esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study's integration of molecular dynamics (MD) simulations with non-covalent adaptable networks (NANs) and corroborative wet lab experiments offers a comprehensive approach to understanding the interactions between ritonavir (RTV) and polymers in supersaturated solutions. This multifaceted study not only explored the stabilization mechanisms facilitated by NANs but also examined the influence of polymer selection on the pharmaceutical properties of RTV, a class III compound known for its slow crystallization rate. This research utilized molecular dynamics simulations to model the intermolecular interactions between RTV and two polymers, Polyvinylpyrrolidone (PVP) K30 and Eudragit L100. These simulations were specifically designed to incorporate the effects of NANs, highlighting their dynamic nature and potential to enhance drug stability and solubility. Simultaneously, wet lab experiments were conducted to measure the nucleation induction times and observe the crystallization behavior of RTV under varying conditions of polymer presence. The experimental data demonstrated a significant extension in nucleation induction time, prolonging the duration from 12 to approximately 64 h when PVP K30 and Eudragit L100 were present. This substantial delay in crystallization was attributed to the strong intermolecular interactions between RTV and the polymers, which were effectively stabilized by the non-covalent bonds within the NANs. These findings were consistently confirmed across both computational and experimental settings, illustrating how NANs can effectively inhibit crystallization and enhance the supersaturation state of RTV. This study successfully demonstrates how the physical and chemical properties of polymers influence the crystallization process of poorly water-soluble drugs such as RTV. Leveraging the synergy between computational simulations and empirical laboratory data, this research provides deep insights into the mechanisms at play, ensuring that drug formulations are optimized for both stability and performance.  
supersaturation level; non-covalent adaptable networks (NANs); ritonavir (RTV) compound; molecular dynamics simulations; nucleation induction times +
Footnote †: journal: Applied sciences  
Article
## 3 Results and Discussion
### Delamination Factors and Optimization Process
#### 3.2.1 Optimization of Fd and Fa

Table 4: ANOVA for second response (F\({}_{\rm d}\)). Figure 6: The main effect of rotational speed, feed rate, and angle of the layers on the third response (F\({}_{\rm d}\)) (a) Feed rate. (b) Rotational speed. (c) Angle of layers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F-Value** & **\(p\)-Value** & \\ \hline Model & 0.4336 & 3 & 0.1445 & 67.62 & \(<\)0.0001 & **significant** \\ A-Feed rate & 0.2904 & 1 & 0.2904 & 135.87 & \(<\)0.0001 & \\ B-Rotational speed & 0.1426 & 1 & 0.1426 & 66.72 & \(<\)0.0001 & \\ C- Angle of layers & 0.0006 & 1 & 0.0006 & 0.2817 & 0.5983 & \\ Residual & 0.0940 & 44 & 0.0021 & & & \\ Lack of Fit & 0.0315 & 14 & 0.0022 & 1.08 & 0.4143 & **not significant** \\ Pure Error & 0.0626 & 30 & 0.0021 & & & \\ Cor Total & 0.5276 & 47 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ANOVA for third response (F\({}_{\rm a}\)). Figure 7: The main effect of rotational speed, feed rate, and layer orientation on the second response (F\({}_{\rm a}\)) (a) Feed rate. (b) Rotational speed. (c) Angle of layers.
## 5 Conclusions  
In this paper, we proposed new communication hardware that is capable of full-duplex communication in two-wire communication and implemented parallel operation control using it.  
1. The communication transmitter generates two levels of signals for data transmission. However, due to the characteristics of the proposed communication hardware, three-level electrical signals are generated in the communication line according to the transmission data state of each module. The communication speed is improved by implementing full-duplex communication through the generated three-level electrical signal.
2. When performing parallel operation using the proposed communication hardware, power quality can be improved under the same conditions by controlling more than twice as fast as a serial communication method, such as a conventional Modbus communication. In addition, it is expected that the data protocol can be more simplified than the full-duplex communication by time division by implementing the full-duplex communication method by the hardware proposed in this paper.
