esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods  
#### 2.2.1 Overview  
The MS-DAN model, as shown in Figure 4, was built on the traditional RNN model with the addition of the attention mechanism and feature selection proposed in this paper. Based on the different stages of bearing degradation, it dynamically adjusted the weights of each feature path to more accurately capture degradation information. The MS-DAN model first performed feature selection and converted the time-domain signal into the frequency-domain signal through DFT to extract useful features. Then, the frequency-domain features were downsampled using kernel average pooling, and important features were highlighted by SoftMax weighting. The most important K features were retained, and the frequency-domain features were converted back to the time domain through Inverse Discrete Fourier Transform (IDFT). Next, the data entered the feature learning stage, where the features were further optimized through the feature extractor and enhanced feature modules. Through the attention mechanism, the model automatically assigned weights to different features, strengthened the areas containing more degradation information, and helped the model focus on important features. The cross-attention mechanism further enhanced the interactive information between features. In the RNN module, the model used recurrent neural networks to capture long-term dependencies in the time series. After passing through the RNN model, the loss L was calculated, and the model parameters were optimized through backpropagation.  
Figure 3: Dataset of bearing.  
Ultimately, this method not only enhanced the accuracy of RUL predictions but also improved the model's adaptability to various operating conditions, providing more reliable support for maintenance decision-making of mechanical equipment. The pseudocode of the algorithm is shown in Algorithm 1.  
```
Input: A set of dataset samples Learning_set = {(X\({}_{1}\)), (X\({}_{2}\)),..., (X\({}_{n}\))}. The Full_Test_Set is the test set. The number of learning epochs is M. Output: the optimal model and its predicted RUL
1. Load the training set and validation set;
2. Begin:
3. Initialize all wights and biases.
4. For m = 1, 2,..., M do
5. Extract features through Multiscale model \(\to F_{R}\);
6. Input \(F_{R}\) to MS-DAN;
7. Calculate the output for the MS-DAN;
8. Input the feature of \(F_{MS}\) into sequence \(X\), and input RNN;
9. Calculate the output for the RNN layer;
10. Calculate the RUL;
11. Model Fit (Adam, (train \(X\))) \(\rightarrow\) M(m);
12. Model Evaluate (M(m), (Val \(X\))) \(\rightarrow\)\(R_{\textit{max}}\)(m).
13. End For
14. Save the optimal model which has min \(R_{\textit{max}}\) in M epochs.
15. End
16. Load the testing set;
17. Load the optimal model in terms of RUL performances
```  
**Algorithm 1**Applying proposed to Prediction
# Intensified Pozzolanic Reaction on Kaolinite Clay-Based Mortar
## 4 Conclusions  
The results of this study are summarized as follows.  
1. In the heat of hydration test, all samples showed two main peaks regardless of SF addition. SF addition increased the hydration heat emission. The interesting result is that the cumulative hydration heat of SF_15 was only 5% higher than that of SF_7.5, although SF content in SF_15 was almost twice than that in SF_7.5. This result reveals that the excessive addition of SF has no chemical effect on the hydration reaction.
2. The XRD analysis showed that the mortar cured for 28 days contains quartz, kaolinite, illite, remaining calcium hydroxide, calcium carbonate, and clinoptiilolite. Unlike Ref, calcium hydroxide was barely detected in two SF samples (SF_7.5 and SF_15), i.e., it was mostly consumed by the intended pozzolanic reaction between hydrated lime and SF. Thus, the intensified hydration reaction that was confirmed by the hydration heat test, was attributed to the intended pozzolanic reaction by adding two mineral admixtures.
3. The total pore volume of the two samples with SF was lower than that of Ref. Between the two samples with SF, the proportion of the pores, whose size range is similar with that of SF particles, was significantly lower in SF_15 than SF_7.5. This verifies the physical filling effect provided by excessive (unreacted) SF particles. However, in the pore size distribution of SF_15, the first peak was changed from 9 to 17 nm between 28 and 91 days. This change can be related to the long-term strength reduction in a fine pore structure of SF_15.
4. The compressive strength of the mortar was increased with SF addition. The main contribution was the intended pozzolanic reaction, but the filling effect also contributed to the strength gain as well. Based on the compressive strength, it was confirmed that the developed kaolinite clay-based mortar with SF has sufficient mechanical properties as a structural material.  
