esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Congestion-Avoidance and Vehicle-Detection Modules of ACO-PT for Vehicles
### Automatic-Vehicle-Detection Module
#### 4.2.2 Contour Detection  
After enhancing the image quality, contour detection is used to detect and extract the shapes of objects in an image. It aims to find the boundaries of objects within an image. Contours are defined as the boundaries of an object, represented by a set of points in an image. These points can then be used to create a representation of the vehicle. Since all contour-detection results are easily affected by noise in the image, it is vital to filter out noise in the image to decrease false detection. The Gaussian filter is applied for the smooth image \(\hat{S}_{im}\), which can be given by:  
\[\hat{S}_{im}=\frac{1}{2\pi\sigma}\exp\left(-\frac{(i-(k_{1}+1))^{2}+(j-(k_{2}+ 1))^{2}}{2\sigma^{2}}\right) \tag{22}\]  
where \(\sigma\) is the standard deviation, \(k_{1}\) is the distance of the vehicle from the source on the horizonal axis, and \(k_{2}\) is the distance of the vehicle from the source on the vertical axis.  
The second stage uses the Laplace operator to detect the contours of objects \(Laplace(f)\), which can be determined as follows:  
\[Laplace(f)=\frac{\partial^{2}f}{\partial x^{2}}+\frac{\partial^{2}f}{\partial y ^{2}} \tag{23}\]  
where \(\frac{\partial^{2}f}{\partial x^{2}}\) is the second derivative of the image pixels.  
An edge in an image may point in a variety of directions, so the improvement in the Laplacian-operator Canny method with image filters to detect vertical, horizontal, and diagonal edges in the blurred image \(B_{im}\) can be determined as follows:  
\[B_{im}=\sqrt{G_{x}^{2}+G_{y}^{2}} \tag{24}\]  
From this calculation, the edge gradient and direction can be determined:  
\[\theta=\tan^{-1}(G_{x}^{2}+G_{y}^{2}) \tag{25}\]  
where G\({}_{x}\) is the first derivative in the horizontal direction, G\({}_{y}\) is the vertical direction, and \(\theta\) is the edge gradient.
# Towards Efficient Sink Mobility in Underwater Wireless Sensor Networks
## 5 Simulation Results
### Network Lifetime and Stability Period  
Network lifetime of the proposed routing protocol is improved as shown in Figure 3. In SEEC, two mobile sinks have not been used efficiently. These sinks are moved in sparse regions only. This kind of movement can bring two sinks in adjacent regions e.g., Region 6 and Region 7. Considering the transmission range, it is not appropriate to use mobile sinks on the basis of sparse regions. In the proposed scheme, MS1 is restricted in the left region and MS2 is restricted in the right region giving priority to sparse regions. Moreover, clustering is also kept in dense regions. Both mobile sinks are moved alternatively in each round. This movement of mobile sinks with clustering in dense regions increased the network lifetime of the proposed routing protocol.  
Sensor nodes are deployed randomly in the network field. There may be a case where sparse regions are less in number at the time of network deployment; however, the number may increase due to the ocean movement. In SEEC, the searching of sparse and dense regions is done only once during initial stages. In the proposed scheme, in addition to searching of sparse regions, due to the the alternate movement of mobile sinks, the stability period is better than SEEC, EEDBR and DBR as shown in Figure 3. Moreover, the restricted movement of mobile sinks on the left side and right side also enhances the stability period.
