esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Formulations
### Sensing-Based Spectrum Sharing under Perfect Sensing Conditions
```
Set parameters: \(\sigma_{\lambda}>0,\sigma_{\mu}>0\): Error tolerances; \(\alpha>0\), \(\beta>0\): Step sizes. \(m\): Iteration number; Initialization: \(\epsilon_{\lambda}\)=\(\lambda_{0}\), \(\epsilon_{\mu}\)=\(\mu_{0}\), \(m=1\); While\(\epsilon_{\lambda}>\sigma_{\lambda}\) and \(\epsilon_{\mu}>\sigma_{\mu}\)  Calculate \(P_{s,m}^{\left(i\right)}\) and \(P_{s,m}^{\left(b\right)}\) using (26) and (27), respectively;  Update \(\lambda\) and \(\mu\) by subgradient method as follows: \(\lambda_{m+1}=\max\left(0,\lambda_{m}-\alpha\left(P_{av}-E\{P_{s,m}^{\left(i \right)}\}P\left(\mathcal{H}_{i}\right)-E\{P_{s,m}^{\left(b\right)}\}P\left( \mathcal{H}_{b}\right)\right)\right)\) \(\mu_{m+1}=\max\left(0,\mu_{m}-\beta\left(I_{av}-E\left\{G_{t}\left(\theta^{ \prime}\right)G_{BS}L_{sp}h_{sp}P_{s,m}^{\left(b\right)}\right\}\right)\right)\); \(\epsilon_{\lambda}\)=\(abs\) (\(\lambda_{m+1}-\lambda_{m}\)); \(\epsilon_{\mu}\)=\(abs\) (\(\mu_{m+1}-\mu_{m}\)); End; \(P_{s}^{\left(i\right)*}=P_{s,m}^{\left(i\right)},P_{s}^{\left(b\right)*}=P_{s,m}^{\left(b\right)}\); \(\lambda^{*}\)=\(\lambda_{m+1}\); \(\mu^{*}\)=\(\mu_{m+1}\). ```  
**Algorithm 1** Iterative Power Allocation Algorithm under Perfect Sensing Conditions.
# Energy-Efficient Multi-User Routing in a Software-Defined Multi-Hop Wireless Network
###### Abstract  
Software-defined networking (SDN) is an innovative architecture that designs a logical controller to manage and program the network based on the global view, providing more efficient management, better performance, and higher flexibility for the network. Therefore, applying the SDN concept in a multi-hop wireless network (MWN) has been proposed and extensively studied to overcome the challenges of MWN. In this paper, we propose an energy-efficient global routing algorithm for a software-defined multi-hop wireless network (SDMWN), which is able to get transmission paths for several users at the same time to minimize the global energy consumption with the premise of satisfying the QoS required by users. To this end, we firstly propose a Lagrange relaxation-based aggregated cost (LARAC) and K-Dijkstra combined algorithm to get the top \(K\) energy-minimum paths that satisfy the QoS in polynomial time. Then, we combine the alternative paths of each user obtained by K-LARAC and propose an improved genetic algorithm to solve the global routing strategy. The simulation results show that the proposed K-LARAC and genetic algorithm combined method has the ability to obtain an approximate optimal solution with lower time cost.  
