 Method
### F2 MF-Based Multi-Level Feature Forecasting  
Dense semantic forecasting estimates future semantic predictions from observed frames. Recently, a method based on feature-to-feature (F2F) forecasting achieved a great accuracy/efficiency ratio [29]. In order to be efficient, this method requires a single-frame recognition model with no skip connections. However, this requirement comes at the cost of reduced single-frame recognition accuracy, which affects the forecasting performance. We propose to mitigate this issue by training the recognition model with the MixSkip procedure and combining it with F2 MF module adapted for multi-level feature forecasting.  
The F2 MF module forecasts future features by combining the predictions from two heads. The F2F head forecasts the features directly. The F2M head forecasts by separately warping each past feature representation into the future according to the predicted flow. The multiple forecasts are blended according to the densely predicted weights. The original work only considers single-level applications of the F2 MF module to custom dense recognition architectures. Our extension enables application of the F2 MF module to ladder-style architectures. In particular, we reuse the flow and weight predictions from the F2M module to warp the representations coming from skip connections. This minimally affects the forecasting efficiency since most of the computation is still done on the most-condensed representation of the recognition model. However, the predicted feature flow and blending weights must be upsampled prior to warping in order to match the resolution of the corresponding skip connections. This is suboptimal because the upsampled flow is coarse and noisy. Consequently, we train our recognition model to be more resistant to noise in skip connections. Additionally, we fine-tune the upsampling path in order to adapt to the forecasted features.  
Figure 3 shows our dense-semantic-forecasting system. The inference pipeline is as follows. First, we extract features from each of the four past images independently. The  
Figure 2: Code illustration of our MixSkip training procedure.  
feature extraction involves the backbone (yellow) and the SPP module (green). As usual, the F2 MF module (red) maps past SPP features into the future counterparts. Furthermore, we bring the predicted feature flow and blending weights to the three warp modules (purple). Each warp module additionally takes the past features from the corresponding skip connection. First, it upsamples the feature flow and the blending weights. Note that the flow vectors also have to be scaled with the upsampling factor. Second, it warps and blends the skip connections features into the future representation. Finally, all of the forecasted features (SPP and skip connections) are interpreted by the upsampling path in order to recover the future semantic predictions.
## 2 Materials and Methods
### Unstructured System Identification Using Laguerre Functions  
Laguerre functions are a complete orthonormal set in \(L_{2}\)\((0,\infty)\) space, and they are particularly popular due to their simple realization. These functions are defined as a functional series [27; 28; 30; 31]:  
\[\Phi_{i}(t)=\sqrt{2p}\frac{e^{pt}}{(i-1)!}\frac{d^{i-1}}{dt^{i-1}}\left[t^{i-1}.e^{2 pt}\right]\,\ \ i=1,2,\ldots,\infty \tag{3}\]  
where \(p\) is a constant called time scaling factor, and \(t\in[0,\infty)\) is a time variable. The Z-transform of the Laguerre functions is [27; 28; 30; 31]:  
\[\Phi_{i}(z)=\mathcal{Z}\{\Phi_{i}(t)\}=\frac{\sqrt{1-p^{2}}}{1-pz^{-1}}\bigg{(} \frac{z^{-1}-p}{1-pz^{-1}}\bigg{)}^{i-1},\ \ i=1,2,\ldots,\infty \tag{4}\]  
Any open-loop stable system can be approximated by \(n\) order Laguerre series as [30; 31]:  
\[Y_{m}(z)=\sum_{i=1}^{n}C_{i}\Phi_{i}(z)U(z)=\sum_{i=1}^{n}C_{i}l_{i}(z) \tag{5}\]  
Among the different methods for expressing the Laguerre ladder network, the state space form is more suitable for system identification. Hence, the system outputs could be derived in the time domain directly. Using Laguerre functions, the system can be expressed in the discrete form as [27; 28; 30]:  
\[L(k+1)=AL(k)+Bu(k)y(k)=CL(k) \tag{6}\]  
In (6), \(L(k)\) is a \(N\times 1\) state vector and \(u(k)\) denotes the system input. \(A\) is a \(N\times N\) lower triangular system matrix. \(y(k)\) is the system output. \(B\) and \  
Elements of \(A\) and \(B\) are determined off-line, hence reducing the computational burden considerably. The Recursive Least Squares (RLS) [23; 34; 35] algorithm is utilized for online determining of the elements of the \(C\) vector:  
\[\begin{array}{c}C(k)=C(k-1)+\{M(k)[y(k)-C(k-1)L(k-1)]\}^{T}\\ M(k)=\frac{P(k-1)L(k-1)}{\lambda+L^{2}(k-1)P(k-1)L(k-1)}\\ P(k)=\frac{1}{\lambda}\big{[}P(k-1)-M(k)L^{T}(k)P(k-1)\big{]}\end{array} \tag{9}\]  
where \(0<\lambda\ \leq\ 1\), \(\lambda\) is the forgetting factor in RLS algorithm.  
