esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Bayesian Estimation  
Different from frequentist methods, the advantage of Bayesian estimation resides in its capacity to integrate prior knowledge and sample data, enabling more stable parameter inference in scenarios with small samples. In this section, we derive the Bayesian estimates and the corresponding interval estimates in the case where the prior distribution of \(a_{j}\) is related to \(b\).  
We suppose that the parameter \(b\) is assigned an exponential prior distribution with the mean equal to \(\frac{1}{\gamma}\), while each \(a_{j}\) follows a Gamma prior distribution with the shape parameters \(\theta_{j},j=1,2,\ldots,s\) and the rate parameter \(b\). [PERSON] [18] also employed a similar assumption. Moreover, \(a_{1},a_{2},\ldots,a_{s}\) are independent of each other. The PDFs of the prior distributions of \(a_{1},a_{2},\ldots,a_{s}\) and \(b\) are presented as:  
\[\pi_{0}(b)=\lambda e^{-\lambda b},b>0;\lambda>0,\]\[\pi_{j}(a_{j}|b)=e^{-ba_{j}}\frac{b^{\theta_{j}}a_{j}^{\theta_{j}-1}}{\Gamma( \theta_{j})},a_{j}>0;\theta_{j}>0,j=1,2,\ldots,s,\]  
where \(\lambda\) and \(\theta_{j}\) are known.  
So the joint prior density can be given by:  
\[\pi(\mathbf{a},b)=(\prod_{j=1}^{s}\pi_{j}(a_{j}|b))\pi(b)=\lambda e^{-\lambda b }b^{\theta^{*}}e^{-ba^{*}}\frac{\prod_{j=1}^{s}a_{j}^{\theta_{j}-1}}{\prod_{j= 1}^{s}\Gamma(\theta_{j})}, \tag{30}\]  
where \(\theta^{*}=\sum_{j=1}^{s}\theta_{j}\).  
Combining (9) and (30), the PDF of the joint posterior distribution of \(a_{1},a_{2},\ldots,a_{s}\) and \(b\) is derived as:  
\[\pi(\mathbf{a},b|data)\propto b^{\theta^{*}+n_{1}}e^{-b(a^{*}+\lambda)}( \frac{1}{a^{*}})^{m_{2}}\prod_{j=1}^{s}a_{j}^{m_{j}+\theta_{j}-1} \tag{31}\]  
The squared error loss function (\(L_{SEL}(\hat{\xi},\zeta)=(\hat{\xi}-\zeta)^{2}\)) is widely used in statistics, where \(\zeta\) represents the true parameter value and \(\hat{\xi}\) is the corresponding estimated value. Squared error loss in Bayesian estimation leads to the posterior mean as the optimal estimator, naturally integrating prior knowledge with observed data to reduce variance and providing inherent uncertainty quantification through the posterior distribution. Under the squared error loss function, the Bayesian estimators for \(a_{1},a_{2},\ldots,a_{s}\) and \(b\) are the posterior conditional expectations, calculated based on (31).
# Integration of Wavelet Denoising and HHT Applied to the Analysis of Bridge Dynamic Characteristics
## 3 Time-Frequency Analysis Method Based on Wavelet Threshold Denoising and HHT
### Wavelet Threshold Denoising  
The wavelet threshold method is popular in signal denoising, whose basic idea is to denoise the non-stationary signal as follows: Firstly, multi-scale decomposition is performed on the noise-containing signal. Secondly, an appropriate threshold function is selected to threshold the wavelet coefficients. Lastly, the inverse wavelet transform is used to reconstruct each signal and achieve denoising. If signal \(f(t)\) is a square integrable function, then the wavelet transform of \(f(t)\) is the inner product of the signal and wavelet function \(\psi_{ab}(t)\):  
\[W_{t}(a,b)=\langle f,\psi_{ab}\rangle=|a|^{\frac{1}{2}}\int\limits_{R}\overline {\psi(\frac{t-b}{a})}f(t)dt. \tag{13}\]  
where \(a\) represents the scale factor, \(b\) represents the translation or displacement factor, \(a,b\subset R\), and the conjugate function of \(\psi\) is denoted as \(\overline{\psi}\). Wavelet transform analyzes signal by scaling and shifting the position of \(\psi(t)\). Thus, the wavelet transform results possess good time- and frequency-domain localities when the wavelet function is selected properly. Selecting the appropriate threshold function in the practical application of wavelet threshold denoising is extremely important because it minimizes the noise in noisy data and preserves the local characteristics of the effective signal. Although hard and soft threshold denoising methods are widely used in data filtering, they still have shortcomings. The works in [25; 26; 27; 28; 29] proved that the semi-threshold function shown in Equation (14) may obtain good denoising results.  