software-defined multi-hop wireless networking (SDMWN); QoS routing; Lagrange relaxation; K-Dijkstra; genetic algorithm +
Footnote †: journal:
## 3 Applications
### Origin of the Idea and Proposed Method
For the sensor array shown in Figure 3b, let the positions of the microphones be \(\mathbf{p}_{i}=\left[x_{i},y_{i},z_{i}\right]^{T}\) and the position of the sound source be \(\mathbf{p}_{k}=\left[x_{k},y_{k},z_{k}\right]^{T}\). Accordingly, Equations (16) and (17) can be rearranged as follows:  
\[ \begin{bmatrix}2(\mathbf{p}_{2}-\mathbf{p}_{1})^{T}\\ 2(\mathbf{p}_{3}-\mathbf{p}_{1})^{T}\\ 2(\mathbf{p}_{4}-\mathbf{p}_{1})^{T}\end{bmatrix} \times\left[\mathbf{p}_{k}\right]= \begin{bmatrix} \mathbf{p}_{2}^{T}\mathbf{p}_{2}-\mathbf{p}_{1}^{T}\mathbf{p}_{1}-v_{s}\tau_{21}(v_{s}\tau_{ 21}+2d_{1})\\ \mathbf{p}_{3}^{T}\mathbf{p}_{3}-\mathbf{p}_{1}^{T}\mathbf{p}_{1}-v_{s}\tau_{31}(v_{s}\tau_{ 31}+2d_{1})\\ \mathbf{p}_{4}^{T}\mathbf{p}_{4}-\mathbf{p}_{1}^{T}\mathbf{p}_{1}-v_{s}\tau_{41}(v_{s}\tau_{ 41}+2d_{1})\end{bmatrix}  \tag{24}\]  
\[\mathbf{A}_{2}\mathbf{x}_{2}=\mathbf{b}_{2} \tag{25}\]  
In the new case, the fourth microphone is assumed to be in the z direction and the \(\mathbf{A}_{2}\) and \(\mathbf{b}_{2}\) matrices are rearranged as follows:  
\[A_{2}=2\times \begin{bmatrix}d_{mx}&0&0\\ 0&d_{my}&0\\ 0&0&d_{mz}\end{bmatrix}  \tag{26}\]  
\[\mathbf{b}_{2}= \begin{bmatrix}d_{mx}^{2}-v_{\mathrm{s}}^{2}\times\tau_{\mathrm{z} }^{2}-2v_{s}\tau_{x}d_{k}\\ d_{my}^{2}-v_{\mathrm{s}}^{2}\times\tau_{\mathrm{z}}^{2}-2v_{s}\tau_{\mathrm{z }}d_{k}\\ d_{mz}^{2}-v_{\mathrm{s}}^{2}\times\tau_{\mathrm{z}}^{2}-2v_{s}\tau_{\mathrm{z }}d_{k}\end{bmatrix}  \tag{27}\]  
After reducing Equation (27) by making similar approximations as in Equation (20), the \(\mathbf{p}_{k}\) position vector is as follows:  
\[ \begin{bmatrix}x_{k}\\ y_{k}\\ z_{k}\end{bmatrix} \approx-v_{s}d_{k} \begin{bmatrix}\frac{\tau_{x}}{d_{mx}}\\ \frac{\tau_{y}}{d_{mx}}\\ \frac{\tau_{y}}{d_{mx}}\end{bmatrix}  \tag{28}\]  
If we substitute the results found in Equation (28) into Equation (2), the \(\varphi\) elevation angle becomes as follows:  
\[\varphi\approx\text{atan2}\left(-\left(\frac{\tau_{z}}{d_{mz}}\right),\sqrt{ \left(\frac{\tau_{x}}{d_{mx}}\right)^{2}+\left(\frac{\tau_{y}}{d_{my}}\right) ^{2}}\right) \tag{29}\]In the case where the distances \(d_{m}\) between the microphones are equal, the Equations (23) and (29) take the following special form:  
\[\theta\approx atan2\big{(}-\tau_{y},-\tau_{x}\big{)} \tag{30}\]  
\[\varphi=atan2\big{(}-\tau_{z},\sqrt{\tau_{x}^{2}+\tau_{y}^{2}}\big{)} \tag{31}\]  
The sensor array layouts that satisfy Equations (23) and (29) are shown in Figure 6.
