esa-sceva/satcom-chunk-collection huggingface dataset:
## Methods
### IFC Application and EEG Measurements  
Participants were instructed to close their eyes to prevent eye-induced artifacts during EEG recordings. Participants rested comfortably in bed for 10 min before the experiment to enhance psychological stability, minimize EEG artifacts, and reduce errors due to postural or behavioral changes [12]. IFC is produced by the interaction between two medium-frequency currents from two electrodes. The currents pass through the tissue simultaneously and interfere with each other, generating an interference current that stimulates the tissue [32]. A pad electrode (4 x 4 cm; Protens Electrodes; Bio-Protech Inc., Gangwon-do, Korea) was attached to the transverse process 2 cm from the left spinous processes, corresponding to the T1-T4 spinal cord segment levels. IFC stimulation was applied for 20 min using the ENDOMED 582 IFC device (Enraf Nonius, Rotterdam, The Netherlands). After a preliminary test, the experiment was conducted in three groups: HF-LI group (burst frequency: 100 bps, intensity: 10-20 mA), LF-HI group (burst frequency: 5 bps, intensity: 45-50 mA), and HF-HI group (burst frequency: 100 bps, intensity: 80-90 mA) [33; 34; 35].
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 7 Evaluation
### Experimental Setup
#### 7.1.3 Evaluation Metrics  
To verify that our PSDL scheme improves model accuracy, we use **test accuracy** and **test error rate** as evaluation measures on different training datasets. In the case of different numbers of malicious **MCs**, the results of our PBSL scheme were verified by using the no-attack FedSGD scheme [38] as the baseline, and the Krum scheme [16], Bulyan [16], Trim-mean [39] as the control.
## 3 Engineering Applications
### Water Diversion Project from the Three Gorges Reservoir to Hanjiang River  
#### 3.2.1 Water Diversion Project from the Three Gorges Reservoir to Hanjiang River Profile  
The Water Diversion Project from the Three Gorges Reservoir to Hanjiang River was started in Danjiangkou City, Hubei Province on 7 July 2022. The project of the water diversion project from the Three Gorges Reservoir to Hanjiang River is the first project in the follow-up project of the South to North Water Diversion Project. The project takes water from Longtan Creek on the left bank of the Three Gorges Reservoir of the Yangtze River, passes through Yichang, Xiangyang and Danjiangkou of Hubei Province and ends at the Anle River mouth on the right bank of Hanjiang River, which is downstream of Danjiangkou Reservoir. The total length is 194.8 km, and the design diversion flow is 170 m\({}^{3}\)/s. The location and water delivery route of the project is shown in Figure 9. The whole route of the project adopts tunnels for water conveyance, which is the longest pressurized water conveyance tunnel under construction in China. Approximately 2.2 \(\times\) 10\({}^{10}\) m\({}^{3}\) of tunnel much would be taken out from the underground [31].  
After the implementation of the project, it will connect the South-to-North Water Diversion Project and the Three Gorges project, as well as the Three Gorges Reservoir and the Danjiangkou Reservoir, and, moreover, link the Yangtze River, the Han River Basin and the Beijing-Tianjin-Hebei-Henan area and improve the national important water network layout. In the meantime, it will also supplement water to the middle and downstream areas of the Hanjiang River, which play an important role in improving the water resources  
Figure 9: The location and water delivery route of the Water Diversion Project from the Three Gorges Reservoir to Hanjiang River.  
allocation capacity of the Hanjiang River Basin and the water ecological environment of the middle and downstream area of the Hanjiang River [32; 33]. The project adopts the pressure single tunnel artesian water conveyance, which is the most difficult long-distance water diversion tunnel project under construction in China. The maximum buried depth is 1182 m, and the tunnel with a buried depth of over 600 m accounts for 45%. The route is long, and the buried depth is large. With high mountains, deep valleys, developed faults and folds, the complex terrain and geological conditions, the project faces multiple challenges, such as water inrush, mud inrush, large faults, etc.