The following assumptions are considered for the system:  
A.1. \(C\) is bounded and the state space of the system is stable (\(p>0\)), observable, and controllable.  
A.2. Laguerre functions are an accurate description of the system.
## 3 Incorporation of DANNC and Collision-Obstacle Avoidance for a Group of UUVs
### Leader-Follower Formation Tracking
#### 3.1.1 Formation Control Term  
To do a formation control, we define an auxiliary variable vector, \(e_{i}\in R^{6}\) for the UUV \(i\) as  
\[e_{i}=\sum_{j\in N_{i}}a_{ij}\left(\mathbf{x}_{i}-\delta_{i}-\left(\mathbf{x} _{j}-\delta_{j}\right)\right)+a_{iL}\left(\mathbf{x}_{i}-\delta_{i}-\mathbf{x }_{L}\right) \tag{12}\]  
where \(a_{ij}\) and \(a_{iL}\) are described in Section 2.1.1.  
A distributed adaptive formation tracking control is then designed based on the consensus tracking protocol and the feedback linearization technique, which is given as  
\[u_{iC}=c_{i}Ke_{i} \tag{13}\]  
Figure 2: The overall distributed adaptive neural networks formation tracking control with collision and obstacle avoidance for the UUV follower.  
where \(K=-B^{\top}P\in R^{3\times 6}\) is a feedback control gain, \(c_{i}\in R\) is a coupling weight updated as  
\[\dot{c}_{i}=\Gamma_{ic}\Pr\left(c_{i},e_{i}^{\top}PBB^{\top}Pe_{i}\right) \tag{14}\]  
where \(\Gamma_{ic}\) is a positive constant, and \(\mathrm{P}\) is the positive definite solution based on the algebraic Riccati equation  
\[A^{\top}P+PA+Q-PBB^{\top}P=0 \tag{15}\]  
where \(Q\in R^{6\times 6}\) is a positive-definite matrix.
## 3 Results
### Personal Opinions (Open-Ended and Multiple-Choice Questions)  
#### 3.4.1 Preferences Between Activities: Multiple-Choice Question  
To explore participants' preferences between the two main ICT-supported activities, a multiple-choice question asked \"Which activity did you find most interesting?\" (Q21, see Appendix A). The response options were as follows: (a) the activities with digital  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Items** & **Grade** & **Group A** & **Group B** & **Group C** & **Total** \\  & **of Satisfaction** & **(_n = 28_)** & **(_n = 28_)** & **(_n = 21_)** & **(_N = 77_)** \\ \hline Q13 & Unsatisfactory 1 & 0.0 & 0.0 & 4.8 & 1.3 \\  & Neutral & 7.1 & 0.0 & 4.8 & 3.9 \\  & Satisfactory 2 & 92.9 & 100.0 & 90.5 & 94.8 \\ Q14 & Unsatisfactory 1 & 3.6 & 0.0 & 9.5 & 3.9 \\  & Neutral & 17.9 & 10.7 & 0.0 & 10.4 \\  & Satisfactory 2 & 78.6 & 89.3 & 90.5 & 85.7 \\ Q15 & Unsatisfactory 1 & 3.6 & 0.0 & 4.8 & 2.6 \\  & Neutral & 28.6 & 21.4 & 4.8 & 19.5 \\  & Satisfactory 2 & 67.9 & 78.6 & 90.5 & 77.9 \\ Q16 & Unsatisfactory 1 & 3.6 & 0.0 & 4.8 & 2.6 \\  & Neutral & 17.9 & 10.7 & 9.5 & 13.0 \\  & Satisfactory 2 & 78.6 & 89.3 & 85.7 & 84.4 \\ Q17 & Unsatisfactory 1 & 3.6 & 0.0 & 4.8 & 2.6 \\  & Neutral & 21.4 & 7.1 & 4.8 & 11.7 \\  & Satisfactory 2 & 75.0 & 89.3 & 90.5 & 84.4 \\ Q18 & Unsatisfactory 1 & 0.0 & 0.0 & 4.8 & 1.3 \\  & Neutral & 14.3 & 7.1 & 0.0 & 7.8 \\  & Satisfactory 2 & 85.7 & 92.9 & 90.5 & 89.6 \\ \hline \hline \end{tabular}  
* Unsatisfactory or very unsatisfactory. 2 Satisfactory or very satisfactory.  
