esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
To improve the efficiency and accuracy of multisource remote sensing image registration in the process of earthquake disaster assessment, a new automatic registration method for multisource RS images was proposed in this paper. Based on the construction of a pre-earthquake database, the proposed method employed a combination of the Shi_Tomasi and SIFT methods to extract tie points using the image partitioning strategy based on geographic information constraints. Additionally, a greedy algorithm was introduced to eliminate redundant tie points and thus to achieve even spatial distribution of matched tie points. The accuracy and robustness of the proposed method were compared with the traditional SIFT method and the Patch-SIFT method using three pairs of high-resolution images covering regions affected by severe earthquakes. The experimental results show that the proposed method outperformed the other two methods in terms of the accuracy of the registered image, running time, and number and distribution of matched tie points. The larger number and more even distribution of the tie points extracted by the proposed method contribute to the improvement in accuracy. The experimental results indicate that the proposed method is more suitable than the Patch-SIFT and SIFT methods in the case of the registration of post-earthquake high-resolution RS images used for earthquake damage assessment. According to the experimental results of a test using a GF-1 image covering urban areas (with a size of \(5354\times 5354\) pixels), the proposed method can also be used for satellite images covering plains (including urban areas), and shows significant advantages in running time over the two comparison methods for large-size RS images.  
All authors made significant contributions to this work. [PERSON] and [PERSON] designed the experiments and analyzed the datasets; [PERSON] writing the original draft and edited the English language; [PERSON], [PERSON], and [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Finance Science and Technology project of Hainan Province, China, grant number 418 MS113, the National Natural Science Foundation of China, grant number 41801259, the National Key Research and Development Program of China, grant number 2017 YC1500902, the Aerospace Information Research Institute, Chinese Academy of Sciences, grant number Y951150 ZEF, the Science and Technology Major Project of Xinjiang Uygur Autonomous Region, grant number 2018A03004, and the National Natural Science Foundation of China, grant number 41972308.  
The authors would like to thank the Linhai Jing Research Group for providing the experiments datasets and learning environments.  
The authors declare no conflict of interest.
# Combining the Transformer and Convolution for Effective Brain Tumor Classification Using MRI Images
## 3 Research Methodology  
Exploring solely local or global aspects for the classification of tumors in an image may be effective in some circumstances. In this study, we suggest TECNN to boost classification performance. The CNN and transformer advantages are combined in the hybrid model known as TECNN. Figure 3 depicts the planned TECNN structure. A CNN's features extractor and a transformer are the two primary parts of the proposed model. An example of a CNNs in the beginning, convolutions, and max pooling layer are employed for local features extraction. The input image goes through max pooling to keep the most crucial local characteristics and draw attention to features in the pooling operation. Afterward, the feature maps are fed into the CNN to further select optimal features using a separate transformer pathway. Convolution processes are cascaded in the CNN feature extractor to gather spatial information. The self-attention modules are used in the transformer pathway for global feature extraction. In this situation, the Transformer and CNN work concurrently. In each CNN step, feature maps with a high concentration of local features are transmitted to the transformer pathway to supply the spatial inductive bias that the transformer lacks. The FFM encourages a smooth transition from feature maps to PE in this situation and fuses the features of two sources. Next, the self-attention of the transformer simulates distant, nonlocal dependencies. The PE are passed back through the FFM for CNN feature extraction after leaving the transformer stages, where they are integrated with feature maps in the IMM. IMM can automatically combine both local and glob features coming from the CNN and transformer, respectively. afterward, the global features extracted from the transformer contribute to improving the CNN features extraction capability. In order to create the final prediction of our model, the output of IMM-2 is connected to a classifier.
## 2 Dynamics Modelling and Problem Formulation
### Windshear Model  
Windshear is a rapid variation in the velocity and direction of air flows. If the diameter of the windshear is less than 4 km, then it is called a microburst, otherwise a macoburst. Although windshear might last only a few minutes, it is one of the most dangerous factors in the takeoff and landing of an aircraft at low altitude because of its extreme speed and variation.  