# Radiation-Hardened 16T SRAM Cell with Improved Read and Write Stability for Space Applications
## 4 Conclusions  
In this paper, we introduced a radiation-hardened, high-stability SRAM cell designed to meet the demanding requirements of space applications. The proposed RHHS16T cell demonstrates exceptional robustness against radiation-induced soft errors, enabling it to  
Figure 12: Relative EQM for various comparison cells on log scale.  
effectively recover to its initial state even if a radiation particle impacts a sensitive node and causes a bit flip. This level of resilience is particularly crucial for environments with high radiation exposure, where data integrity must be reliably maintained. Additionally, the RHHS16T cell is engineered to withstand and recover from SEMNU occurrences at the storage node pair, further enhancing its data retention capabilities. Moreover, the proposed RHHS16T cell offers high read stability and write stability by isolating the storage node from the bit line during read operations and maintaining feedback loops between nodes during write operations. Therefore, the RHHS16T cell can ensure reliable data through its resilience to soft errors, as well as its high read and write stability. In conclusion, radiation-hardened SRAM cells designed for space applications must provide high reliability for stored data. The high reliability of the RHHS16T cell makes it a highly suitable candidate for space SRAM applications, where long-term and stable operation is essential. We have demonstrated the RHHS16T cell's effectiveness through extensive simulations, highlighting its potential as a reliable solution for radiation-hardened memory systems in space applications.  
Conceptualization, J.-Y.O. and S.-H.J.; methodology, J.-Y.O.; validation, J.-Y.O.; data curation, J.-Y.O.; writing--original draft preparation, J.-Y.O.; writing--review and editing, S.-H.J.; visualization, J.-Y.O.; supervision, S.-H.J.; project administration, S.-H.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The EDA Tool was supported by the IC Design Education Center, Korea.  
The authors declare no conflicts of interest.
## 4 Results and Discussion
### Research Results
#### 4.1.3 Evaluation of Fuzzy Integration
\begin{bmatrix} 0.24&0.56&0.2&0\\ 0.32&0.62&0.05&0.01\\ 0.25&0.60&0.1&0.05\\ 0.32&0.66&0.02&0\\ 0.24&0.55&0.21&0\\ \end{bmatrix} =(0.28,0.60,0.11,0.01)\] \[\rm B_{3}^{2} =\rm W_{3}^{2}\cdot R_{3}=(0.57,0.43)\cdot \begin{bmatrix}0.25&0.2 6&0.48&0.01\\ 0.30&0.22&0.44&0.04\\ \end{bmatrix} =(0.27,0.24,0.46,0.02)\] \[\rm B_{4}^{2} =\rm W_{4}^{2}\cdot R_{4}=(0.40,0.60)\cdot \begin{bmatrix}0.29&0.2 8&0.43&0\\ 0.27&0.33&0.40&0\\ \end{bmatrix} =(0.28,0.31,0.41,0)\]  
As shown in Table 6, \(\rm W^{1}=(0.30,0.25,0.20,0.24)\), \(\rm B^{1}\) is obtained. \[\rm B^{1}=\rm W^{1} \begin{bmatrix}\rm B_{1}^{2}\\ \rm B_{2}^{2}\\ \rm B_{4}^{2}\end{bmatrix} =(0.30,0.25,0.20,0.24)\cdot \begin{bmatrix}0.58&0.34& 0.06&0.03\\ 0.28&0.60&0.11&0.01\\ 0.27&0.24&0.46&0.02\\ 0.28&0.31&0.41&0\\ \end{bmatrix} =(0.37,0.38,0.24,0.02)\]  
As shown in Table 8, \(\rm S_{1}^{2}\), \(\rm S_{2}^{2}\), \(\rm S_{3}^{2}\), and \(\rm S_{4}^{2}\) denote the scores of the fuzzy integrated evaluation in terms of functionality, ease of use, system performance, and safety, respectively; \(\rm S^{1}\) denotes the score of the fuzzy integrated evaluation of the evaluation indexes of the AI painting software based on the GAN. The calculated results are as follows. \[\rm S_{1}^{2}=\sum_{j=1}^{4}\rm b_{j}v_{j} =0.58\times 100+0.34\times 80+0.06\times 60+0.03\times 50=89.53\] \[\rm S_{2}^{2}=\sum_{j=1}^{4}\rm b_{j}v_{j} =0.28\times 100+0.60\times 80+0.11\times 60+0.01\times 50=83.10\] \[\rm S_{3}^{2}=\sum_{j=1}^{4}\rm b_{j}v_{j} =0.27\times 100+0.24\times 80+0.46\times 60+0.02\times 50=75.48\] \[\rm S_{4}^{2} =\sum_{j=1}^{4}\rm b_{j}v_{j} =0.28\times 100+0.31\times 80+0.41\times 60+0\times 50=77.32\] \[\rm S^{1}=\sum_{j=1}^{4}\rm b_{j}v_{j} =0.37\times 100+0.38\times 80+0.24\times 60+0.02\times 50=82.23\]
## 6 Conclusions  
In this paper, a HVSC dynamic model composed by driver's NMS model, 2 DOF vehicle dynamic model and MPC controller is proposed for the shared control of AV. Specifically, the influence of the significant factors of the arms and legs, such as stiffness, damping and inertia are considered into the driver's NMS model. Field experiments were conducted for the validation of the model. Based on the subsequent experimental results, the HLS algorithm and UKF observer are implemented to identify and estimate the important NMS parameters, respectively. Finally, the covariance analysis shows consistency with the identified parameter of the driver's NMS characteristics. The results show that the model we proposed has a high level of accuracy with actual value. This paper will serve as a theoretical basis for the control strategy allocation between human and vehicle for L3 class AVs.  
