esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 2 Literature Review
### Convolutional Neural Network for Voiceprint Recognition  
Convolutional neural networks are a class of deep learning models primarily designed for tasks involving structured grid-like data, such as images and videos. CNNs have shown remarkable performance in tasks like image classification, object detection, and even speech recognition, when combined with certain preprocessing techniques like MFCCs. The distinguishing feature of CNNs is their ability to automatically and adaptively learn spatial hierarchies of features from input data. They consist of multiple layers, including convolutional layers, activation layers, pooling layers, and fully connected layers, each of which plays a specific role in feature extraction and classification.  
A CNN is a powerful deep learning framework that excels in identifying structured patterns within data. While originally designed for image processing, CNNs have proven highly effective in speech and voice recognition tasks by leveraging feature representations such as spectrograms and MFCCs.  
In this study, CNNs are utilized to process MFCC features extracted from voice samples. The CNN architecture comprises convolutional layers that detect intricate patterns within the MFCCs, pooling layers that reduce spatial dimensions while retaining critical features, and fully connected layers that perform final classification. The inclusion of dropout layers mitigates overfitting, ensuring the model's robustness. This design allows the network to capture unique voice characteristics, enabling precise speaker identification and verification.  
By focusing on extracting and classifying voice features, the CNNs in this methodology are optimized for performance in low-resource and diverse voiceprint recognition scenarios. The model's parameters, such as the number of convolutional filters and kernel sizes, were selected based on empirical testing to balance computational efficiency and recognition accuracy.
## 4 Results and Discussion
### Effect of Particle Diameter  
The pressure drop of the straight pipe hardly varied with the particle diameter at the static state (Figure 9A). After vibration, the pressure drop decreased with the increase of particle diameter, showing a nonlinear relationship. The analysis led to the following reasons for this phenomenon: the smaller the particle diameter, the more particles entered the flow field under the same IPF conditions, and the disturbance was larger after particles were subjected to vibration, resulting in more chaotic movement. Combined with Figure 8, it can be seen that vibration was the main reason for the increase of pressure drop, and vibration increased the flow resistance. For small particles (0.1-0.3 mm), the 0\({}^{\circ}\) and 90\({}^{\circ}\) section wear rates were relatively high. The vibration led to irregular rebound of small particles in the straight section, and the secondary flow played a significant role; the large number of particles and frequent impacts led to serious wear in straight pipe segment (Figure 9B). For large diameter particles (0.3-1.0 mm), the effect of vibration and secondary flow on the particles on the inertial force was greatly reduced, resulting in uniform wear distribution at the pipe elbow and small differences in the maximum wear rate (Figure 9B). In summary, the trajectory of large particles was controlled by equilibrium forces with the increase of particle diameter to a critical value. The variation trend of the maximum wear rate at each angle section was the same as that along the path (Figure 9B,C). The vibration action was dominant when the particle size was within the range of 0.1-0.3 mm. Particles collided with the side wall of the straight pipe frequently, and the abrasion rate was relatively high at the 0\({}^{\circ}\) and 90\({}^{\circ}\) sections. Once the particle size exceeded 0.3 mm, the inertia force, buoyancy force and vibration at the elbow reached a balance, and the influence of secondary flow was weakened, resulting in uniform abrasion distribution at the elbow, and the wear rate at the cross section of 90\({}^{\circ}\) was not as large as that at the cross section of the small angle.
# Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images
## 4 Experimental Results
### Experiment 1  
From the observation of Figure 3 and Table 1, we can draw a conclusion that the relationship between \(\sigma_{d}\) and \(\sigma_{r}\) nearly obeys Equation (11) where the LRD is a constant. On the whole, \(\sigma_{r}\) falls down with the increasing of the value of \(\sigma_{d}\), see Figure 3a. Along with the increasing of the value of \(\sigma_{d}\), the value of MR first falls sharply and at last tends to be stable, see Figure 3b.  
It is concluded that the parameters of \(\sigma_{d}\) and \(\sigma_{r}\) should be set simultaneously when using the BF for the application of BUS image in order to preserve the smoothness of homogeneous region and sharpness of the edge region, simultaneously. From Figure 3, we can see that \(\sigma_{d}~{}=~{}12\) should be the best choice for the BF. In this experiment, we set \(\sigma_{d}\) in the BF to be 4, 8, 12, 16, 20, 22, and 24,respectively, and LRD = 0.836 \(\pm\) 0.001 is a constant. Corresponding value of MR (1/VMR) and \(\sigma_{r}\) are shown in Table 1.