3. It is expected that long-distance communication is possible by increasing the voltage of the transmission line by constructing the transmission circuit with a half-bridge structure.
4. The voltage of the communication line is generated by the transmitter transmission data and the receiver transmission data signal. Therefore, it is expected to be excellent in terms of security since only two devices communicating with each other can analyze signal information of a communication line.  
In conclusion, the communication hardware proposed in this paper is expected to be actively applicable to the system requiring large capacity through parallel operation. It is also expected to improve load sharing imbalances and problems with circulating currents that may arise in systems that require parallel operation.  
Conceptualization, S.-K.L., S.-M.P., and S.-J.P.; Data curation, S.-K.L., H.-R.C., and S.-M.P.; Formal analysis, S.-K.L., H.-R.C., and S.-M.P.; Funding acquisition, S.-J.P.; investigation, S.-K.L. and S.-J.P.; Project administration, S.-J.P.; Writing--original draft, S.-K.L.; Writing--review and editing, H.-R.C. and S.-J.P.  
This research was funded by the support of the Korea Institute of Industrials Technology as \"Development of Chassis Platform for Semi-autonomous Electric Vehicle with EMS Function\" (KITECH NW-19-0050).  
The authors declare no conflicts of interest.
# Efficient Secure Mechanisms for In-Vehicle Ethernet in Autonomous Vehicles
## 4 Simulation Experiment and Result Analysis  
### Simulation of Network Traffic and Attacks  
This section delves into the simulation process of network traffic and various attack scenarios within the context of in-vehicle networks. The primary objective of this simulation is to evaluate the efficacy of the proposed security mechanisms under realistic network conditions.  
For our experimental simulation, the software utilized was CANoe from Vector, a leading tool for automotive network simulation. We began by editing ECU node information and creating a database with Vector AUTOSAR Explorer (2.0.23 version). System variables were then configured, and a virtual panel was designed in Panel, setting parameters in Properties to establish the simulation environment.  
In CANoe, we constructed the network topology architecture, which integrates multimedia and control signals with in-vehicle Ethernet serving as the backbone network, as illustrated in Figure 7.  
In Figure 7a, the multimedia signals encompass AMP (Audio Multimedia Processor), CAMF (Camera Module for Video Feed), and HU (HeadUnit), where AMP is responsible for editing multimedia-related information, CAMF acts as the video sender node, and HU serves as the video playback module. The control signals include IC (Instrument Cluster) and VGW (Vehicle Gateway), with IC simulating the dashboard module and VGW emulating the centralized vehicular gateway's function, facilitating communication between the vehicle's powertrain systems.  
Figure 5: Encryption flowchart of AES-ECC data encryption algorithm.  
Figure 6: Decryption flowchart of AES-ECC data encryption algorithm.  
Figure 7b demonstrates the control of the simulated car through the configuration of system variables, which enables the implementation of these functions on the designed panel interface. As illustrated in Figure 7b, the simulated car system variables include settings for parameters such as turn signals, steering wheel, and hazard lights. Taking the left turn signal as an example, 0 signifies off, while 1 indicates on; the steering wheel parameter is set to \(-1\) for a left turn and 1 for a right turn. The pedal is represented by a percentage scale of 0-100, indicating the intensity of the pedal press.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 6 Scheme Analysis
### Performance Analysis
#### 6.2.3 Storage Overhead  
To test the impact of the different sizes of data on the blockchain's storage overhead in our scheme, we conducted tests on the storage overhead in both the blockchain and IPFS. By uploading data of various sizes and performing multiple tests to obtain averages, we compared the storage overhead generated by the blockchain and IPFS. In this context, the returned CID from the IPFS is a constant 46 bytes (SHA-256, Base64 encoding). As shown in Figure 5, it can be observed that the data size has almost no effect on the storage overhead on the blockchain network.  
Figure 4: Time overhead comparison between AES-PRE and DReC.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **General Method** & **Our Method** \\ \hline
1 & 182 & 72 \\
2 & 293 & 68 \\
3 & 344 & 59 \\
4 & 518 & 56 \\
5 & 847 & 51 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Query time overhead results (ms).