Conceptualization, H.W. and Y.W.; Data curation, X.C. and J.X.; Funding acquisition, H.W.; Supervision, X.C.; Writing--review & editing, Y.W. All authors have read and agreed to the published version of the manuscript. This research was funded by [National Key Research and Development Program of China (Grant No. 2018 YFB1600500)]; [National Natural Science Foundation of China (Grant No. 51305472)]; [Natural Science Foundation Project of the Chongqing Municipal Science and Technology Commission (Grant No. KJQN201800714)]. This study has no conflict of interest.  
Figure 12: Covariance analysis of each subject’s state value.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**No.** & **Parameter** & **Coefficient** & **No.** & **Parameter** & **Coefficient** \\ \hline
1 & Lateral speed & 0.8621 & 9 & Steering assistance torque & 0.7156 \\
2 & Yaw rate & 0.8246 & 10 & Motion of leg & 0.82323 \\
3 & Lateral offset & 0.7653 & 11 & Movement speed of leg & 0.74421 \\
4 & Yaw & 0.8956 & 12 & Pedal torque & 0.7021 \\
5 & Steering wheel angle & 0.8663 & 13 & Leg contraction torque & 0.74421 \\
6 & Steering wheel angular rate & 0.8421 & 14 & Leg tendon motion & 0.746 \\
7 & Arm active contraction torque & 0.73421 & 15 & Pedal position & 0.716 \\
8 & Steering wheel torque & 0.8646 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Covariance analysis results between the actual value and Unscented Kalman Filter (UKF) estimated value.
# Seismic Ground Response Estimation Based on Convolutional Neural Networks (CNN)
###### Abstract  
One of the purposes of earthquake engineering is to mitigate the damages in buildings and infrastructures and, therefore, reduce the impact of earthquakes on society. Seismic ground response analysis refers to the process of evaluating the ground surface motions based on the bedrock motion. On the other hand, deep learning techniques have been developing fast, and they are establishing their application in the civil engineering field. This study proposes two convolutional neural network (CNN) models to estimate the seismic response of the surface based on the seismic motion measured at 100 m level beneath the surface, and selected the one which outperforms the other as the main model. The performances of the main model are compared with those of a physical software SHAKE2000. Twelve sites that include 100 earthquake datasets, whose moment magnitude is higher than 6 and PGA is higher than 0.1 g, were selected. In addition, the corresponding earthquake datasets are used for the CNN model. Whereas the conventional software overestimated the values of the amplitudes for most of the sites, the proposed CNN model predicts fairly well both the values of the amplitudes and the natural periods where responses amplify the most with few exceptions. The proposed model especially outperforms the conventional software when the natural periods range from 0.01 to 0.3 s. For specific sites, the average mean squared errors of the proposed model are even dozens of times lower than those of the conventional physical software.  
earthquake; seismic ground response analysis; convolutional neural networks; physical analysis software; acceleration response spectrum +
Footnote †: journal: Applied Sciences
# Privacy Preserving Classification of EEG Data Using Machine Learning and Homomorphic Encryption
## 2 Methods
### Optimization Formulation
Furthermore, overfitting can be addressed by using larger training datasets: the more samples are used for training, the more difficult it is for the model to learn the error or noise. Thus, according to [29], the number of samples should be at least 10-15 for each term in the model to avoid overfitting. In our use cases, this condition is met in all experiments, as the number of samples exceeds the required threshold (e.g., 9200 training samples for 80 features in the seizure detection use case). For those experiments where EEG signals are used in the unencrypted form, we also applied SVM methods, as they previously delivered state-of-the-art results in studies employing EEG information [30; 31]. Specifically, we used nuSVR from the scikit-learnlibrary [32] on plaintext data and compared the results with the performance obtained by applying polynomial regression on both plaintext and encrypted data. We performed a grid search for the parameters of the SVM classifier (\(nu\), which controls the number of support vectors, and the penalty parameter \(C\)) to determine their optimal values. The parameters obtained are reported in the Results section. The matrix multiplication operation was parallelized to reduced the computational time (using the Python multiprocessing package [33]). The elements of the resulting matrix were computed by distributing the corresponding rows and columns of the input matrices across multiple processes. All experiments were run on a machine equipped with an Intel Core i7 CPU running at 4.2 GHz and 32 GB RAM.
