esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Reflective Surfaces for Wireless Networks:
## 3 IRS-Assisted Wireless Networks
### Channel Estimation
#### 3.2.3 MmWave Systems  
In [43] a compressed-sensing approach is considered to estimate the cascaded BS-IRS-user channel in a MISO mmWave downlink system with NLOS conditions by addressing the channel estimation as a sparse signal recovery problem. The results are presented in terms of the normalized mean-square error (NMSE) and the method proposed shows higher performance than that obtained in [44]. Thus, the LS estimator in [44] requires many more measurements to achieve a performance similar to that of the method proposed in [43], which results in a more computationally efficient estimator.  
In [45] a compressive sensing (CS)-based channel estimation solution for the downlink of a multiuser OFDM IRS-aided hybrid mmWave MISO cellular system is proposed. The angular channel sparsity caused by large-scale arrays present in mmWave is exploited to perform the CSI acquisition with reduced pilot overhead. The authors assume that the BS-IRS channel is known and then employ a distributed orthogonal matching pursuit (OMP) algorithm for the IRS-users channel estimation. The results are presented in terms of the NMSE and the algorithm proposed is compared with that developed in [44]. The results show that the algorithm proposed leads to a NMSE lower than that obtained in [44] with the LS algorithm since the performance of this latter method is limited by the interference corresponding to the NLOS paths.  
In [46], the high-resolution channel estimation problem for IRS-assisted mmWave MIMO systems was studied. Again, the cascaded BS-IRS-user channel estimation is formulated as a sparse recovery problem. A matching pursuit (MP)-based high-resolution estimation is performed, which outperforms the scheme in [35] in terms of the NMSE. The computational complexity of this algorithm depends on the dictionary matrix which leads to a prohibitive complexity in practical implementations. Nevertheless, the authors propose to exploit the coarse angular domain information obtained by beam training in order to reduce the size of the dictionary matrix and thus the complexity.  
The work in [47] also considers a DL framework for channel estimation in a multiuser IRS-assisted mmWave MISO systems with non-ideal switching. A twin CNN architecture is designed and it is fed with the received pilot signals to estimate both the direct and the BS-IRS-users cascaded channels by considering LOS conditions between the users and the BS. The user has a deep network that is fed by the received pilot signals from the BS in the downlink to estimate the direct and the cascaded channels. The results show lower minimum mean square error (MMSE) with respect to the approach in [37].
# Dynamic Induction Heating Temperature Field Analysis of Spiral Bevel Gears
## 3 Simulation Results and Discussion
### Effect of Coil Structure on the Heating Rate of the Gear Shot-Peening Area  
One point each on the tooth top (TT) and tooth root (TR) of the spiral bevel gear in the middle of the shot-peening area was selected as the study object. This selection accurately analyzed the tooth surface temperature of the spiral bevel gear during the induction heating process, thereby ensuring the uniformity of the tooth surface temperature in the gear shot-peening area (coil opening area). The electrical parameters were set to 15 kHz and 1000 A, and the temperature distribution data from the simulation results were extracted (Figure 9).  
Spiral bevel gear shot peening area with TC heating to 170 \({}^{\circ}\)C took 85 s, whereas the LC heating took 118 s. During induction heating, with the rotation of the spiral bevel gear, the research target point periodically passed through and away from the shot peening area of the coil opening. Hence, the temperature curve of the study target point on both the top and root of the tooth changed in a periodic fashion, and the temperature repeatedly increased and decreased. When the research target point was in the coil opening area, the  
Figure 8: Effect of current frequency on temperature distribution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency/kHz** & **GSPA (TC)/\({}^{\circ}\)C** & **GSPA (LC)/\({}^{\circ}\)C** & **GSTD (TC)/\({}^{\circ}\)C** & **GSTD (LC)/\({}^{\circ}\)C** \\ \hline
10 & 167.3 & 131.2 & 56.8 & 29.7 \\
12.5 & 196.3 & 152.7 & 69.8 & 35.6 \\
15 & 220 & 170  
magnetic field strength near the research point decreased. Thus, the electromagnetic heat generated in this area was lower than the energy dissipated at this time, resulting in a lower temperature. When the research target point was far from the coil opening area and located below the heating coil, the magnetic field strength near the research point significantly enhanced again. Thus, the electromagnetic heat at the point was significantly increased again, resulting in a temperature increase.  