## 2 Signal Modeling of Communication Radiation Sources  
MPSK signals are communication signals that are created by digital baseband pulses attaching modulation information to the carrier phase. The modulation information is represented by a variety of different carrier phases. MPSK signals are commonly used in the communications industry, and when taking into account the complex signal model, they can be expressed as follows:  
\[s(t)=\sqrt{2P_{\mathrm{t}}}\sum_{k=-\infty}^{\infty}d_{k}(t)p(t-kT)e^{[\theta _{k}(t)-\pi/2]}, \tag{1}\]  
where \(P_{\mathrm{t}}\) is the total signal power in the passband; \(d_{k}(t)\triangleq A_{k}e^{i\theta_{k}}\) is the complex modulation of the \(k\)th symbol, where \(A_{k}\) is the normalized amplitude; and \(\theta_{k}=[2q_{k}+(1+(-1)^{M/2})/2]\pi/M\) is the phase modulation of the \(k\)th MPSK with independent, uniformly distributed \(q_{k}\in\{0,1,\cdots,M-1\}\). \(T\) is the symbol period; \(\theta_{c}(t)\) is the carrier phase; and \(p(t)\) is the pulse shape to satisfy the \(T^{-1}\int_{0}^{T}p^{2}(t)\mathrm{d}t=1\). MPSK signals have the advantages of strong noise immunity, high bandwidth utilization, good coherence detection, and large data throughput. Commonly, there are Binary Phase Shift Keying (BPSK) signals (\(M=2\)),Quadrature Phase Shift Keying (QPSK) signals (\(M=4\)), 8 Phase Shift Keying (8 PSK) signals (\(M=8\)), etc.  
Assuming that the signal from the target communication radiation source is stable during the observation time and that the signal received in the observatory contains Doppler information, the received signal can be expressed as follows:  
\[r(t)=\sqrt{2R}\sum_{k=-\infty}^{\infty}d_{k}(t)p(t-kT)e^{i[\omega_{\rm r}t+ \theta_{\rm d}+\theta_{\rm c}(t)-\pi/2]}+n(t), \tag{2}\]  
where \(\omega_{\rm r}=\omega_{\rm c}+\omega_{\rm d}\), \(\omega_{\rm c}\) is the carrier frequency at the transmitter; \(\omega_{\rm d}\) is the frequency shift; and \(n(t)\) denotes the noise term obeying zero-mean Gaussian white noise.
# Comparative Study for Multi-Speaker Mongolian TTS with a New Corpus
## 4 Speech Synthesis Experiments
### Robustness Analysis  
All baseline models achieved good results in both naturalness and speaker similarity assessment. However, the end-to-end TTS model often suffers from the robustness issue, such as repeating, skipping words, etc. In this section, we further conduct robustness analysis to check their performance.  
We analyzed the synthesized speech in terms of six classes: (1) repetitive words, words that repeat speech in the sentence; (2) skipped words, words that were missing from the sentence, and skipped directly over words that were not spoken; (3) mispronunciation words, words that were spoken but mispronounced in the sentence; (4) incomplete words, words that are pronounced correctly in a sentence but are incomplete; (5) long pauses, an unusually long pause in a sentence; and (6) non-verbal sounds, other sounds in the sentence that are not verbal. The length of the text to be synthesized was set at 13 words, as either too long or too short sentences would not accurately reflect the synthesis. We synthesized 50 speech items for each speaker from the test set, and randomly selected 20 of them for robustness analysis. Finally, we invited five native Mongolian volunteers to evaluate the synthesized speech for six errors.  
The final statistical results are shown in Table 4. For FastSpeech2+HiFi-GAN, the speech samples of F1, F2, and F3 produced six, nine, and three errors in total, respectively. The VITS model also produced 2, 10, and 3 errors for F1, F2, and F3. The difference between the two models was not significant for F2 and F3, except for F1 where the number of errors was significantly lower than that of FastSpeech2+HiFi-GAN. It is worth noting that the speech samples of F2 produce more errors than F1 and F3 for both models. The main reason may be due to the data distribution again. For the F2 dataset, from Figure 2b we can find that the length distribution was not concentrated. There were more sentences with improper length, that were too short and too long, than the other two speakers. Therefore, it will lead to long speech samples, such as 13 words, in the test set of F2, making it easier to produce errors. In conclusion, the robustness analysis shows that our MnTTS2 can be used to build robust TTS systems to some extent.
