esa-sceva/satcom-chunk-collection huggingface dataset:
# Train Speed Estimation from Track Structure Vibration Measurements
## 3 Procedures for Train Speed Estimation
### Train Speed Estimation from Acceleration Dynamic Response Signals
#### 3.1.3 Train Speed Calculation  
The train running speed can be calculated by combining the time of the bogie passing the sensor with the train dimension. The train dimension diagram is shown in Figure 7.  
Applying the peak point screening method described above, the identified times of the train bogie passing through the sensor are \(t_{1}\), \(t_{2}\),..., \(t_{n}\). The time differences \(\Delta T\) of adjacent bogies passing through are as shown in Equation (3):  
\[\Delta T=\{\Delta t_{i}=t_{i+1}-t_{i},\ i=1,2,\ldots,n-1\} \tag{3}\]  
When the train speed \(v\) is constant, if \(l_{a}>l_{b}\), the time inferences can be divided into two categories, as shown in Equation (4):  
\[\{\Delta t_{a}|T_{a}\}>\Delta\vec{t}>\{\Delta t_{b}|T_{b}\} \tag{4}\]  
where \(T_{a}\) is the set of the time for the train to travel \(l_{a}\) (in Figure 7); \(T_{b}\) denotes the set of the time for the train to travel \(l_{b}\) (in Figure 7); and \(\Delta\vec{t}\) is the sample average of \(\Delta T\). Then, the train speed is estimated in Equation (5):  
\[V=\frac{l_{a}}{T_{a}}\cup\frac{l_{b}}{T_{b}} \tag{5}\]  
where \(V\) is the set of calculated train speeds. To avoid unpredictable situations in field tests, the Grubbs criterion is introduced to ensure the reliability of results. The Grubbs criterion is a method to distinguish abnormal values of samples obeying normal distribution when the total standard deviation \(\sigma(x)\) is unknown. It states that the data should be removed when the residual \(V_{i}\) corresponding to a measured value \(x\) satisfies Equation (6):  
\[|V_{i}|=\left|x_{i}-\vec{x}\right|>g(n,a)\times\sigma(X) \tag{6}\]  
where \(\vec{x}\) is the sample mean, \(g(n,\ a)\) is a coefficient depending on the number of measurements \(n\), and the significance level \(a\), \(\sigma(X)\) is the standard deviation of the sample. After judging by the Grubbs criterion, the average value of the remaining elements in set \(V\) is the final estimated train speed.
## 4 Results  
The Resnet-50 model was evaluated based on training, validation, and classification accuracy. Training accuracy shows how well the model performs on the training data, while validation accuracy measures its ability to generalize to new and unseen data. The validation data was separated from the training data using the hold-out cross-validation method. This approach ensured an unbiased evaluation of the model's performance. Table 3 indicates that the ResNet-50 model achieved impressive results, with a training accuracy of 98.7% and a validation accuracy of 92.4%. These high accuracy scores confirm that the model effectively captures the underlying patterns in the training data. It is important to highlight that the skip connections in the ResNet-50 architecture played a crucial role in accelerating convergence and optimizing computation time. This is particularly valuable in real-time applications, as it ensures timely predictions and speeds up on-site decision-making. Figure 8 displays the confusion matrix obtained for the ResNet-50 model, representing the predicted results for the test set. Using the confusion matrix, we can easily assess TN, FN, FP, TP, and the classification accuracy. The model achieved a classification accuracy of 91.8%, indicating its successful generalization from the test data and its ability to handle real-world scenarios. From Figure 8, out of the 500 normal datasets, the model accurately classified 454 data samples as normal (TP). However, it incorrectly identified 46 normal datasets as abnormal (FN). On the other hand, the model commendably identified 464 datasets as abnormal (TN). It is important to note that there were 36 instances where the model failed to detect actual abnormalities (FP).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Value** \\ \hline Optimization algorithm & Adaptive moment estimation (ADAM) \\ Learning rate & 0.001 \\ Loss function & Cross entropy \\ Epochs & 5 \\ Batch size & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters used in the experiment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training** & **Validation** & **Classification** & **Computation** \\  & **Accuracy (\%)** & **Accuracy (\%)** & **Time (s)** \\ \hline ResNet-50 & 98.7 & 92.4 & 91.8 & 7329 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the ResNet-50 model.