## 5 Results and Discussion
### Discussion
#### 5.3.5 Graphical Comparison of the Four Models  
Figure 14 compares the accuracy obtained for each epoch in the four models. It can be seen that LSTM behaved the worst, even showing erratic behavior, with negative slopes in several periods, highlighting a very pronounced negative slope at Epoch 10, with an accuracy below 95%. The BiLSTM model gave better results than LSTM. Although the training accuracy improved notably, according to Figure 11, the same did not occur with the validation accuracy, which had a negative slope at Epoch 10 and a pronounced negative slope at the end of Epoch 20. GRU and BiGRU gave better results in training than those offered by LSTM and BiLSTM since it was observed that, in both of them, an accuracy that constantly increased from the first epoch was obtained. Analyzing GRU versus BiGRU, it  
Figure 12: GRU accuracy.  
Figure 13: BiGRU accuracy.  
was observed that BiGRU gave better results, obtaining in the last Epoch 20 100% for its training accuracy, over the 99% obtained in GRU. Even in Figure 15, it can be seen that the model with the lowest loss was BiGRU, followed by GRU. On the other hand, of the four algorithms analyzed, the one that performed worst was LSTM.  
To define which of the DL algorithms worked best with the data embedded with GloVe, we performed the execution of the four algorithms using the K-fold cross-validation technique, where K = 5 and shuffle = true. The run results in Table 6 indicate that our model is acceptable, as all four algorithms performed with a mean accuracy above 96.70%. The DL algorithm with the best results was the BiGRU algorithm, with 97.39%.
# MeLa: A Programming Language for a New Multidisciplinary Oceanographic Float
## 2 A Programming Language Based on Models
### Application Verification  
#### 2.5.1 Static Analysis of Applications  
The static analysis consists of computing properties of the applications from models without executing them. We use it to verify that the applications do not exceed the instrument capacities during their development. In its current version, MeLa is able to compute the processor usage, the battery life time, and the amount of data to transmit by satellite, but other properties, such as memory usage, can be added to the analysis. Each function of the MeLa library is associated with information about execution time used to compute the processor usage. The execution time can be a constant value or an equation with parameters, such as the size of arrays used during the call of the function. Indeed the execution time can change by several orders of magnitude for different sizes of arrays. The functions can also have a specific meaning, such as \"this function requests the ascent of the float\" or \"this function records data to transmit by satellite\", which are interpreted by the analysis tools.
## 3 Application Instance  
### Ground Pressure Monitoring System  
This study takes the ground pressure monitoring system of Zhongjin Lingnan Panlong Lead-Zinc Mine in Guangxi as the engineering background to verify the effectiveness and feasibility of the proposed method. The network structure of the ground pressure monitoring system, as illustrated in Figure 4, consists of 24 acoustic emission monitoring channels, a sampling interval of 200 microseconds, and a signal generated every 1024 sampling points.  
Figure 3: Identification and classification model of mine acoustic emission signals.
## 5 Results and Discussion
### Fault Prediction Phase
#### 5.1.2 Algorithm Optimizer Selection  
According to the equipment operation parameter data set, an AF data item is selected to optimize the algorithm model. This experiment applied cross-validation to compare a total of five algorithm optimizers: Adam, SGD, Adagrad, RMSprop, and Nadam. After comparison of multiple groups of experiments, it was found that the multi-layer GRU neural network model could better reflect the influence of different optimizers, so the model was displayed using multi-layer GRU neural network. Figure 16 shows the prediction results.  
The parameter settings of each optimization algorithm are the best results proposed in the literature. Most current neural networks use Adam to optimize the loss function. The advantage of the Adam algorithm is that it converges quickly and can deal with high noise and sparse gradient problems. The proposed Nadam optimization algorithm changes the Adam momentum and speeds up the convergence of the model.  
