esa-sceva/satcom-chunk-collection huggingface dataset:
# Parallelized Swarm Intelligence Approach for Solving TSP and JSSP Problems
## 3 Results
### MPA Performance versus other Approaches  
Further experiments aimed at comparing the performance of the MPA with performance of other, state-of-the-art algorithms. Table 5 presents results obtained for instances of the TSP for different values of the _searchTime_. Calculations were carried out using the set of agents consisting of **bestMove**, **randomReverse** and **crossover**. Computations were carried out using 175 threads. The stopping criterion was defined as no improvement in the best fitness for 5 rounds. In Table 6 the results are compared with some other results known from the literature,  
Figure 1: Mean error versus the number of threads for some example (**a**) traveling salesman (TSP) and (**b**) job-shop scheduling (JSSP) datasets. Figure 2: Mean computation time (s) versus the number of threads for some example (**a**) TSP and (**b**) JSSP datasets. A comparison with [27] is especially interesting as in the CII very similar one-argument functions were applied to a single initial solution. CII is much faster (time is expressed in milliseconds in this case) than MPA. In return, our method outperforms CII in terms of the computation errors. Table 7 presents results obtained for instances of the JSSP. In this case the set of agents consisting of **randomMove, randomReverse** and **crossover** were used. _searchTime_ of one round and stopping criterion was defined as 2 deciseconds and 2 rounds with no improvement in the best solution for la01 to la14, and 5 s and 5 rounds with no improvement for la15 to la49. In Table 8 the MPA results are compared with some other recently published results. Errors for [33] and [32] were computed on the base of results given in the respective papers. In [32] only mean errors were reported with no information on the computation time. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **MPA** & & **DSOS**[21] & & **NDDE**[19] & & **CII**[27] & \\
**Dataset** & **Err (\%)** & **Time (s)** & **Err** & **Time (s)** & **Err** & **Time (s)** & **Err** & **Time (ms)** \\ \hline kroA100 & 0.00\% & 2.50 & 0.60\% & 127.5 & 0.21\% & 2.8 & 0.80\% & 3.5 \\ pr107 & 0.00\% & 6.40
# Overview of FEM-Based Resistance Models for Local Buckling of Welded Steel Box Section Columns
## 3 Finite Element Model
### Material Model  
Mechanical properties govern the behavior of the material in a computational model. In the case of overall buckling phenomenon in welded sections, the material law has a clear influence on the buckling resistance as it was shown in [38]. For general stability problem, material behavior takes a special importance as it defines the shape of the behavior curve and the ultimate load capacity. Post-buckling resistance may be expected for the elastic buckling of plates, though not for elastic-plastic buckling [11]. Since the data collected cover studies around the world, material composition of the steel and its treatment will not follow a uniform standard. To standardize the computational analysis, the material's constitutive law will be determined based on the yield strength of the steel used. For normal-strength steel with a yield strength below 500 MPa, the material model proposed by the prEN 1993-1-14 [39] is used. For high-strength steel with a yield strength equal or exceeding 500 MPa, the Ramberg-Osgood-type material model was used following the proposal of prEN 1993-1-14 [39].
# Space-Based Displacement Monitoring of Coastal Urban Areas: The Case of Limassol's Coastal Front
###### Abstract  
In the last five years, the urban development of the city of Limassol has rapidly increased in the sectors of industry, trade, real estate, and many others. This exponentially increased urban development arises several concerns about the aggravation of potential land subsidence in the Limassol coastal front. Forty six Copernicus Sentinel-1 acquisitions from 2017 to 2021 have been processed and analyzed using the Sentinel Application Platform (SNAP) and the Stanford Method for Persistent Scatterers (StaMPS). A case study for the identification and analysis of the persistent scatterers (PS) in pixels in a series of interferograms and the quantity of the land displacements in the line of sight of the Limassol coastal front is presented in this research, with subsidence rates up to about (\(-5\) to 4 mm/year). For the validation of the detected deformation, accurate ground-based geodetic measurements along the coastal area were used. Concordantly, considering that there is a significant number of skyscrapers planned or currently under construction, this study attempts a preliminary assessment of the impact these structures will pose on the coastal front of the area of Limassol.  