# Lightweight End-to-End Speech Enhancement Generative Adversarial Network Using Sinc Convolutions
## 6 Experimental Setup
### Evaluation Metrics  
We evaluate the performance of the proposed system on six classic objective evaluation criteria for speech enhancement (the higher the better):  
* PESQ: Perceptual evaluation of speech quality, using the wide-band version recommended in ITU-T P.862.2 [42] (in the range of \([-0.5,4.5]\))
* STOI: Short-Time Objective Intelligibility [43] (in the range of \([0,100]\))
* SSNR: Segmental SNR [44] (in the range of \([0,+\infty)\))
* CBAK: Mean Opinion Score (MOS) prediction of the intrusiveness of background noises [45] (in the range of \([1,5]\))
* CSIG: MOS prediction of the signal distortion attending only to the speech signal [45] (in the range of \([1,5]\))
* COVL: MOS prediction of the overall effect [45] (in the range of \([1,5]\))  
All metrics compare the enhanced signal with its corresponding clean reference over the test set as defined in Section 6.1 (All criteria are calculated based on the implementation demonstrated in [1], and all implementations are published on [[https://www.crcpress.com/downloads/K14513/K14513_CD_Files.zip](https://www.crcpress.com/downloads/K14513/K14513_CD_Files.zip)]([https://www.crcpress.com/downloads/K14513/K14513_CD_Files.zip](https://www.crcpress.com/downloads/K14513/K14513_CD_Files.zip)) (accessed on 15 February 2021)).
## 2 Materials and Methods
### Complexity Analysis  
To demonstrate the innovation more comprehensively, we discuss the complexities of our method and the classical Chirp Scaling (CS) algorithm and assume that the input data are \(D\subset R^{N\times K}\). For fast Fourier transform (FFT) computing, we always extend the size to the nearest power of two: \(D^{\prime}\subset R^{N_{2}\times K_{2}}\), \(N_{2}=2^{m}\geq N\), \(K_{2}=2^{n}\geq K\). The time cost of 128 FFT points and 128 dot product points on DSP are 4 us and 2 us, respectively. The major time cost of the CS algorithm is summarized in Table 1. From the table, we can obtain the total time cost of CS algorithm as  
\[T_{CS}=\frac{3 KN_{2}+8K_{2}N_{2}}{256} \tag{9}\]  
Similarly, we can summarize the major time cost of our method in Table 2. Additionally, the total time cost of our method is  
\[T_{our}=\frac{3 NK_{2}}{256} \tag{10}\]  
Comparing \(T_{CS}\) and \(T_{our}\), it is well-known that the complexity of our method is less than that of the CS algorithm. Our method just determines the FFT and the dot products once, and CS algorithm conducts them multiple times.
# Studies on a Piezoelectric Cylindrical Transducer for Borehole Dipole Acoustic Measurements
## 2 Principles and Numerical Analyses
### Directivity Patterns  
The numerical simulation and test measurement results of horizontal directivity of the proposed transducer at 979 Hz, 3690 Hz, and 4816 Hz are compared and shown in Figure 9. The difference between them is due to the practical measurement error because the minimum angle measurement interval is not as small as the simulation result. The results show that the performance of the developed transducer is a pure dipole source in the operating frequency range.  
Figure 8: Comparison of the simulation (solid black line) and test (dashed blue line) transmitting voltage response (TVR) results of the proposed transducer with the frame in water.  