\end{table}
Table 4: Percentages (%) of pre-service teachers’ ratings on activity involving digital news.  
newspaper articles, (b) the activity using TikTok, (c) both activities equally, and (d) neither the press nor the TikTok activity.  
Overall (see Table 5), the majority of pre-service teachers (59.7%) selected the TikTok activity as the most interesting, followed by 29.9% who valued both activities equally. A smaller proportion preferred the digital news activity (7.8%), and only 2.6% reported that neither activity was particularly interesting. When disaggregated by group, the following observations were made:  
* Group A showed a 57.1% preference for TikTok, 25% for both activities equally, 10.7% for digital news, and 7.1% for neither activity.
* Group B showed a stronger preference for TikTok (64.3%), 25% for both, and 10.7% for digital news, with no participants selecting \"neither\".
* Group C, by contrast, revealed a different trend: 42.9% reported liking both activities equally, while 57.1% selected TikTok, and none chose digital news or neither.  
A chi-square test of independence was conducted to examine potential differences among the three groups in their responses to the multiple-choice question (Q21). The results indicated no statistically significant association between group membership and activity preference, \(\chi^{2}\)(6, \(N\) = 77) = 7.533, \(p\) = 0.274.  
These results suggest a clear overall preference for the TikTok-based activity, with some variation across groups and a notable proportion recognising value in both instructional approaches.
## 4 Impact Analysis of Cushion Blocks on Winding Axial Strength
### Impact of Cushion Block Offset on Winding Axial Strength  
To investigate the influence of cushion block offset on the axial strength of the winding, three cushion block offset structures are set up, including a single-end offset, two-end same-direction offset, and two-end anisotropic offset. Four offset angles are considered, including 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\). Three cases with offset angles of 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\) are presented in Figure 16. The line chart depicting the maximum axial bending stress under conditions is shown in Figure 17.  
Figure 15: Maximum axial bending stress in the winding with different cushion block unbalanced heights.  
\begin{table}
\begin{tabular}{c c c} \hline
**Cushion Block** & **Flexural Modulus** & **Maximum Axial** \\
**Unbalanced Height** & **of Elasticity** & **Bending Stress** \\
**[mm]** & **[MPa]** & **[MPa]** \\ \hline
0 & 89.97 & 20.21 \\
2 & 84.86 & 22.64 \\
4 & 59.89 & 24.57 \\
6 & 52.22 & 26.51 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of test results under different unbalanced heights.  
It can be observed that, for windings with different cushion block offset structures, as the cushion block offset angle increases, the trend in the change of the maximum axial bending stress in the windings is the same, showing an increasing pattern. Moreover, as the offset angle increases to a certain degree, the magnitude of the increase in the maximum axial bending stress gradually rises, exacerbating the impact of the offset angle on winding strength.  
Different offset structures have varying effects on winding strength. Under the same cushion block offset angle, windings with cushion blocks deviating in opposite directions consistently exhibit the highest maximum axial bending stress, while windings with single-end offset structures consistently have the lowest maximum axial bending stress. In the  
Figure 16: Winding axial bending stress cloud for different cushion block offsets.  