The magnitude of the temperature fluctuations at the study point also reflects the heating efficiencies of the two coils. The small temperature fluctuations indicate that the temperature of the tooth surface point does not drop significantly within one cycle of gear rotation, and the heating efficiency is high. According to Figure 9, in one rotation cycle, with the same electromagnetic parameters, the temperature fluctuation of the gear heated by the horizontal coil is approximately 35 \({}^{\circ}\)C, and the tooth root temperature in the shot peening area is 5 \({}^{\circ}\)C lower than the tooth top. The temperature fluctuation of the vertical coil heated gears was 15 \({}^{\circ}\)C, and the root and top temperatures in the shot peening area were approximately the same.
## 3 Methodology
### PSO Deep-ConvLSTM Network  
Although ConvLSTM models have demonstrated their aptitude for handling time series data, determining optimal hyperparameters remains a formidable challenge. Undoubtedly, these hyperparameters substantially dictate the resulting network model's topology. Consequently, different hyperparameter configurations can lead to varying degrees of predictive performance. Therefore, the judicious selection of model hyperparameters is of paramount importance [21]. However, current research predominantly relies on subjective means, relying on intuition and trial-and-error methods for hyperparameter selection. Such an approach obscures the accuracy of determining the model's  
Figure 3: Structure of a ConvLSTM cell.  
optimal network structure hyperparameters, resulting in suboptimal prediction accuracy and inefficient training procedures.  
To mitigate these challenges, this study introduces a pioneering integration of the particle swarm optimization (PSO) algorithm with ConvLSTM, culminating in the PSO Deep-ConvLSTM model. The model's primary goal is to facilitate an automated search for the optimal network structure hyperparameters of ConvLSTM by leveraging the PSO algorithm. Drawing inspiration from avian foraging behaviors, PSO stands as an intelligent optimization algorithm [22]. By simulating the collaborative interactions of birds, the algorithm steers a swarm toward their food source. This is achieved through iteratively updating their positions based on individual best positions and relative swarm positions, eventually forming an optimal configuration [23].  
The PSO algorithm further boasts an array of appealing features, including ease of implementation, accelerated convergence rates, and the capacity to search for global optima. Therefore, the integration of the PSO algorithm enables swift, accurate searching for optimal ConvLSTM neural network structural hyperparameters. The proposed framework of the PSO Deep-ConvLSTM model is illustrated in Figure 4.
## 2 Basic Theory of Non-Negative Tensor Factorization
### HALS Algorithms for Sparse Non-Negative Tensor Factorization  
Similar to the sparse nonnegative matrix factorization, the sparseness constraints are imposed on the decomposed vectors for the improvement of the sparsity level and the ability of local representation. The SNTF problems can be solved by HALS algorithm as demonstrated below. For convenience, we abbreviate this method as HALS-SNTF.  