In theory, the dipole sound field is rather pure due to the cylindrical shell acting as the radiation surface driven by the piezoelectric actuator. Firstly, it is because of an infinite cylinder which oscillates at the velocity of \(u_{a}(t)=u_{0}\phi^{i\omega t}\) in the \(x\)-axis direction (\(\varphi=0\)), perpendicular to the vertical axis of the cylinder, the sound pressure (supposing the \(z\)-axis coincides with the vertical axis of the cylinder, then sound pressure \(p\) is unrelated to the \(z\)-coordinate axis) can be expressed as [19]:  
\[p(r,\varphi,t)=\frac{-j\rho cu_{0}\phi^{i\omega t}}{J_{1}^{\prime}(ka)-jN_{1} ^{\prime}(ka)}\big{[}J_{1}(kr)-jN_{1}(kr)\big{]}\cos\varphi \tag{10}\]  
where \(r\) and \(\varphi\) represent the radial and angular coordinate, respectively; \(\rho\) and \(c\) are the density and sound velocity of the medium, respectively; \(a\) is the radius of the circular cross section; and \(\omega\), \(k\) and \(t\) are the angular frequency, wave number and time, respectively [19]. The normalized form of the horizontal radiation pattern is:  
\[DI(\theta)=\frac{(p)_{\theta}}{(p)_{\theta=0}}=|\cos\varphi| \tag{11}\]  
In this work, although the cylindrical shell is finite in length, the horizontal directivity pattern can be considered similar to that of the ideal infinite cylinder because the wavelength is much more than the geometric size of the shell. Hence, the transducer with the cylindrical shell would perform as a pure dipole source. Moreover, with a full azimuthal angle of radiation surface, the vibration of the piezoelectric actuator is confined in the space of cylindrical housing so that the vibration otherwise coupling into the fluid of the  
Figure 9: Measured and simulated directivity patterns of piezoelectric cylindrical transducer at 979 Hz (**a**,**b**), 3690 Hz (**c**,**d**), and 4816 Hz (**e**,**f**), respectively.  
borehole is effectively inhibited from interfering with the sound field by the outer radiation surface [17].
## 3 Experiments and Data Analysis
### Experiment on Measuring the Pose of a Rigid Body with a Camera  
We set the motion mode of the body rigid body to swing mode, with the swing parameters: an amplitude of 20 degrees, frequency of 0.5 Hz, and time of 10 s. To ensure that the high-speed camera recorded the entire motion of the turntable, it was operated at 500 frames/s, and 16.68 s of video was collected. The test scene is shown in Figure 5.  
Figure 4: Coordinate systems of the three-dimensional turntable.  
Figure 5: Scene of the experiment.  
In verifying the ability to make repeated measurements adopting the proposed method, the camera was placed at three positions to measure the swing mode of the central axis of the turntable. These three positions were set parallel to the measured object at intervals of 0.1 m. As the camera used a Nikon F series variable-focal-length lens, the quality of the image was guaranteed by adjusting the focal length within a certain range. The camera was thus placed at any position perpendicular to the rigid body being photographed, and the measured distance from this position to the rigid body was 2.21 m, as shown in Figure 6.