Figure 17: Maximum winding axial bending stress in the winding for different cushion block offsets.  
range of offset angles from 0\({}^{\circ}\) to 15\({}^{\circ}\), the impact of different cushion block offset structures on winding axial strength is relatively small. However, as the offset angle increases, the magnitude of the increase in maximum axial bending stress for each structure also increases. Among these structures, those with opposite-direction offset show the most significant increase, leading to the greatest reduction in winding axial strength. The overall simulation trends align well with the experimental results, validating the effectiveness of the experiments.
## 3 Results
### Dominant Ions (Ca\({}^{2+}\), Mg\({}^{2+}\) and HCO\({}_{3}\)\({}^{-}\))  
The saturation of the water in the first tank with helium and then that of the second tank with hydrogen caused the degassing of the natural gases initially present, mainly an admixture of CO\({}_{2}\), N\({}_{2}\), and O\({}_{2}\). The degassing of CO\({}_{2}\) notably upsets the calco-carbonic equilibrium in the water, which seems to have led to the precipitation of CaCO\({}_{3}\) at the bottom of the tanks and MgCO\({}_{3}\) to a lesser extent, totaling a probable loss of around 29 mg\(\cdot\)L\({}^{-1}\) of dissolved elements. The water injected into the aquifer is therefore undersaturated with calcite and dolomite, which explains the observed decrease in the concentrations of Ca\({}^{2+}\), Mg\({}^{2+}\), and HCO\({}_{3}\)\({}^{-}\) in the samples taken during the first day (Figure 22). The cumulative concentration of these three ions decreased from 401 mg\(\cdot\)L\({}^{-1}\) before injection to a minimum of 372 mg\(\cdot\)L\({}^{-1}\) during injection, which corresponds to a 9.1% drop in their molar concentration shortly after the passage of the injected plumes. A maximum of 427 mg\(\cdot\)L\({}^{-1}\) is then observed at t = 2.80 days. This is interpreted as the dissolution of the carbonaceous aquifer rock by the injected water, which became more aggressive with respect to calcite and dolomite. The concentrations then reached their normal baseline values between the third and sixth days, with a cumulative concentration of 405 mg\(\cdot\)L\({}^{-1}\) until the end of monitoring.  
These cycles of variations in concentrations does not appear at any of the piezometers located further downstream (Figure 23). This shows that the aquifer has already returned to its natural calco-carbonic equilibrium at a distance of 10 m downstream from the injection well.  
Figure 21: Comparative evolution of the pH and the tracer concentration at PZ2 BIS (5 m downstream): (**a**) over a period of one month; and (**b**) during the first 24 h.  
Figure 22: Comparative evolution of the concentrations of dominant ions and tracer at PZ1 (20 m upstream) and PZ2 BIS (5 m downstream) during the week following the injection of dissolved hydrogen.
## 3 Results
### Principal Component Analysis  
PCA was conducted to compare all SRIs and to show which SRIs are close to plant traits. The results from PCA showed that the first factor (PC1) explained 53.1%, 66.5%, 53.8%, and 69.7% of the total variation whereas the second factor (PC2) explained 30.7%, 11.7%, 28.1%, and 14.1% of the total variation at the BT, AN, EMG, and combined three growth stages, respectively (Figure 5). At the BT stage, the three measured traits were grouped together with three water-related SRIs (WI-M, NDWI-P, and NDWI-M2) and one published vegetation-related SRI (RENDVI-P) in a negative PC1 slope, with an acute angle between the vectors of three traits and these four SRIs. The three traits were grouped with the same three water-related SRIs (WI-M, NDWI-P, and NDWI-M2) and all vegetation-related SRIs with a positive PC1 slope at the AN stage and in the combined three stages, and in a negative PC1 slope (except RENDVI-P) at the EMG stage, with an acute angle between the vectors of three traits and these SRIs (Figure 5). At each growth stage and in the combined three stages, the angle between the vectors of the three traits and other water-related SRIs was obtuse and straight (Figure 5). In general, an acute angle indicates a strong positive association between SRIs and the three traits. The opposite is indicated with obtuse and straight angles.