esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 5 Experimental Evaluation
### Ablation Study
#### 5.2.2 Ablation Study with UR-Fall Dataset  
Table 3 presents the ablation study of the proposed model using the UR-FALL multi-modal dataset. The models are evaluated with both skeleton data in Stream-1 and sensor data in Stream-2. The study examines five different configurations by varying the number of GSTCN modules in Stream-1 while using a fixed BiLSTM-CNN combination in Stream-2. The results show the performance of each configuration in terms of accuracy, precision, recall, and F1-score. The configuration with 3 GSTCN modules achieves an accuracy of 99.14%, with a precision of 99.06%, recall of 99.041%, and F1-score of 99.04%. Increasing the number of GSTCN modules to 4 results in a slight improvement, with an accuracy of 99.15%, precision of 99.19%, recall of 98.81%, and F1-score of 98.99%. The performance continues to improve with 5 GSTCN modules, reaching an accuracy of 99.24%, a precision of 99.12%, recall of 99.19%, and F1-score of 99.15%. The best performance is achieved with 9 GSTCN modules, which yield an accuracy of 99.32%, a precision of 99.23%, recall of  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multirow{2}{*}{**Ablation**} & \multicolumn{2}{c}{**Stream-1 Skeleton**} & \multicolumn{2}{c}{**Stream-2 Sensor**} & \multicolumn{2}{c}{**Result with UR-FALL (10 Fold Mean)**} & \multicolumn{1}{p{42.7 pt}}{} \\  & \multicolumn{1}{p{42.7 pt}}{**Yes or No Stream-1**} & \multicolumn{1}{p{42.7 pt}}{**No of GSTCN**} & \multicolumn{1}{p{42.7 pt}}{**Yes or No Stream-2**} & \multicolumn{1}{p{42.7 pt}}{**Model Name**} & \multicolumn{1}{p{42.7 pt}}{**Accuracy**} & \multicolumn{1}{p{42.7 pt}}{**Precision**} & \multicolumn{1}{p{42.7 pt}}{**Recall**} & \multicolumn{1}{p{42.7 pt}}{**F1-Score**} \\ \hline
1 & No & - & Yes & Only CNN & 97.78 & 93.79 & 92.92 & 93.02 \\ \hline
2 & No & - & Yes &
\begin{tabular}{} \end{tabular} & 99.04 & 96.92 & 97.24 & 96.91 \\ \hline
3 & No & - & Yes &
\begin{tabular}{} \end{tabular} & 99.07 & 96.63 & 97.21 & 96.75 \\ \hline
4 & Yes & 3 & No & - & 91.57 & - & - & - \\ \hline
5 & Yes & 4 & No & - & 91.56 & - & - & - \\ \hline
6 & Yes & 6 & No & - & 91.86 & - & - & - \\ \hline
7 & Yes & 9 & No & - & 91.67 & - & - & - \\ \hline
8 & Yes & 3 & Yes &
\begin{tabular}{} \end{tabular} & 98.53 & - & - & - \\ \hline
8 & Yes & 9 & Yes &
\begin{tabular}{} \end{tabular} & 98.66 & - & - & - \\ \hline
9 & Yes & 6 & Yes &
\begin{tabular}{} \end{tabular} & 99.09 & 97.06 & 97.18 & 96.99 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study of the proposed model for the UP-Fall multi-modal dataset.  
99.19%, and F1-score of 99.21%. These results demonstrate that increasing the number of GSTCN modules significantly enhances the model's performance, with the highest accuracy and F1-score achieved with 9 GSTCN modules.
## 2 Authentication in the Smart Grid (SG) Network
### Authentication Types in the Smart Grid (SG) Network
#### 2.1.1 Device-to-Device Authentication  
In the smart home environment, various smart devices and HAs communicate with each other via HAN. Secure communication among devices is a vital for SG security. The authentication server (AS) of the SG system must authenticate all the associated SMs of HAN. Apart from this, the SMs and the other HAs must be secured from various attacks such as impersonation, data forgery attack, _etc._ A couple of identity-based authentication and public key infrastructure (PKI) mechanisms are proposed by [PERSON] _et al._[25] to offer mutual authentication between smart devices. However, the security analysis of the scheme is not discussed.
# SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
## 5 Potential Applications and Future Work
### Compliance Framework Extensions  
As AI regulations expand, reliable record-keeping and provable security claims gain further importance. SecureLLM's modules could be extended to producing cryptographic proof of compliance (e.g., zero-knowledge proofs) or automatically generating official reports for regulatory bodies. Additional extensions may involve hardware enclaves, advanced MPC protocols, and cross-border data transfer compliance.  