Researchers have developed many different models of windshear. [PERSON] and [PERSON] developed the vortex-ring model [26]. This paper uses the simplified vortex-ring downburst model [27] in simulations. As the lateral motion is ignored, the horizontal and vertical wind speeds are given, as follows:  
\[\begin{array}{c}W_{x}=f_{x}\left(\frac{100}{\left[\left(x-3D/2\right)/200 \right]^{2}+10}-\frac{100}{\left[\left(x-D/2\right)/200\right]^{2}+10}\right) \\ W_{h}=-f_{h}\left(\frac{0.4h}{\left[\left(x-D\right)/400\right]^{2}+10}\right) \end{array} \tag{7}\]  
where \(W_{x}\) is the horizontal wind speed, \(W_{h}\) is the vertical wind speed, \(D=V_{0}T/2\), \(x=V_{0}t\), \(V_{0}\) is the approaching speed, \(T\) is the total time during which the aircraft flies in the windshear, and \(f_{x}\) and \(f_{h}\) are the strengths of the windshear.  
As the windshear speeds will influence the aircraft landing maneuver, Equation (7) needs to be embedded into the aircraft dynamics in the flight simulation. Additionally, the longitudinal motion equations can be written, as follows:  
\[\begin{array}{l}\dot{x}=V\cos(\gamma)+W_{x}\\ \dot{h}=V\sin(\gamma)+W_{h}\\ \dot{V}=\frac{1}{m}\left[T\cos(\alpha+\varepsilon_{T})-D-mg\sin(\gamma)-m\dot {W}_{x}\cos(\gamma)-m\dot{W}_{h}\sin(\gamma)\right]\\ \dot{\gamma}=\frac{1}{mV}\left[T\sin(\alpha+\varepsilon_{T})+L-mg\cos(\gamma)+ m\dot{W}_{x}\sin(\gamma)-m\dot{W}_{h}\cos(\gamma)\right]\end{array} \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \(\delta_{e}\) & \(\delta_{e}\) & \(\delta_{t}\) & \(\dot{\delta_{t}}\) \\ \hline \(0.35\)(rad) & \(0.26\)(rad/s) & \(0.088\)(rad) & \(0.017\)(rad/s) \\ \hline \end{tabular}
\end{table}
Table 3: The maximum deflections and rates.
# Graphene Nanoplatelets Impact on Concrete in Improving Freeze-Thaw Resistance
## 3 Results and Discussion
### Elastic Modulus Evolution  
The elastic modulus of concrete is usually defined as either a secant or a chord modulus. A secant modulus of concrete is calculated from the starting point to a specified point on the stress-strain curve. The specified point often is selected between 30% and 60% of the specimen's ultimate strength. We take 30% of the ultimate strength of the specimen as the defined point. Table 3 summarizes the elastic modulus of concrete samples. We found that GC0.05 has the strongest ability to resist elastic deformation, and the elastic modulus of GC0.05 is close to that of C60. The dates implied that GNP addition increases modulus of the concrete. It may have contributed to the high stiffness of graphene and the increasing compressive strength. We also found that for all samples, the modulus was obviously changed after 200 cycles. The elastic modulus of graphene concrete showed a lower decrease during the whole test. Comparing with the control sample before F-T actions, the ordinary cement concrete showed a drop up to 25.4% in the elastic modulus at 200 cycles. Nevertheless, the sample with the additives of 0.05%, 0.2%, and 0.3% GNP showed 17.57%, 19.7%, and 16.4% decrease in elastic modulus, respectively. After 200 F-T cycles, the elastic modulus of GC0.05 is still close to 30.0 GPa. This indicated that the addition of GNP can enhance the elastic deformation resistance of concrete in F-T actions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**F-T Cycles** & **GC0** & **GC0.02** & **GC0.05** & **GC0.1** & **GC0.2** & **GC0.3** & **GC0.4** \\ \hline
0 & 30.895 & 35.593 & 36.060 & 35.485 & 33.740 & 31.900 & 30.355 \\
10 & 29.445 & - & - & 34.010 & 33.490 & 31.445 & 29.830 \\
20 & 29.375 & - & - & 32.415 & 33.215 & 30.655 & 29.395 \\
30 & 28.265 & - & - & 32.790 & 31.840 & 30.835 & 29.190 \\
50 & 28.295 & 32.019 & 33.420 & 31.755 & 30.410 & 30.430 & 27.550 \\
100 & 26.700 & 31.440 & 32.670 & 30.380 & 29.865 & 28.575 & 25.560 \\
150 & 25.175 & 29.375 & 30.393 & 28.825 & 28.210 & 27.835 & 25.060 \\
200 & 23.045 & 28.460 & 29.725 & 28.140 & 27.080 & 26.665 & 23.315 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Elastic modulus of concrete samples after F-T cycles (unit: GPa).
# Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network
## 5 Conclusions  
We proposed a DQN-based intelligent signal control system to determine the order of green signals at multiple intersections. The purpose of this study is to distribute signals efficiently while maximizing the throughput at the intersections. Through pre-experiments, it was confirmed that the important factors affecting the performance differ depending on the traffic signaling method. By applying various parameters to the proposed model, the performance was compared with that of other signal control methods. This study classified two reward parameters, average queue length and standard deviation of the queue length, to evaluate the performance and compared them with the fixed-time signal, QT-CDQN [8] and CRL [16]. In terms of the wait time, the proposed model performs approximately 30% better when applying the standard deviation of the queue length than when applying the average queue length. In addition, considering the standard deviation of the queue length, it is interpreted that the length of the lanes within the intersection is similarly adjusted by efficiently distributing the green signals.  
Because we proposed a traffic signal control method applied at multiple intersections, large-scale signal control studies, including in urban areas by expanding the range, also need to be conducted. In addition, it will be necessary to expand the research area by applying federated learning methods for faster learning with more diverse data [18].  
Conceptualization, H.J. and Y.L.; Methodology, H.J.; Software, H.J.; Writing--Review & Editing, H.J. and Y.L.; Supervision. Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.2021R1F1A1047113).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 3 Information Mapping of Encoded Radio Transmission under Time-Varying Disturbance  
In this section, an MI-based CQI is obtained to evaluate the impact on RSTT based on the joint statistical characteristics of disturbances. We first map the statistics of the disturbances during a single code block to RBIR, which indicates the quality of digital transmission using the FEC codes. Furthermore, due to the presence of dynamic EMI in the HSR environment, a method based on nonlinear optimization is proposed to obtain the equivalent metric RBIR\({}_{\rm L}\) from a series of RBIR values.
# A Feasibility Study of the Use of PZT Actuators for Active Control to Enrich Engine Sound
## 5 Conclusions  
This paper investigated the feasibility of a proposed ASE system using a PZT-plate as a secondary actuator to enrich the insufficient engine sound of an ICEV. The ASE system was devised with a purely feedforward control approach. Three PZT actuators were bonded on a component steel plate of the front cross member of the car roof. The control filter of the ASE algorithm was designed using an inverse method to compensate for the secondary path. Then, a real-time control experiment was carried out to examine the tracking performance of a pre-defined target sound of three engine orders using the proposed ASE system.  
This study contributes to clarifying the relationship between a PZT-plate actuator and its generated sound using proper equations to consider the design of the inverse control filter for compensating the secondary path and to examine the characteristics of the vibro-acoustic secondary path related to the PZT-plate actuator.  
The control experiment results showed that the SPLs of the three engine orders were greatly enriched by about 5-30 dBA with small errors. This result indicates that the proposed ASE system using a PZT-plate actuator and the control filter can be an alternative for actively generating sound to achieve a target sound quality in a car.  
Especially for EVs, this feasibility study suggests the ASE system is an affordable approach to mask exotic noises due to absence of engine sound which exists in ICEVs. For  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{\(L_{\text{TE,RMS}}\) (dBA)} \\ \hline Engine order & C3 & C5 & C6 \\ \hline Before ASE & 20.0 & 17.4 & 19.1 \\ \hline After ASE & 0.9 & 1.5 & 1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the control performance of the proposed ASE system for the C3, C5 and C6 orders.  
further practical verification of the proposed ASE system, its effects will be investigated in the future with real-time control experiments while driving on a road.  