# Multi-Scale Feature Learning for Language Identification of Overlapped Speech
## 4 Experiments and Discussion
### Limitation  
This paper proposes a SE-Res2 Net-CBAM-BiLSTM model, a language identification method based on a multi-scale feature extraction network. This model improves the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Loss Function** & **Model** & **Acc (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score** \\ \hline Cross-entropy loss & CNN-CBAM- & 94.57 & 94.64 & 94.91 & 94.77 \\ Focal loss & BiLSTM & 95.65 & 95.68 & 94.95 & 95.31 \\ Cross-entropy loss & SE-Res2 Net- & 96.26 & 96.11 & 95.07 & 95.59 \\ Focal loss & CBAM-BiLSTM & 97.31 & 96.97 & 96.86 & 96.91 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Graph of language identification results with different loss functions.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Languages** & **Acc (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score** \\ \hline zh-cn (Mandarin) & 61.51 & 63.71 & 60.62 & 62.12 \\ id-id (Indonesian) & 58.72 & 58.42 & 59.43 & 58.92 \\ ja-jp (Japanese) & 67.91 & 66.31 & 70.02 & 68.11 \\ ko-kr (Korean) & 56.12 & 43.83 & 73.34 & 54.87 \\ vi-vn (Vietnamese) & 66.23 & 68.41 & 64.45 & 66.37 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results for multilingual cocktail party data with a 100% overlap rate.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Languages** & **Acc (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score** \\ \hline zh-cn (Mandarin) & 71.45 & 73.14 & 69.91 & 71.57 \\ id-id (Indonesian) & 69.42 & 68.52 & 70.13 & 69.32 \\ ja-jp (Japanese) & 79.14 & 77.41 & 81.24 & 79.37 \\ ko-kr (Korean) & 68.91 & 59.63 & 83.16 & 69.46 \\ vi-vn (Vietnamese) & 76.82 & 79.12 & 75.24 & 77.13 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results of improved models on multilingual cocktail party data.  
recognition performance compared to the baseline, but it still faces some difficulties in dealing with multilingual overlapping speech scenarios. One of the reasons is that this study only used public datasets and our synthesized multilingual cocktail dataset, which simulated the situation of different energy ratios with only two speakers. However, actual multilingual overlapping speech scenes are more complex and may involve three or more speakers and languages. Therefore, future research can explore more realistic and diverse datasets, as well as more advanced feature extraction and recognition techniques. The aim of this paper was to propose a novel language identification method that can handle multilingual overlapping speech effectively. We will continue to improve the performance of the model while studying more complex multilingual cocktail party scenarios.
# A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
## 4 Waveform Selection
### Studies on Single Carrier Waveforms  
In this subsection we will briefly study some of the works done on massive MIMO systems with single-carrier waveform.  
In [36] BER of SC-FDP massive MIMO system in the uplink and in presence of frequency selective channel has been investigated. The results show that when the number of BS antennas is large, even with simple linear detectors, the BER performance of this system converges to the BER performance of a SISO system with flat fading channel. In [37], an iterative low complexity receiver for uplink of SC-FDP massive MIMO system has been designed. It was shown that even when the number of BS antennas is not very large, the performance of the designed receiver reaches that of single user system after only few iterations. The performance of different detectors in uplink of SC-FDP massive MIMO system has been studied in [38]. The authors of [39] studied the power efficiency of SC-FDP massive MIMO system with MF detector in the uplink. It was assumed that the users had multiple antennas and correlation between transmit antennas was considered. The results indicate that this system can have better performance than the system with single antenna users. In [40], the BER and PAPR of SC-FDMA and OFDMA in downlink of massive MIMO systems were studied through simulations. It was shown that SC-FDMA had lower PAPR and better BER performance than OFDMA.