Zero-knowledge proof (ZKP) integrations let organizations attest to meeting certain security thresholds--e.g., the maximum noise levels or key rotation intervals--without revealing sensitive operational details [64]. Storing these proofs on chain or on a tamper-evident ledger will allow third parties to verify compliance without direct access to the raw logs or proprietary model weights.  
As the adoption of AI becomes increasingly international, the issue of cross-border data transfers emerges. SecureLLM's decentralized model helps ensure that data never leave their jurisdiction in plaintext, greatly simplifying the compliance with diverse national data protection mandates. Encrypted inference and partial model updates can also be distributed globally without exposing raw training examples [65].  
Finally, future developments of MPC can extend beyond gradient aggregation into full \"privacy-preserving inference\", where entire query sequences are processed under encryption. SecureLLM's potential to incorporate zero-knowledge proofs of the correct inference further boosts confidence in the model's security posture. It thus enables responsible innovation under mounting regulatory pressures and heightened public concern over data misuse.
###### Abstract
They have classified these IEQ models into three categories: subjective-objective models, objective models, and subjective models, based on each model's data collection approach, and discussed the analytical methods applied in these studies. Moreover, they have summarized five criteria for evaluating the quality of IEQ models, based on which criteria they proposed a scoring system and ranked the 25 IEQ models accordingly. The existing literature has developed various IEQ models. However, the included variables and applied methods varied greatly from one study to another, and no clear patterns emerged. Therefore, the need is still to systematically provide a theoretical basis and practical guidance in IEQ model development and interpretation. Most studies focused on the development of the models while lacking a discussion of the application of these models. To address the research gaps, this study introduces the theoretical basis of the IEQ models, summarizes the methods of data collection, data analysis, and validation used in IEQ model developments, and provides the possible scenarios and challenges for IEQ model applications in the form of 10 questions. Questions 1 and 2 deal with the definition and importance of IEQ models. Questions 3 and 4 refer to the data collection methods used for selecting indicators and the related measurement methods. Question 5 addresses the issue of data analysis methods. Questions 6 to 9 concern the application and validation of IEQ models. Question 10 explores the challenges and future direction for IEQ model development and application. The answers to these questions reveal the challenges and limitations in the current research on IEQ models, in an attempt to shed light on the future development of IEQ models.
# Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications
## 4 Simulation and Numerical Results
### Benchmark Schemes for Comparison  
After the RBF-GAT was trained offline, we compared its performance with the following benchmarks:  
Upper Bound: Let the CSI of all channels is perfectly known at the IRS and the LEO satellite, and we optimize the sum-rate maximization problem by the block coordinate descent (BCD) algorithm proposed in [14], which can be treated as the system performance upper bound but, in reality, it is difficult to realize. We stop the BCD algorithm after 2000 iterations.  
Deep Learning(GNN): Adopt a GNN architecture proposed in [24] to capture the interactions among all users and the LEO satellite. The user locations and received pilots  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Name** & **Input Dimension** & **Output Dimension** & **Activation Function** \\ \hline GAT1 & \(K\times 10\) & \(K\times 512\) & LeakyReLU \\ GAT2 & \(K\times(512+2\text{ML})\) & \(K\times 1024\) & LeakyReLU \\ \(f_{1}\) & \(K\times 1024\) & 2048 & ReLU \\ \(f_{2}\) & 2048 & 1024 & ReLU \\ \(f_{3}\) & 1024 & 512 & ReLU \\ \(f_{w}\) & 512 & \(2\text{MK}\) & ReLU \\ \(f_{\Theta}\) & 512 & \(2\text{N}\) & ReLU \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of RBF-GAT.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Definition** & **Value** \\ \hline \(P_{L}\) & Satellite maximum transmit power & 30 dBW \\ \(\
u/\lambda\) & Carrier frequency & 20 GHz \\ \(G_{k}/T\) & User received gain per to noise & 15 dB/K \\ \(B\) & Bandwidth & 25 MHz \\ \(\varphi_{k,m}\) & Beam angles between IRS/user and & 0.01\({}^{\circ}\)\(\sim\)0.5\({}^{\circ}\) \\ \(\varphi_{k,3 dB}\) & 3-dB angle & 0.4\({}^{\circ}\) \\ \(b_{max}\) & Maximal satellite antenna gain. & 52 dBi \\ \(\mu_{m}\) & Rain fading mean & \(-2.6\) dB \\ \(c_{m}^{2}\) & Rain fading variance & 1.63 dB \\ \(\xi\) & Rician factor & 10 \\ \(\widetilde{h}_{0}\) & ILS fading parameters between & (19.4, 0.158, 1.29) \\ \(\widetilde{h}_{k}\) & satellite to IRS channel & (0.739, 0.063, \(8.97\times 10^{-4}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters in the IRS-assisted LEO satellite communication network.  
are directly concatenated as the input feature, and then train the model offline in an unsupervised manner.  