# Routing Design for Pipe System Considering Natural Frequency
## 6 Simulations and Results  
Three experiments were carried out to demonstrate the feasibility and effectiveness of the proposed algorithms. The first and second cases were based on a well-designed pipe routing problem in a 2D plane, and it was used to verify the optimization performance of the algorithms, for instance, the path searching ability, the convergence rate and speed, as well as the robustness. The third experiment involved applying the proposed algorithms to an aero-engine model with a complex and narrow routing space. This case aimed to demonstrate the practicality and effectiveness of the algorithms in handling real-world engineering scenarios with numerous obstacles and constraints. The optimization performance was evaluated in terms of the ability to find optimal or near-optimal pipe paths that consider the natural frequency, while also meeting various engineering constraints.
# Temperature Prediction of Chinese Cities Based on GCN-BiLSTM
## 3 Materials and Methods
### Methods
#### 3.2.3 GCN-BiLSTM Model  
After preprocessing the original meteorological time series data, such as data cleaning and standardization, the data set is divided into training sets, validation sets, and test sets. After the initial data pass through the two-layer GCN network, the feature extraction of meteorological information from the surrounding area is achieved. After this, the information contains spatial features. The output of the previous layer is further fed into the BiLSTM network. Then, the final output \(\{y_{1},y_{2},\ldots y_{n}\}\) is obtained after dropout optimization and 6 fully connected layers. When the input data is the training set, the output values \(\{y_{1},y_{2},\ldots y_{n}\}\) and the actual temperature \(\{T_{1},T_{2},\ldots,T_{n}\}\) are used to calculate the loss of mean squared error. The model is continuously optimized by the Adam optimizer. When the input data are the test set, no loss calculation and model optimization are performed, and the output values are the predicted temperature values of the GCN-BiLSTM model. The detailed workflow of the GCN-BiLSTM model is shown in Figure 6.  
Figure 5: The network structure of BiLSTM.
# Benefit Analysis of Gamified Augmented Reality Navigation System
## Discussion  
### Analysis Results  
* Due to current issues with technologies combining AR and GPS, the AR guide's position shifted even when the user was standing still. Thus, this method is not recommended for outdoor venues. These contexts would be better served by fixed-location display methods, in which the virtual guide is displayed after images are scanned at each exhibit.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multirow{2}{*}{**No. of Samples**} & \multicolumn{5}{c}{**Time (s)**} \\ \cline{3-6}  & & **Mean (n)** & **Standard Deviation (SD)** & \(t\)**-Value (t)** & **Degree of Freedom (df)** & \(p\)**-Value (\(p\))** \\ \hline Free & 45 & 340.20 & 606.334 & & & \\ \hline Game & 54 & 243.78 & 382.659 & 0.962 & 97 & 0.339 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary table of \(t\)-test of exhibition-viewing time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multirow{2}{*}{**No. of Samples**} & \multicolumn{5}{c}{**Distance travelled (m)**} \\ \cline{3-6}  & & **Mean (n)** & **Standard Deviation (SD)** & \(t\)**-Value (t)** & **Degree of Freedom (df)** & \(p\)**-Value (\(p\))** \\ \hline Free & 45 & 329.889 & 549.8836 & & & \\ \hline Game & 54 & 201.050 & 250.7699 & 1.451 & 59.099 & 0.152 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary table of \(t\)-test of exhibition-viewing distance travelled.  
Figure 7: (**a**) Path distributions of participants using game mode (N = 54); (**b**) path distributions of participants using free mode (N = 45).  
Analysis showed no significant differences between the participants in terms of time or distance travelled, meaning that gamification did not impact the personal pace of the participants or the time they spent viewing the exhibition, and the overall distance that the participants travelled did not differ significantly. This means that using gamification to control the time spent viewing exhibits or distance travelled by visitors would be difficult.
* The paths presented by the participants using gamified AR were more regular, and therefore we can speculate that visitors follow the gamified path instructions while viewing exhibitions. This is a significant finding, as it can be applied to divert crowd flows. However, the starting points of the participants remained scattered. We speculate that this was because the participants needed time to familiarize themselves with the map and because their smartphone hardware and GPS positioning conditions can affect AR stability. This requires improvement.