\[T_{\lambda}(d)=\left\{\begin{array}{ll}0&|d|\leq\lambda_{B}\\ \mathrm{sgn}(d)\frac{C\lambda_{B}(|d|-\lambda_{B})}{(C-1)\lambda_{B}}&\lambda_ {B}<|d|\leq C\lambda_{B}\\ d&|d|>C\lambda_{B}\\ \end{array}\right.. \tag{14}\]  
where \(\lambda_{B}\) is the Bayes-hrink thresholding, \(\lambda_{B}=\widehat{\sigma}_{N}/\widehat{\sigma}_{Y}\), and \(C\) is a constant that usually has a value greater than one. The threshold function has continuity and high-order conductivity. It overcomes the discontinuity and oscillation problems of the signal reconstructed from the hard threshold function and addresses the deficiency that although the soft threshold function is continuous, the denoising results need to be subtracted from the threshold; systematic deviation exists when the coefficient exceeds the threshold.
# High-Dimensional Data Analysis Using Parameter Free Algorithm Data Point Positioning Analysis
## 7 Conclusions  
This study proposed a novel clustering algorithm (DPPA) to efficiently cluster the higher-dimensional dataset. The proposed algorithm does not depend on any predefined parameter value, meaning no need to analyze the values of the different parameters, like DBSCAN. For this purpose, this approach calculates 1-NN and Max-NN without requiring the initial manual parameter assignment by examining the positions of data points in the dataset. Therefore, it is no longer required to define the appropriate radius Eps and the minimum number of points MinPts, since the radius range, \(\lambda\), is automatically computed by evaluating the position and distance of the data points. The proposed algorithm was validated using two publicly available higher-dimensional datasets. To compare the performance of the proposed model, this study also implemented four popular clustering algorithms. The experimental outcomes demonstrated that the proposed algorithm could identify the right number of clusters from the higher-dimensional dataset.  
The following are the main benefits of our proposed method over previous non-parameter-free techniques:  
* This study proposed a novel approach for clustering data, called data point positioning analysis (DPPA), to enhance the efficiency of high-dimensional dataset clustering.
* In this method, there is no need to pre-specify the number of clusters; whereas traditional clustering methods often require the number of clusters to be determined beforehand. This makes parameter-free methods more flexible and adaptable to different datasets.
* This proposed parameter-free clustering algorithm is better able to handle noisy or outlier data points since it uses density-based clustering techniques that do not depend on distance measures alone.
* The study compared the proposed method to four popular clustering algorithms and demonstrated that the proposed method achieves superior performance in identifying clusters.  
In the future, we intend to compare the performance of the proposed method with various datasets. Future research directions for decision-dependent uncertainty in clustering algorithms in critical fields, such as electricity grids and petroleum offshore platforms, include exploring ensemble clustering techniques to improve the reliability of results, and incorporating prior knowledge or domain expertise to enhance the accuracy and effectiveness of the results. Developing methods for quantifying and evaluating uncertainty, as well as investigating the impacts of different evaluation metrics on decision-making processes, are also important areas of future work. Furthermore, applying clustering in other critical fields and evaluating its effectiveness would contribute to the advancement of clustering algorithms and improve decision-making processes in these fields.  
The work is funded by Zayed University under the start-up grant--R21043.
# Exploiting Data Similarity to Improve SSD Read Performance
## 2 Background and Research Motivation  
### SSD Architecture  
Figure 1 shows the internal organization of an SSD. The components of an SSD are divided into two groups :the front-end and the back-end. The front-end is responsible for managing the back-end resources and issuing I/O transactions to the back-end channels [7]. The Host Interface Logic receives an I/O request from the host, splits it into page-sized sub-requests with specific Logical Page Number (LPN) [8], and inserts them into device queues for internal processing. The Flash Translation Layer (FTL) maintains a mapping table that tracks the current physical location, known as the Physical Page Number (PPN), of each LPN. When handling read sub-requests, the FTL searches the mapping table to find the corresponding PPN based on the LPN. For write requests, the FTL employs a page allocation scheme to assign free pages for data storage. The page allocation determines the target channel ID, chip ID, die ID, and plane ID of a page-sized transaction, following a specific priority order of the parallelism levels [9].  