Deep Learning(MLP): Design a multi-layer perceptron (MLP), which is composed of a simple network including multiple layers with several neurons, to establish the mapping from pilots and location to beamforming. This method has been studied in [30].  
Without IRS: Let \(N=0\), and then the precoding matrix of the LEO satellite is optimized using the alternating optimization algorithm presented in [14].  
Random Phase: The IRS phase shift matrix is initialized with random value, and the alternating optimization algorithm proposed in [14] is then applied to optimize the precoding matrix of the LEO satellite.
## 3 Results and Analysis
### Thermal Conductivity Analysis
The test mixture ratios with maximal and minimal unit weights and thermal conductivities were MA0 and FA30, respectively, indicating that the unit weight is correlated with the thermal conductivity. In general, fewer internal voids would lead to a heavier unit weight; this is due to the cement being replaced by mineral admixtures. However, the opposite trend is found in the cases of DLS10 and DLS30; DLS30, with smaller unit weight, had a higher thermal conductivity than that of DLS10, which is due to hydration product properties. Thus, this effect should also be considered in the mixture. Overall, the unit weight had a slight effect on the thermal conductivity but with a low correlation shown by the linear regression correlation coefficient of R\({}^{2}\) = 0.4732. (3) Changes in the water absorption ratio  
Table 6 and Figure 12 show that the water absorption ratio and thermal conductivity index of the controlled sample were 4.41% and 0.484 kcal/m.h. \({}^{\circ}\)C, respectively. The water absorptions of FA10 and FA30 are 5.61% and 7.85%, and the thermal conductivity is reduced by 14.7% and 25.7% respectively. The water absorptions of BFS10 and BFS30 are 4.62% and 5.61%; the thermal conductivity is reduced by 4% and 14.2% respectively. The water absorptions of DLS10 and DLS30 are 6.13% and 8.93%, and the thermal conductivity is reduced by 1.9% and 17.2% respectively. The water absorptions of GLP10 and GLP30 are 6.07% and 7.22%, and the thermal conductivity is reduced by 22.2% and 21.6% respectively. The water absorption ratios of the mineral admixtures were greater than that of the controlled sample, indicating that, as the cement is replaced by mineral admixture, the derived concrete porosity varies; specifically, greater porosity did produce a higher water absorption ratio and smaller thermal conductivity. This is due to air acting as an insulator and reducing heat transfer. Figure 12: The relationship between unit weight, water absorption, and thermal conductivity.
###### Abstract  
An integrated method for fault detection of bearing using wavelet packet energy (WPE) and fast kurtogram (FK) is proposed. The method consists of three stages. Firstly, several commonly used wavelet functions were compared to select the appropriate wavelet function for the application of WPE. Then the analyzed signal is decomposed using WPE and the energy of each decomposed signal is calculated and selected for signal reconstruction. Secondly, the reconstructed signal is analyzed by FK to select the best central frequency and bandwidth for the band-pass filter. Finally, the filtered signal is processed using the squared envelope frequency spectrum and compared with the theoretical fault characteristic frequency for fault feature extraction. The procedure and performance of the proposed approach are illustrated and estimated by the simulation analysis, proving that the proposed method can effectively extract the weak transients. Moreover, the analysis results of gearbox bearing and rolling bearing cases show that the proposed method can provide more accurate fault features compared with the individual FK method.  
wavelet packet energy (WPE); fast kurtogram (FK); wavelet packet parameters; rolling element bearing +
Footnote †: journal: Applied Sciences
## 4 Conclusions  
The presented experiments are the first studies on the effect of pre-treatment with SCO\({}_{2}\) on the efficiency of anaerobic digestion of MSS in continuous reactors. The available literature data present only the results of tests carried out in batch reactors. So far, the impact of the applied OLR on the efficiency of MSS methane fermentation has not been assessed, which is also a novelty of the conducted research.  