## 4 Experimental Test Rig
### Sensing System  
The whole sensing system was composed of different kinds of sensors as shown in Figure 8. The sensors installed were: input and output torque and speed sensors (outside the vacuum chamber), input and output rotation and position contactless sensors, six inner temperature sensors in the stator part, and four magnetic field sensors all along the height in order to characterize thermal and magnetic behavior. In addition to those main sensors, some secondary sensors were added for the temperature control system: three temperature sensors and two level detectors for the LN2 deposits.  
The output and input torque/speed sensors were two DATAFLEX 16-10 from KTR Company. The temperature sensors were PT-111 model from Lakeshore. The temperature control system sensors were composed of PTC sensors and a proportional-integral-derivative (PID) controller. The magnetic field sensors were HGCA-3020 model from Lakeshore. All the sensors were monitored by a PXI model system from National Instruments.  
Figure 7: Thermal-vacuum chamber and external test rig elements.  
A contactless position and rotation sensor based on Hall Effect probes had to be specifically designed for measuring position and rotation of the two floating axles, shown in Figure 9. Two position and rotation sensors were needed for the input and output axle, respectively. In order to keep the Hall probes in the right temperature operation range, the sensors had a heater and a PID external controller.  
These new sensors monitored the X, Y and Z position and rotation of both axles in order to measure run-outs and tilting of the floating axles. The precision of the sensors was \(\pm\)1 \(\upmu\)m in linear direction and \(\pm\)1 arcmin in rotation.  
Figure 8: Sensors of the test rig scheme.  
Figure 9: Rotation and position contactless sensor for the floating axles.
## 5 Results and Discussion
### Environmental Effect Compensation Study  
Our developed interface electronics enable temperature and humidity compensation by subtracting frequency changes caused by these factors between two SAW devices at the mixer. Three types of experiments were conducted under varying external conditions to evaluate the effectiveness of this compensation. The experiments utilized a signal generator as the reference signal, a reference SAW device without HfO\({}_{2}\) sensing material, and a reference SAW device with HfO\({}_{2}\) sensing material. When the signal generator was used for the reference signal, no compensation for temperature and humidity changes was achieved, resulting in significant frequency variations. In contrast, using a reference SAW device without HfO\({}_{2}\) showed that while temperature and humidity compensation was effective for the LN substrate, a small frequency change was still observed due to environmental factors affecting the HfO\({}_{2}\) sensing material. Finally, when a reference SAW device coated with HfO\({}_{2}\) was used, it was confirmed that environmental interference factors were completely eliminated (Figure 11). This demonstrates that our sensor system effectively compensates for temperature and humidity by subtracting the frequency changes of the two SAW devices with HfO\({}_{2}\) sensing material at the mixer.  
Figure 11: Frequency variations in the sensor system, both with and without reference SAW devices, are observed under environmental changes: (**a**) fluctuations in relative humidity ranging from 10% to 90% RH and (**b**) changes in temperature between 25 \({}^{\circ}\)C and 90 \({}^{\circ}\)C.
# Practical Inner Product Encryption with Constant Private Key +
## 5 Efficiency Analysis and Implementation Results  
Herein, we compare the efficiency of the proposed IPE scheme with the schemes proposed in [1, 3, 5, 6, 7, 15, 16, 22, 23, 24, 25, 26, 27, 28, 29, 30, 36] (Because [4, 17] are the complete versions of [16, 24], we only compare our work with [16, 24]). As shown in Table 1, we compare our scheme to others in two aspects: the size of the private key and the number of pairing operations for decryption. The type of group order is also presented because the efficiency of prime order groups is higher than that of composite order bilinear groups. As is evident in Table 1, our proposed scheme has the shortest private key length and smallest number of pairings. Moreover, both the private key length and the number of pairings in our proposed scheme are independent of the length of the predicate and attribute vectors. The most efficient existingscheme is [29], where the private key length is three group elements and three pairings are needed for decryption. In our scheme, the private key is only an element of \(\mathbb{G}\) and an element of \(\mathbb{Z}_{p}\), and only one pairing is necessary during decryption. Furthermore, in [5], the private key length (\(2m|\mathbb{G}|\)) and the number of pairings (\(2m\)) are independent of the lengths of the vectors, where \(m\) is the leakage-resilience parameter. However, \(m\) must be at least equal to or greater than 2. Therefore, the private key length and pairing number are still larger than those obtained with our approach (this is because their scheme degenerates to a conventional IPE scheme without leakage resilience when \(m=1\)). We also implemented our scheme and the schemes of [15; 17; 29] to compare efficiency. We chose these three schemes for the following reasons:  