Table 5 shows the prediction evaluation indexes of MLGRU combined with different optimizers. The results show that the prediction accuracy of the Nadam optimizer is higher, so the Nadam optimizer is selected.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Optimizer**} & \multicolumn{5}{c}{**Prediction Evaluation Index**} \\ \cline{3-8}  & & **MSE** & **RMSE** & **MAE** & **MAPE** & **R\({}^{2}\)** & **EVS** \\ \hline \multirow{4}{*}{MLGRU} & Adam & 0.00163 & 0.04031 & 0.01803 & 0.29742 & 0.99992 & 99.99\% \\  & SGD & 0.14438 & 0.37998 & 0.25664 & 4.95808 & 0.99255 & 99.26\% \\ \cline{1-1}  & Adagard & 0.12446 & 0.35279 & 0.27877 & 5.66912 & 0.99358 & 99.37\% \\ \cline{1-1}  & RMSprop & 0.02471 & 0.15721 & 0.10667 & 1.43667 & 0.99872 & 99.88\% \\ \cline{1-1}  & Nadam & 0.00123 & 0.03509 & 0.01227 & 0.23866 & 0.99994 & 99.99\% \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of the predicted results of evaluation indexes from different optimizers.
## 2 Related Work  
As encryption becomes ubiquitous, detecting malicious behavior concealed in encrypted traffic has emerged as a central challenge in cyber security [5,16]. The adoption of TLS/SSL protocols renders traditional payload-based intrusion detection ineffective, with reports indicating that nearly 70% of all cyberattacks now exploit encrypted channels [13]. To address this, detection strategies generally fall into two categories: one extracts observable side-channel features to detect threats without decryption [5], while the other analyzes endpoint behaviors such as system calls and session patterns [17]. While signature-based methods struggle with encrypted and evolving threats [18], statistical- and flow-based models can identify anomalies by learning from traffic-level characteristics like timing variance and size distribution [1]. Recent advances apply ML and deep learning to extract discriminative features from encrypted traffic [18], but their performance relies heavily on large labeled datasets and stable protocol conditions, limiting their adaptability in dynamic environments [17].
Previous studies have examined the corrosion behavior of ductile cast iron in soils with varying NaCl concentrations [25], or under specific conditions such as in nuclear power plants, where tests were conducted at a low NaCl concentration of 1.6 ppm and at a temperature of 25 \({}^{\circ}\)C [26]. Some studies have also investigated the effectiveness of corrosion inhibitors, including organic (triethanolamine phosphate) and inorganic (sodium hexametaphosphate) compounds [27]. However, no research has been found that considers the environmental conditions specific to marine engine applications and that investigates the role of nitrite-based additives, accordingly. Unlike land-based systems, marine engine cylinder liners (FCD-500) are exposed to complex environmental conditions, includingthe intrusion of seawater into the cooling water (with chloride concentrations ranging from 0 to 500 ppm Cl\({}^{-}\)) and elevated cooling water temperatures of around 80 \({}^{\circ}\)C [28]. Therefore, studying the corrosion behavior and effectiveness of corrosion inhibitors under these conditions can provide a robust academic foundation for understanding the role of such inhibitors in marine cooling systems. Furthermore, it is expected to serve as a practical study that proposes effective management strategies for the operation of corrosion inhibitor-containing coolants to prevent corrosion in marine engines. This study aims to evaluate the corrosion behavior of FCD-500 in CW conditions, simulated according to the case of an engine on a marine merchant vessel. The experiments are designed to examine the effects of varying inhibitor dosage and chloride ion levels. In addition, high chloride ion concentration levels that may occur in marine vessels were considered. Moreover, temperature is one of the key factors influencing corrosion and inhibitor performance in CWSs. Therefore, this study considers the temperature dependence of a commercial nitrite-based inhibitor, by evaluating the corrosion behavior at two representative CW temperatures commonly operated in marine vessel engines, namely 50 and 80 \({}^{\circ}\)C. This study is expected to contribute to the improvement of corrosion control strategies for CWSs in marine propulsion applications.
