esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Fracture Development and Failure Patterns  
Generally, under the action of loads, the compressive stress or tensile stress will concentrate around the opening. As the applied load increases, the stress concentration factor rises accordingly. As a consequence, tensile or shear cracks will occur when the tensile strength or cohesion is surpassed. Meanwhile, strain localization is gradually formed at the stress concentration zones. Therefore, the DIC technology was used to monitor the fracture process of samples under uniaxial loading. By importing the recorded speckle photos into GOM Correlate software, both the real-time strain and displacement distributions of each sample can be visualized. Figure 5 shows the principal strain contours of five representative samples during uniaxial loading. In the figure, the numbers 1, 2, 3, 4 and 5 marked represent the elementary tensile cracks, posterior tensile cracks, sidewall slabbing cracks, shear cracks and surface spalling cracks, respectively. For the lowercase letters that located on the upper-right corner of the number, they denote the occurrence sequence of the same pattern of cracks.  
propagated slowly towards the direction of the maximum compression. When the axial stress was 65 MPa, it was found that the sidewall slabbing cracks 3\({}^{\text{a}}\) and 3\({}^{\text{b}}\) occurred on the two sides of the opening, resulting in the appearance of V-shaped notches. Two posterior tensile cracks 2\({}^{\text{a}}\) and 2\({}^{\text{b}}\) were also gradually formed at the upper-left and lower-left corners of the opening in turn. However, compared to the situation at 40 MPa, the length of the crack 1\({}^{\text{a}}\) was shortened. At the peak point, the cracks 1\({}^{\text{a}}\) and 2\({}^{\text{a}}\) disappeared completely, while the crack 2\({}^{\text{b}}\) was getting longer. Additionally, another posterior tensile crack 2\({}^{\text{c}}\) at the upper-right corner of the opening was observed, and some surface spalling cracks 5 appeared on the right side of the opening because of the high level of concentrated compressive stress. At 78 MPa after the peak, the spalling area enlarges and a shear crack 4\({}^{\text{a}}\) emerged in the upper-right corner of the sample. This shear crack would continue propagating along the counter diagonal until it merged with the right V-shaped notch. After that, the other shear crack 4\({}^{\text{b}}\) appeared in the lower-left corner of the sample and intersects with the left V-shaped notch, leading to the sample instability.
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 4 Search Patterns
### Random Walk: A2  
The simplest solution to explore the area is to walk randomly. Every time an agent reaches the center of a search cell, it randomly decides which surrounding cell to visit next. Given a present heading \(\psi_{i}\), the next course will be then \(\psi_{i+1}=\psi_{i}+\Delta\psi_{i}\), being:  
\[\Delta\psi_{i}\sim\mathcal{N}\left(0,\sigma_{rw}^{2}\right) \tag{49}\]  
where \(\mathcal{N}\) indicates normal distribution and \(\sigma_{rw}\) is its standard deviation, which has been chosen as \(\sigma_{rw}=\pi/2\). Since the movement is restricted to the surrounding eight cells, \(\psi_{i+1}\) is rounded to the closest possible heading pointing to any of those cells. In addition to this behavior, the collision  
Figure 7: Schematic representation of the modeling process. Y represents the scenario parameter space, while \(\Omega\) is the configuration space of the algorithm (the value of the 23 parameters). The black points represent the dataset, i.e., the suboptimal configurations \(\Omega^{*}\) found with the genetic algorithm for each of the scenarios Y\({}_{i}\), with \(i=1,...,100\). The blue line represents the model of the suboptimal configurations based on GPs. Each parameter has been modeled independently (one GP for each parameter). For any new scenario \(j\), the suboptimal configuration \(\tilde{Y}_{j}^{*}\) is predicted (red circle).  
avoidance is also implemented as explained in Section 3.4. In Figure 8, an example of a random walk has been represented for two agents.
## 2 Preliminaries
### Acoustic Propagation Model  
Here, the Thorp model [PERSON] (1977) is adopted for the underwater acoustic channel.  
The attenuation of an underwater acoustic signal, characterized by frequency \(f\) (in kHz) at transmission distance \(l\) (in meter), can be described as  
\[A(l,f)=l^{k}a(f)^{l}, \tag{1}\]  
where \(k\) represents the spreading coefficient, \(a(f)\) is the absorption coefficient, and  
\[10\log a(f)=0.11\frac{f^{2}}{1+f^{2}}+44\frac{f^{2}}{4100+f^{2}}+2.75\times 1 0^{-4}f^{2}+0.003. \tag{2}\]  
The energy expended by the sensor node for transmitting an \(m\)-bit data packet to another sensor node located at a distance \(l\) (\(l<l_{\rm max}\)) can be expressed as  
\[E_{\rm t}(m,l)=mP_{0}A(l,f),(E_{\rm t}<E_{\rm remain}), \tag{3}\]  
where \(P_{0}\) represents the minimum power necessary for the node to transmit data, \(l_{\rm max}\) denotes the maximum communication distance of the sensor node, \(E_{\rm remain}\) is the remaining energy of the sensor node, and \(E_{\rm remain}\) represents the upper limit of \(E_{\rm t}\).  