## 4 Long-Term Settlement Prediction Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Unit Weight kg/m\({}^{3}\)** & **Elasticity Modulus** & **Unit Weight kg/m\({}^{3}\)** & \(\beta\) & \(\psi\) & \(\sigma_{c}\) & \(k\) \\  & & /MPa & **Unit Weight kg/m\({}^{3}\)** & **(\({}^{\circ}\))** & **(\({}^{\circ}\))** & **(kPa)** & \\ \hline Fill material & 2080 & 38 & 0.30 & 38 & 38 & 30.2 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Linear Drucker–Prager model parameters. Considering the Drucker-Prager model parameters for a plane strain problem with the assumption of k = 1 and using the associated flow rule where \(\psi=\beta\), we obtain Equations (6) and (7). \[tan\beta=\frac{\sqrt{3}sin\varphi}{\sqrt{1+\frac{1}{3}sin^{2}\varphi}},d=\frac{ \sqrt{3}cos\varphi}{\sqrt{1+\frac{1}{3}sin^{2}\varphi}}c \tag{6}\]  
\[\sigma_{c}=\frac{1}{1-\frac{1}{3}tan\beta}d,\sigma_{t}=\frac{1}{1+\frac{1}{3} tan\beta}d \tag{7}\]  
According to the above formula, \(\beta(\psi)\), \(\sigma_{c}(\sigma_{t})\) can be obtained. Based on the parameters of the existing Mohr-Coulomb model, the parameters of the Drucker-Prager model were calculated, and the results are shown in Table 5.  
The maximum long-term settlement (10 years) for the typical cross-section is shown in Figure 7. Based on numerical simulation results, the maximum pavement settlement-time curves for different degrees of compaction of the typical cross-section are illustrated in Figure 8.  
From these two figures, we can observe that the relationship between the maximum roadbed settlement and the degree of compaction for the typical cross-section is nonlinear. Figure 8: Road surface maximum settlement–time curves for different compaction levels. Figure 7: Maximum long-term settlement (10 years) at different compaction levels. Particularly, as the degree of compaction increases from 85% to 90%, the variation in roadbed deformation decreases. When the degree of compaction exceeds 90% and falls within the 90% to 95% range, the maximum pavement settlement remains relatively stable. For red bed soft rock fill materials with different compaction densities, under the same load, lower compaction density results in a larger porosity and lower compaction degree. Macroscopically, this manifests as smaller stiffness, weaker resistance to deformation, larger ultimate creep strain, and, consequently, greater pavement settlement [26; 27; 28]. With an increase in compaction density, the material's porosity decreases, compaction degree increases, macroscopically exhibiting higher stiffness, enhanced resistance to deformation, reduced ultimate creep strain, and, ultimately, smaller pavement settlement.
# A Rapid Method for the Determination of SBS Content Based on the Principle of Orthogonal Testing
## 3 Results and Discussion
### Rheological Test Results
#### 3.1.2 Bend Beam Rheometer Test Results  
The creep stiffness modulus \(S\), creep rate \(m\) of the matrix asphalt and the three modified asphalts in long-term aging were determined by bending beam rheometer at different test temperatures. The test results are recorded in Table 3.  
According to the above test results, the linear fitting curves of the stiffness modulus \(S\) and the creep slope \(m\) with the SBS content at different test temperatures are plotted respectively, as shown in Figure 3. It is obvious that at \(-18\)\({}^{\circ}\)C, the creep stiffness \(S\) of modified asphalt decreases with the increase of the SBS content, which indicates that as the SBS content increases, the asphalt becomes more elastic, more viscous and less brittle, and the resistance to deformation increases. At \(-12\)\({}^{\circ}\)C, although the \(S\) value of modified asphalt is smaller than that of matrix asphalt, its variation with the SBS content is not stable.  
Figure 2: Calibration curves of SBS content based on rutting factor.  
At the same time, the creep stiffness \(m\) value of modified asphalt is lower than that of matrix asphalt at both test temperatures, both decreasing with the increase of SBS content. Moreover, when the SBS content is the same, the lower the test temperature, the lower the \(m\) value, which means that with the decrease of temperature, the anti-deformation ability of the asphalt decreases, and the low temperature performance becomes worse.  
It can be seen from the figures that the linear fitting results of creep slope \(m\) with SBS content at both test temperatures are more precise. The stiffness modulus \(S\) and SBS content also have a good linear relation at \(-18\)\({}^{\circ}\)C, but the linear fitting results at \(-12\)\({}^{\circ}\)C are not accurate. This is because, as can be seen from Table 3 the stiffness modulus of the matrix asphalt has reached 125 MPa at \(-12\)\({}^{\circ}\)C, which is not much different from that of the modified asphalt after the addition of 2% modifier. Therefore, the change of \(S\) value with the increase of the amount at \(-12\)\({}^{\circ}\)C is not obvious. The fitting result of creep slope \(m\) with SBS content can be used to calibrate the SBS content in modified asphalt, but the good linear relation of the result is also related to temperature, which means the calibration curves differ at different temperatures. The problem with this method is also the same as the calibration method based on the rutting factor.