# Prediction of Ground Deformation during Pipe-Jacking Considering Multiple Factors
## 3 Methodology
### Influence Factors
#### 3.2.2 Grouting Pressure  
The ground movement caused by grouting pressure will be firstly calculated in the sectional direction then overlaid along the penetrating direction. In the sectional direction, the ground movement induced by grouting pressure is assumed to be a cavity expansion process in an elastic half-plane. [PERSON] [66] has given equations for calculating the deformation with uniform stress applied at the cavity boundary. The solution uses the complex variables to calculate the ground movement. The displacement of a picked point should satisfy the following equation:  
\[\frac{E}{1+\mu}(v-iw)=\frac{3-\mu}{1+\mu}\varphi(Z)-Z\overline{\varphi^{\prime }(Z)}-\overline{\psi(Z)}=S(y,z) \tag{23}\]  
Then [PERSON] solution can be expressed as:  
\[\varphi(Z)=iM_{T}\biggl{[}-2\Bigl{(}1+\alpha^{2}\Bigr{)}+\frac{2(Z-t)}{Z+t}+ \frac{2\alpha^{2}(Z+t)}{Z-t}\biggr{]} \tag{24}\]  
\[\psi(Z)=iM_{T}\biggl{[}-3\Bigl{(}1+\alpha^{2}\Bigr{)}+\frac{2\alpha^{2}(Z-t)} {Z+t}+\frac{2(Z+t)}{Z-t}+\frac{\alpha^{2}(Z+t)^{2}}{\left(Z-t\right)^{2}} \biggr{]} \tag{25}\]  
\[\alpha=\frac{h-\sqrt{h^{2}-{R_{s}}^{2}}}{{R_{s}}} \tag{26}\]  
\[t=-ih\frac{1-\alpha^{2}}{1+\alpha^{2}} \tag{27}\]  
\[M_{T}=-\frac{\alpha^{2}Th}{(1-\alpha^{2})(1-\alpha^{4})} \tag{28}\]  
Figure 4: Districts of surrounding soils under the disturbance of pipe-jacking.  
where, \(T\) is the grouting pressure, which can be calculated by:  
\[T=\frac{nT_{0}d^{2}}{4\left(R^{2}-{R_{s}}^{2}\right)} \tag{29}\]  
where, \(n\) is the proportion of grouting volume and gap volume, \(T_{0}\) is the grouting pressure of jetting machine, and \(d\) is the diameter of grouting pipeline. This equation determines the proportion of these two pressures by the sectional area. The permeation of slurry during the grouting process is also considered by the proportion of grouting volume and gap volume.  
Along the penetrating direction, the Boussinesq solution is used to overlay the ground movement in the sectional direction. Figure 5 shows overlaying coefficients in the sectional direction (\(m\)) and penetrating direction (\(n\)). Then the ground movement caused by grouting pressure can be expressed as:  
\[u_{3}=\frac{1+\mu}{E}|S(y,z)|\times\left(1-\mu\right)\exp\left[-0.5\left(x+L \right)^{2}\right] \tag{30}\]  
\[v_{3}=\frac{1+\mu}{E}\text{Re}[S(y,z)]\times\left\{1-\frac{1}{1+\exp[-\left(x +L\right)]}\right\} \tag{31}\]  
\[w_{3}=\frac{1+\mu}{E}\text{Im}[S(y,z)]\times\left\{1-\frac{1}{1+\exp[-\left(x +L\right)]}\right\} \tag{32}\]  
where, \(u_{3}\) (\(v_{3}\), \(w_{3}\)) is the ground movement along the \(x\) (\(y,z\)) axis caused by grouting pressure.
# Accuracy of Code GNSS Receivers under Various Conditions
## 4 Results
### Static Session  
Figure 14 shows an NE graph of the static session on the roof of a building with almost perfect sky visibility conditions (Figure 4d). The true (known) position of the antennas was determined in the same way as in Sections 4.1-4.3 by using RTK mode on the roof of the building; the coordinates were 50\({}^{\circ}\)03\({}^{\prime}\)58.54\"N, 19\({}^{\circ}\)55\({}^{\prime}\)13.36\"E, 227 m. In the case of the G_2 receiver, some of coordinates had an error larger than 10 m. For better interpretation, the scales of Figures 14 and 15 are the same as the corresponding graphs in Sections 4.1-4.3. Moreover, the accuracy of G_1 and the smartphones were very close each other.  
Figure 14: Plot of NE component deviations of static measurements with code receivers.  
Figure 15: Plot of U-component deviations of static measurements with code receivers.  
Figure 15 shows the height error of the Garmin receivers. In this case, the G_1 receiver accuracy varied between \(-\)25 m and 25 m of error, while the G_2 error was stable, but much larger, around 40-45 m for the whole period.  