Similarly, the energy consumed by the sensor node to receive an \(m\)-bit data packet can be expressed as  
\[E_{\rm r}(m)=mP_{\rm r}, \tag{4}\]  
where \(P_{\rm r}\) is the reception coefficient.  
Using the attenuation \(A(l,f)\) can evaluate the signal-to-noise ratio (SNR) observed at a receiver over a distance \(l\) when the transmitted signal is a tone of frequency \(f\). Neglecting the directivity indices and losses other than the path loss, the narrow-band SNR (\(SNR(l)\)) is given by  
\[SNR(l)=\frac{E_{\rm b}/A(l,f)}{N_{0}}, \tag{5}\]where \(E_{\text{b}}\) is the average energy consumed to transmit one bit of data, and \(N_{0}\) represents the noise power spectral density in an additive white Gaussian noise (AWGN) channel.  
In this paper, binary phase shift keying (BPSK) modulation technology [28] is employed. When the propagation distance is \(l\), the bit error rate can be computed as  
\[p_{\text{e}}(l)=\frac{1}{2}\Bigg{(}1-\sqrt{\frac{SNR(l)}{1+SNR(l)}}\Bigg{)}. \tag{6}\]  
Consequently, the probability of successfully transmitting \(m\)-bit data is  
\[p(m,l)=[1-p_{\text{e}}(l)]^{m}. \tag{7}\]
# A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks
## 3 Methodology
### Dataset Collection  
We obtained the dataset from publicly accessible online data on kaggle.com to detect brain tumors [45]. Images from magnetic resonance imaging (MRI) were used to construct the dataset. We selected MR images for our research since MRI is the best technique for detecting brain tumors. Meningioma (937 photos), no tumor (500 images), pituitary tumor (900 images), and glioma tumor (926 images) were the four different types of brain tumor data that we used in our study. In total, we used 3264 MRI data in our dataset. Table 2 displays the breakdown of the dataset, and Figure 4 shows the MR images according to the various forms of brain tumor.  
Figure 2: Proposed CNN Architecture.
# Impact of Intracavity Power Variations toward Ultrashort Pulse Generation
###### Abstract  
This study demonstrates a passive mode-locked erbium-doped fiber laser with a graphene nanoplatelet-saturable absorber (GNP-SA) that generates ultrashort pulses within femtosecond pulse duration. The GNP-SA is fabricated via a direct transfer approach by mechanically exfoliated graphene on a fiber ferrule. Its characteristics include 0.8% modulation depth, 8.7 MW/ cm\({}^{2}\) saturation fluence, and 36.8% absorbance. The quality of ultrashort pulses is studied with a variation of intracavity circulating powers that is controlled through an optical coupler. By changing the light intensity in the cavity, the optical amplification property in the erbium-doped fiber is also impacted. The increment of the output coupling ratio increases the population inversion in the active gain medium, which leads to the change of lasing wavelength from 1558 to 1532 nm. Using a 50% output coupling ratio, the fiber laser generates 960 fs pulse duration, 11.08 MHz repetition rate, and 6.05 mW output power. This study contributes to the understanding of oscillating light behavior while changing its intracavity power that affects the optical amplification properties.  
graphene; ultrashort pulse; erbium Article
# Inertial-Navigation-Aided Single-Satellite Highly Dynamic Positioning Algorithm
###### Abstract  
Nowadays, research on global navigation satellite systems (GNSS) has reached a certain level of maturity to provide high-precision positioning services in many applications. Nonetheless, there are challenging GNSS-denial environments where a temporarily deployed single-satellite positioning system is a promising choice. To further meet the emergency call of highly dynamic targets in such situations, an augmented single-satellite positioning algorithm is proposed in this paper. First, the initial location of the highly dynamic target is found by real-time displacement feedback from the inertial navigation system (INS). Then, considering the continuity of position change, and taking advantage of the high accuracy and robustness of the unscented Kalman filter (UKF), target location is through iteration and fusion. Comparing this proposed method with the least-squares Newton-iterative Doppler single-satellite positioning system and the pseudorange rate-assisted method under synthetic error conditions, the positioning error of our algorithm was 10% less than the other two algorithms. This verified the validation of our algorithm in the single-satellite system with highly dynamic targets.  
