esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation of The Proposed Approach
### Experimental Results
However, a paired t-test suggested that the amplitudes from participants significantly differed in regard to the distance that each could cover with wrist-and-finger movement. Our finding supports the results reported in [33], according to which it is difficult for an operator to accurately move his or her hand to a desired position without the aid of visual information. \begin{table}
\begin{tabular}{c} **Accuracy** \\ **(Mean)** \\ \end{tabular} \\ \hline
**Average** & 1.0500 & 1.5000 & 6.9329 & 0.7015 & 0.0770 & 0.3408 \\ \hline
**Standard** & 0.4550 & 0.3983 & 1.1774 & 0.3377 & 0.0514 & 0.1780 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison between the precision and accuracy obtained from the conventional approach and the proposed approach. Figure 9: Plot comparisons of the (**a**) variance and (**b**) mean between the conventional approach and the proposed approach. Meanwhile, the average accuracy before filtering was 0.7014, which is 53.09% better than that of the conventional approach at 1.4952. It suggests that the proposed approach helps participants to stay closer to the target line more on average than the conventional approach does, which ensures that the filtered output will be close to the target throughout the operation. After filtering had taken place, the average precision of the proposed approach was 0.0783, whereas that of the conventional approach was 1.0231, indicating an improvement in precision of 92.34%. Likewise, the average accuracy of the proposed approach was 0.3381, whereas that of the conventional approach was 1.4952, indicating an improvement of 77.39%. The results from the paired t-test statistically prove that the proposed approach can minimize the variation in position caused by human arm movement error significantly. This improvement in total variation arises from the improvements in both precision and accuracy. Thus, the proposed arm movement pattern incorporating the Kalman filter significantly improves the precision and accuracy of the output. This is because moving the master controller across the target instead of parallel to it helps to reduce the chance of missing the target line so that accuracy is maintained. Therefore, the error in accuracy is minimized (close to target). Then, the Kalman filter improves the precision (straightness). Moreover, Figure 9 shows that our approach improved consistency for individuals and reduced the difference among the participants.
# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
## 3 Result and Discussion
### MSE and BER  
This section of the study focuses on evaluating the mean square error (MSE) and bit error rate (BER), as well as assessing the effectiveness of zero forcing (ZF), minimum mean square error (MMSE), and ELMx-based signal detection approaches in confirming the performance of several CNN-SD algorithms. These strategies were evaluated in ultra-massive MIMO systems that included 256 transmitting and receiving antennas, each with 256 QAM modulation mapping and a certain number of pilots. The attributes and approaches of the ELMx-based and CNN-SD algorithms are explained in Algorithms 1 and 2, correspondingly. The results of the comparative mean squared error (MSE) performance are shown in Figure 5. It was noted that ZF, because of its more rudimentary approach, was less successful compared to the other four methodologies. On the other hand, MMSE outperformed the least squares (LS) method, because of its increased complexity. The ELMx-based methodology, a machine learning technique, outperformed both ZF and MMSE by effectively using large training and testing datasets to accurately replicate the necessary data. The CNN-SD approach distinguished itself by showing a superior performance, which was attributed to its unique structural arrangement, which is optimized for exceptional results.  
Figure 6 shows the performance of BER for all signal detection techniques in an ultra-massive MIMO-based communication system, which included 256 transmitting antennas (MT) and 256 receiving antennas (MR). The CNN-SD outperformed the fundamental approaches such as LS, MMSE, and ELMx-based machine learning in signal detection. The  
Figure 5: The performance of MSE in ultra-massive MIMO systems.  
results revealed that the BER performance of the CNN-SD algorithm was the best among all methods.
# A Reliability Scheduling Algorithm for the Static Segment of FlexRay on Vehicle Networks +
## 2 Proposed Methodology
### Message Generator  
A set of messages, \(M\), is generated by the message generator module in the static segment. \(M\) = {\(M_{1}\), \(M_{2}\),..., \(M_{n}\)}, and each message \(M_{i}\) consists of four parameters, as in Equation (2), where \(T_{i}\) denotes the generation rate of \(M_{i}\), and \(D_{i}\) is the relative time of \(M_{i}\) since it was produced until the end of transmission. \(PF_{i}\) and \(S_{i}\) are the probability of failure during transmission and the size of \(M_{i}\) in bits, respectively. \(PF_{i}\) can be computed as Equation (3), where the bit error rates (BER) value is decided according to the type of wire and the environment [15]. On the other hand, when the message  
Figure 2: The proposed fast reliability scheduling algorithm (FRSA) architecture.  
is stored in a slot, its parameter \(D_{i}\) should be less than or equal to \(T_{i}\) to avoid being overwritten by other messages before transmission, as in Equation (4):  
\[M = \{M_{i}\left(T_{i},D_{i},PF_{i},S_{i}\right)\text{, }i=1,2,\ldots,n\} \tag{2}\] \[PF_{i}\ =\ 1\ -\ \left(1\ -\ \text{BER}\right)^{S_{i}}\] (3) \[\forall\ m\ \in M,\ D_{i}\ \leq\ T_{i} \tag{4}\]
## 2 Materials and Methods
### Multi-Frequency GPR Data Fusion
#### 2.2.2 Evaluation Method for Multi-Frequency GPR Data Fusion  
To verify the fusion effectiveness of the GPR data, \"Information Entropy (\(IE\))\", \"Space Frequency (\(SF\))\" and \"Laplacian Gradient (\(LG\))\" are employed to quantitatively evaluate the fusing performance of the multi-frequency GPR data.  
