esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Result
As for the temperature rise of the prototype motor, considering that the limited temperature value is around 180 \({}^{\circ}\)C, the load experiments are carried out with two different kinds of loads at the same speed of 3000 rpm. The constant power load is 30 kW while the constant torque load is 191 N\(\cdot\)m, and those conditions represent the rated operation and limited condition, respectively. The temperature curves of the motor are illustrated in Figure 25.  
It can be seen from Figure 25a that the steady-state temperature of the prototype motor at the rated load is around 110 \({}^{\circ}\)C and the original temperature is 100 \({}^{\circ}\)C; both of them are under the limited temperature of 180 \({}^{\circ}\)C. When the motor at the rated load and different speeds is in normal situations, the temperature of the motor can stabilize before 30 min, and our testing usually lasts for around 12 h to confirm the temperature can remain stable. It proves that the prototype motor can safely run in the rated operation condition for a long time. The simulation analysis in Section 3.4 shows that the maximum temperatures of the same condition are 105 \({}^{\circ}\)C and 100 \({}^{\circ}\)C, respectively, which is consistent with the experimental result. And Figure 25b shows that in the limited condition, the prototype motor temperature is 20 \({}^{\circ}\)C higher than the original one because of the worse heat dissipation capacity brought by the new winding arrangements and the reduction in motor volume. With the 191 N\(\cdot\)m load, the motor temperature will reach 180 \({}^{\circ}\)C when the operating time is 250 s, which is quicker than the original motor, and the rise trend is close to the simulation results shown in Figure 20. Although the temperature rise is bad for the safety of the motor, to improve the power density and reduce the torque ripple, it is still acceptable in the safe range. Under the rated condition, the average output torque was measured, and the torque ripple was calculated from the waveform of the output torque. The records of the measured torque and harmonic distribution are shown in Figure 26, which verify the simulation content in Figure 11. And the calculated torque ripples are listed in Table 14. From Table 6 in Section 3.2, by applying the optimized structures, the torque ripple is reduced from 14.9% to 7.1% without the skewed slot. Based on the experimental test, the actual corresponding value changes from 18.5% to 5.4%. The simulation and experimental results both validate the improvement in the torque ripple by the optimized structure. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **The Original Motor** & **The Optimized Prototype** \\ \hline Average torque & 95.3 N\(\cdot\)m & 94.8 N\(\cdot\)m \\ Torque ripple & 18.5\% & 5.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 14: The torque of the experiment data. Figure 25: The temperature curves of the motor at rated load and limited load: (**a**) rated condition temperature; and (**b**) limited condition temperature.
# GA-StackingMD: Android Malware Detection Method Based on Genetic Algorithm Optimized Stacking
## 4 Experiments
### Comparison with Literature
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Year** & **Data Sets** & **Experiment Description** \\ \hline \multirow{4}{*}{[40]} & \multirow{4}{*}{2021} & \multirow{4}{*}{CIC-AndMal2017} & \multicolumn{3}{c}{Feature Type: Permission} \\  & & & Classifier: Ensemble model (ET, XGB, and RF) \\  & & & Experimental Result: Accuracy—90.40%; Precision—90.40\%; \\  & & & Recall—90.40\%; F1-score—90.40\% \\  & & & Feature Type: Permission \\  & & & Classifier: CNN and ANFIS \\
[41] & 2022 & \multirow{4}{*}{2022} & \multirow{4}{*}{\
\begin{tabular}{c} [PERSON] \\ CICMalDroid2020 \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} Feature Type: Permission \\ (CNN, adaptive network-based fuzzy inference system) \\ Experimental Result: Accuracy—92.00\%, 94.67\%; Precision—92.15\%, 94.78\%; \\ Recall—92.00\%, 94.67\%; F1-score—92.01\%, 94.66\% \\ Feature Type: Network Traffic \\
[42] & 2022 & \multirow{4}{*}{
\begin{tabular}{c} [PERSON] \\ CIC-MalDroid2020 \\ \end{tabular} } & Classifier: F2 DC (A novel traffic encoding scheme called F2D and CNN) \\  & & Experimental Result: Precision—96.30\%, 82.06\%; Recall—96.03\%, 81.60\%; \\  & & F1-score—96.08\%, 81.34\% \\ Feature Type: System Calls \\