single-satellite system; highly dynamic positioning; inertial navigation system (INS); unscented Kalman Filter (UKF); pseudorange difference
## 3 The Proposed Model
### System Model  
In this work, the network method is regarded as a collection of SNs randomly positioned in a network range with an N\(\times\)N dimension. The nodes are under a random deployment with a Poisson distribution. All the sensors have different IDs and coordinate data. SNs gather environmental data and transfer the information to their corresponding nodes. Each node is heterogeneous with respect to energy. Furthermore, the BS is static in nature.  
In this work, the first order radio energy model is used [23; 24]. Once an SN receives or transmits information, it expends energy based on the distance D among the receiving and transmitting nodes. The free space communication model (\(D^{2}\) model) for the direct or single-hop communication is adapted while the D is smaller and when D is larger the multipath models (\(D^{4}\) model) are adapted. The amount of energy consumed \(E_{TX}(l,D)\) to transmit \(l\)-bit packet over distance \"D\" is given as follows [23]:  
\[E_{TX}(l,D)=\left\{\begin{array}{ll}l*E_{elec}+l*\varepsilon_{fs}*D^{2},&if \ D<D_{0}\\ l*E_{elec}+l*\varepsilon_{amp}*D^{4},&D\geq D_{0}\end{array}\right. \tag{1}\]  
The received energy is defined as:  
\[E_{RX}(l,D)=l*E_{elec} \tag{2}\]  
In Equation (2), the size of the data packet is \(l\) whereas \(E_{elec}\) characterizes the energy expended for each bit. \(\varepsilon_{fs}\) characterizes the free space energy and \(\varepsilon_{amp}\) signifies the multipath energy model. The \(D_{0}\) characterizes a threshold distance that controls states either to use \(\varepsilon_{amp}\) or \(\varepsilon_{fs}\):  
\[D_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{amp}}} \tag{3}\]
## 5 FPGA Implementation
### FPGA Implementation of the Code Acquisition Module  
The FPGA implementation of the code acquisition module is shown in Figure 27. The module consists of the correlator, operation and judgment modules. The yihuo_mult module in Figure 27 is the correlator module. The main function of correlator module is to multiply local PN code by received signal. The integer_capture module in Figure 27 is the operation module. The functions of the operation module are summarized as follows:  
(1). Segmental integration of the output values of the correlator module.  
(2). Acquisition of the absolute values of the segmental integrated values.  
(3). Summation of all the absolute values in one cycle.  
(4). Threshold judgment of the final added values to judge whether the k and the s signal have been acquired.  
Judge module in Figure 27 is the judgment module. The main function of the judgment module is to judge whether the code acquisition is successful given the values of the k and s signals. G_out = 1 when the code acquisition is successful, in which case the code tracking loop can be initialized.  
Figure 26: The waveform of carrier loop synchronization.  
After creating the FPGA code of the code acquisition module, a timing simulation of the programmed FPGA circuit was conducted. The simulation result is shown in Figure 28. The m_out signal in Figure 28 is the local PN sequence. m_out3 is the simulative received PN code sequences. k is the flag of unsuccessful acquisition. s is the flag of successful acquisition. When m_out and m_out3 are show that acquisition has been unsuccessful, the output of k is a high level signal while the output of s is a low level signal after one computing cycle. When m_out and m_out3 are acquired in phase, the output of k is a low level signal while the output of s is a high level signal after one computing cycle. These results confirm the proper function of our FPGA design for the code acquisition module.  
After the timing simulation, the code acquisition module was debugged with the use of SignalTap II. The debugging result is shown in Figure 29. m2 is the local PN code signal generated by the receiver. cos_out is the received PN code signal. G_out16 is the successful flag of code acquisition. From Figure 29, we can see that the output of G_out16 is at the high level when the phase difference between the local PN code signal and the received PN code signal is less than the minimum chip width.  
From the figure, we also see that the proposed improved code acquisition loop system implements the function of the code acquisition module successfully.  
Figure 27: The top-level design of the code acquisition module.  
Figure 28: Timing simulation of the code acquisition module.  
Figure 29: The waveform of the code acquisition module.