By employing the L\({}_{1}\)-norm as the sparsity regulation term, we shall solve the minimization problems as follows:  
\[D_{F}^{(j)}(\underline{\mathbf{Y}}^{(j)}\|\mathbf{a}_{j}\odot\mathbf{b}_{j} \odot\mathbf{c}_{j})=\frac{1}{2}\|\underline{\mathbf{Y}}^{(j)}-\sum_{j=1}^{J} \mathbf{a}_{j}\circ\mathbf{b}_{j}\circ\mathbf{c}_{j}\|_{F}^{2}+\alpha\| \mathbf{a}_{j}\|_{1}+\alpha\|\mathbf{b}_{j}\|_{1}+\alpha\|\mathbf{c}_{j}\|_{ 1}, \tag{17}\]  
for \(j=1,2,\ldots,J\) subject to nonnegativity constraints, where \(\alpha>0\) represents the unified sparseness regulation parameters. Analogous to the method discussed in Section 2.2, it is efficient to obtain the learning rule for vector \(\mathbf{a}_{j}\):  
\[\mathbf{a}_{j}\leftarrow\left[\frac{\mathbf{Y}_{(1)}^{(j)}(\mathbf{b}_{j} \odot\mathbf{c}_{j})-\alpha 1_{\text{L}\text{S}1}}{(\mathbf{b}_{j}^{T}\mathbf{b}_{j}).*( \mathbf{c}_{j}^{T}\mathbf{c}_{j})}\right]_{+},(j=1,2,\ldots,J) \tag{18}\]  
which can be rewritten as \(\mathbf{a}_{j}=\left[\mathbf{Y}_{(1)}^{(j)}(\mathbf{b}_{j}\odot\mathbf{c}_{j })-\alpha 1_{\text{R}\text{S}1}\right]_{+}\) when \(\mathbf{b}_{j}\) and \(\mathbf{c}_{j}\) are normalized. Also, the update rules for \(\mathbf{b}_{j}\) and \(\mathbf{c}_{j}\) are generated in a similar way alternatively.
## 1 Introduction
Additionally, the performance of the pre-trained denoiser in combination with each linear AECs was investigated to determine which of the outputs contained residual echo that resembled noise more closely than speech. The results indicated that, contrary to the NLMS, the outputs of the NSLMS were more akin to noise than speech. Therefore, the preceding linear AEC choice had an even more significant impact when employing a pre-trained speech denoiser model for the residual echo suppression task. With the NSLMS, a speech denoiser might be a suitable alternative when insufficient data is available to train an RES model. Finally, the advantages and efficacy of the proposed RES model over a larger pre-trained denoiser model are shown. To summarize the contributions of the presented study, the main findings are highlighted below:  
* The performance of the NSLMS is superior to that of the common NLMS, both as a standalone linear AEC and combined with a deep-learning residual echo suppressor. More generally, the reported findings indicated that the linear AEC significantly impacted the performance of the following residual echo suppressor and should be carefully chosen. * When combined with a pre-trained speech denoiser, the NSLMS brings a more significant performance improvement than when combined with a residual echo suppressor. This indicated that the outputs of the NSLMS were less structured and more akin to noise than the NLMS outputs. Therefore, with the NSLMS, employing a pre-trained speech denoiser might be a viable alternative to training a residual echo suppressor. * The DCCRN architecture, initially proposed for speech enhancement, is offered to perform residual echo suppression. While requiring only a minor modification to adapt to the residual echo suppression task, the proposed residual echo suppressor outperformed the larger, pre-trained speech denoiser. The presented study focused on challenging real-life scenarios, such as echo-path changes, low signal-to-echo ratios (SERs), and real-time considerations. Following is the outline of the manuscript. In Section 2, formulation of the residual echo suppression problem is provided, the relevant signals are denoted, the different systems and their components are described, and details regarding the datasets and experimental procedures are provided. In Section 3, the experimental results are provided. The results are discussed and interpreted in this section as well. The manuscript is concluded in Section 4.
## Case Study
### Effect of the Train System on the Prediction Accuracy
#### 4.3.2 The Number of Train Carriages  
Based on the previous analysis, the performance of predicting the train-induced bridge responses with a varying number of carriages will be discussed in this section. Figure 19 shows the non-dimensional metrics for evaluating the prediction performance of the displacement and acceleration of a railway bridge as the number of carriages varies from 6 to 22. The riding speed of the train is set as 50 km/h. The results show that the increase in the carriage number causes a similar changing trend with the train speed. To be specific, the increase in carriage number leads to a decrease in prediction accuracy. The error level of displacement prediction is relatively stable, and the acceleration prediction is more sensitive to the change in train sections.  
Figure 17: The impact of train speed on displacement prediction accuracy.  
Figure 18: The impact of train speed on acceleration prediction accuracy.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 4 Ecosystem
### Enabling Technologies
#### 4.3.2 Quantum Computing  
[PERSON] et al. [195] proposed a novel image encryption algorithm involving a quantum chaos map by applying quantum correlation theory to the classic logistic map. The resulting map exhibits strong chaotic behavior and is used to generate chaotic streams for permuting the pixels of the image in question.