displacement monitoring; land subsidence; coastal areas; PSI; SAR; leveling; Cyprus +
Footnote †: journal: remote sensing  
Article
## 3 Beamforming Optimization with AN  
In this paper, we propose a beamforming optimization scheme to provide security in a MISO SWIPT CR-NOMA network. We consider a practical scenario where the SU-Tx does not have the CSI of the eavesdropper. To further increase the secrecy performance of the network, AN is generated to tackle eavesdropping and guarantee the security of the confidential information of the unlicensed users. In particular, we minimize the transmit power for the SUs and EH users, subject to the constraints of a minimum SINR for the SUs, minimum EH by the SUs and EH users, maximum available power for the SU-Tx, and maximum interference power to ensure the PUs' legitimate transmissions are not affected. The idea of minimizing transmit power for the SUs and EH users is to use the rest of the available power at the SU-Tx to maximize AN and protect transmitted messages against an eavesdropper. In addition, SDMA and ZF baseline schemes are developed along with MISO CR and SWIPT for comparison purposes.
# Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks
## 2 Related  
### Traditional Handover Algorithm and Intelligent Handover Algorithms  
Traditional handover algorithms, such as the 3 GPP Event A3, have been widely adopted in wireless communication systems. These algorithms primarily rely on signal strength measurements, such as Reference Signal Received Power (RSRP), to make handover decisions. Studies have shown that the Event A3 algorithm, while robust, can lead to frequent handovers and increased signaling overhead, particularly in dense urban areas and high-mobility situations [10]. In a highly dynamic environment, traditional handover algorithms may struggle when the signal strengths at the junction of two base stations are nearly identical, leading to frequent handovers, known as the ping-pong effect. To address this issue, our research leverages machine learning to predict future signal strengths and uses these predictions as thresholds in the handover algorithm. This dynamic adjustment aims to reduce the occurrence of the ping-pong effect, resulting in more stable connections for the user equipment.  
In recent years, machine learning (ML) techniques have been increasingly applied to enhance handover decisions. Supervised learning models, such as Support Vector Machines (SVM) and Decision Trees, have been used to predict handover triggers based on historical data [11; 12]. These approaches have demonstrated improvements in handover accuracy and a reduction in unnecessary handovers. However, they often require large labeled datasets and may not generalize well to unseen environments.
## 2 Signal Model
### Equivalent Range Model  
Since the SAR slant range defined from the antenna phase center to the targets largely determines the focusing accuracy of the targets [25], thus the accurate slant range form was derived based on the SAR imaging geometry model of the hypersonic platform.  
For DPCAM SAR of the hypersonic platform, each sub-aperture is arranged linearly in the along-track dimensional, thus the \(q\)th receiving sub-aperture deviates from the center of the transmitting sub-aperture can be expressed by  
\[\triangle x_{q}=\bigg{(}\frac{Q+1}{2}-q\bigg{)}d,\,q=1,2,\cdots,Q, \tag{1}\]  
where \(d\) is the spacing of the adjacent phase center of receiving sub-apertures, and \(Q\) is the number of receiving sub-apertures, which is an odd number.  
Refer to Figure 2, the instantaneous slant range from the phase center of the transceiver sub-aperture to the targets can be expressed by  
\[R_{a}(\eta)=\sqrt{L^{2}+r^{2}-2 Lr\cos(\omega_{s}\eta)+h^{2}}. \tag{2}\]  
Furthermore, since the azimuth multichannel sampling of the hypersonic platform SAR adopts the mode of single transmitter and multiple receivers, based on which the slant range from the phase  
Figure 2: Azimuth multichannel sampling for hypersonic platform SAR.  
