esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
With the development of coal mine IoT, a great number of intelligent miner's lamps are put into use which has led to the multiplication of monitoring data. Data compression appears as an effective solution to high-efficiency transmission and storage.  
In view of the long linear structure of the coal mine roadway and the time sparsity of gas sensors, the paper studies the relationship between data transmission and compressed sensing in multi-hop networks and a Total Variation Sparsity based on Multi-Hop (TVS-MH) algorithm is proposed, which takes advantage of the time correlation of the mobile gas monitoring data in multi-hop networks and realizes the sparsity of monitoring data by introducing total variation.  
The current study analyzed the sparseness characteristics of gas data, investigated the network model suitable for the transmission of gas data flow and used a pseudorandom measurement matrix to replace a random measurement matrix for performance improvement. Finally, through the intrusion of total variation of gas data, gas data were accurately and efficiently reconstructed using the TVS-MH algorithm. The present results indicate that high-efficiency acquisition and transmission of the measured data by mobile sensors in a distributed measurement network are possible.  
[PERSON] proposed the algorithm and performed the experiments. [PERSON] and [PERSON] wrote the paper. [PERSON] and [PERSON] reviewed and edited the manuscript. All authors read and approved the manuscript.  
This work is supported by the National Key Research and Development Project of China (No. 2017 YFC0804404).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Processor** & **Memory** & **Hard Disk** & **Operating System** & **Simulation Tool** \\ \hline Intel (R) Core (TM) & 16.0 GB & 512 GB NVM & 64-bit Windows 10 Home & MATLAB R2018a \\ i7-7700 HQ CPU @2.80 GHz & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The testing environment.  
Figure 12: The relation between CPU operation time and compression ratio for a same sparse gas signal using different reconstruction algorithms.
## 3 Experimental Analysis
### Grommet Making and Testing  
A grommet was fabricated using compound rubber according to the derived ratio. The grommet produced was a bellows-type grommet [9]. The temperature of the grommet was kept at 180 \({}^{\circ}\)C for 900 s. Three grommets with the existing formulation and three with the formulation derived through mixture design were fabricated under the same processing conditions. The manufactured grommet was a test standard for use in vehicle wiring required by the automobile industry. Figure 4a shows the conceptual diagram of the bellows-type grommet. Figure 4b shows the panel fixing jig for the grommet mounting experiment.  
To measure the mounting force, the wire holding part was fixed using fixing tape to the universal testing machine and the wire was pulled until the grommet was firmly fitted to the steel plate. Force was applied until the grommet was fitted in the panel and when being released. The speed of the universal testing machine head was 50 mm/min to measure the insertion and withdrawal forces. Figure 5 shows the measurement of the insertion and separation forces. Figure 5a is a photograph showing a grommet mounted on a jig to measure the insertion force of the grommet and shows a process in which the grommet is inserted before being pulled and the grommet is stretched. Figure 5b shows the drawing of the grommet to pull out the grommet with the grommet inserted into the plate initially to measure the separation force.  
Figure 4: Grommet and jig diagram: (**a**) bellows-type grommet; (**b**) jig for fixing the panel.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Mechanical Characteristics**} & \multicolumn{4}{c}{**Vulcanization Characteristics**} \\ \cline{2-7}
**No.** & \begin{tabular}{c} **Hardness** \\ **(Shore A)** \\ \end{tabular} & \begin{tabular}{c} **Tensile Strength** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **Elongation** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **T MAX** \\ **(Nm)** \\ \end{tabular} & \
\begin{tabular}{c} **TC10 (s)** \\ \end{tabular} &
\begin{tabular}{c} **TC90 (s)** \\ \end{tabular} \\ \hline
1 & 51 & 13.03 & 745 & 1.89 & 76 & 196 \\ \hline
2 & 51 & 11.95 & 738.1 & 1.85 & 76 & 189 \\ \hline
3 & 52 & 11.81 & 750.5 & 1.88 & 77 & 195 \\ \hline
4 & 51 & 13.02 & 727.4 & 1.92 & 76 & 200 \\ \hline Average & 51.25 & 12.45 & 740.25 & 1.88 & 76.25 &
## Appendix B The Calculus for the Projection Lengths  
Let us consider the \(A_{0}TT_{x}\) and \(A_{x}TT_{x}\) right-angle triangles in Figure 19. Let us note the length of segments \(A_{0}T_{x}\), \(TT_{x}\), and \(A_{0}A_{x}\) by \(x\), \(y\), and \(d\), respectively.  