[43] & 2022 & CIC-AndMal2017 & Classifier: DT, KNN, LR, SVM, MLP \\  & & Experimental Result: Precision—85.00\%; Recall—85.00\%; \\  & & F1-score—85.00\% \\  & & & Feature Type: Textual Features Analysis and Visual Features Analysis (Network-based byte streams) \\
[44] & 2022 & \multirow{4}{*}{
\begin{tabular}{c} CIC- \\ InvesAndMal2019 \\ \end{tabular} } & Classifier: Voting-Based Ensemble Learning (Gaussian Naive Bayes, SVM, DT, LR, RF) \\  & & Experimental Result: Accuracy—97.76\%, 98.44\% \\ Feature Type: The RGB graphics \\
[45] & 2023 & \multirow{4}{*}{\
\begin{tabular}{c} CIC-AndMal2017 \\ CICMalDroid2020 \\ \end{tabular} } & Classifier: CNN, DCNN(VGG-16) [46] \\  & & Experimental Result: Accuracy—97.81%; Precision—97.98\%; \\  & & Recall—97.63\%; F1-score—97.78\% \\  & & & Feature Type: Permission, API, Dalvik opcode, Intent, and Hardware \\ This paper & 2023 & \multirow{4}{*}{
\begin{tabular}{c} CIC-AndMal2017 \\ CICMalDroid2020 \\ \end{tabular} } & Classifier: GA-StackingMD (SVM, KNN, LGBM, CatBoost, and RF) \\  & & Experimental Result: Accuracy—98.43\%, 98.66\%; Precision—98.28\%, 99.15\% \\  & & Recall—93.44\%, 99.06\%; F1-score—95.80\%, 99.10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: GA-StackingMD comparison with the relevant studies in the literature. with relevant models, the GA-StackingMD, which was proposed as a GA-based ensemble approach in our work, demonstrated more competitive performance.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 5 Parameters Affecting the Performance of a Submerged Hollow Fiber System
### Hydrodynamics in Submerged HF Membranes
#### 5.2.3 Effect of Gas Flowrate  
It has been reported in many studies that the filtration performance can be improved by controlling the bubbling rate [15, 16, 209, 210]. On the one hand, the critical flux improves in an approximately linear fashion with respect to gas flow in submerged HF systems [100, 101, 102, 204]. On the other hand, for  
Figure 2: Fouling and cake control mechanisms by bubbles outside fibers [14]. Reproduced with permission from [14], Copyright Journal of Membrane Science, 2003.  
different sizes of fibers, it has been shown that a modest gas flow can increase the final flux by a factor of 3-6 in a yeast suspension, but the enhancement quickly plateaus at higher gas flowrates such that further increases in the gas flowrate achieve negligible enhancement [187]. This disparate behavior stems from the slugging phenomenon, whereby large changes in the gas flow have a negligible effect on the velocity of the film that is formed adjacent to the surface of the fiber [211]. Similar plateaus have been observed in submerged HF systems for sewage treatment and drinking-water treatment [191; 210]. It has been observed that at high gas flowrates (namely, 40 L/h), a maximum flux was observed, after which the flux started to decline [212]. This phenomenon was explained by the relationship between the bubble size and air flowrate; since bubble size tends to increase with the air flowrate, the bubbles become so large after an optimum air flowrate that they start to prevent the liquid from reaching the membrane surface, a phenomenon that is also linked to slugging flow.
## 6 Hardware Experimental Setup  
Figure 5 shows the experimental hardware setup. The \(\text{Sprient}^{\text{TM}}\) GSS6700 GNSS simulation system [21] is utilized in this research to create a fully controlled environment to assess the performance of the vector-based GPS SDR in various signal degraded conditions using both static and dynamic scenarios. The \(\text{Sprient}^{\text{TM}}\) simulation system is run by \(\text{SimGEN}^{\text{TM}}\) simulation software [22]. \(\text{SimGEN}^{\text{TM}}\) enables the simulation of terrain obscuration, several multipath scenarios, variable antenna reception parameters, the trajectory generation of air, sea, land and space vehicles, ionospheric and tropospheric degradation of GNSS signals, and several jamming signals, including continuous wave (CW), swept continuous wave (SCW), amplitude-modulated (AM) and frequency-modulated (FM) [23], to name a few. The output RF signal is equivalent to that from real satellites as if the antenna is mounted on a vehicle on an actual road trajectory at the location and time of the simulation. The jamming signal is generated using the Agilent interference signal generator (ISG) that is connected to \(\text{Sprient}^{\text{TM}}\) GSS6700 via the \(\text{Sprient}^{\text{TM}}\) GSS8366 interference combiner unit (ICU).  