## 6 Selection of Seed Ground Motions for UHS and CMS
As shown in Figures 9a and 10a, the acceleration response spectra of the synthesized time histories, obtained by increasing the magnitude of the earthquake using the EGFM, showed greater similarity to the CMS and UHS than to those of the 912 Gyeongju Earthquake. Therefore, by using ground motions recorded during moderate earthquakes, it is possible to synthesize ground motions that are compatible with the risk assessment spectrum while maintaining the actual seismic characteristics of the site. The acceleration response spectrum selected to match the CMS lacks a spectral value compared with the UHS in the short-period range of less than 1.0 s, as shown in Figure 9a. However, because the UHS has a larger spectral value than the CMS, the acceleration response spectrum selected to match the UHS exceeds that of the UHS in the short-period range of less than 1.0 s, as shown in Figure 10a. Figure 8: The MSE trend according to hypocentral distance based on the EGFM results, considering the distance between the rupture starting point in the finite fault model and the virtual seismic station. In this graph, each MSE value represents the sum of the MSE values of the two horizontal components: (**a**) The MSE with respect to the UHS presented in Table 4, plotted against hypocentral distance; (**b**) The MSE with respect to the CMS presented in Table 5, plotted against hypocentral distance. Figure 9: Selection of acceleration time histories with a response spectrum to best match the CMS: (**a**) Comparison of acceleration response spectra selected from the EGFM synthetic ground motion time histories; (**b**) Acceleration time histories recorded at MKL station from the 912 Gyeongju Earthquake; (**c**) Acceleration time histories synthesized using the EGFM. Figures 9b and 10b present the recorded acceleration time histories of the 912 Gyeongju Earthquake, while the synthesized acceleration time histories generated using the EGFM are shown in Figures 9c and 10c. Because the magnitude increased from finite fault modeling, there was a corresponding increase in both the seismic intensity and strong motion duration of the synthesized acceleration time histories. In addition, although the acceleration time histories were synthesized for the same closest distance using the EGFM, there were differences in both the time histories and response spectra. These results indicate that the EGFM can be utilized to generate ground motion time histories that reflect earthquake characteristics of the target magnitude and distance at the site.
## 5 Results
### Wavelet Analysis Features  
The results of the wavelet analysis method indicate 11,484 total features. Subject No. 1 performed remarkably well across all classifiers, with the highest being 83.48% for general LinearSVM. Subject No. 2 showed lower performance across all classifiers, with LinearSVM being the most optimal, at 68.51%. Subject No. 3 attained excellent results, with LinearSVM reaching 89.62% and GSVM slightly close behind (85.55%). Subject No. 7 also showed high performance, with LinearSVM at 90.03%. Subject No. 6 still showed low accuracy, with LinearSVM reaching 50.16% and CART being the lowest, at 31.93%.  
Regarding the classifiers' overall average success rate, the lowest classification was attributed to CART (58.95%). SVM with a polynomial kernel is better, reaching 71.44% accuracy; GSVM scores reach 75.25%; and the highest success rate is reached with LinearSVM (77.25%). The results of applying wavelet analysis methods for feature extraction are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **LinearSVM** & **CART** & **GSVM** & **SVM (Kernel = “Poly”)** \\ \hline Subject No. 1 & 83.12 & 66.01 & 71.77 & 67.01 \\ Subject No. 2 & 68.14 & 41.48 & 49.62 & 41.48 \\ Subject No. 3 & 89.62 & 72.59 & 52.59 & 56.66 \\ Subject No. 4 & 63.70 & 38.92 & 53.70 & 48.07 \\ Subject No. 5 & 77.89 & 54.20 & 66.05 & 48.13 \\ Subject No. 6 & 47.85 & 32.83 & 31.05 & 33.79 \\ Subject No. 7 & 90.40 & 85.64 & 69.38 & 61.62 \\ Subject No. 8 & 86.70 & 64.41 & 54.16 & 59.11 \\ Subject No. 9 & 86.46 & 77.62 & 52.76 & 54.40 \\ \hline Avg. Success Rate & 77.10 & 59.30 & 55.68 & 52.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of the statistical features.
## 3 Queuing Model and Dynamic Spectrum Algorithm
### Dynamic Spectrum Allocation Algorithm  
The change of arrival rates of SU and PU can change the usage of the channel. Correspondingly, the CBS should also change the priority factor \(p\) so as to reduce the system blocking coefficient by changing the allocation of the channels.  
The CBS first senses whether there is a PU. If the PU occupies the channel, it informs all SUs to stop using the channel. If not, then it determines whether there is an idle channel. If there is an idle channel, a fixed priority factor is used to directly respond to the SU's request and the CBS assigns the channel. When an idle channel does not exist, the CBS evaluates the status of the channel occupied by \(SU_{q1}\) and \(SU_{q2}\) in a sensing period, so as to adjust the channel allocation by changing the priority factor \(p\).  