This shows how, in this kind of handheld receiver, good initialization is important, in this measurement disturbed by high sky obstructions, which particularly affected the G_2 receiver.
## 2 Related Works
### Geographic Routing
#### 2.1.1. Sender-Based Geographic Routing  
Sender-based underwater routing protocols relying on geographic information for routing purpose are relative distance based forwarding (RDBF) [13], routing and multi-cast tree-based geo-casting (RMTG) [14], and adaptive routing protocol (ARP).  
In RDBF, an efficient route search towards destination is performed using location information. For finding suitable node for forwarding process, based on distance, a fitness function is defined with respect to destination. Hence, nodes nearby sink have greater probability to get selected as forwarder nodes. In order to avoid redundant transmissions and collisions, a node maintains neighbor information and if the same packet is transmitted from other node, it simply drops the packet. Residual energy threshold is maintained for efficient energy consumption. However, accurate position information is required for each node for successful communication which is hard to obtain  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protocols** & **Void Handling** & **Geographic Information** & \
\begin{tabular}{c} **Communication** \\ **Overhead** \\ \end{tabular} &
\begin{tabular}{c} **Energy** \\ **Efficiency** \\ \end{tabular} & **Delay** \\ \hline VBF [11] & No & Position information & High & Medium & High \\ DBR [13] & No & Depth information & Low & High & High \\ DS-DBR [14] & No & Depth information & Low & Medium & Low \\ H2-DAB [15] & No & Depth information & Low & High & Medium \\ RDBF [16] & No & Location information & High & High & Low \\ RMTG [17] & By pass void hole & Location information & High & Low & Medium \\ ARP [18] & Avoids void hole & Location information & High & High & Medium \\ DVRP [19] & No & Location information & Medium & High & Medium \\ VAPR [20] & Avoids void hole & Location information & Medium & Low & Medium \\ Hydrocast [21] & Depth recovery & Pressure information & High & Low & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of existing routing protocols.  
Figure 1: Classification of existing routing protocols.  
in underwater environment [13]. The RMTG geocast routing protocol relies on multiple piece of information, such as location information of nodes and their neighbors, route discovery for selection of node closest to the destination and route maintenance. This protocol has addressed problems like void hole and link breakage problems. A multicast shortest path is formed for packet transmission within the intended geographic region [14].  
In ARP, data packets are assigned different delivery priorities that depend on application requirement. Higher priority packets are sensitive to delay. So, trade-off between throughput and latency in ARP exists. Also, this uses location information and it is an energy efficient protocol however, this incurs high overhead [15].
## 3 DDoS Attack Detection Methods
### DDoS Attack Detection Studies Using Signature-Based Methods
#### 3.2.2 Correlation of IP Address  
As attackers are notorious for forging packets from originating IPs, it is straightforward to localize the DDoS attack by filtering the attack traffic if the spoofed IP address can be successfully recognized. This method examines and compares the difference between the attacker's spoof IP and the host server's IP. When these IP addresses are not uniform, a DDoS attack alarm is triggered. A method to efficiently identify and block spoofed source IPs was presented by [PERSON] et al. [55]. The detection accuracy of the proposed approach is relatively high. However, because it must constantly communicate with the source side, there is a significant increase in traffic. In [PERSON] and [PERSON] [56], network traffic distribution was analysed, and IP address correlation-based non-uniformity was found. The amount of IP data packets throughout a period was determined in this study, and the amount of data packets in a sliding window was approximated. The correlation coefficient between the IPs for the two subsequent periods was then calculated. Under typical network traffic conditions, the correlation between the source IP address access and the target network is steady. Due to the dispersion of the IPs during an attack scenario, the correlation coefficient is dramatically reduced. With this analysis, the approach could identify the presence of an attack because of the distribution of IPs when attacks occur. Several other notable studies that report the use of IP address correlation and analysis for DDoS attack detection are reported in [57, 58]. In [57], an attack detection method using the analysis of source and destination IP address databases was presented, with a reduced false alarm rate. In [PERSON] et al. [58], this approach was demonstrated for attack detection in a wired network. Results demonstrate that attack traffic was distinguished from normal traffic. In addition, detection accuracy ranging from 91% to 96% was reported when examined on internet data, a data centre traffic trace, and the KDD99 dataset.  