* Among all the existing IPE schemes, the first scheme of [16] requires the smallest number of pairings for decryption (only two pairings required);
* Among the schemes supporting constant private key length, the schemes of [15; 29] require the smallest number of pairings for decryption (only three pairings required). The environment of the implementation is presented in Table 2, and the implementation results are shown in Table 3. We implemented these schemes by using the Charm-Crypto library [37] and Python language. For schemes constructed over symmetric paring groups (the approach in [16] and our method), we selected the pairing group SS512 in [38] (also known as type A groups), and for the schemes constructed over asymmetric pairing groups (in [15; 29]), we chose the pairing group BN254 in [39] (also known as type F groups). The SS512 group is a supersingular elliptic curve group where the size of the base field order is 512 bits and the embedding degree is two. For a bilinear map \(e:\mathbb{G}\times\mathbb{G}\rightarrow\mathbb{G}_{T}\) over the SS512 group, the bit lengths of elements in \(\mathbb{G}\) and \(\mathbb{G}_{T}\) are 64 and
# An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers
## 4 Performance Evaluation  
The performance evaluation presented here was carried out exploiting the simulated correlation value shown in Equation (4) and executing the discriminator/pre-filter algorithms discussed in Sections 2 and 3. The tracking error dynamic and signal strength were varied in the carefully designed test scenes (i.e., test Scenes A, B, C, D) in order to evaluate the algorithms comprehensively. The algorithms evaluated in this paper include ATAN/ATAN2/NC-EMLE discriminators, the coherent pre-filter, and the enhanced non-coherent pre-filter. For the sake of brevity, the coherent pre-filter will be abbreviated to \"Coh\", and the enhanced non-coherent pre-filter abbreviated to \"Non-coh\". The metric used for evaluation is the root mean square (RMS) value of estimation error.  
The common non-coherent pre-filter without FDE is not chosen as a method to be evaluated. There are two reasons why we did not do this. Firstly, in previous work, the observation noise variance matrix of the non-coherent pre-filter is determined by filter tuning [13; 16]. Secondly, the advantage of the enhanced non-coherent pre-filter over ordinary non-coherent pre-filter has been analyzed in Section 3.2. We would also discuss this advantage in the following experiments.
## 2 Deflecting Cavity Design
### Mode Separation  
TM\({}_{110}\) is the working mode in the deflecting cavity which exists degenerated modes. The present methods of mode separation are to break the rotational symmetry of the cavity and fix the polarization direction, which include adding holes on the disk [20], using racetrack shaped RF coupling iris [9], cutting slots on the edge of the cavity [12; 16], adding longitudinal rods in the cavity [21], using an elliptical racetrack shaped cavity [22], etc. Due to the exist of coupling slot in the middle cell, the symmetry has been broken and the frequency separation value of the two degenerated modes is about 3 MHz. To further separate the frequency, adding polarization alignment holes is selected due to the 1.3 GHz  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & \(b_{m}\) & \(b_{e}\) & \(d\) & \(t\) & \(R\) & \(a\) \\ \hline Value & 132.29 & 136.14 & 57.24 & 16 & 33 & 17.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The geometric dimension of the 3-cell deflecting cavity.  
Figure 2: The relationship between \(R_{\perp}\) and the electron beam \(\beta\).  
deflecting cavity has large geometric size. As the polarization direction of the working mode is \(y\)-axis, the two polarization alignment holes are chosen to locate on \(x\)-axis with center-to-center distance of 128 mm. The holes locate where the degenerated mode has stronger electric field, and the working mode has stronger magnetic field to acquire the most outstanding mode separation effect. The frequency deviation of the two modes with different hole radius are shown in Figure 4, finally the radius of 15 mm is determined, and the frequency deviation of the two modes is greater than 10 MHz.  
The final parameters of the deflecting cavity are shown in Table 2.  
Figure 4: The influence of the polarization alignment holes radius on the mode frequency separation.  
Figure 3: The electromagnetic field of the working mode.