The length of the hypotenuse, \(R\), is known from round-trip time measurements. A phase difference measurement, \(\Delta\phi\), of a signal transmitted from \(T\) and arriving at points \(A_{0}\) and \(A_{x}\) is carried out. The length of segment \(TA_{x}\) is less than the length of segment \(TA_{0}\) with the product of the phase difference \(\Delta\phi\) and the wavelength \(\lambda\).  
Applying Pythagoras theorem on the triangles \(A_{0}TT_{x}\) and \(A_{x}TT_{x}\) one obtains the following equation system:  
\[\left\{\begin{aligned} x^{2}+y^{2}=R^{2}\\ (x-d)^{2}+y^{2}=(R-\lambda\Delta\phi)\end{aligned}\right.^{2}. \tag{20}\]  
Expanding the second equation one can get the following:  
\[x^{2}-2 xd+d^{2}+y^{2}=R^{2}-2R\lambda\Delta\phi+\lambda^{2}\Delta\phi^{2}. \tag{21}\]  
On can observe that \(x^{2}+y^{2}\) on the left side is equal to \(R^{2}\) on the right side; thus, this can be optimized and Equation (15) yields the following:  
\[-2 xd=-2R\lambda\Delta\phi+\lambda^{2}\Delta\phi^{2}-d^{2}. \tag{22}\]  
Finally, the length of the projection on the \(x\)-axis is as follows:  
\[x=\frac{R\lambda\Delta\phi}{d}-\frac{\lambda^{2}\Delta\phi^{2}}{2d}+\frac{d}{2} \tag{23}\]
## 3 Experimental Results
### Results of Feature Evaluation and Selection  
Using single biomedical signals such as ECG or PPG to estimate the BP including systolic blood pressure (SBP) and diastolic blood pressure (DBP) can increase the value of smart devices [3; 23]. Therefore, the effects of ECG and PPG signals are examined and then three types of inputs were designed as I(ECG), I(PPG), and I(ECG, PPG). I(\(\bullet\)) is used to represent the selected signals and the experimental results are shown in Table 2. Using both ECG and PPG signals, the proposed system can achieve the best performance. However, the performance of using a single ECG or PPG still achieves acceptable results. For practical applications, the complexity of the proposed approach can be reduced without severely losing accuracy. Thus, the proposed structure of the neural network is suitable for practical applications.  
Next, the effects of input signals represented as time and frequency sequences are examined and the results are shown in Table 3. The results show that the performance using time and frequency signals is higher than that using only time or frequency signals. Previous studies showed that the relationship between ECG and PPG signals in the time domain can be used to predict BP [2]. Moreover, the performance using time signals is higher than that using frequency signals. Therefore, the proposed approach can learn the relationship between ECG and PPG signals in the time domain, and then the accuracy can be increased. However, integrating with frequency information, the accuracy can be effectively increased by using the proposed neural network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Neurons** & **Parameters** & **Selected Value** \\ \hline \multirow{3}{*}{Convolutional layer 1 and 2} & Number of kernel size & 64 \\  & Number of filter size & 10 \\  & Number of pooling size & 2 \\ \hline \multirow{3}{*}{Convolutional layer 3} & Number of kernel size & 32 \\  & Number of filter size & 10 \\  & Number of pooling size & 2 \\ \hline LSTM layer 1 and 2 & Number of hidden units & 64 \\ \hline Fully connected layer 1,2, and 3 & Number of hidden units & 64, 32, 16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of selected parameters of the proposed approach.