However, there is an optimal SF to binder ratio in the developed mortar. Excessive SF addition resulted in the degradation of long-term compressive strength. Considering price competitiveness and long-term strength, a SF to binder ratio of 7.5%, rather than 15%, is suitable. However, it should be noted that this optimum amount of SF can be different depending on the binder selection (i.e., the ratio of kaolinite clay and calcium hydroxide). The investigated mix design herein can be further expanded to develop other sustainable structural materials, such as natural minerals with low reactivity or waste materials with nonbinding properties.  
This research was supported by a paper fee from the Institute of Engineering Research, Seoul National University, Republic of Korea and Singapore Ministry of Education Academic Research Fund Tier 1.  
[PERSON] and [PERSON] conducted experiments, analyzed results, and wrote the manuscript. [PERSON] and [PERSON] designed the experiments and advised on analysis, data interpretation, and manuscript revision.  
The authors declare no conflict of interest.
## 4 Machine Learning Algorithms
### Model Construction  
This study employed a Bayesian Optimization-SHAP joint parameter tuning framework to enhance model performance through a three-stage optimization process. After 100 iterations, the optimal parameter combination for the TOC prediction model was determined (Figure 7): the number of decision trees was set to 200 through systematic convergence testing where the coefficient of variation (CV) for RMSE reached \(\leq\)1.5% across 10-fold cross-validation, the minimum samples per leaf node to 3, and the maximum tree depth to 12, resulting in an 18.6% reduction in root mean square error (RMSE) compared to initial parameters.  
\[CV=\frac{\sigma_{RMSE}}{\mu_{RMSE}}\times 100\% \tag{5}\]  
Figure 7: Comparative analysis of RFR performance with varying decision tree quantities.  
Building upon the trained RFR model, a weighted feature voting mechanism was introduced to improve generalization capability. Ten rounds of Monte Carlo cross-validation demonstrated robust model stability, with a standard deviation of R\({}^{2}\) = 0.032 \(\pm\) 0.004 based on cross-validation trials (Figure 8). To evaluate model performance, RMSE (ranging from 0 to +\(\infty\)) and coefficient of determination (R\({}^{2}\), ranging from 0 to 1) were adopted as evaluation metrics. The TOC prediction model exhibited a positive correlation with RMSE and a negative correlation with R\({}^{2}\). Following model tuning and validation, the optimized RFR model was applied to predict TOC content across five sub-layers at various well locations in the Qiongzhusi Formation.  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{{{(T_{i}-P_{i})}^{2}}}}} \tag{6}\]  
\[R^{2}=1-\frac{\frac{1}{n}{\sum\limits_{i=1}^{n}{{{(T_{i}-P_{i})}^{2}}}}}{\sum \limits_{i=1}^{n}{{{(T_{i}-T_{a})}^{2}}}} \tag{7}\]  
where _Ti_ measures TOC content of the _i_-th sample. _Pi_ predicts TOC content of the _i_-th sample. _Ta_ means value of the measured TOC content.
###### Abstract  
This study proposes a GPU-accelerated Position-Based Dynamics (PBD) system for realistic and interactive cloth simulation in Extended Reality (XR) environments, and comprehensively evaluates its performance and functional capabilities on standalone XR devices, such as the Meta Quest 3. To overcome the limitations of traditional CPU-based physics simulations, we designed and optimized highly parallelized algorithms utilizing Unity's Compute Shader framework. The proposed system achieves real-time performance by implementing efficient collision detection and response handling with complex environmental meshes (RoomMesh) and dynamic hand meshes (HandMesh), as well as capsule colliders based on hand skeleton tracking (OVRSkeleton). Performance evaluations were conducted for both single-sided and double-sided cloth configurations across multiple resolutions. At a 32 \(\times\) 32 resolution, both configurations maintained stable frame rates of approximately 72 FPS. At a 64 \(\times\) 64 resolution, the single-sided cloth achieved around 65 FPS, while the double-sided configuration recorded approximately 40 FPS, demonstrating scalable quality adaptation depending on application requirements. Functionally, the GPU-PBD system significantly surpasses Unity's built-in Cloth component by supporting double-sided cloth rendering, fine-grained constraint control, complex mesh-based collision handling, and real-time interaction with both hand meshes and capsule colliders. These capabilities enable immersive and physically plausible XR experiences, including natural cloth draping, grasping, and deformation behaviors during user interactions. The technical advantages of the proposed system suggest strong applicability in various XR fields, such as virtual clothing fitting, medical training simulations, educational content, and interactive art installations. Future work will focus on extending the framework to general deformable body simulation, incorporating advanced material modeling, self-collision response, and dynamic cutting simulation, thereby enhancing both realism and scalability in XR environments.  