After a sensing period, if the SU's arrival rate and status of the channel do not change, the CBS does not change the priority factor; if one of them changes in the next sensing period, the CBS adjusts the priority factor \(p\) to reduce the blocking coefficient of the system.  
When the arrival rates of \(SU_{q1}\) and \(SU_{q2}\) vary, the system congestion will inevitably change. The algorithm optimizes the system blocking coefficient by changing the priority factor \(p\), which depends on the numbers of different SUs in the system. It is easy to write the average number of \(SU_{q1}\) and \(SU_{q2}\). If \(E_{s1}\) and \(E_{s2}\) are the average numbers of \(SU_{q1}\) and \(SU_{q2}\), then we obtain  
\[E_{s1}=\sum_{i=0}^{N}\sum_{j=0}^{N-i}iP_{i,j} \tag{13}\]  
\[E_{s2}=\sum_{i=0}^{N}\sum_{j=0}^{N-i}jP_{i,j}. \tag{14}\]When there are more users of \(SU_{q1}\) in the system, there should be a greater probability of preempting \(SU_{q2}\)'s channel. We obtain  
\[1-p=E_{s1}/(E_{s1}+E_{s2}) \tag{15}\]  
\[p=E_{s2}/(E_{s1}+E_{s2}). \tag{16}\]  
The CBS adjusts the priority factor \(p\) to realize the dynamic allocation of the spectrum according to the condition of the system. Algorithm 1 is the DSA algorithm.  
```
1 if PU occupy the Channel
2 Tell SU stop using the channel
3 elseif Any channel idle
4 Allocate channels to \(SU_{q1}\&SU_{q2}\)
5 else
6 do
7 Evaluate the status of users in system
8 Adjust the priority factor \(p\)
9 While (Arrival rates of SU change)
10 Adjust the channels allocation Of \(SU_{q1}\&SU_{q2}\)
11 end
```  
**Algorithm 1**Dynamic Spectrum Allocation Algorithm
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 10 Summary
\begin{tabular}{c c} CP & Cyclic Prefix \\ CPE & Common Phase Error \\ CSI & Channel State Information \\ CW & Codeword \\ CN & Core Network \\ D2D & Device-to-Device \\ DAI & Downlink Assignment Index \\ DEL & Deep Learning \\ DCI & Downlink Control Information \\ DCAE & Deep-Convolutional Autoencoder \\ DCTF & Differential Constellation Trace Figure \\ DL & Downlink \\ DMRS & Demodulation Reference Signals \\ DoS & Denial of Service \\ DDoS & Distributed Denial of Service \\ DM-RS & DeModulation Reference Signals \\ DWT & Discrete Wavelet Transform \\ EPNRE & Energy Per Null Resource Elements \\ EPC & Evolved Packet Core \\ EPS & Evolved Packet System \\ eNB & Evolved Node B \\ FBS & False Base Station \\ gNB & gNodeB \\ FDD & Frequency Division Duplex \\ FR & Frequency Ranges \\ FR1 & Frequency Range 1 \\ FR2 & Frequency Range 2 \\ FR3 & Frequency Range 3 \\ GP & Guard Period \\ GPRS & General Packet Radio Service \\ GPS & Global Positioning System \\ GTP & GPRS Tunnelling Protocol \\ GSM & Global System for Mobile communications \\ GT & Guard Time \\ HARQ & Hybrid Automatic Repeat reQuest \\ ICI & Inter-Carrier Interference \\ IMSI & International Mobile Subscriber Identity \\ IoT & Internet of Things \\ IP & Internet Protocol \\ IQ & In-Phase Quadrature \\ JCaS & Joint Communication and Sensing \\ LAA & License Assisted Access \\ LoRa & Long Range \\ LPWAN & Low-Power Wide Area Network \\ LTE & Long Term Evolution \\ LSTM & Long Short-Term Memory \\ MAC & Multiple Access Channel \\ MIB & Master Information Block \\ MIMO & Multiple-Input Multiple-Output \\ MITM & Man-In-The-Middle \\ ML & Machine Learning \\ ML & Maximum Likelihood \\ MLP & Multi-Layer Perceptron \\ mMIMO & massive-MIMO \\ MSA & Multi-head self attention \\ MSE & Mean Square Error \\ MTC & Machine Type Communication \\ MU-MIMO & Multi User Multiple-Input Multiple-Output \\ NACK & Non-ACKnowledged \\ NB-IoT & Narrow Band IoT \\ NAS & Non-Access Stratum \\ NLP & Natural Language Processing \\ NR & New Radio \\ NR-U & New Radio Unlicensed \\ \end{tabular}