The output RF signal is then processed by FireHose D17088 front-end (FE) by NovAtel [24] to generate the raw in-phase (I) and quadrature-phase (Q) signals. Inside the FE, the received signal is downconverted from RF to baseband via several down-conversion stages. Next, the baseband signal is sampled and digitized using an analog-to-digital converter (ADC). The FireHose-FE can collect GPS L1/L2 signals at bandwidths of 2.5 MHz, 5.0 MHz, 10 MHz, and 20 MHz. Furthermore, the number of quantization bits can vary from 1-bit up to 8-bit. Eventually, the digital signal is sent to the computer using an ethernet connection. These data are stored for post-processing by the SDR.  
Figure 4: Flowchart of vector tracking architecture.
# Modeling Intelligent Control Switch IEC 61850 Based Substation Automation Communication
## 3 Network Traffic Management  
The network communications have many functions that help to share the Ethernet network which help to exchange messages such as Sampled Values (SV), GOOSE and so on, between devices and the control room. Several standards, such as IEEE standard 1588, have recommended the Precision Time Protocol (PTP) for SV messages synchronization [20]. The traffic volume of PTP is low, generally around 300 bytes per second, which in turn cannot influence the operation of the SV or GOOSE. Figure 4 depicts the sharing network communication between the switchyard devices and the control room. The traffic of the Ethernet flows in a bi-directional manner, with a possibility of interaction in the bay and the core Ethernet of the switches. The Ethernet switches will be straightforward tickers if  
Figure 3: Publisher and subscriber GOOSE technique [17].  
PTP is utilized for synchronization, because of the prerequisite of the PTP power profile to utilize the peer delay technique.  
Traffic management is essential in the communication network environment especially with the multicast technique that is associated with SV, GOOSE and PTP. Multicast and VLAN filtering methods are utilized for stopping overloads on the edges of the device (such as protection relays), and for limiting the transmission data of the multicast function to specified devices only that are addressed in the data message [21; 22].
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 5 Potential Research for ISAC towards 6G ISTNs
### Security  
Security is a key issue in communication network architectures. For the physical layer, the ISAC signal contains both important communication information and radar perception information, involving real-world environmental conditions and user privacy. Moreover, relying on wireless channels, ISAC signals are highly vulnerable to eavesdropping and unauthorized access. Beamforming, which focuses signals in spatial areas, inevitably increases the possibility of information leakage, necessitating robust security waveform design. In [89], the authors propose integrating security attributes into ISAC from hardware security and waveform design perspectives, actively maintaining information security.  
For the future ISTNs, network layer security is also a worthwhile topic for exploration. For inter-satellite networking and information access, data encryption authentication, route selection, and secure protocols can be employed to prevent routers from being exploited by attackers for malicious operations. Encryption technology and identity authentication can also be used to protect data confidentiality and ensure reliable transmission. Additionally, ISAC towards ISTNs can enhance the system's security in real-world environments. ISAC, with its ability to perceive the surrounding environment, can greatly minimize the occurrence of physical collision accidents. Therefore, discussing the application of security in ISAC towards ISTNs is of great significance.