cloth simulation; Position-Based Dynamics; GPU acceleration; mixed reality; Compute Shader; real-time physics; environmental mesh collision; hand interaction; spatial computing; Meta Quest +
Footnote †: journal: Applied sciences
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 3 Analytical Modeling and Performance Evaluation
### Handover Blocking Probability
#### 3.3.2 Handover Blocking Probability Analysis with Other Approaches  
Suppose the handover latency of a particular mobility management protocol is denoted by \(L_{HO}\)(\({}^{\cdot}\)), where (.) specifies a specific protocol. If the mean value of \(L_{HO}\)(\({}^{\cdot}\)) is given by \(E[L_{HO}\)(\({}^{\cdot}\))] and the residence time in the network is denoted by \(T_{R}\), along with its probability density function \(f_{R}(t)\). Supposing the handover latency is exponentially distributed along with the cumulative function \(F_{T}\)(\({}^{\cdot}\)), for all other protocols with their respective handover latencies, the handover blocking probability can be calculated using:  
\[\rho_{b}=\Pr(L_{HO}^{(\cdot)})>T_{R}=\int_{0}^{\infty}\left(1-F_{T}^{(\cdot)}( u)f_{R}(u)du\right), \tag{20}\]  
\[\rho_{b}=\frac{\mu_{c}E\left[L_{HO}^{(\cdot)}\right]}{1+\mu_{C}E\left[L_{HO}^{ (\cdot)}\right]}, \tag{21}\]  
where \(\mu_{c}\) is the rate of boundary crossing of the MN, i.e., its mobility rate. Supposing that the BMA's coverage area is circular, then \(\mu_{c}\) is calculated with Equation (19) [8; 32; 33; 34].
## 1 Introduction
The advantages and disadvantages of the circular Halbach array have not been well established, which would be helpful for expanding the application of the Halbach array. The contributions of this work are as follows: (1) a theoretical model of electromagnetic energy harvesting in cylindrical coordinates is analyzed and established based on the coordinates of cubic magnet nodes; (2) the magnetic flux density and energy harvesting performance of two magnet arrays in a rotating environment are compared in a simulation and an experiment; and (3) the phase of the induced voltage in the coils is analyzed when the two magnet arrays are applied in energy acquisition. This paper compares the Halbach array with the two-directional magnet array in terms of various aspects of energy conversion. The magnetic flux density, distribution range, and induced voltage under different parameters of the two magnet arrays are compared. It is concluded that the Halbach array adopted in this paper has advantages in energy harvesting only when the distance between the magnet and the coil is sufficiently large. The design and corresponding analysis are presented in Section 2. Section 3 presents a comparison of simulations in COMSOL Multiphysics between the two magnet arrays, including the magnetic flux density and voltage response under different gaps and rotating speeds. Experiments are described and verified in Section 4. Finally, a conclusion is drawn in Section 5.
## 3 Beamforming Design for BIOS-FD Systems
### SIC-WMMSE Approach
For a more concise representation, problem (14) can be rewritten as follows by transforming the scalars into matrices:  
\[ \begin{array}{ll}\min\limits_{\mathbf{\Phi}_{1},\mathbf{\Phi}_{2},\mathbf{F},\mathbf{U}_{\text{D},\text{A}_{\text{D},\text{U},\text{A}_{\text{U},\text{A}_{ \text{U},\text{A}_{\text{U}}}}}}&f=\text{Tr}(\mathbf{A}_{\text{D}}\mathbf{E}_{ \text{D}})-\log\det(\mathbf{A}_{\text{D}})+\text{Tr}(\mathbf{A}_{\text{U}} \mathbf{E}_{\text{U}})-\log\det(\mathbf{A}_{\text{U}})\\ \text{s.t.}&\|\mathbf{H}_{\text{SI}}\mathbf{F}\|_{F}^{2}/N_{\text{R}}\leq \gamma,\\ &|\mathbf{\Phi}_{1}|_{ii}=|\mathbf{\Phi}_{2}|_{jj}=1,\forall i,j,\\ &\text{Tr}\big{(}\mathbf{F}\mathbf{F}^{H}\big{)}\leq P_{\text{s}},\end{array}  \tag{17}\]  