## 3 Results and Discussion  
### Shaping of Bursts  
Based on the technique described above, we have shaped several temporal envelopes of bursts containing 1000 individual pulses at a repetition rate of about 1 MHz. Two examples are shown in Figure 3. Essentially, we achieved a good agreement between the targeted shape and the measurement. Only for pulse energies of less than 5% of the maximum amplitude, the accuracy has dropped. This can partly be attributed to the accuracy of our cross-referenced measurement scheme, when pulses are hidden in the noise originating from the photo diodes used for measurement.  
Besides the oscillatory noise, which was discussed before, the residual high frequency noise deviating from the goal function originates from a random jitter in the triggering system. The standard deviation of this random jitter is around 100 ps. This can in principle be improved by using a better synchronization and a Pockels cell driver with a longer rise time.
## 3 Methods: Bayesian Modelling
### Bayesian Model: White Box
#### 3.2.1 Prior Distribution  
The proxy coefficients are modelled as a Gaussian distribution, given by \(p(\beta)\sim\mathcal{N}(\mu_{0},\sigma_{0})\), whereas the noise variance is assumed as a random variable following an inverse Gamma distribution, given by \(p(\sigma^{2})\sim\text{IG}(a,b)\).  
Informative priors are established by applying nonlinear regression on the training data. First, the variable, \(\mu_{0}\), can be initiated using the estimated variables, \(\hat{\beta}\), where the standard deviation, \(\sigma_{0}\), is chosen to be twice that of the mean value, \(\mu_{0}\). Second, the parameters \(a\) and \(b\) are estimated by taking the squared residual values between the nonlinear regression estimation and the real BC mass concentration data. This provides the residual mean, \(\mu_{r}\), and its corresponding variance, \(\sigma_{r}^{2}\). Using the properties of the inverse Gamma distribution, the parameter \(a\) can be estimated using \(a=(\mu_{r}/\sigma_{r})^{2}+2\), whereas the parameter \(b\) can be computed using \(b=\mu_{r}\big{(}(\mu_{r}/\sigma_{r})^{2}+1\big{)}\) as proposed in [PERSON] et al. [49; 50]. These prior parameters are then used as starting values for the chosen initiation method for running Markov Chain Monte Carlo (MCMC) algorithm.
# Numerical Simulation of Piles in a Liquefied Slope Using a Modified Soil-Pile Interface Model
## 2 Numerical Simulation
### Soil-Pile Interface Model Development
[44] proposed that the friction angle of the interface normal spring decreases as the embedment depth decreases in dry sand. The friction angle of the interface normal spring can decrease due to the liquefaction of the ground. In this study, a linear decrease in the tangential friction angle to 5% of the initial value was applied, with an increase in the excess pore pressure ratio. The residual value of 5% is generally adopted for the residual strength of liquefied ground [22; 45]. Figure 5 illustrates the mechanism of the interface normal spring operating between the pile node and the soil grid containing the node. Here, \(r_{\text{u}}\) refers to the excess pore pressure ratio, \(\varphi^{\prime}_{\text{sp}}\) to the friction angle of the normal spring, and \(\varphi^{\prime}_{\text{c}}\) to the effective confining stress. The normal spring with a sufficiently large coefficient generates a maximum normal force when subjected to the relative displacement between the pile and the soil. However, the normal spring proposed for dry soil does not consider the change in the friction angle due to changes in the excess pore pressure ratios. As a result, the increase in excess pore pressure ratio in the soil grid including the pile node decreases the normal force through a decrease in the effective confining stress (blue path in Figure 5). On the other hand, the proposed method considers the decrease in tangential friction angle due to the increase in the excess pore pressure ratios, leading to a faster decrease in the maximum normal force (red path in Figure 5). While no gap developed between the pile and sandy soil element during cycling [46; 47], the separation between the soil and pile elements should be considered due to the slope failure and soil settlement induced by the liquefaction and dissipation process. The soil-pile separation was simulated by switching on the large strain mode in FLAC2D, which can help to avoid the transfer of large forces at the pile node resulting from severe soil displacement. All parameters of the interface springs were accessed and adjusted using subroutines written in the FISH programming language, which is available in FLAC2D.