Machine learning has been utilized to enhance the accuracy of this approach, as reported in the literature. A three-layer backpropagation neural network (BPNN) was suggested in [59] to detect and categorize attacks against DNS servers. The findings demonstrate that a three-layered BPNN with a 3-7-3 structure can classify direct DoS and amplification assaults with 99% accuracy. In [60], an artificial neural network (ANN) was used to detect TCP, UDP, and ICMP attacks by blocking the spoofed packet before reaching the target. The proposed approach recorded a detection accuracy of 98% when demonstrated on old datasets and 92% on new datasets.  
In Table 6, a summary and comparison of the signature-based DDoS attack detection methods based on their features, advantages, and limitations is provided.
# Review of Traffic Assignment and Future Challenges
## 3 Traffic Assignment: Theoretical Background
### Dynamic Traffic Assignment (DTA)  
Dynamic traffic assignment (_DTA_) considers changes in the perception of travel costs based on demand dynamics, choices, traffic conditions, and network characteristics. Unlike static traffic assignment, the vehicle's departure time significantly influences the perceived cost and, consequently, route choice. It is commonly accepted that a dynamic assignment model should include the following elements:  
* A traffic model in which congestion (travel time) varies over time.
* A time-varying demand.
* An equilibrium that is based on experienced travel cost, not instantaneous travel cost.  
_DTA_ has been studied for over forty years, and numerous research projects have been carried out [47; 48; 49; 50; 51; 52; 53; 54; 55]. The first approaches were based on the formulation of the dynamic assignment problem in the form of mathematical programming ([56; 57; 58; 59]) or optimal control ([60; 61; 62]). More recently, models with greater resolution in terms of time steps, vehicle batches, and traffic interaction [3] are being used.  
Macroscopic traffic models can be used for estimating the generalized costs experienced by users [63; 64; 65; 66; 67; 68; 69]. However, mesoscopic [70] and microscopic [71; 72; 73; 74] models are more suitable. The main advantage of using these models lies in their ability to accurately represent more detailed traffic phenomena, such as shock waves, expansion waves, and queue spillback, thereby enhancing the realism of the assignment [4]. However, we draw the reader's attention to the fact that the microscopic simulation-based traffic assignment raises many difficulties, such as convergence issues [2] and deadlock, that impede simulated vehicles from reaching their destination (see Figure 3A). Over the last decade, DTA has garnered significant attention due to the increased accuracy of the models used to assess traffic performance within the framework of environmental considerations, such as estimates of CO\({}_{2}\) emissions and noise pollution. The use of DTA is not limited to traffic prediction; it can also be leveraged for traffic optimization by comparing various infrastructures or traffic regulation scenarios.
# IoT-Based Smart Surveillance System for High-Security Areas
## 2 Materials and Methods
### Hardware Components Used SS-HSA
#### 2.3.2 Global System for Mobile (GSM) SIM900A  
GSM is used to communicate with mobile-based systems because mobile systems are widely used. GSM uses less power and is cost-effective. Its installation is very simple, with a small footprint of 24 \(\times\) 24 \(\times\) 3 mm. The GSM is digital cellular and open-source technology that plays a vital role in making any system IoT-based. The frequency used to send data and transmit voices is the 900/1800 MHz band. This corresponds to the GSM and GPRS dual-band. For communication purposes, different techniques are used, such as frequency division multiple access and others, while the GSM uses time division multiple access to communicate with a mobile system. The GSM overcomes the use of bandwidth by digitizing and reducing the size of data.