In the subsequent steps, a block ID is assigned to the target plane, and pages are programmed in a predetermined order within the block. Subsequently, the PPN of the block is calculated. After the completion of address translation, the page-sized transactions are delivered to the transaction scheduling unit (TSU) for scheduling their execution on the target die [10]. The front-end manages the back-end resources and issues I/O requests to the back-end channels via the I/O scheduler. The SSD back-end contains multiple channels.  
Each channel is connected to one or more chips. Each chip is made up of one or more chips, each of which has one or more planes. For processing I/O requests, such an organization offers four layers of parallelism (channel, chip, die, and plane).
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
###### Abstract  
In vehicular ad hoc networks (VANETs), helpful information dissemination establishes the foundation of communication. One of the significant difficulties in developing a successful dissemination system for VANETs is avoiding traffic fatalities. Another essential success metric is the transfer of reliable and secure warning messages through the shortest path, particularly on highways with high mobility. Clustering vehicles is a general solution to these challenges, as it allows warning alerts to be re-broadcast to nearby clusters by fewer vehicles. Hence, trustworthy cluster head (CH) selections are critical to decreasing the number of retransmissions. In this context, we suggest a clustering technique called Optimal Path Routing Protocol for Warning Messages (OPRP) for dissemination in highway VANETs. OPRP relies on mobility measured to reinforce cluster creation, evade transmission overhead, and sustain message authenticity in a high mobility environment. Moreover, we consider communication between the cluster heads to reduce the number of transmissions. Furthermore, the cluster head is chosen using the median technique based on an odd or even number of vehicles for a stable and lengthy cluster life. By altering traffic densities and speeds, OPRP is compared with prominent schemes. Simulation results revealed that OPRP offers enhanced throughput, end-to-end delay, maximizing packet delivery ratio, and message validity.  
vehicle: 21 June 2022  
Accepted: 5 September 2022  
Published: 9 September 2022  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 4 Test Results Using the IDHIL Simulator
### Differences due to Target SOC in the A-ECMS  
Figure 23 shows the SOC of normal PHEV #3 and connected PHEV #2. They appear to be very different, but a closer look reveals that their charge/discharge tendencies are similar: they each discharge in the same interval, but sometimes the electrical energy usage is limited by the target SOC, as in Figure 23b. In other words, the charge/discharge tendency is the same but the amount of charge/discharge is different. On the other hand, there are cases where the charge/discharge tendencies are opposite, as shown in Figure 23c where the connected PHEV has a discharge tendency and the normal PHEV has a charge tendency. The target SOC in this study is determined for each road segment, rather than a profile as was the case in [25]. This has the advantage of simplicity and low computational load. However, the fuel economy performance is lower compared to the SOC profile. In addition, the long initialization period of \(SOC_{0}\) causes the error between actual SOC and target SOC to be larger. Nevertheless, it can be seen that the charge/discharge tendency changes significantly when compared to a normal PHEV. The connected PHEV using the A-ECMS with a target SOC adjusts the charge/discharge amount and the tendency of the battery according to the information of the road to be traveled in the future. Therefore, it can perform more efficient power distribution than a normal PHEV that only considers the torque demanded by the driver's pedal operation and the vehicle state.
## 3 Results
### Confirmatory Factor Analysis of the RAC-Qm  
The construct validity of the initial model (model 1) was determined using CFA. Model 1 contains items similar to the original version. In general, model 1 had a reasonable goodness of fit, with a chi-square statistic divided by the degree of freedom of 1.43, SRMR of 0.07, RMSEA of 0.06, CFI of 0.92, and TLI of 0.90. Model 1 also showed good reliability, with a Cronbach's alpha of 0.85. The standardized factor loadings for all items were >0.40, except that for question number eight, which was 0.40, as shown in Table 4. Model 2 was created by excluding question number eight. Overall, model 2 did not have significantly better indices, and removing question number eight did not improve any other item's factor loading, as shown in Table 5. Figure 2 shows the path diagram of the RAC-Q. There was no multicollinearity between items, and all the factor loadings were acceptable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Factor Loading** & **Cronbach’s Alpha** & **Composite Ratio** \\ \hline Q1 & 0.48 & 0.857 & 0.857 \\ Q2 & 0.43 & & \\ Q3 & 0.73 & & \\ Q4 & 0.74 & & \\ Q5 & 0.63 & & \\ Q6 & 0.71 & & \\ Q7 & 0.68 & & \\ Q8 & 0.40 & & \\ Q9 & 0.53 & & \\ Q10 & 0.56 & & \\ Q11 & 0.67 & & \\ Q12 & 0.68 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Factor loading and internal consistency reliability of RAC-Qm.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Model** & \(\mathbf{x^{2}}\) **(df)** & \(\mathbf{x^{2}}\)**/df** & \(\mathbf{p}\) & **SRMR** & **RMSEA** & **90\% CI** & **CFI** & **TLI** & **AIC** & **BIC** \\ \hline Model 1 & 77.7 (54) & 1.43 & 0.019 & 0.07 & 0.06 & 0.03, 0.08 & 0.92 & 0.90 & 1137.52 & 1207.77 \\ Model 2 & 59.4 (44) & 1.35 & 0.059 & 0.07 & 0.05 & 0.02, 0.07 & 0.94 & 0.93 & 1006.14 & 1070.54 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confirmatory factor analysis of RAC-Qm.