## 1 Introduction
Finally, option (c) places the satellite link between the User Equipment (UE) and the RAN, relaying the New Radio (NR) signal. The Uu interface connects the UE to the gNB. The affected interfaces in each architecture are subject to a distinct set of performance requirements and limitations, indicating that their relay through satellite links has an impact on their operation or even on their technical viability. This paper contributes to the exploration of 5G NTN transparent architectures by studying their implementation with mega-constellations, an approach that, to the best of our knowledge, has not been addressed before. We present a methodology to deploy a testbed, based on open-source software, which combines the required 5G functionality with real mega-constellation connectivity. From this testbed, we collect time metrics from each of the transparent architectures proposed in Figure 1, which are later utilized to evaluate their overall performance. The obtained results provide a valuable insight into the potential of mega-constellations as a viable approach for transparent architectures, introducing the community to this kind of 5G NTN implementation. Nevertheless, it is necessary to note that the integration of external commercial services into the testbed, as it is the case of the mega-constellation, inevitably triggers a number of assumptions throughout the work due to their inherent black-box nature, as well as posing technological limitations related to the abstraction of low-level aspects for end users. This paper is organized as follows: Section 2 briefly provides additional context about the evolution of the space communications sector, the current industry of mega-constellations, and the concept of the split gNB. Section 3 gathers related works and discusses their contributions. Section 4 explains the development of the testbed after presenting a characterization of the Starlink service. Section 5 describes the methodology to be followed in the collection of metrics. Section 6 analyzes technically the metrics obtained from tests. Section 7 discusses the findings of this paper from a practical perspective. Finally, Section 8 concludes this paper.
## 3 Review of Gaussian Process Regression Models
### GP-NARX Models  
Quite often, we seek to model time series data as a function of exogenous inputs and an autoregressive function of past observations. A class of GP models incorporating both non-linear autoregressive structure and exogenous predictors (typically abbreviated as GP-NARX) offer a principled way to propagate uncertainty when forecasting.  
An early example comes from [21], who proposed a GP-NARX model in which the inputs at time \(t\) consist of the past \(L\) lags of the response time series \(y_{t}\), as well as available exogenous inputs \(\mathbf{c}_{t}\in\mathbb{R}^{n_{c}}\). The input vector at time \(t\) in the GP-NARX model is the tuple \((\mathbf{x}_{t}^{\prime},\mathbf{c}_{t}^{\prime})^{\prime}\), where \(\mathbf{x}_{t}=(y_{t-1},\dots,y_{t-L})^{{}^{\prime}}\). Since \(y_{t-1},\dots,y_{t-L}\) are known during the training phase of model fitting, estimation is performed using maximum likelihood or _maximum a posteriori_ methods.  
Although training the GP-NARX model is similar to training the GP regression model and is straightforward, predicting future values is more challenging. Suppose our goal is to generate \(k\)_-step ahead_ forecasts for future responses \(y_{T+1},\dots,y_{T+k}\) given training data \(\mathbf{y}=(y_{1},\dots,y_{T})^{\prime}\in\mathbb{R}^{T}\). Because all or part of \(\mathbf{x}_{t}\) is unobserved in the holdout period (since it involves \(y_{T+j-1},\dots,y_{T+j-L}\), \(j=1,\dots,k\)) and is an uncertain input during forecasting, direct application of (8) would fail to take into account this inherent uncertainty.  