Conceptualization, Y.-S.L. and E.Y.; methodology, Y.-S.L.; software, S.R.; validation, Y.-S.L.; formal analysis, Y.-S.L., S.R. and E.Y.; investigation, J.L. and E.Y.; resources, J.L.; data curation, S.R.; writing--original draft preparation, Y.-S.L.; writing--review and editing, Y.-S.L. and S.R.; visualization, J.L.; supervision, E.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Incheon National University Research Grant in 2018.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Concepts and Theory
### Tropospheric Scattering
#### 2.3.3 Radar Transmission Loss  
The received signal in radar is, essentially, the unmodulated carrier wave transmitted by the radar itself. Remote sensing methods using radar and wireless communication share similarities in the way they work. However, radar places greater emphasis on changes in the echo of the carrier due to the impact of the wireless channel, while communication focuses on maintaining the quality of the transmitted signals. To match the troposcatter transmission loss with the radar reflectivity factor, the transmission loss of weather radar, \(L_{\text{R}}\), is expressed as follows:  
\[L_{\text{R}}=-10\text{log}(P_{\text{r}}/P_{\text{t}}) \tag{22}\]  
By substituting Equations (1) and (2), \(L_{R}\) can be defined simply as  
\[L_{\text{R}}=-\bigg{[}10\,\text{log}\bigg{(}\frac{\pi^{3}G^{2}\theta^{2}IK^{2} }{1024\text{ln}2\lambda^{2}}\bigg{)}+10\text{log}\bigg{(}\frac{Z}{R^{2}}\bigg{)} \bigg{]} \tag{23}\]  
Thus, using \(L_{\text{R}}\) in Equation (23), the troposcatter transmission loss and the radar transmission loss can be linked to radar parameters and the radar reflectivity factor. However, there is an associated issue. Because the receiving and transmitting ends of wireless communication are independent, the grazing angle is half of the scattering angle. Radar is an integrated transmitter and receiver device that only receives backscattered signals (regardless of multiple scattering and multipath effects). Therefore, to compare the transmission loss of radar with that of wireless communication beyond the line of sight, a calibration coefficient is needed to correct for the difference in intensities between the grazing direction and backscatter.  
It is well known that when an electric wave is incident from a medium with dielectric constant \(\varepsilon_{r1}\) to an infinite interface with a medium of dielectric constant \(\varepsilon_{r2}\), the Fresnel reflection coefficient, \(r_{\text{f}}\), can be simplified as follows:  
\[r_{\text{f}}\approx-\frac{\varepsilon_{r2}-\varepsilon_{r1}}{4\text{sin}^{2}\varphi} \tag{24}\]  
where \(\varphi\) is the elevation of the radar beam (i.e., the same as the glancing angle).  
Then, referring to Equation (24), the calibration coefficient, \(g_{\text{s}}\), can be defined as  
\[g_{\text{s}}=\text{sin}^{-2}\varphi \tag{25}\]  
Thus, the radar equivalent transmission loss (\(L_{\text{Re}}\), transmission loss in short hereafter) is as follows:  
\[L_{\text{Re}}=-\bigg{[}10\,\text{log}\bigg{(}\frac{\pi^{3}G^{2}\theta^{2}hK}{1 024\text{ln}2\lambda^{2}}\bigg{)}+10\text{log}\bigg{(}\frac{Z}{R^{2}}\bigg{)} -20\,\text{log}(\text{sin}\,\varphi)\bigg{]} \tag{26}\]
###### Abstract  
This research aims to establish a secure system for key exchange by using post-quantum cryptography (PQC) schemes in the classic channel of quantum key distribution (QKD). Modern cryptography faces significant threats from quantum computers, which can solve classical problems rapidly. PQC schemes address critical security challenges in QKD, particularly in authentication and encryption, to ensure the reliable communication across quantum and classical channels. The other objective of this study is to balance security and communication speed among various PQC algorithms in different security levels, specifically CRYSTALS-Kyber, CRYSTALS-Dilithium, and Falcon, which are finalists in the National Institute of Standards and Technology (NIST) Post-Quantum Cryptography Standardization project. The quantum channel of QKD is simulated with Qiskit, which is a comprehensive and well-supported tool in the field of quantum computing. By providing a detailed analysis of the performance of these three algorithms with Rivest-Shamir-Adleman (RSA), the results will guide companies and organizations in selecting an optimal combination for their QKD systems to achieve a reliable balance between efficiency and security. Our findings demonstrate that the implemented PQC schemes effectively address security challenges posed by quantum computers, while keeping the the performance similar to RSA.  
post-quantum cryptography; quantum key distribution; NIST; CRYSTALS-Kyber; CRYSTALS-Dilithium; Falcon; Qiskit 2024
###### Abstract  
Strong variability of Ka-band radar backscattering from short wind waves on the surface of water covered with surfactant films in the presence of internal waves (IW) was studied in wave tank experiments. It has been demonstrated that modulation of Ka-band radar return due to IW strongly depends on the relationship between the phase velocity of IW and the velocity of drifting surfactant films. An effect of the strong increase in surfactant concentration was revealed in convergent zones, associated with IW orbital velocities in the presence of a \"resonance\" surface steady current, the velocity of which was close to the IW phase velocity. A phenomenological model of suppression and modulations in the spectrum of small-scale wind waves due to films and IW was elaborated. It has been shown that backscatter modulation could not be explained by the modulation of free (linear) millimeter-scale Bragg waves, but was associated with the modulation of bound (parasitic) capillary ripples generated by longer, cm-dm-scale waves--a \"cascade\" modulation mechanism. Theoretical analysis based on the developed model was found to be consistent with experiments. Field observations which qualitatively illustrated the effect of strong modulation of Ka-band radar backscatter due to IW in the presence of resonance drift of surfactant films are presented.  