# Optimization of Acoustic Communication Links for a Swarm of AUVs: The COMET and NEMOSENS Examples
## 5 Physical Layer
### Modern Performance with Adaptive Parameters  
In order to evaluate the system performance when the modern operates with the optimization procedure, we performed simulations in a noisy environment. In this case, the system utilized channel encoder. The Watermark simulator [34; 35] was utilized for emulating the channel. We added white Gaussian noise to the signal that passed through the channel. The signal-to-noise ratio (SNR) is measured at the receiver side, and it is defined as:  
\[SNR=10\log_{10}\left(\frac{E_{\text{b}}}{N_{0}}\right) \tag{11}\]  
where \(E_{\text{b}}\) is the bit energy, and \(N_{0}\) is the noise power spectral density.  
The modem was set with the following parameters for the simulations performed considering this framework. The channel encoder utilized is the convolutional, with a code rate of \(1/2\), and constraint length \(7\). The bits are modulated with a D-QPSK modulation, and the sampling rate is \(f_{s}=192\) kHz. In addition to that, each simulation is repeated \(100\) times. For each channel, and for a given overhead value, the optimization algorithm is executed for obtaining the number of subcarriers (\(N_{\text{sub}}\)) and the cyclic prefix length (\(T_{\text{CP}}\)).  
We ran simulations regarding the environment of KAU1 channel. Figure 15 shows the BER as a function of the SNR. The curves considering the optimized points are indicated in the legend as \(\rho_{\text{max}}=0.15\) and \(\rho_{\text{max}}=0.3\). The other two curves were generated considering a parameters pair randomly selected, i.e., the unoptimized case. For \(\rho_{\text{max}}=0.15\), it is possible to observe the performance gain when utilizing the optimization algorithm. This gain increases with the augmentation of the SNR. The same behavior is observed for \(\rho_{\text{max}}=0.3\). In this case, the gain is even more significant: for SNR values higher than 12 dB, the BER is improved in at least one order of magnitude.  
Figure 14: Corresponding overhead for each parameters pair for KAU1 channel.  
All these results show the benefits the optimization algorithm can bring to the system.
# SAET: The Non-Verbal Measurement Tool in User Emotional Experience
## 4 Validation of the SAET Images
### Pilot Study
#### 4.1.1 Participants  
The participants were master and undergraduate students from Tongji University in China who were enrolled in a user research and interaction design course. A total of 9 male and 24 female students participated (\(N=33\)). Their ages ranged from 20 to 26 years old (\(M=22.09\), \(SD=2.3\)).
# Empirical Study of Fully Homomorphic Encryption Using Microsoft SEAL
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data Size**  
The comparative analysis of CPU load revealed that the Jetson Nano operates within a higher utilization range across all dataset sizes and _poly_modulus_degree_ values compared to the PC. For small datasets, the difference in CPU load between the two platforms was minimal, showcasing the Nano's capability to efficiently manage basic encryption tasks. However, as the computational demands increased, the Nano's CPU load reached its peak, often hitting 100% utilization, while the PC showed a more gradual increase in CPU load, maintaining better overall performance stability. Similarly, memory utilization trends have highlighted significant constraints on the Jetson Nano, especially with large datasets. The PC's ample memory capacity allowed it to handle extensive cryptographic operations with relative ease, whereas the Jetson Nano struggled with memory saturation, leading to execution failures in the most demanding scenarios. This limitation points to the critical need for memory-efficient cryptographic algorithms and optimizations tailored for edge computing devices. Traditional computing environments and edge devices like the Jetson Nano reveal critical insights for implementing homomorphic encryption in edge computing. There is a pronounced need for optimization to adapt encryption practices to the limitations of edge hardware, requiring algorithmic refinements that minimize computational and memory demands. Furthermore, the performance gap underscores the necessity for both cryptographic algorithm innovations and advancements in edge device capabilities. Tailoring encryption methods to meet the specific needs of edge computing can significantly improve operational efficiency, making secure, privacy-preserving computations feasible within IoT ecosystems. Moreover, the deployment of homomorphic encryption technologies must consider application-specific demands, including data sensitivity, computational requirements, and resource availability. The analysis highlights the importance of edge-specific optimizations and parameter adjustments to meet these needs effectively.