Ref. [21] deals with the uncertain inputs issue by assuming that for each \(j=1,\dots,k\), \(\mathbf{x}_{T+j}\sim\mathcal{N}_{L}\Big{(}\mathbf{\mu}_{\mathbf{x}_{T+j}},\mathbf{ \Sigma}_{\mathbf{x}_{T+j}}\Big{)}\). Then, given the training data \(\mathcal{D}=\{\mathbf{c}_{t},\mathbf{x}_{t},y_{t}\}_{t=L_{x}+1}^{T}\), and a set of exogenous inputs \(\mathbf{c}_{T+j}\), \(j=1,\dots,k\), the posterior predictive distribution for \(y_{T+j}\) is  
\[p(f_{T+j})=\int p(f_{T+j}|\mathbf{x}_{T+j},\mathbf{w}_{T+j},\mathcal{D})p(\mathbf{x}_{T+ j})d\mathbf{x}_{T+j}. \tag{18}\]  
Although there is no closed form for (18), the moments of the posterior predictive distribution can be obtained via Monte Carlo sampling or one of several different approximation methods [22].  
Recently, these ideas have been extended to sparse GP models. Ref. [23] developed an approximate uncertainty propagation approach to be used alongside the sparse pseudo input GP regression method, known as the Fully Independent Sparse Training Conditional (FITC) model [16]. Ref. [24] derived uncertainty propagation methods for a wide variety of competing sparse GP methods, and [25] extended sparse GP-NARX time series modeling to an online setting.
###### Abstract  
Coda wave interferometry (CWI) has been used to measure the relative wave-velocity change (\(dV/V\)) caused by small changes in materials. This study uses the stretching processing technique which has been used for CWI analysis to investigate velocity changes of direct longitudinal (P) wave, direct shear (S) wave, and coda wave in concrete by choosing different time windows of ultrasonic signals. It is found that the obtained wave-velocity change depends on the time window position, because the relative contribution of P wave and S wave is different in each signal window. This paper presents three experimental scenarios of velocity change in concrete: early-age hydration, temperature change, and uniaxial loading. In early-age concrete, the S wave has a larger relative velocity change than the P wave, which is consistent with the microstructure development due to the hydration process. Temperature change causes a larger \(dV/V\) on the P wave than on the S wave, and the difference between P and S wave-velocity changes may be used to determine nonlinear elastic constants of materials. In the uniaxial loading experiment, analysis of the direct P wave can distinguish the acoustoelastic effects in the stress direction and the non-stress direction, which may potentially be used for stress evaluation in prestressed structures. However, the coda wave does not show this directional property to stress due to multiple scattering in the medium.  
stretching technique; coda wave interferometry (CWI); hydration; temperature; uniaxial loading; nondestructive evaluation; concrete +
Footnote †: journal: Applied Sciences
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
## 4 Performance Evaluation and Results
### Simulation Results  
In the following experiments, four methods are compared in terms of thermal noise performance and multipath performance, which are shown in Table 1. The first method is the original DBT, where both the SPLL and the PLL use prompt correlators. The second method is called the OC-P, where the SPLL uses the OC technique, and the PLL uses the prompt correlator. The third method is called the OC-OC, where both the PLL and the SPLL use the OC technique. The fourth method is called the PAOC-PAOC, where both the PLL and the SPLL use the PAOC technique. The first two methods, DBT [10] and OC-P [22], are old methods that have been proposed before. The last two methods, OC-OC and PAOC-PAOC, are new methods proposed in this paper for the first time.  
Figure 8: Tracking jitters of the DBT, OC, and PAOC techniques for the BOC (15, 2.5) signal with a bandwidth of 40.96 MHz: (**a**) subcarrier noise error; (**b**) carrier noise error.
# Terpenes as Potential Anti-Alzheimer's Disease Agents
## 2 Activities of Terpenes on the Mechanisms Associated with AD
### Inhibition of Acetylcholinesterase (AChE)  
#### 2.4.1 Monoterpenes  
From the essential oils of several plants, many monoterpenes are obtained, such as carvone (**93**), colimene (**94**), menthone (**95**), and pulegone (**96**). These four monoterpenes are weak inhibitors of AChE with IC\({}_{50}\) values of 2900, 4700, 9000, and 9000 \(\upmu\)M, respectively. All the other monoterpenes, such as linalool (**1**), 1,8-cineol (**47**), \(\alpha\)-pinene (**48**), \(p\)-cymene (**49**), citral (**97**), citronellal (**98**), terpinene-4-ol (**99**), \(\beta\)-myrcrene (**100**), \(\Upsilon\)-terpinene (**101**), isopulegol (**102**), menthol (**103**), \(\alpha\)-terpinene (**104**), and \(\alpha\)-phellandrene (**105**) are inactiveagainst AChE (IC\({}_{50}\) > 10.000 uM) [121]. These results suggest that the compounds with a carbonyl group in a cyclic monoterpene present some activity.