Ka-band radar backscattering; internal waves; short wind waves; radar modulation transfer function; surfactant films 2021
# Development of Cybersecurity Technology and Algorithm Based on Quantum Computing
## 4 Transforming the Classical AES Algorithm into a Quantum-Gate-Based AES Algorithm
### Quantum Computing Programming
#### 4.3.1 Classical AND Gate Using Toffoli Gate  
Descriptions 1 to 4 below describe Figure 14.  
1. Initialize qubit's initial state to \(\ket{0}\).
2. Calculate from the last digit (fourth) of the input values. The last digit of Input1 is 1. The last digit of Input2 is 0. That is, qubit_0(q424_0) should be \(\ket{1}\), and qubit_1(q424_1) should be \(\ket{0}\)> [4]. Therefore, apply the Pauli-X gate to qubit_1. Qubit_2(q424_2) must be \(\ket{0}\)> because it must satisfy the AND operation. The measurement of qubit_2 returns the result of the AND operation.
3. Calculate the third digit of the input values. The third digit of Input1 is 0. The last digit of Input2 is 1. That is, qubit_3(q424_3) should be \(\ket{0}\), and qubit_4(q424_4)  
Figure 12: Classical XOR gate.  
Figure 13: CNOT gate.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Input**} & \multicolumn{2}{c}{**Output**} \\ \cline{2-5}  & **A** & **B** & **A** & \(\mathbf{A\oplus B}\) \\ \hline \multirow{3}{*}{XOR (classical)} & 0 & 0 & X & 0 \\  & 0 & 1 & X & 1 \\  & 1 & 0 & X & 1 \\  & 1 & 1 & X & 0 \\ \hline \multirow{3}{*}{CNOT (quantum)} & 0 & 0 & 0 & 0 \\  & 0 & 1 & 0 & 1 \\ \cline{1-1}  & 1 & 0 & 1 & 1 \\ \cline{1-1}  & 1 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: XOR Gate vs. CNOT Gate.  
should be 11>. Therefore, apply the Pauli-X gate to qubit_4. The measurement then returns the results of the AND operation.
4. Iterate this process for each digit.
## 2 Relation between Tool Position Data and Machine Tool Axis Displacement  
The tool path denotes the tool's motion trajectory in CNC machining, acquired by determining the contact point and the optimal orientation between the tool and the workpiece based on the component's geometry and process requirements. In general, a tool path is created based on the tool position data, which is typically generated using CAM software. The tool position data consist of the tool's center coordinates (\(x,y,z\)) and its axis vector (\(i,j\), \(k\)). The center coordinates are used to determine the path of the machining, while the axis vector determines the tool's position. Figure 1 illustrates that the tool path comprises the trajectory of the tool's center point and the trajectory of the unit-length point on the tool axis. The vector of the tool axis is represented by the blue that links the two trajectories.  
To transform a five-axis five-linkage trajectory into a five-axis four-linkage trajectory, it is essential to comprehend the correlation between the tool position data and the motion of the machine tool axes.  
This paper focuses on a Sinumerik 828D controlled five-axis CNC machine tool that boasts a rotary table and a swinging head; the schematic diagram is shown in Figure 2. This machine tool is capable of simultaneously performing any four-axis linkage.  
The machine tool under consideration has five axes in motion, comprising three linear axes (X/Y/Z) and two rotational axes (the tool swing shaft B and the worktable rotating shaft A). This type of machine tool is classified as a swing head and rotary table five-axis machine tool. The structure of this machine tool is intermediate, between the double swing head and the double rotary table types. Due to the workpiece rotation on the A axis, this machine tool can process workpieces of a relatively broad size range, making it a highly versatile CNC machine tool. The parameters of the machine tool are shown in Table 1.