## 4 Results and Discussion
Figures 21 and 22 also display the area under ROC (AUC) for each of ACC combined with EMG signals of the forearm and biceps brachii muscles, respectively, which are equal to 1 in both cases. Comparing the results of the proposed ML approaches and measurement set to the results presented in Table 1, it is quite clear that the proposed approach outperformes all relative techniques from the literature. More specifically, The best achieved classification accuracies were for the proposed technique of combined sEMG at the forearm and accelerometer at the Palmer wrist with accuracies of 86.5% to 99.6% for the different investigated classifiers. In all investigated scenarios, the lowest classification accuracies were achieved using LDA classifier, and best results were for bagged tree classifier. Thus, the proposed approach with bagged tree classifier achieves the best classification accuracy of 99.6%. This is due to two facts, first the right selection of the sensor types and positions, and second the application of the appropriate signal processing and ML techniques. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Biceps brachii (EMG) and Wrist (Acc)** & **Forearm (EMG) and Wrist (Acc)** \\ \hline Decision Tree & 97.3\% & 99.2\% \\ LDA & 73\% & 86.5\% \\ KNN & 92.3\% & 97.5\% \\ SVM & 93.4\% & 98.2\% \\ Boosted Tree & 97.6\% & 99.6\% \\ Bagged Tree & **98.5\%** & **99.6\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification accuracies based on sensor-fusion. Figure 19: Confusion matrix of accelerometer (ACC) and EMG measured from forearm muscle bagged tree classifier. Figure 20: Confusion matrix of ACC and EMG measured from biceps brachii muscle bagged tree classifier. Figure 21: ROC of ACC and EMG measured from biceps brachii muscle bagged tree classifier. Figure 22: ROC of ACC and EMG measured from biceps brachii muscle bagged tree classifier.
## 1 Introduction
The effectiveness of the multiple-injection strategy in reducing EGEs applied to a heavy-duty diesel marine engine has been demonstrated and is available to be referred to in our previous research [19]. On the remaining aspect, the injector SA strongly affects the combustion and emission characteristics of engines, because it determines the targeting point of the injection into the engine's combustion chamber [20]. The targeting point of the fuel injection, together with the shape of the piston surface strongly affects the turbulence of the fluid flow inside engine cylinders. Additionally, the distance between the injection targeting point and the piston surface determines the liquid fuel film formed on the piston surface, due to the wall-wetting phenomena [21; 22]. These factors strongly affect the combustion quality and emission formation in ICEs, especially soot. Therefore, the study on the injector SA is very important and has practical implications for both engine designers and operating engineers. [PERSON] et al. [17] numerically studied the effects of the SA in a swirl chamber combustion system of DIEs by using the AVL-FIRE code. The research concluded that the SA of the injector has a strong influence on the fuel-air equivalence ratio and the temperature distribution inside the combustion chamber. It also changes the combustion and emission characteristics of the engine. The research found that an injector SA of 150\({}^{\circ}\) produces the best emission performance. [PERSON] et al. [20] experimentally investigated the impact of the fuel SAs and injection timing on the combustion and emissions of a diesel engine. The research found that the fuel SA has a significant influence on the combustion, performance, and emissions of the engine. They concluded that smaller SAs (narrow SAs) produce a higher maximum combustion pressure and heat release rate than wider SAs. [PERSON] et al. revealed the same conclusion in their study [23]. [PERSON] and [PERSON] [24] revealed in their publication, that an appropriate injector SA can reduce NOx and soot emissionswithout affecting the engine fuel consumption because of the improvement of the air-fuel mixture quality. An optimization for the fuel injector of a heavy-duty 2-stroke direct injection DF marine engine was carried out and can be referred to in our previous study [25]. The present study numerically studied the effects of the injector SA on the combustion process and emission formations of a 4-stroke port-injection NG-diesel DF marine engine by using the CFD method. The combustion of the fuel and emission formations inside the engine cylinder were simulated by the AVL FIRE code. The final goal of this study is to specify the optimal injector SA for the engine, with respect to reducing EGEs while retaining the engine power. The CFD models were verified by the measured results reported in the technical measurement data of the engine. The study successfully pointed out the optimal injector SA for the engine to achieve certain emission reductions.