center of the transmitter to the targets can be substituted by the instantaneous slant range of the transceiver sub-aperture. Then, the accurate two-way slant range of this platform SAR can be given by  
\[R_{arc,a}(\eta)=R_{a}(\eta)+\sqrt{L^{2}+r^{2}-2 Lr\cos\left(\omega_{s}\eta- \triangle x_{q}/L\right)+h^{2}}. \tag{3}\]
# Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
## 3 Enhanced Labeling Workflow
### Dimensionality Reduction Algorithm  
In order to get the best visualization of the detected geological features by each attribute, the multi-attributes analyses can be combined and reduced into some meaningful principal components. This idea is known as Principal Components Analysis (PCA) and is classified as an unsupervised learning method. However, this technique is based on linear transformation, whereas seismic multi-attributes analysis is a non-linear problem; thus, it will not fit well in the seismic multi-attributes analysis [34].  
To address this problem, there is a kernel based PCA technique that is fit for processing non-linear problems, and can reduce the dimension of the data without much loss of information [35]. The methods behind the KernelPCA algorithm are the same as PCA,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Seismic Attributes** & **Seismic Properties** & **Geological Features** \\ \hline Reflection Strength & Amplitude & Channels, bright spots, and stratigraphy \\ RMS Amplitude & Amplitude & Channels, bright spots, and stratigraphy \\ Sweetness & Amplitude & Channels, bright spots, and stratigraphy \\ Envelope & Amplitude & Channels, bright spots, and stratigraphy \\ Average Frequency & Frequency & Shadows and stratigraphy \\ Bandwidth & Frequency & Shadows and stratigraphy \\ Tuning Frequency & Frequency & Shadows and stratigraphy \\ Instantaneous Phase & Phase & Faults, salts, and stratigraphy \\ Response Phase & Phase & Faults, salts, and stratigraphy \\ \hline \hline \end{tabular}
\end{table}
Table 1: Seismic attributes with key seismic properties and the geological applications.  
which tends to maximize the variance of the transformed result in feature space. The main difference is the kernel technique that have been used in KernelPCA. This kernel is used to implement PCA in hyperspace, which can induce the originally scattered samples to aggregate along the eigenvector direction. The main idea of the kernel method was applied to the Support Vector Machine (SVM) algorithm firstly [36].
## 4 Influence of Dynamic on Waveform  
To reduce the influence of thermal and speckle noise, delay waveform samples are incoherently averaged. Because of the high dynamics of LEO and GNSS satellite, the snapshot moves each other when incoherent averaging is performed as  
\[\langle|Y(\tau)|^{2}\rangle=\frac{1}{N_{\text{incoh}}}\sum_{i=1}^{N_{\text{ incoh}}}|Y_{i}(\tau-\Delta\tau_{i})|^{2} \tag{9}\]  
where \(\Delta\tau_{i}\) is the delay difference between the \(i\)th and first waveform sample, which is determined by the geometry configuration of GNSS and LEO satellites, such as the hight, elevation, moving direction of satellite. The red solid lines in Figure 4 give the simulated results of the change of the delay difference between direct and reflected signals, in which the delay difference changes show an approximately linear relationship over the time in a short period of time. The blue dotted lines in Figure 4 show the linear curves with the slope of 0.54 and \(-\)0.52 for Figure 4a,b, from which it is found that the disagreements between the delay difference and the linear curves increase over the time. This behavior illustrates that in fact the delay difference is not linear as shown in the following where Delay Difference Change Rate (DDCR) is time-varying. Figure 5 shows the delay waveform with the tracking refresh period of 1 ms, 1 s, 3 s, from which it is seen that if the waveform samples are not aligned when incoherent averaging is conducted, incoherent averaging causes a distortion of averaged waveform, moreover, the longer the tracking refresh period is, the more serious the distortion is. This distortion results in a degradation of the retrieval performance as analyzed in [21] in which the results show that the performance of spaceborne GNSS-R altimeter is seriously degraded without a proper alignment of the waveform samples. As proposed in [22], the ideal case is that the correlation window is refreshed on each coherent integration period (e.g., 1 ms). However, a high refresh rate requires too many instrument resources and too much data to be uploaded from the ground station. Therefore, it is necessary to find an alternative with the less tracking refresh rate.
# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 3 Security and Safety Issues
### Physical Layer Security (PLS)
In greater detail, prospect theory (PT) was used to formulate a subjective smart attack game for the UAV transmission, in which a smart attacker selected its attack type from jamming, spoofing, and eavesdropping without knowing the attack detection accuracy of the UAV system. Simultaneously, the UAV transmit power across different channels was appropriately set to resist these smart attacks. Probability weighting and value functions were applied by PT in order to model the subjective decision-making processes incorporating the fact that people tend to avoid risks due to incurring losses. Then, RL-based UAV power allocation was proposed to safeguard the transmission against smart attacks without being aware of the attack model and the channel model. Performance evaluation indicated that the proposed UAV power allocation method can decrease the impact of smart attacks and can increase the secrecy capacity of the UAV transmission by 16% and the utility by 22%, as compared with the Q-learning based scheme. Protection against global positioning system (GPS) spoofing attacks on UAV networks was the subject of the paper in Reference [54]. As fake signals can confuse the UAV and the air traffic controllers, mitigating the impact of this attack type is vital for UAV systems. Thus, a supervised ML approach based on ANN for detecting the GPS spoofing signals was developed. In order to classify the GPS signals and to train the ANN, characteristics such as pseudo range, Doppler shift, and SNR were acquired. An important feature of this solution is its applicability in current UAV systems, as it does not require any modification of the GPS equipment. The results illustrated that the ANN-based solution exhibits high detection probability of spoofing signals and reduced false alarm probability, compared to the stochastic gradient descent (SGD), searching for the minimum using conventional Newton-Raphson iterative solving based on a subset of samples and the adaptive moment (Adam) estimation employing dynamic per-parameter learning rate to calculate the minimum of the cost function. Then, the potential of using unsupervised learning for the detection of active eavesdropping in UAV-aided relay-based networks was investigated in Reference [55], where the uplink was responsible for the authentication procedure. More importantly, one-class SVM and K-means clustering were exploited for the detection of possible attacks during the authentication. To create training data sets for the ML models and to facilitate the training process, the wireless signals and the statistical knowledge of CSI were used. According to the results, the one-class SVM was more stable than k-means. However, k-means is preferable, provided that the eavesdropper's transmitted power has a sufficiently high value.
## 1 Introduction
### Proposed Methodology and Contributions  
In this paper, a new approach to initializing the population of genetic algorithms (GAs) is presented with the goal of improving the quality of the solutions. The Iterative Approximation Method (IAM-TSP) is the main component of our solution that solves the Traveling Salesman Problem (TSP). This approach, which we described in full in our earlier work [41], yields an approximate polynomial solution for the TSP and acts as the basis for initializing the GA.  
The IAM-TSP approach starts with the identification of four extreme cities along the route. Each city is then successively incorporated into the route by comparing and calculating the costs at different insertion sites. By using this approach, the overall path cost is reduced by placing each city in the best possible position. We next apply local constant permutations to the IAM-TSP outputs to further improve the quality and efficiency of the solution, yielding an improved final path called IAM-TSP+.  
This work makes several important and varied contributions, mainly to the field of improved GA performance by means of a sound initial population approach. The particular contributions of this study are as follows:  
* Improvement of Quality: By using a new method to initialize the GA population, our proposed method guarantees that the beginning point is closer to the optimal solution, resulting in a significant improvement in the quality of the solution.
* Durability: The proposed method shows resilience in generating high-quality solutions in a variety of TSP instances, highlighting its durability and applicability to various scenarios.
* Computational Efficiency: We accomplish computational efficiency by lowering the temporal complexity involved in locating a near-optimal route in TSPs, which improves the GA's overall performance, allowing it to converge faster. This is made possible by the usage of IAM-TSP+.  