# Numerical Analysis of Fluid Stream Division to Supply Multiple Hydraulic Receivers
## 4 Discrete Model and CFD Analysis
### Meshing and Mesh Quality Assessment  
The geometrical fluid model was created in PTC Creo. Then, it was meshed using prism elements at boundary layers and tetrahedrons in the bulk flow. A pressure-based solver was used to achieve the pressure-velocity coupling, taking into account the convergence condition \(10^{-4}\) for both mass and momentum residuals. After the initial mesh generation according to ANSYS recommendations, the flow simulation was performed at the fixed flow rate \(Q=25.0\) \(\text{dm}^{3}\,\text{min}^{-1}\) and the spool position \(x_{1}=2.0\) \(\text{mm}\). Subsequent simulations were then performed by applying the progressive mesh refinement technique. As a result, mesh density was increased; however, the computation time was significantly extended. The geometric model in longitudinal section is shown in Figure 5a, while an exemplary mesh (case 3) is shown in Figure 5b. The pressure drop values obtained as a function of the mesh density are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Kinematic Viscosity** & **Density** & **Temperature** & **Flow Rate** & **Outlet Pressure** & **Reynolds Number** \\ \hline \(\
u\) & \(\rho\) & \(T\) & \(Q_{in}\) & \(p_{net}\) & \(Re\) \\ \(\text{m}^{2}\cdot\text{s}^{-1}\) & \(\text{kg}\,\text{m}^{-3}\) & \({}^{\circ}\text{C}\) & \(\text{dm}^{3}\,\text{min}^{-1}\) & MPa & \(-\) \\ \hline \(41\cdot 10^{-6}\) & 870 & 50.0 & 25 & 0.1 & \((2.1-26.5)\cdot 10^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical model parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Kinetic Energy** & \multicolumn{2}{c}{**Kinetic Energy**} & \multicolumn{2}{c}{**Turbulent Viscosity**} & **Turbulence** & **Length** \\
**Constant** & **Dissipation** & **Constants** & **Constant** & **Intensity** & **Scale** \\ \hline \(s_{k}\) (-) & \(s_{\varepsilon}\) (-) & \(C_{1\varepsilon}\) (-) & \(C_{2\varepsilon}\) (-) & \(C_{\mu}\) (-) & \(I\) (\%) & \(\ell\) (mm) \\
1.0 & 1.3 & 1.44 & 1.92 & 0.09 & 4.5–6.1 & 0.14–0.49 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Turbulence model parameters.  
The first mesh refinement caused the resulting pressure drop value to change by 13.2% and the second by 5.3%. However, further refinement did not change the result significantly, with a considerable extension of the calculation time. Hence, the case 3 model was adopted for further analysis.
## 2 Computational Model  
There are many commercially available general-purpose MBD software, e.g., MSC Adams, Recurdyn, and Simpack, among others. The software mentioned initially, MSC Adams, is used in this paper. The multibody dynamics software share the basic idea of dynamics modelling, so the procedures and principles in this paper may be applied to other MBD software as well. The chosen MBD software uses the Euler-Lagrange method to generate equations of motion in the form of:  
\[\mathbf{M}(\mathbf{q})\ddot{\mathbf{q}}+\mathbf{\Phi}_{\mathbf{q}}^{\mathrm{ T}}(\mathbf{q})\boldsymbol{\lambda}=\mathbf{Q}\big{(}\dot{\mathbf{q}}, \mathbf{q},t\big{)}, \tag{1}\]where \(\mathbf{M}(\mathbf{q})\) represents the generalized mass matrix, \(\mathbf{\Phi}_{\mathbf{q}}^{\mathrm{T}}(\mathbf{q})\mathbf{\lambda}\) is the reaction force to the action force \(\mathbf{Q}(\hat{\mathbf{q}},\mathbf{q},t)\) acting on the generalized coordinates \(\mathbf{q}\) of size \(n\), \(\mathbf{\lambda}\) reflects the Lagrange multiplier, and \(t\) indicates time. The generalized coordinates used here are Cartesian coordinates for position and Euler angles for the orientation of the body centroidal reference frames. If flexible bodies are present, the set of generalized position coordinates is augmented with the deformation modes. The solution must satisfy a set of \(m\) kinematic constraint equations \(\mathbf{\Phi}\):  
\[\mathbf{\Phi}(\mathbf{q},t)=0. \tag{2}\]  
A total number of dynamic degrees of freedom \(s\) is:  
\[s=m-n. \tag{3}\]  
Equations (1) and (2) yield a system of differential algebraic equations that are solved for unknowns \(\mathbf{q}\) and \(\mathbf{\lambda}\). The solution can be found using numerical techniques, e.g., the backward differentiation formulas or the HHT integrator [24], which is based on the \(\alpha\)-method proposed by [PERSON], [PERSON] and [PERSON] [25]. The HHT integrator is second-order accurate, unconditionally stable, and presents high-frequency numerical damping to avoid numerical oscillations in the Lagrange multipliers. Therefore, the HHT integrator is favored in the integration of large finite element systems [26].