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
## 3 U-Shaped Roadway Model and Reflection Path Validity  
### Valid Mirror Points  
The U-shaped roadway consists of two roadways along the \(y\)-axis direction and one roadway along the \(x\)-axis direction, which contain eight reflecting surfaces and one relay node \(P\). As shown in Figure 1, there is a reflecting surface coinciding with the YOZ coordinate plane \(m_{1}\); the plane connecting \(m_{1}\) is \(m_{2}\); the plane connecting \(m_{2}\) is \(m_{3}\); the plane closest to \(m_{1}\) and parallel is \(m_{4}\); the plane parallel to \(m_{2}\) is \(m_{5}\); the plane closest to \(m_{3}\) and parallel is \(m_{6}\); the top plane is \(m_{7}\); the bottom plane is \(m_{8}\). The lengths of the roadways along the \(y\)-axis are \(L_{1}\) and \(L_{3}\), and their width is \(a\). The length of the roadway along the \(x\)-axis is \(L_{2}\), and the width is \(c\). The height of all roadways is \(b\).
## 1 Introduction
Secondly, the accuracy of the BEM method will become progressively worse when moving closer to the domain boundary if an inaccurate far field radiation boundary condition is imposed at the truncated computational domain boundary. In the present method, we applied accurate first-order non-reflecting boundary conditions (NRBCs) in the truncated domain boundary. We utilized the 2D normal mode method to obtain the propagation wave modes near the domain boundary, which are needed for the application of the first-order NRBC. The accuracy of the truncated boundary condition was validated against existing benchmark solutions. Due to the full-field simulation capability, our method could serve as a benchmark solution for comparing and assessing the performance of the classical established model: the 3D Parabolic Equation model. To do that, we conduct 3D simulations to benchmark low-mid frequency acoustics over kilometer ranges for the prediction of 3D sound scattering in three complex underwater environments: a 3D Acoustical Society of America (ASA) wedge, a 3D seamount, and an underwater canyon. We also demonstrate the practical 3D+T simulation capability of PFFT-BEM for underwater acoustics scattering problems. Here, 3D+T refers to time-stepped, frozen-field modeling of underwater sound. Because sound moves at 1.5 km/s in the ocean, pulsed sound traveling hundreds of kilometers could, in principle, be modeled by updating the medium condition over the excursion time of hundreds of seconds as the sound moves away from the sound sources. Using 3D+T PFFT-BEM (Version 2019, Massachusetts Institute of Technology, Cambridge, MA, USA, 2019), we simulate sound scattering variations due to the change in medium properties caused by internal traveling waves, and we compare the numerical results with field tests.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 2 Preliminary Materials
### Commonly Used Protocols
#### 2.7.1 ZigBee  
ZigBee is a wireless networking protocol based on the IEEE 802.15.4 technical standard [16]. It has a low data rate, low power consumption, low cost, and encrypted communication using the Advanced Encryption Standard (AES) with a 128-bit key. ZigBee is a great technology for Internet of Things devices that need a long battery life and a low data transfer rate. The ZigBee data rate is 250 kbps at 2.4 GHz (global), 40 kbps at 915 MHz (Americas), and 20 kbps at 868 MHz (Europe). Some applications of ZigBee can be found in smart homes, smart buildings, the Internet of Medical Things (IoMT), and more [17; 18].
## 3 Results  
### Evaluation Method  
To evaluate the agreement between the predicted value obtained through MLA and the measured value, along with the MLA error, the coefficient of variation (CV), root-mean-square error (RMSE), mean absolute error (MAE) and mean absolute percent error (MAPE) was used. The CV was obtained by dividing the standard deviation by the average and comparing datasets with different units of measure. The RMSE is an objective error index used to study the difference between the model-predicted value and the measured value. The MAE, i.e., the absolute value of the difference between the predicted value and the measured value, indicates the accuracy (reliability) of the model. The MAPE supplements the disadvantages of the MAE and indicates how much relative error has occurred.  