MSS pre-treatment with SCO\({}_{2}\) was found to be successful in the partial disintegration of microbial cells, as evidenced by the subsequent dissolution of organic matter and nutrients. The disintegrated MSS had higher levels of soluble COD, TOC, and mineralized nitrogen/phosphorus.  
The present study shows that MSS pre-treatment with SCO\({}_{2}\) at an SCO\({}_{2}\)/MSS ratio of 0.3 (by volume) significantly improves AD performance in terms of methane production and feedstock mineralization. The pre-treatment was found to have no negative effect on the long-term continuous operation of the reactor. The organic load rate played a much larger role in determining the ultimate performance of AD compared to the other variables tested.  
The pre-treatment was found to enhance AD performance within the OLR range of 3.0-4.0 gVS/dm\({}^{3}\)-day. These values produced significantly higher biogas yields and CH\({}_{4}\) fraction contents, both in terms of daily production and per VS added. The most significant impact of SCO\({}_{2}\) on VS removal from MSS was found for OLR = 4.0 gVS/dm\({}^{3}\)-day. The SCO\({}_{2}\) pre-treatment was not found to affect pH, FOS/TAC, or the anaerobic bacterial community composition. Instead, these parameters were mainly affected by the OLR.  
The highest gross energy gain of 187.07 \(\pm\) 1.5 Wh/day was obtained at an OLR of 3.0 gVS/dm\({}^{3}\)-day. The production of SCO\({}_{2}\) in technologies dedicated to biogas upgrading and its use for MSS pre-treatment fits directly into the assumptions of the circular economy and corresponds to material recycling. It also supports the idea of reducing carbon dioxide emissions through its capture and use in a closed cycle.  
Conceptualization, J.K.; methodology, J.K. and M.D.; validation, J.K.; formal analysis, J.K.; investigation, J.K.; resources, J.K., M.D. and M.Z.; software, J.K.; data curation, J.K., M.D. and M.Z.; supervision, J.K.; writing--original draft preparation, J.K. and M.D.; writing--review and editing, J.K., M.D. and M.Z.; visualization, J.K. and M.D.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.  
The manuscript was supported by the work WZ/WB-IIS/3/2022, funded by the Minister of Education and Science, and was financially supported by the Minister of Education and Science for the program entitled \"Regional Initiative of Excellence\" for the years 2019-2023, project no. 010/RID/2018/19, amount of funding: 12,000,000 PLN.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Dynamic Selection Adaptive Genetic Algorithm (DSAGA)
#### 2.3.3 Adaptive Selection Operation  
This study introduces a genetic algorithm that incorporates an evolutionary-state dynamic selection strategy [35]. This method dynamically adjusts the selection strategy by real-time monitoring of the population's genetic diversity and fitness indicators [36]. Detailed descriptions of the mathematical models and decision points can be found in the following sections.  
The population diversity \(H(t)\) is initially defined as a measure of genetic diversity, comparable to the Shannon diversity index in ecology [37]:  
\[H(t)=-\sum_{i=1}^{n}p_{i}\text{log}(p_{i}) \tag{13}\]  
where \(p_{i}\) represents the frequency of the \(i\)th allele within the population [37]. This metric assists in the monitoring of genetic diversity within the population each generation.  
Furthermore, the average fitness \(A(t)\) and the best fitness \(B(t)\) are calculated to evaluate the performance of the overall population and the optimal individuals. These metrics are defined by the following formulas [27]:  
\[A(t)=\frac{1}{N}\sum_{i=1}^{N}f(x_{i}) \tag{14}\]  
\[B(t)=max\{f(x_{i})1\leq i\leq N\} \tag{15}\]  
where \(N\) denotes  
\[C_{B}:\ \frac{B(t)-B(t-1)}{B(t-1)}<\Delta B \tag{18}\]  
Here, \(\Delta H\), \(\Delta A\), and \(\Delta B\) are sensitivity thresholds for a decrease in diversity, slow growth in average fitness, and best fitness, respectively [38]. The selection function \(S(t)\), based on the aforementioned conditions, controls when and how to transition between different selection strategies to enhance the search capacity of the genetic algorithm and prevent premature convergence. The strategies employed include and transition between roulette wheel selection [27], tournament selection [39], and elite strategy [36].  