# Maintaining Secure Level on Symmetric Encryption under Quantum Attack
## 2 Literature Review
### [PERSON] Proposed AES-512 Bits Method  
[PERSON] et al. [15] proposed the AES enhanced algorithm for quantum computing, and the length of the original encrypted key was increased from 256 bits to 512 bits. In order to be able to decrypt it, the original length of 128 bits was changed to 512 bits on the FPGA, but there are several disadvantages involved with this method.  
First, unlike the original AES architecture, the security cannot be compared. Second, since each block expands in length, the overall space complexity is greatly increased. Third, since the block length is at least 512 bits, security from Level-1 to Level-3 cannot be achieved.
# Numerical Analysis on Self-Propulsion of a Waterjet-Propelled Ship with Different Propulsion Models
## 3 Results and Discussions
### Computations of Self-Propulsion
#### 3.2.3 Internal Ingested Flow of the Control Volume
The thrust deduction fraction relates the resistance of the bare hull to the gross thrust and is defined as follows:  
\[1-t=\frac{R_{bh}}{T_{\text{\small gross}}} \tag{15}\]  
Similarly to the conventional thrust deduction fraction employed in propeller/hull interaction theory, the resistance increment fraction \(t_{r}\) based on the bare hull resistance and the net thrust of the waterjet system is defined as follows:  
\[1-t_{r}=\frac{R_{bh}}{T_{\text{\small net}}} \tag{16}\]  
Usually, at the self-propulsion point, the net thrust of waterjet system is equal to the resistance of the self-propelled hull. Therefore, the resistance increment fraction \(t_{r}\) reflects the change in the resistance of the waterjet-propelled hull compared to the bare hull resistance. In order to relate the net thrust to the gross thrust, the jet system thrust deduction fraction \(t_{j}\) is defined as follows ([PERSON], 2014):  
\[1-t_{j}=\frac{T_{\text{\small net}}}{T_{\text{\small gross}}} \tag{17}\]  
Thus, the relationship among the thrust deduction fraction \(t\), resistance increment fraction \(t_{r}\) and jet system thrust deduction fraction \(t_{j}\) is  
\[1-t=(1-t_{r})(1-t_{j}) \tag{18}\]  
Table 7 shows the results of \(1-t_{r}\), \(1-t_{j}\) and \(1-t\) with the MRF model and body-force model. With the increase in speed, the resistance increment fraction \(t_{r}\) decreases, and the jet system thrust deduction fraction \(t_{j}\) increases, which is similar to the results for \(Fr<0.55\)  
by [PERSON] et al. (2022). Additionally, the relative errors of \(1-t_{r}\), \(1-t_{j}\) and \(1-t\) between the two models are less than 1.9%.
# Challenges Associated with Implementing 5G in Manufacturing
## 2 The Potential of 5G in Manufacturing
signals is smaller, which is limiting to the signal penetration capability. 5G will initially be allocated spectrum at the 3 to 6 GHz range (depending on national regulations). Within a heavy engineering environment, with dense, generally metallic machinery, signal penetration should be factored into the design. To calculate the penetration of a 5G signal the wavelength of the signal is needed, as signal penetration is proportional to its wavelength, i.e., longer wavelengths penetrate obstacles better. This is calculated by using the frequency and the speed of light (in the medium), in Equation (1) [13]. \[\lambda=c/f \tag{1}\]  
where;  
\(\lambda\) (Lambda) = Wavelength in meters  
**c** = Speed of Light (299,792,458 m/s)  
**f** = Frequency (MHz)  
Applying the formula above, it becomes clear that there may be issues around signal penetration and distance issues when transmitting at the bands allocated for 5G. As signal penetration is directly related to signal wavelength, shorter wavelengths are more easily reflected or refracted. As the frequencies utilized by 5G increases, the signal penetration decreases, i.e., it is more susceptible to attenuation. Accordingly, manufacturing should choose their operating band wisely as it may have a significant impact. A summary comparison of 5G and 4G LTE (Long-Term Evolution) is shown below in Figure 3.