where \(\mathbf{E}_{\text{D}}=\text{diag}\big{(}e_{\text{D},1},\cdots,e_{\text{D},K_{ \text{D}}}\big{)}\) and \(\mathbf{E}_{\text{U}}=\text{diag}\big{(}e_{\text{U},\text{I}},\cdots,e_{\text{U},K_{\text{U}}}\big{)}\), respectively, denote diagonal matrices containing the MSEs of each DL and ULUE. \(\mathbf{A}_{\text{D}}=\text{diag}\big{(}a_{\text{D},1},\cdots,a_{\text{D},K_{ \text{D}}}\big{)}\) and \(\mathbf{A}_{\text{U}}=\text{diag}\big{(}a_{\text{U},1},\cdots,a_{\text{U},K_ {\text{U}}}\big{)}\), respectively, denote diagonal matrices containing the weighting coefficients of each DL and ULUE. \(\mathbf{U}_{\text{D}}=\text{diag}\big{(}u_{\text{D},1},\cdots,u_{\text{D},K_ {\text{D}}}\big{)}\) denotes the diagonal matrix containing the receive coefficients of each DL UE, and \(\mathbf{U}_{\text{U}}=\big{[}\mathbf{u}_{\text{U},1},\ldots,\mathbf{u}_{\text{U},K_{\text{U}}}\big{]}\) denotes the matrix containing the receive combiners of each ULUE. Although we have transformed the SRM problem into an equivalent WMMSE problem (17), it is still difficult to solve it directly due to the highly non-convex constraints. Thus,we propose an efficient way to deal with the residual SI power constraint (13b). Specifically, move the SI suppression term into the objective function with a penalty factor \(\rho\), resulting in a new optimization problem as follows:  
\[ \begin{split}\mathbf{\Phi}_{1,\mathbf{\Phi}_{2},\mathbf{F},\mathbf{ U}_{\text{D},\mathbf{A}_{\text{D}},\mathbf{U}_{\text{U}},\mathbf{A}_{\text{U}}}}& f=\text{Tr}(\mathbf{A}_{\text{D}}\mathbf{E}_{\text{D}})- \log\text{det}(\mathbf{A}_{\text{D}})+\text{Tr}(\mathbf{A}_{\text{U}}\mathbf{ E}_{\text{U}})-\log\text{det}(\mathbf{A}_{\text{U}})+\rho\zeta\\ \text{s.t.}&\text{s.t.}\end{split}  \tag{18}\]  
where \(\zeta=\frac{1}{N_{\text{B}}}\|\mathbf{H}_{\text{SI}}\mathbf{F}\|_{F}^{2}\) is the power of the SI signal. In order to facilitate the optimization of each variable, we substitute all variables into objective function (18) to derive a more detailed expression of \(f\), which can be expressed as follows:  
\[f=f_{\text{D}}+f_{\text{U}}+f_{\text{SI}}, \tag{19}\]  
where  
\[
## 2 Related Works
The collaborative strategy ensured the efficiency and the robustness of the data processing, while limiting the required communication bandwidth. There were many parameters that affected the impact of cooperation on sensor network lifetime such as number of domains, node density, network area, propagation environment, network topology, and base station deployment [21]. [PERSON] _et al._ investigated cooperation strategies within a linear programming framework to prolong sensor network's lifetime for use in multi-domain wireless sensor networks. In [22], [PERSON] _et al._ proposed a novel collaborative signal and information processing (CSIP) algorithm based on virtual fields excited by sensor nodes in wireless heterogeneous sensor networks. Experimental results demonstrated that the proposed approach can reduce energy consumption in sensor nodes, meanwhile, the information gain efficiency and the network lifetime were also increased. Wireless sensor networks have been explored for adoption to improve the performance of the centralized and cable-based structural health monitoring systems. [PERSON] _et al._ designed a multi-agent system and evaluated it for the adoption of collaborative wireless sensor networks in large structure health monitoring [23]. [PERSON] and [PERSON] proposed a method utilizing the measurement results of co-occurrence to identify data redundancy, and a novel collaborative data gathering approach utilizing the co-occurrence to offer a trade-off between the communication costs and communication errors of data gathering for estimating the sensor measurements at the base station [24]. The distributed node wakeup of wireless sensor networks was in the scope of collaborative optimization. [PERSON] and [PERSON] proposed an effective clustering protocol for information potential fields navigation in wireless _ad-hoc_ sensor networks [25]. The proposed distributed infectious disease model is utilized to derive the collaborative sensor wakeup method. Visual target tracking which is always performed in single-view system is challenging in complex situations. [PERSON] and [PERSON] presented a distributed multi-view tracking system using collaborative signal processing for distributed wireless sensor networks [26]. The potentiality of WSN-controlled agricultural activities and different environmental compartments for integrated water quality management had been presented and the limitations of WSNs in agriculture and water quality monitoring have been identified. [PERSON] _et al._ proposed a case of collaborative networks at a catchment scale to enable the cooperation among individually networked activities for integrated water quality monitoring, control and management [27]. [PERSON] and [PERSON] proposed an improved lower bound for Bayesian network based on clustering protocols [28].