## 4 System Model, Algorithms, and Security Model
### Algorithms
#### 4.2.2 Encryption  
Encrypt(K, \(\psi\), GPP, \(\cup\) APK\({}_{\text{k}}\)) \(\rightarrow\) (CT): This algorithm takes as input the GPPs, a symmetric key K by which data are encrypted, an access policy structure \(\psi\), and a collection of public keys of applicable authorities. It generates the ciphertext.
# Research on Mechanical Properties and Damage Constitutive Model of Water-Bearing Coal
## 4 Damage Constitutive Model
### Random Distribution Variable of Micro-Element Strength  
To derive the damage constitutive equation of coal, it is important to identify the precise expression of \(S\) in Equation (13). In this paper, the Drucker-Prager strength criterion commonly used in geotechnical materials is used as a reference.  
The expression of the random distribution variable \(S\) of the micro-element strength corresponding to the Drucker-Prager strength criterion is:  
\[S=\alpha I_{1}^{*}-\sqrt{I_{2}^{*}} \tag{14}\]  
In Equation (14): \(I_{1}^{*}\) is the first principal invariant of the effective stress tensor, \(I_{2}^{*}\) is the second principal invariant of the effective stress deviation, and \(\alpha\) is the material parameter. The following equations can be used to calculate \(I_{1}^{*}\) and \(I_{2}^{*}\):  
\[I_{1}^{*}=\sigma_{1}^{*}+\sigma_{2}^{*}+\sigma_{3}^{*} \tag{15}\]  
\[I_{2}^{*}=\frac{\left(\sigma_{1}^{*}-\sigma_{2}^{*}\right)^{2}+\left(\sigma_{2 }^{*}-\sigma_{3}^{*}\right)^{2}+\left(\sigma_{3}^{*}-\sigma_{1}^{*}\right)^{2} }{6} \tag{16}\]According to the equivalent strain hypothesis [40], the relationship between effective stress and nominal stress is as follows:  
\[\sigma_{i}^{*}=\frac{\sigma_{i}}{1-D}(i=1,2,3) \tag{17}\]  
From Equations (14)-(17), the expression of random distribution variable \(S\) can be obtained:  
\[S=\frac{\left(\alpha I_{1}+\sqrt{J_{2}}\right)E\epsilon_{1}}{\sigma_{1}-\mu( \sigma_{2}+\sigma_{3})} \tag{18}\]  
In Equation (18): \(I_{1}\) is the first principal invariant of the nominal stress tensor, \(J_{2}\) is the second principal invariant of the nominal stress deviation. \(I_{1}\) and \(J_{2}\) can be calculated by the following equations:  
\[I_{1}=\sigma_{1}+\sigma_{2}+\sigma_{3} \tag{19}\]  
\[J_{2}=\frac{(\sigma_{1}-\sigma_{2})^{2}+(\sigma_{2}-\sigma_{3})^{2}+(\sigma_{ 3}-\sigma_{1})^{2}}{6} \tag{20}\]  
In the case of uniaxial compression, there are \(\sigma_{1}=\sigma\), \(\sigma_{2}=\sigma_{3}=0\), \(\epsilon_{1}=\epsilon\), and Equation (18) can be simplified:  
\[S=\left(\alpha+\frac{1}{\sqrt{3}}\right)E\epsilon \tag{21}\]
# The Influence of Electrode Impedance on the Parameters of Visual Evoked Potentials
###### Abstract  
The choice of a proper electrode is determined mainly by the type of examination. For recording spontaneous activity of the brain (EEG), two types of reusable electrodes should be considered, i.e., cup (Au, Ag, Ag/AgCl) and sintered bridge electrodes (SBEs) with a gold or silver pin. In the case of evoked potentials (EPs), only cup electrodes are used. To test the effect of electrode choice on the parameters of evoked potentials, twenty reusable electrodes of each type, i.e., Au, Ag, Ag/AgCl, were used to record visual evoked potentials (VEPs). The electrodes were then further tested by the electrochemical impedance spectroscopy (EIS) method. The presented research confirms the existence of an effect of the number of examinations performed with the same set of reusable cup electrodes on the values of the VEP parameters. A prolongation of the latencies and variability in the amplitudes for all the individual VEP components were observed. The obtained results suggest possible advantages of using reusable cup electrodes only once. An alternative to these electrodes may be solutions based on materials composed of polymers or dry electrodes.  