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & \multicolumn{6}{c}{Early Milk-Grain Stage} & & \multicolumn{1}{c}{BY} \\ \hline BNDVI-P & **0.36** & **0.54** & **0.54** & **0.54** & **0.66** & **0.70** & **0.61** & **0.71** & **0.74** & **0.65** \\ BNDVI-M & **0.26** & **0.51** & **0.48** & **0.43** & **0.63** & **0.64** & **0.54** & **0.68** & **0.72** & **0.52** \\ GNDVI-P & **0.34** & **0.53** & **0.52** & **0.53** & **0.62** & **0.68** & **0.63** & **0.69** & **0.72** & **0.66** \\ GNDVI-M & **0.24** & **0.51** & **0.47** & **0.43** & **0.61** & **0.64** & **0.56** & **0.67** & **0.71** & **0.56** \\ RNDVI-P & **0.41** & **0.56** & **0.57** & **0.56** & **0.67** & **0.71** & **0.65** & **0.73** & **0.73** & **0.71** \\ RNDVI-M & **0.34** & **0.56** & **0.54** & **0.51** & **0.68** & **0.69** & **0.63** & **0.73** & **0.74** & **0.68** \\ RENDVI-P & 0.09 & \(-\)0.24 & \(-\)0.09 & \(-\)0.04 & \(-\)0.23 & \(-\)0.16 & \(-\)0.17 & \(\mathbf{-0.30}\) & \(\mathbf{-0.31}\) & **0.26** \\ \hline WI-M & 0.19 & \(-\)0.10 & 0.12 & 0.23 & \(-\)0.13 & 0.16 & 0.08 & \(-\)0.08 & 0.02 & **0.5
## 3 Results
### Feature Map Refinement  
[PERSON] et al. (2023) [24] explored the enhancement of feature maps by employing a Multi-Stage U-Net. Their proposed architecture consists of three U-Nets, with the encoding paths replaced by a VGG16 model. The first network is trained on 2D TRUS data to obtain a preliminary segmentation. The encoded feature maps then serve as input for a second network, which produces a second segmentation. This step is repeated for a third network. The interim segmentations are fused by element-wise multiplication to generate the final segmentation outcome. Compared to a single U-Net with VGG16 integration, the DSC scores improved by 3.7%. Interestingly, model performance decreased when interim segmentation outcomes were not included in the final segmentation, indicating that different networks learn specific aspects of the prostate boundary.  
In the Adaptive Detail Compensation Network (ADC-Net) proposed by [PERSON] et al. (2023) [31], the feature space is refined with a Detail Compensation Module (DCM). The primary aim was to estimate PV with a 3D U-Net. To reduce the complexity of 3D convolution, the authors decreased the feature channel dimensions. Although this reduces network complexity, it also results in the loss of detailed image information. The DCM enriches the remaining feature maps by embedding a pre-trained ResNet-34 model, which encodes detailed feature maps merged with the feature maps of the 3D U-Net. The effect of the DCM was evaluated in an ablation study, showing improvements of 3%, 4%, 3%, and 3% in DSC, JC, Precision, and Recall, respectively.
# Energy-Efficient Multicast Precoding for Massive MIMO Transmission with Statistical CSI
## 3 Energy-Efficient Multicast Precoding for Massive MIMO
Moreover, the complexity of calculating DE involved in the objective of each sub-problem is relatively low due to the quick convergence [28] in solving (19). ```
0: Initialization power allocation \(\mathbf{\Lambda}^{(0)}\), beam domain statistical CSI \(\mathbf{\Omega}_{k}\), threshold \(\epsilon\)
0: Beam domain energy-efficient multicast power allocation pattern \(\mathbf{\Lambda}\)
1: Initialization: \(\ell=0\), calculate \(\eta^{(\ell)}\) using (16)
2:while\(\left|\underset{k}{\min}\ W\overline{R}_{k}\left(\mathbf{\Lambda}^{(\ell)}\right)- \eta^{(\ell)}P\left(\mathbf{\Lambda}^{(\ell)}\right)\right|\geq\epsilon\)do
3: Update \(\ell\leftarrow\ell+1\)
4: Calculate \(\mathbf{\Lambda}^{(\ell)}\) via solving (17) with \(\eta^{(\ell-1)}\)
5: Calculate \(\eta^{(\ell)}\) using (16)
6:endwhile
7: Return \(\mathbf{\Lambda}=\mathbf{\Lambda}^{(\ell)}\)
```  
**Algorithm 1** Beam Domain Energy-Efficient Multicast Power Allocation Algorithm