We evaluated the proposed algorithms against standard TSP datasets to determine how they would perform in terms of key performance measures. Our promising experimental results show that our new method is effective in terms of error percentage, mean convergence, and time. Among the achievements of this paper are new approaches to initial population creation in the context of solving TSPs as compared to random, NN, and regression strategies.  
The rest of this research work is organized into the following structure: Section 2 discusses some relevant background ideas. The proposed approach is explained in Section 3. Section 4 evaluates the experimental outcomes. A brief conclusion is presented in the Section 5.
# Design of a Novel Linear Shaped Charge and Factors Influencing its Penetration Performance
## 5 Parametric Influences on Penetration Performance
### Explosive Type  
Based on the penetration performance of the BLSC with 0.6 mm liner, another four sets of DOP tests on steel targets with the explosives of COMP B, C-4, TNT, and LX-19 were further simulated. Table 4 shows the corresponding parameters of these explosives [20]. Figure 12 shows the velocity distribution with the above five different explosives before penetration. Figure 13 further illustrates the sectional penetration cracks, and the values of penetration depths are shown in the parentheses.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **COMP B** & **C-4** & **TNT** & **LX-19** \\ \hline Density(g/cm\({}^{3}\)) & 1.717 & 1.601 & 1.630 & 1.942 \\ A (kPa) & 5.24 \(\times\) 10\({}^{8}\) & 6.10 \(\times\) 10\({}^{8}\) & 3.71 \(\times\) 10\({}^{8}\) & 1.64 \(\times\) 10\({}^{9}\) \\ B (kPa) & 7.68 \(\times\) 10\({}^{6}\) & 1.30 \(\times\) 10\({}^{7}\) & 3.21 \(\times\) 10\({}^{6}\) & 1.86 \(\times\) 10\({}^{7}\) \\ R1 & 4.2 & 4.5 & 4.15 & 6.5 \\ R2 & 1.1 & 1.4 & 0.95 & 2.7 \\ W & 0.34 & 0.25 & 0.3 & 0.55 \\ C–J Detonation velocity (m/s) & 7,980 & 8,190 & 6,930 & 9,208 \\ C–J Energy/unit volume (kJ/m\({}^{3}\)) & 8.50 \(\times\) 10\({}^{6}\) & 9.00 \(\times\) 10\({}^{6}\) & 4.30 \(\times\) 10\({}^{6}\) & 1.15 \(\times\) 10\({}^{7}\) \\ C form alae J Pressure (kPa) & 2.95 \(\times\) 10\({}^{7}\) & 2.80 \(\times\) 10\({}^{7}\) & 2.10 \(\times\) 10\({}^{7}\) & 4.30 \(\times\) 10\({}^{7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of four explosives.  
Figure 11: Penetration depth of BLSCs with different liner thickness.  
Figure 12: Velocity distribution of different explosives.  
According to these figures, it should be noted that the penetration depth is a result of a multitude of factors such as jet tip velocity, jet length, velocity distribution and so on. The jet tip velocities of BLSCs with 8701, COMP B, and TNT are much higher than those with C-4 and LX-19. The jet/slug lengths of BLSCs with 8701, COMP B, and TNT are also longer than those of the others. As shown in Figure 13, the penetration depth of BLSC with COMP B is the deepest, followed in turn by that of BLSCs with TNT, 8701, C-4, and LX-19, which is the smallest. Although the jet/slug length of BLSC with LX-19 is not the shortest, it has the minimal penetration depth, indicating that the velocity has greater influence on the penetration depth of BLSC than jet/slug length.
## 3 Results
### Experimental Results
#### 3.1.1 Dispersant Effectiveness
In this graph, four diagonal lines represent the values of energy dissipation and power at a range of mixing speeds (rpm values), as calculated from the equations presented in Section 2, for three cases of the laboratory scale, pilot scale paddle mixer, and paddle pilot scale helical mixer. Then, the individual DE results are plotted, with the DE levels notated per data point. This graph shows how the helical impeller is able to achieve high DE levels at lower power input values, as compared to the paddle impeller at the pilot scale.