# Closed Form Solutions for Thermal Buckling of Functionally Graded Rectangular Thin Plates
## 4 Numerical Results and Discussion
However, for type-I FGM plate, which is made from temperature-dependent materials, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**B.C.** & **T.E.** & _a/b = 2_ & _a/b = 3_ & _a/b = 4_ & _a/b = 5_ & _a/b = 6_ & _a/b = 7_ \\ \hline \multirow{3}{*}{CSCS} & uniform & 30.0846 & 52.8369 & 85.1409 & 125.3608 & 172.2154 & 224.5874 \\  & linear & 62.9589 & 110.1631 & 176.7119 & 258.9990 & 354.3651 & 460.7078 \\  & nonlinear & 66.0438 & 115.5227 & 185.2298 & 271.3522 & 371.0771 & 482.1803 \\ \hline \multirow{3}{*}{CSCC} & uniform & 45.1642 & 92.3546 & 155.1861 & 229.9260 & 313.6459 & 403.9628 \\  & linear & 94.2791 & 191.5108 & 319.7465 & 471.5480 & 642.0001 & 828.0680 \\  & nonlinear & 98.8767 & 200.7238 & 334.8863 & 493.5001 & 671.3553 & 865.1898 \\ \hline \multirow{3}{*}{CCCC} & uniform & 74.6176 & 162.6580 & 273.1802 & 398.4099 & 532.6899 & 671.3248 \\  & linear & 155.0859 & 334.9406 & 559.4693 & 816.5263 & 1101.5178 & 1417.3660 \\ \cline{1-1}  & nonlinear & 162.5833 & 350.7718 & 585.2726 & 853.1769 & 1149.2782 & 1475.2611 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Critical buckling temperature \(\Delta T_{\rm cr}\) of type-I FGM rectangular thin plate with different \(a/b\) and various B.C. (\(a/h=100\), \(n=2\), \(T_{0}=300\) K). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**B.C.** & **T.E.** & _T_**0 = 300** & _T_**0 = 350** & _T_**0 = 400** & _T_**0 = 450** & _T_**0 = 500** & _T_**0 = 550** \\ \hline \multirow{3}{*}{CSCS} & uniform & 30.0846 & 29.1293 & 28.2271 & 27.3736 & 26.5646 & 25.7966 \\  & linear & 62.9589 & 61.0372 & 59.1907 & 57.4131 & 55.6986 & 54.0411 \\  & nonlinear & 66.0438 & 64.0307 & 62.0950 & 60.2304 & 58.4306 & 56.6896 \\ \hline \multirow{3}{*}{CSCC} & uniform & 45.1642 & 43.7545 & 42.4210 & 41.1575 & 39.9584 & 38.8184 \\  & linear & 94.2791 & 91.4769 & 88.7779 & 86.1741 & 83.6574 & 81.2201 \\  & nonlinear & 98.8767 & 95.9427 & 93.1148 & 90.3846 & 87.7440 & 85.1846 \\ \hline \multirow{3}{*}{CCCC} & uniform & 74.6176 & 72.3631 & 70.2244 & 68.1925 & 66.259 & 64.4162 \\  & linear & 155.0859 & 150.7014 & 146.4609 & 142.3542 & 138.3707 & 134.4997 \\ \cline{1-1}  & nonlinear & 162.5833 & 157.9968 & 153.5573 & 149.2545 & 145.0775 & 141.0154 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Critical buckling temperature \(\Delta T_{\rm cr}\) of type-I FGM rectangular thin plate with different initial temperature \(T_{0}\) (\(a/h=100\), \(a/b=2\), \(n=2\)). \(\Delta T_{\rm cr}\) decreases almost linearly as \(T_{0}\) increases, and \(T_{\rm cr}\) increases almost linearly, but the increment of \(T_{\rm cr}\) is less than that of \(T_{0}\). Figure 4: The relation between the critical buckling temperature \(\Delta T_{\rm cr}\) and power law index \(n\) of type-I FGM rectangular thin plate (\(a/h=100\), \(a/b=2\), \(T_{0}=300\) K). Figure 3: The relation between the critical buckling temperature \(\Delta T_{\rm cr}\) and the length–thickness ratio \(h/a\) of type-I FGM rectangular thin plate (\(a/b=2\), \(n=2\), \(T_{0}=300\) K).
# Density Functional Theory Study on the Adsorption of Co(II) in Aqueous Solution by Graphene Oxide
## 5 Experimental Verification of Adsorption Properties of GO  
To verify the Gaussian calculation results, experimental methods were used to determine the effects of pH value, temperature and interfering ions on the adsorption solution.
# Spectral Coexistence of QoS-Constrained and IoT Traffic in Satellite Systems
## 3 Asymptotic Analysis  
In order to gather preliminary insight on the performance of the system, we first consider an asymptotic setting in which the codewords transmitted by \(\mathsf{s}_{\mathsf{a}}\), as well as those by users of \(\mathcal{I}_{\mathsf{b}}\) over a slot, can be made long enough to approach the _classical_ information theoretic results.