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Characteristic**} & \multicolumn{4}{c}{**2020**} & \multicolumn{4}{c}{**2021**} & \multicolumn{4}{c}{**Mean 2020-2021**} \\ \cline{2-9}  & **Control** & **AF2030** & **AMSSOC** & **Control** & **Seovered Extract** & **Control** & **AF2030** & **AMSSOC** \\ \hline First-pole height [\(\mathrm{sn}\)] & \(9.8\pm 1.2\)b & \(13.4\pm 1.8\)a & \(11.3\pm 1.2\) a & \(10.4\pm 1.05\) b & \(14.1\pm 1.23\) a & \(11.8\pm 1.33\) a & \(10.1\pm 1.74\) b & \(13.5\pm 1.83\) a & \(11.6\pm 1.9\) a \\ Plant height [\(\mathrm{cm}\)] & \(88.8\pm 3.35\) c & \(108.9\pm 3.56\) a & \(98.6\pm 3.32\) b & \(80.8\pm 3.42\) b & \(10.9\pm 3.08\) a & \(96.3\pm 3.26\) a & \(84.7\pm 5.42\) c & \(106.4\pm 4.05\) a & \(97.4\pm 3.21\) b \\ Pot count & \(15.3\pm 1.04\) a & \(17.5\pm 1.24\) a & \(17.2\pm 1.11\) a & \(14.0\pm 1.25\) b & \(16.9\pm 1.04\) a & \(16.4\pm 0.92\) a & \(16.4\pm 1.77\) b & \(17.2\pm 1.23\) a & \(16.8\pm 0.93\) a \\ Topal error weight [\(\mathrm{gs}\)] & \(208.0\pm 2.71\) a & \(158.4\pm 3.23\) b & \(205.3\pm 2.64\) b & \(204.3\pm 2.22\) a & \(194.5\pm 2.56\) b & \(202.4\pm 2.54\) b & \(206.2\pm 3.00\) a & \(196.5\pm 3.37\) b & \(230.8\pm 2.77\) a \\ Yield [\(\mathrm{gs}\) m\({}^{-1}\)] & \(273.1\pm 11.8\) b & \(337.7\pm 17.9\) a & \(352.8\pm 8.9\) a & \(264.5\pm 15.36\) b & \(364.7\pm 15.64\) a & \(366.8\pm 7.14\) a & \(26.7\pm 13.39\) b & \(351.2\pm 20.98\) a & \(38.0\pm 13.83\) a \\ \hline \multicolumn{9}{c}{Values not sharing a common letter indicate significant difference at \(p\leq 0.05\).} \\ \end{tabular}
\end{table} 
Table 2: Soybean biometrics (mean \(\pm\) SD). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Characteristic**} & \multicolumn{4}{c}{**2020**} & \multicolumn{4}{c}{**2021**} & \multicolumn{4}{c}{**Mean 2020-2021**} \\ \cline{2-9}  & **Control** & **S**overed Extract** & **Control** & **S**overed Extract** & **Control** & **SA**overed Extract** \\ \hline \multirow{2}{*}{Total protein [\(\%\)]} & \multirow{2}{*}{32.52 \(\pm\) 1.36 b} & \multirow{2}{*}{36.41 \(\pm\) 1.78 a} & \multirow{2}{*}{35.74 \(\pm\) 2.03 ab} & \multirow{2}{*}{33.16 \(\pm\) 2.08 b} & \multirow{2}{*}{38.34 \(\pm\) 2.73 a} & \multirow{2}{*}{37.13 \(\pm\) 2.98 ab} & \multirow{2}{*}{32.84 \(\pm\) 2.19 b} & \multirow{2}{*}{37.38 \(\pm\) 3.16 a} & \multirow{2}{*}{36.44 \(\pm\) 3.16 b} \\ Total fat [\(\%\)] & \(17.21\pm 1.42\) a & \(17.79\pm 1.55\) a & \(17.24\pm 1.76\) a & \(17.63\pm 1.36\) a & \(17.28\pm 1.83\) a & \(17.81\pm 2.01\) a & \(17.42\pm 1.62\) a & \(17.04\pm 2.14\) a & \(17.53\pm 2.31\) a \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Protein and fat content in soybean seeds (mean \(\pm\) SD).