## 6 Conclusions  
Critical transportation infrastructure implemented with IoT technology can be targeted by multiple threats due its cyber-physical nature. The knowledge gap between security and civil engineering domain, a lack of statistical data related to cyber-attacks and emerging threats from cyber and physical space make the assessment of risk more difficult. Additionally, the exposure of IoT devices in physical areas brings new opportunities for attackers, which have been previously overlooked. To address this gap, in this paper a novel cyber-physical risk assessment approach is proposed. This is based on cyber-physical characteristics of threat sources, vulnerabilities, and types of physical impacts. The approach utilizes a decision scale and importance indexes, to assist stakeholders in the assessment of the level of vulnerability, threat source and physical impact. To overcome the lack of data and the premature level of IoT adoption in transportation infrastructure, an illustrative but realistic case study of an IoT enabled bridge subjected to a cyber-physical attack in the physical space was presented. Results from Monte Carlo simulations, indicate that IoT enabled transportation infrastructure is susceptible to cyber-physical attacks when the threat source can physically access IoT devices that have limited physical and cyber control barriers, resulting in high risk levels in a significant number of cases. Control barriers that operate in both cyber and physical space, individually or combined, resulted in the reduction in cyber-physical risk. Comparison with other studies that do not thoroughly consider the importance of characteristics, led to an underestimation of cyber-physical risk. Overall, this risk assessment approach can constitute a valuable resource towards the future assessment of IoT enabled transportation infrastructure under its cyber-physical perspective.  
All the researchers collaborated for the main activities of the study, main contributions may be identified as follows: Conceptualization, K.N. and D.P.M. and L.P.; methodology, K.N. and D.P.M. and L.P.; software, K.N.; validation, K.N. and D.P.M. and L.P.; formal analysis, K.N. and D.P.M. and L.P.; investigation, K.N. and D.P.M. and L.P.; resources, D.P.M. and L.P.; data curation, K.N.; writing--original draft preparation, K.N.; writing--review and editing, K.N. and D.P.M. and L.P.; visualization, K.N.; supervision, D.P.M. and L.P.; project administration, D.P.M. and L.P.; funding acquisition, D.P.M. and L.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University College of Dublin Advanced PhD Scholarship Scheme 41063.  
Not applicable.  
Data Availability StatementData is contained within the article.  
**Acknowledgments:** This work is financially supported by a University College of Dublin Advanced PhD Scholarship Scheme.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Method for Extracting Acoustic Water Surface Waves Based on Phase Compensation
## 3 Method  
Radar uses the microwave to detect the sea surface. Then, the phase of the range bin where the peak point is located for each pulse is extracted. According to the analysis in the second section, the extracted phase history may be discontinuous under wave interference. The Wavelet-Kalman Filtering filters out low-frequency sea surface waves through wavelet transform, and then the Kalman Filter filters out the phase noise to extract acoustic water surface waves. Wavelet decomposition convolves signals with a set of wavelet basis functions of different scales to obtain wavelet coefficients of different scales. Therefore, the wavelet coefficients at a point may be affected by the discontinuous values of the signal away from that point. The advantage of a second-order difference over wavelet transform is that the second-order difference operation is only related to the values of adjacent points, and discontinuous points have less influence on other points in the time series. This section introduces the method for extracting acoustic water surface waves based on phase compensation. The phase history is compensated based on whether the range bin changes. Then, the phase jump is amplified into up- and down-oscillation data through a second-order difference. Subsequently, outlier detection and removal can reduce the impact of phase discontinuity on time-frequency analysis and communication bit error rate. The algorithm flowchart is shown in Figure 4.  
Figure 3: Phase jumping example II: (**a**) range–time graph, (**b**) range bin and the phase history.
# Noncoherent Channel Combining for GNSS Signal Tracking with an Adaptive Antenna Array
## 4 Simulation Results  
Several simulations are carried out in this section to verify the performance of the proposed method and compare it with existing methods that have been mentioned earlier.