# Voice Activation for Low-Resource Languages
## 3 Experiments
### Training Procedure  
Our experiments for the English data set follow the same procedure as the TensorFlow reference for the Google Speech Commands data set [38]. The data set is split into training (80% of the total number of samples), validation (10%), and test (10%) sets. For the Lithuanian data set, we follow the procedure from [31]: 326 records for training, 75 for validation, 88 for testing (the rest of the samples are background noise samples). At the train stage, background noise is added to a training sample with a probability of \(P_{n}\). We use a stochastic gradient descent for optimization with initial learning rate \(L\), momentum \(M\) and batch size \(B\). At each \(S\) step, the validation accuracy is computed. If the value is not improved, the best checkpoint is reloaded, and the learning rate is divided by \(L^{\prime}\). The training for each experiment is stopped after 6 learning rate drops. Finally, the test accuracy is computed. We run each experiment three times and report the average test accuracy. The distributions and values for a random hyperparameter search can be seen in Table 2 (\(U\) is a uniform or discrete uniform distribution).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Architecture Name** & **Residual Blocks** & **Feature Maps** & **Average Pooling** & **Dilation** \\ \hline res8 & 3 & 45 & \((4\times 3)\) & no \\ res8-narrow & 3 & 19 & \((4\times 3)\) & no \\ res15 & 6 & 45 & no & yes \\ res15-narrow & 6 & 19 & no & yes \\ res26 & 12 & 45 & \((2\times 2)\) & no \\ res26-narrow & 12 & 19 & \((2\times 2)\) & no \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of ResNet architectures used in the experiments. The names follow [15; 31].  
Figure 4: The scheme of the res8 neural network used in our experiments. The shape of the resulting tensor is specified after each block in square brackets. FC stands for the fully-connected layer, AP stands for the average pooling layer, RB stands for residual block (Figure 3). \(T\) is the number of frames in a sample (98 in all our experiments). \(B\) is the number of channels in the audio features (80 for log-mel filterbanks, 512 for wav2 vec features). \(C\) is the number of classes to discriminate. \(M\) is the number of feature maps (45 for res8).
# Analysis of Lightweight Cryptographic Algorithms on IoT Hardware Platform +
## 2 Background
Specifically, the focus was on the \"Satumin\" family of ciphers. The study analyzed how well Saturnin performs when implemented in a specific resource-constrained environment. In addition, to evaluate the results, a comparison with the Advanced Encryption Standard (AES) was conducted using an experimental setup. The findings showed that substantial performance improvements can be achieved as Saturnin, which is based on the design of the AES, can be almost twice as fast as the AES in such restricted environments. In [33], the authors aimed to present an overview of the current state of the lightweight cryptography algorithms used in IoT environments. A comprehensive review of the literature was conducted to identify different algorithms and extract relevant data, such as the level of security, encryption and decryption performance, execution time, memory usage, clock speed, latency, and frequency. The data were then presented in comparison tables for a further analysis, evaluation, and assessment. The study provided insight into the performance of the lightweight cryptography algorithms used in IoT environments and devices. Additionally, it suggested future research directions to build on the findings of the study. Almost all the cited works were interested in the implementation of specific LWC schemes, while in this work we performed a software implementation of almost 119 different schemes and compared their performance using two different hardware platforms.