## 1 Introduction
### Contributions  
In this study, we propose to implement enhancement on the GFDM model. In addition, the traditional GFDM model is enhanced by using polyphase decomposition and FT transformations. The proposed hexagonal GFDM is such a candidate scheme with a smaller symbol duration and has lower complexity. The main innovations and contributions are given as follows:  
1. To decrease symbol duration and also latency, we combine hexagonal time-frequency lattice structure with the GFDM, which is referred to as the _hexagonal GFDM_. The hexagonal lattice structure is a more optimal time-frequency allocation way than the rectangular structure. The symbol duration and thus latency is decreased by using proposed hexagonal GFDM.  
2 To induce computational complexity, we use the polyphase decomposition of the prototype filter. Thus, the computational complexity is reduced by using parallel processing techniques of polyphase decomposition.
3 We distinguish the proposed model and the traditional GFDM model and show that the symbol duration and computational complexity are decreased. The number of symbols on the same time and frequency interval is risen by using the hexagonal GFDM model.  
In this way, the proposed hexagonal GFDM model provides robustness to the channel effects and low latency dependent on the time slot structures with its hexagonal structure. Filtering with polyphase decomposition decreases the computational complexity and supplies low complexity and long battery life.
# Optimal Base Station Density of Dense Network:
## 4 Throughput of MAC Layer in SmBS Tier
### Analysis of the Channel Load  
To obtain the channel load, we need to analyze the average number of users that connect to an SmBS. Based on the aforementioned analysis of user association, it has the following results.  
**Lemma 5**: _The average load of an SmBS is obtained by_  
\[\mathrm{G}=\frac{\lambda_{\mathrm{u}}\lambda_{\mathrm{data}}}{\hat{\mathcal{C}} \lambda_{\mathrm{M}}+\lambda_{\mathrm{m}}}, \tag{14}\]  
_where \(\hat{\mathcal{C}}=\left(\frac{P_{\mathrm{M}}}{BP_{\mathrm{m}}}\right)^{\frac{ 2}{\alpha}}\)._  
Proof of Lemma 5.: See Appendix C.  
Lemma 5 gives the insight for channel load control. Besides some objective parameters, the load of the SmBS depends on its density \(\lambda_{\mathrm{m}}\). As the density of the SmBS increases, there are more SmBSs and their average load decreases.