# End-to-End Mispronunciation Detection and Diagnosis Using Transfer Learning
## 2 Related Work
### Technical Basis  
In speech-related domains, several SSP models have been designed, such as APC [32], wav2 vec [33], and wav2 vec2.0 [12]. They are used as the building blocks of models for many tasks, e.g., ultra-low resource ASR [34] and emotion recognition [35].  
This study uses the pretrained model wav2 vec2.0 [12] to obtain the acoustic representations. The architecture of wav2 vec2.0 is shown in the left part of Figure 1. It is composed of three modules: a CNN-based acoustic encoder, a transformer-based context network, and a vector quantizer. The encoder module built with seven blocks of temporal convolution layers encodes the raw audio sample \(\mathcal{X}\) into latent speech representation \(\mathcal{Z}\). The encoder module compresses about a 25 ms region of 16 kHZ audio every 20 ms. After that, the encoded representation \(\mathcal{Z}\) will be used to provide inputs for two branches. For the branch of the transformer-based context network, a certain proportion of consecutive time steps of latent representation \(Z\) are masked and used to learn the contextual representations \(\mathcal{C}\). For the branch of the vector quantizer, vector quantization (VQ) [36] is used to discretize the unmasked \(Z\) in continuous space to a finite number of entries \(\mathcal{Q}\), which is detailed in the upper-right part of Figure 1. During pretraining, a contrastive predict coding (CPC) [37] criterion is adopted. The outputs of the transformer-based context network and quantized outputs of vector quantizer are used to compute the contrastive loss so that the pretrained model can distinguish the latent representation from a series of distractors sampled from other masked time steps. In the meantime, VQ is expected to learn the underlying speech units that are reported to correspond to phonemes. Alternatively, learning general acoustic representations can be achieved using other pretrained models, e.g., HuBERT [38]. In this study, we chose the wav2 vec2.0 model for reasons of its popularity and ease of implementation.  
As for transferring linguistic knowledge from prior text for E2E MDD, the common way is to convert sentence transcription to textual embeddings and then incorporate them with attention operation. Specifically, the input of the model is the phoneme sequence converted from prior text. Suppose an \(N\)-length phoneme sequence of the input sentence \(S=[s_{1},...,s_{i},...,s_{N}]\) where \(s_{i}\) is the phoneme at the \(i\)-th position in the prior phoneme sequence, the audio sample \(\mathcal{X}\) and phoneme sequence \(S\) can be converted to acoustic and textual representations by an audio encoder and text encoder, respectively, using the following equations:  
\[h^{Q}=\texttt{Audio\_encoder}(\mathcal{X}) \tag{1}\]  
\[h^{K},h^{V}=\texttt{Text\_encoder}(S) \tag{2}\]where \(h^{Q},h^{K}\), and \(h^{V}\) are the query, key, and value, respectively, for the attention operations. Next, a fixed-length context vector can be obtained using the following equation:  
\[c=\texttt{Attention}(h^{Q},h^{K},h^{V}). \tag{3}\]
## 1 Introduction
The contribution of this paper mainly includes the following three aspects. 1. The proposed approach generates high-quality solutions using an end-to-end architecture based on reinforcement learning. The models can be trained without expert knowledge and labeled data, and the trained models can automatically extract features from the problem. 2. The PFSP is innovatively regarded as a complete graph. Two multi-layer GIN are used to encode the constraint features and processing time features in the PFSP. The GINs are able to efficiently aggregate the nodes' own features and other neighbors' features to obtain a contextual representation of each node. 3. An improved iterative greedy method is proposed. The RL model is able to obtain high-quality initial solutions in a short time and the IG method is used to improve the initial solutions. Experimental results show that the RL + IG method surpasses many excellent heuristic and meta-heuristic algorithms. The rest of this article is organized as follows. Section 2 first describes the PFSP of minimizing the late work objective and models it as a sequential decision process, then describes the deep reinforcement learning architecture used and the training method of the model to generate an initial solution to the problem. Section 3 proposes a hybrid iterative greedy algorithm to further improve the generated initial solution. Section 4 illustrates the experimental setup of this article and shows the results of comparing the proposed algorithm with other methods. Section 5 concludes the article and presents several directions for future work.