electrochemical impedance spectroscopy (EIS); reusable cup electrodes; visual evoked potential (VEP) +
Footnote †: journal: Applied Sciences
## 2 System Structure and Principle  
The system structure of the parking space and obstacle detection based on the vision sensors and the laser device is shown in Figure 1. In addition to the original 360\({}^{\circ}\) camera sensors installed on the body of the vehicle, the system is also equipped with a checkerboard grid effect laser transmitter. The images of the parking space and the surrounding environment are captured by the 360\({}^{\circ}\) cameras. Then, the visual processor carries out the subsequent image processing and connects with the parking controller through the communication serial port.  
Figure 1: System construction.  
The principle of the system is shown in Figure 2. The laser emitting devices are illuminated on the ground to present the shape of the checkerboard grid. When obstacles exist on the ground, such as vehicles in the parking space, the shape of the laser mesh will change. Similarly, when there are obstacles such as stones, potholes, walls, and floor locks on the ground, the laser mesh will produce different changes. The cameras capture the changes of the laser mesh, making it easier to recognize the parking spaces and the obstacles from images. As a result, the recognition efficiency and success rate will be improved. Furthermore, when parking at night, the laser emitting devices can emit light autonomously, which reduces the requirements for lighting conditions. In addition, the safety and applicability of the parking system will be improved.  
After acquiring the environment image of the parking space, the system first preprocesses the image, which mainly includes gamma conversion image enhancement, image graying, mean filtering by smoothing and denoising, and binarized edge detection. Then, the region of interest will be extracted. After that, contour detection and convex hull detection are performed in the region of interest. Finally, the contour and convex hull will be displayed in the images. The algorithm flow is shown in Figure 3.
## Materials and Methods
### Feature Extraction
#### 2.4.2 Co-occurrence Matrix Features  
Co-occurrence Matrix (COM) features are also called second-order statistical features. They are obtained from the distance and the angle between pixels, based on the gray-level co-occurrence matrix (GLCOM) [28]. For this study, eleven second-order texture features were calculated in four dimensions, 0, 45, 90, and 135\({}^{\circ}\), up to five-pixel distance. Eleven second-order COM features were acquired,namely, entropy, inertia, correlation, inverse difference, and energy, etc. First of all, energy is defined in Equation (13). Energy is calculated in the distribution between gray-level values, i.e., \(\zeta\).  
\[Energy=\sum_{i}\sum_{j}\Bigl{(}\zeta_{i,j}\Bigr{)}^{2} \tag{13}\]  
Correlation describes the pixel similarity at particular pixel distance and is described in Equation (14).  
\[Correlation=\frac{1}{\sigma_{\alpha}\sigma_{\beta}}\sum_{x}\sum_{y}(x-\mu_{ \alpha})\Bigl{(}y-\mu_{\beta}\Bigr{)}\zeta_{i,j} \tag{14}\]  
Entropy measures the total content of the image. It is described in Equation (15).  
\[Entropy=-\sum_{i}\sum_{j}\zeta_{i,j}log_{2}\zeta_{i,j} \tag{15}\]  
The local homogeneity of image is called inverse difference, and it is presented in Equation (16).  
\[Inverse\;Diff\;ference=\sum_{i}\sum_{j}\frac{\zeta_{i,j}}{\left|i-j\right|} \tag{16}\]  
The contrast describes the inertia, which is defined in Equation (17).  
\[Inertia=\sum_{i}\sum_{j}(i-j)^{2}\zeta_{i,j} \tag{17}\]