# Comparative Aerodynamic Performance Analysis of Camber Morphing and Conventional Airfoils
## 4 Results
Figure 5:2 **(A–C)** Down each column, comparison of **(a)**\(C_{L}\), **(b)**\(C_{D}\), and **(c)** L/D of morphing (yellow) and conventional airfoils (purple). In the overlapping region, on average, at least a 1.7% improvement in the \(L/D\) ratio can be expected for all cases. Outside the overlapping region, where the \(L\) generated is not approximately the same for both cases, cambered configurations generate more \(L\) and less \(D\), thus improving the \(L/D\) ratio significantly. In addition, the morphing airfoils stall at a higher \(\alpha\) than the corresponding conventional airfoil which also improves the \(L/D\) tremendously. Though the percentage improvement for \(\alpha\) outside of the overlapping regions could have been ignored, it was included in Figure 12A,B for considering actual implementation and flight of morphing aircraft with given angles. As shown in Figure 12A, NACA 7412 or a 7% camber-morphed airfoil shows a higher \(L/D\) improvement rate. However, it is noted from Table 2 that the NACA 7412 airfoil has the smallest overlapping region, so the improvement rate correlates to the fact that its corresponding conventional airfoil configuration generated lesser \(L\) and more \(D\) (as shown in Figure 11). The \(L/D\) improvement rate increases gradually as the camber rate increases in Figure 12B, and the greatest improvement can be expected when morphing with 7%, 8% or 9% camber rates. Consequently, the high values for the improvement rate in Figure 12 highlights the benefits of morphing. It is also implacable and meaningful to compare the \(D\) generated by different morphed and deflected configurations to visualize the reduction in drag due to morphing. Figures 13-15 show the \(C_{D}\) plots that were cut off at the stall angle for better visualization. As seen in Figure 15, it is noteworthy that morphing the camber until the highest rate of 9%, still generates a lesser \(D\) than deflecting the flap 11.75\({}^{\circ}\) (which is the deflected configuration from case 7 that matches the \(L\) of the profile of the NACA 7412 airfoil). An investigation of the behaviour of morphing wings at a low Reynolds number such as 76,630 is not a common practice, but small UAVs or MAVs--for which morphing wings are designed for--fly in flight conditions around this range so it is worthwhile to investigate the benefits of morphing in this flight condition. In the comparative analysis conducted between morphed and deflected airfoils, all morphed configurations output a better L/D ratio than the deflected configuration for every matching lift case studied. Figure 5:3 (**A**) Improvement in the L/D ratio when the camber is morphed instead of deflecting the flap. (_Re_ = 76,630). (**B**) Improvement in the L/D ratio as the camber rate is varied. (_Re_ = 76,630). Figure 5:4 Morphing airfoils \(C_{D}\) plots, generated by varying the maximum camber rate. (_Re_ = 76,630).
## 5 Experimental Analysis
### Jamming Success Rate Optimization  
The jamming success rate is the core indicator of jamming optimization. The total receiver bit error rate in a certain area measured according to actual satellite signals was used to reflect the jamming success rate, where a higher bit error rate indicates a higher jamming success rate. In the experiment testing jamming success rate optimization, assuming that  
Figure 9: Variation curve of jamming coverage rates for different optimization methods.  
LEO satellites send a 1000-bit stream to ground users and under the constraint of at least 90% jamming coverage, the NSGA-III algorithm was tested first, followed by comparison with the c-DPEA, MTCMO, MSCEA, and URCMO algorithms. The changes in the bit error rates corresponding to different optimization algorithms are plotted in Figure 10.  
From Figure 10, it can be observed that during the initial iterations, the NSGA-III algorithm produced the highest bit error rate, while the URCMO algorithm produced the lowest bit error rate. With an increase in the number of iterations, the bit error rate of the NSGA-III algorithm grew the most, surpassing the other four algorithms, while the bit error rate growth of the URCMO algorithm was the smallest. At the end of the iterations, the NSGA-III algorithm generated 102 bit errors; the c-DPEA and MTCMO algorithms produce 51 and 45 bit errors, respectively; the MSCEA algorithm produced 38 bit errors; and the URCMO algorithm produced only 24 bit errors. These results demonstrate the efficiency of the NSGA-III algorithm for optimizing the jamming success rate in distributed jamming of the LEO satellites' downlinks.
# Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems
###### Abstract  
As the informatization of power systems advances, the secure operation of power systems faces various potential network attacks and threats. The false data injection attack (FDIA) is a common attack mode that can lead to abnormal system operations and serious economic losses by injecting abnormal data into terminal links or devices. The current research on FDIA primarily focuses on detecting its existence, but there is relatively little research on the localization of the attacks. To address this challenge, this study proposes a novel FDIA localization method (GA-CNN-LSTM) that combines convolutional neural networks (CNNs), long short-term memory (LSTM), and a genetic algorithm (GA) and can accurately locate the attacked bus or line. This method utilizes a CNN to extract local features and combines LSTM with time series information to extract global features. It integrates a CNN and LSTM to deeply explore complex patterns and dynamic changes in the data, effectively extract FDIA features in the data, and optimize the hyperparameters of the neural network using the GA to ensure an optimal performance of the model. Simulation experiments were conducted on the IEEE 14-bus and 118-bus test systems. The results indicate that the GA-CNN-LSTM method achieved F1 scores for location identification of 99.71% and 99.10%, respectively, demonstrating superior localization performance compared to other methods.  
power systems; attack detection; deep learning; genetic algorithm +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Optimization CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems. _Appl. Sci._**2024**, _14_, 6865. https:////doi.org/10.3390/app14166865  
Academic Editor [PERSON]  
Received: 1 July 2024  
Revised: 3 August 2024  
Accepted: 5 August 2024  
Published: 6 August 2024