\[CV=\frac{\sigma}{m}, \tag{5}\]  
\[\text{RMSE}=\sqrt{\frac{\sum\limits_{i=1}^{m}{(f^{\prime}_{c,pred}-f^{\prime }_{c,meas})^{2}}}{m}}, \tag{6}\]  
\[\text{MAE}=\frac{1}{n}\sum\limits_{i=1}^{m}\left|f^{\prime}_{c,pred}-f^{\prime }_{c,meas}\right|, \tag{7}\]  
\[\text{MAPE}(\%)=\frac{1}{m}\sum\limits_{i=1}^{m}\left|\frac{f^{\prime}_{c,pred }-f^{\prime}_{c,meas}}{f^{\prime}_{c,meas}}\right|\times 100. \tag{8}\]  
where \(\sigma\) is the standard deviation, \(m\) is the mean, \(f^{\prime}_{c,meas}\) and \(f^{\prime}_{c,pred}\) are measured and predicted compressive strength of concrete.
## 2 Summary of the Paper
### Waveform System Model and FPGA System Hardware Testbed  
Wideband (multi-GHz) cognitive radio is in its infancy, compared with its narrowband counterpart IEEE 802.22. It is critical to test key system components in different system settings. Three system models are proposed: (1) MATLAB/C simulation model; (2) Waveform model; (3) Real-time FPGA system model.  
Most research is carried out in the domain of MATLAB/C simulation model. This approach is simple. But many real-world limitations cannot be simulated. The unique approach of this proposal is to combine these three models. Real-time FPGA model (Figure 3(b)) is the ultimate test, but time-consuming. We will use this model when the system is very stable. As a result, most system emulations are based on the waveform model in Figure 3(a). A waveform of 9.6 GHz effective RF bandwidth with 10-bit resolution can be transmitted over the air, and captured after transmission. This system produces high-speed serial waveforms with real life imperfections including noise, jitter, pre/de-emphasis, and multi-level signaling up to 8 Gb/s. This waveform model is made available only recently (to the best knowledge of the first author, this is available less than one year), with the latest A/D conversion. TTU's lab is fortunate to have the NSF MRI grant to make this possible.
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 2 Preprocessing (Lofar, TPSW)  
The raw signals were transformed into time-frequency representations using the Lofar technique, which enhanced the visibility of tonal components in the signal. Additionally, the two-pass split window (TPSW) algorithm was applied to perform noise whitening and improve the signal quality by reducing the background noise and interference.
## 3 Experiments
### Performance Comparison
#### 3.4.2 Leaf Disease Detection
However, the diseased leaves were not discovered in the proposed network. In particular, when the leaves are small or covered by other leaves, and when the spot area is not clearly visible, false detection occurs. Since the leaves vary in size, and the occlusion problem is severe, leaf disease detection is more challenging than conventional object detections such as faces, cars, and pedestrians. Accordingly, the overall mAP values were low compared to those in [32]. Figure 8: Detection results; ground truth bounding boxes (**upper row**), predicted bounding boxes with the proposed AE-YOLO model (**bottom row**).
###### Abstract  
Virtual-force algorithms (VFAs) have been widely studied for accurate node deployment in wireless-sensor-network (WSN) applications. Their main purpose is to achieve the maximum coverage area with the minimum number of sensor nodes in the target area. Recently, we reported a new VFA based on virtual spring force (VFA-SF) and discussed in detail the corresponding efficiency via statistical analysis. The optimized strategy by adding an external central force (VFA-SF-OPT) was presented, which effectively eliminates the coverage hole or twisted structure in the final network distribution. In this paper, the parameter effects on VFA-SF and the VFA-SF-OPT were further investigated: (1) Node velocity dramatically affects the convergence rate of the node-deployment process. (2) A suitable external central force improves equilibrium distance and reduces energy consumption. (3) The effects of VFA-SF and VFA-SF-OPT for different types of obstacles are discussed. Generally, by choosing suitable parameters, both VFA-SF and VFA-SF-OPT can effectively improve node deployment and energy consumption for the whole sensor network. The results give important insight in parameter selection and information fusion in the application of a large-scale WSN.  
wireless sensor network; virtual spring force; pair correlation diversion; parameter analysis Article