###### Abstract  
To mitigate challenges arising from renewable energy volatility and multi-energy load uncertainty, this paper introduces a dynamic hydrogen blending (DHB) strategy for an integrated energy system. The strategy is categorized into Continuous Hydrogen Blending (CHB) and Time-phased Hydrogen Blending (THB), based on the temporal variations in the hydrogen blending ratio. To evaluate the regulatory effect of DHB on uncertainty, a data-driven distributionally robust optimization method is employed in the day-ahead stage to manage system uncertainties. Subsequently, a hierarchical model predictive control framework is designed for the intraday stage to track the day-ahead robust scheduling outcomes. Experimental results indicate that the optimized CHB ratio exhibits step characteristics, closely resembling the THB configuration. In terms of cost-effectiveness, CHB reduces the day-ahead scheduling cost by 0.87% compared to traditional fixed hydrogen blending schemes. THB effectively simplifies model complexity while maintaining a scheduling performance comparable to CHB. Regarding tracking performance, intraday dynamic hydrogen blending further reduces upper- and lower-layer tracking errors by 4.25% and 2.37%, respectively. Furthermore, THB demonstrates its advantage in short-term energy regulation, effectively reducing tracking errors propagated from the upper layer MPC to the lower layer, resulting in a 2.43% reduction in the lower-layer model's tracking errors.  
1  
School of Information Engineering, Nanchang University, Nanchang 330031, China;  
[EMAIL_ADDRESS] (Y.X.); [EMAIL_ADDRESS] (Y.Z.)  
3  
ynamic hydrogen blending; distributionally robust optimization; model predictive control; integrated energy system
## 3 Mathematical Model  
To describe turbulent fluid flow, a mathematical model based on RANS equations and the averaged continuity equation was utilized:  
\[\frac{\partial(\rho\overline{u}_{i})}{\partial t}+\frac{\partial}{\partial x_{j}} \Big{(}\rho\overline{u}_{i}\overline{u}_{j}+\rho\overline{u^{\prime}_{i}u^{ \prime}_{j}}\Big{)}=-\frac{\partial\overline{p}}{\partial x_{i}}+\frac{ \partial\overline{\tau}_{ij}}{\partial x_{j}} \tag{1}\]  
\[\frac{\partial(\rho\overline{u}_{i})}{\partial x_{i}}=0 \tag{2}\]  
where \(\rho\) represents the fluid density, \(\overline{u}_{i}\) denotes the averaged Cartesian components of the velocity vector, \(\rho\overline{u^{\prime}_{i}u^{\prime}_{j}}\) represents the Reynolds stress tensor, and \(\overline{p}\) denotes the mean pressure. The mean viscous stress tensor reads as follows:  
\[\overline{\tau}_{ij}=\mu\Bigg{(}\frac{\partial\overline{u}_{i}}{\partial x_{ j}}+\frac{\partial\overline{u}_{j}}{\partial x_{i}}\Bigg{)} \tag{3}\]  
where \(\mu\) denotes the dynamic viscosity.  
Equations (1) and (2) do not constitute a closed system of equations; thus, a turbulence model must be employed. In this study, the \(k-\omega\) SST (SSTKO) and the realizable \(k-\epsilon\) (RKE) turbulence models were selected. The RKE turbulence model consists of two transport equations, one for the specific turbulent kinetic energy and one for the specific dissipation rate. The RKE turbulence model belongs to the group of eddy viscosity models, which are based on the similarity between the molecular gradient-diffusion process and turbulent motion [18]. It is a relatively recent development and it provides superior performance for flows including rotation, boundary layers with strong adverse pressure gradients, separation, and recirculation [19].
# R&D of an Innovative OFDM Communication Payload for Small-Size AUV
## 1 Introduction
In Section 2, the detailed signal model, time-frequency differential OFDM coding, Doppler estimation and compensation implementation are introduced. Section 3 describes the HIP simulation results to verify the performance of the proposed UWA communication payload. In Section 4, we introduce accuracy and availability analysis for the proposed scheme. Conclusions are summarized in Section 5.