\[S(t)=\left\{\begin{array}{cl}\mathit{roulette wheel selection},&\quad\quad\quad if\ C_{H}\ \mathit{is}\ \mathit{True}\\ \mathit{tournament selection},&\quad\quad\quad if\ C_{A}\ \mathit{and}\ C_{B}\ \mathit{are}\ \mathit{True}\\ \mathit{elitism preservation methods},&\quad\quad\quad otherwise\end{array}\right. \tag{19}\]
# Multimodal Chinese Sarcasm Detection Integrating Audio Attributes and Textual Features
## 5 Experimental Results and Analysis
### Ablation Study  
To better demonstrate the contribution of each module, this paper conducts an ablation study by removing each module individually using a controlled variable approach. The experimental results are summarized in Table 6 and illustrated in Figure 7.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Acc (\%)** & **Pre (\%)** & **Rec (\%)** & **F1 (\%)** & **Macro Pre (\%)** & **Macro F1 (\%)** \\ \hline
**MMCSNN (Ours)** & **86.88** & **84.01** & **92.85** & **88.20** & **86.24** & **86.17** \\ \hline Without Multi-scale Conv & 81.19 & 79.08 & 87.08 & 82.88 & 81.39 & 81.29 \\ \hline Without Audio Attribute & 75.04 & 73.76 & 82.43 & 77.85 & 76.66 & 75.84 \\ \hline Without Feature Integration Layer & 84.95 & 83.74 & 89.74 & 86.63 & 83.74 & 86.64 \\ \hline Early Fusion Baseline & 80.87 & 78.25 & 84.71 & 81.36 & 79.21 & 80.18 \\ \hline Without BatchNorm and Dropout & 82.15 & 80.13 & 86.77 & 83.34 & 81.53 & 82.47 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study results of MMCSNN on the MCSh dataset. **Bold values indicate the best results in each column.**  
Figure 7: Ablation study: performance comparison of MMCSNN Variants.  
To further investigate the impact of different audio attribute subsets, we conducted an ablation study by isolating low-level and high-level descriptor groups, and by removing individual key features. As shown in Table 7, the high-level features (HNR, F1, and F2) yielded slightly better performance compared to the low-level ones (F0, shimmer, and jitter), indicating their superior ability to capture vocal tract characteristics essential for sarcasm recognition. However, removing F0 caused the most significant drop in accuracy, suggesting its crucial role as a tonal anchor. These results validate the complementary nature of prosodic and spectral features in our attribute design.  
In addition to the ablation studies conducted, we further examined the architectural variations suggested by the reviewer. Specifically, for the multi-scale convolution module, we explored different kernel size combinations (e.g., 1-3-5, 3-5-7, and 5-7-9) during the model development. The experimental results showed that, while varying the kernel sizes slightly affected performance, the default setting of \(3\times 3\), \(5\times 5\), and \(7\times 7\) achieved the best trade-off between capturing local fine-grained cues and broader contextual information. Therefore, we adopted \(3\times 3\), \(5\times 5\), and \(7\times 7\) as the default setting for our multi-scale convolutional structure. Additionally, regarding the feature fusion strategy, we employed a late fusion approach rather than early fusion. Early fusion, which directly concatenates raw features from different modalities, often leads to scale mismatches and noisy interactions that obscure subtle sarcastic signals. In contrast, late fusion allows each modality to be deeply encoded before integration, resulting in a more semantically aligned and robust multimodal representation for sarcasm detection.
# Numerical Analysis of the Take-Off Performance of a Seaplane in Calm Water
## 3 Numerical Model
### Governing Equations
Hence, the continuity and momentum equations were solved for a mixed fluid whose density \(\rho\) and viscosity \(\mu\) were calculated by following equations:  
\[\rho=\alpha\rho_{w}+(1-\alpha)\rho_{a} \tag{6}\]  
\[\mu=\alpha\mu_{w}+(1-\alpha)\mu_{a} \tag{7}\]  
The VOF model is very sensitive to the discretization of this convection term. Low order schemes often lead to smearing of the interface due to numerical diffusion, while the use of high order schemes results in numerical oscillations due to their unstable behavior. In this paper, the High-Resolution Interface Capturing (HRIC) scheme is used to discretize the convection term of volume fraction function. The HRIC scheme is currently the most successful advection scheme and is extensively used in many CFD codes. For improving the stability and robustness of the scheme, the software blends the HRIC with the upwind differencing (UD) scheme depending on the Courant-Friedrichs-Lewy (CFL) number. And an upper limit CFL\({}_{\text{U}}\) and a lower limit CFL\({}_{\text{L}}\) are introduced to adjust the blend. For simulations ending with a steady state, the value of CFL\({}_{\text{U}}\) and CFL\({}_{\text{L}}\) must be larger than the local CFL to ensure the HRIC scheme is applied. In this way, a large time step size could be used whilst a sharp interface is retained.