(1) \(IE\). \(IE\) is the basic concept of information theory, which describes the uncertainty of the occurrence of each possible event of a fusion information source. It mainly reflects the average information amount contained in fusion areas; the values of \(IE\) strongly correlate with the richness of the information contained in the fusion areas, which can be computed as [31]  
\[IE=-\sum\limits_{i=0}^{L-1}p(i)\log_{2}(p(i)) \tag{6}\]  
where \(p(i)\) represents the frequency of pixels value at grayscale, \(i\), and \(L\) is the total gray value of the image.  
(2) \(SF\). \(SF\) comes from human visual technology, which represents the overall activity level of an image by coupling the standard scalar quantization of frequency coefficients. Therefore, the larger values of \(SF\) imply that the image is clearer. For an \(M\times N\)-sized image, assume that each pixel processes the gray value, \(F(m,n)\), in position (\(m\), \(n\)); then, the \(SF\) can be calculated as  
\[SF=\sqrt{\left(\frac{1}{MN}\sum\limits_{m=1}^{M}\sum\limits_{n=2}^{N}(F(m,n)-F( m,n-1))^{2}\right)^{2}+\left(\frac{1}{MN}\sum\limits_{n=1}^{N}\sum\limits_{m=2}^{M}(F(m,n)-F( m-1,n))^{2}\right)^{2}} \tag{7}\]  
(3) \(LG\). \(LG\) refers to the second derivative gradient operator applied to the auto-focus system. In this paper, it will be applied as a clarity indicator to evaluate the fusing results. The \(LG\) function of point (\(x\), \(y\)) in image \(I\) (\(x\), \(y\)) can be computed as \[LG=\frac{\partial^{2}I}{\partial x^{2}}+\frac{\partial^{2}I}{\partial y^{2}}= \frac{1}{n}\times\sum_{x}\sum_{y}|I(x+1,y)+I(x-1,y)+I(x,y+1)+I(x,y-1)-4I(x,y)| \tag{8}\]  
where \(n\) is the total number of image pixels. The values of \(LG\) are proportional to the image clarity; usually, the clearer of image, the better the detailed features inside the image can be distinguished.
###### Abstract  
In the simulation of compressible turbulent flows via a high-order flux reconstruction framework, the artificial viscosity model plays an important role to ensure robustness in the strongly compressible region. However, the impact of the artificial viscosity model in under-resolved regions on dissipation features or resolving ability remains unclear. In this work, the performance of a dilation-based (DB) artificial viscosity model to simulate under-resolved turbulent flows in a high-order flux reconstruction (FR) framework is investigated. Comparison is conducted with results via several typical explicit subgrid scale (SGS) models as well as implicit large eddy simulation (iLES) and their impact on important diagnostic quantities including turbulent kinetic energy, total dissipation rate of kinetic energy, and energy spectra are discussed. The dissipation rate of kinetic energy is decomposed into several components including those resulting from explicit SGS models or Laplacian artificial viscosity model; thus, an explicit evaluation of the dissipation rate led by those modeling terms is presented. The test cases consist of the Taylor-Green vortex (TGV) problem at \(Re=1600\), the freely decaying homogeneous isotropic turbulence (HIT) at \(~{}Ma_{0}=0.5\) (the initial turbulent Mach number ), the compressible TGV at Mach number 1.25 and the compressible channel flow at \(Re_{b}=15,334\) (the bulk Reynolds number based on bulk density, bulk velocity and half-height of the channel), Mach number 1.5. The first two cases show that the DB model behaves similarly to the SGS models in terms of dissipation and has the potential to improve the insufficient dissipation of iLES with the fourth-order-accurate FR method. The last two cases further demonstrate the ability of the DB method on compressible under-resolved turbulence and/or wall-bounded turbulence. The results of this work suggest the general suitability of the DB model to simulate under-resolved compressible turbulence in the high order flux reconstruction framework and also suggest some future work on controlling the potential excessive dissipation caused by the dilation term.  
artificial viscosity; dissipation rate; high order method Article
###### Abstract  
Primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG) are prevailing eye diseases that can lead to blindness. In order to provide a non-invasive diagnostic method for glaucoma, we investigated the feasibility of using drop-coating deposition Raman spectroscopy (DCDRS) to discriminate glaucoma patients from healthy individuals based on tear samples. Tears from 27, 19 and 27 POAG patients, PACG patients and normal individuals, respectively, were collected for Raman measurement. For high-dimension data analysis, principal component analysis-linear discriminant analysis (PCA-LDA) was used to discriminate the features of the Raman spectra, followed by a support vector machine (SVM) used to classify samples into three categories, which is called a PCA-LDA-based SVM. The differences in the characteristic peaks of Raman spectra between glaucoma patients and normal people were related to the different contents of various proteins and lipids. For the PCA-LDA-based SVM, the total accuracy reached 93.2%. With the evaluation of 30% test dataset validation, the classification accuracy of the model was 90.9%. The results of this work reveal that tears can be used for Raman detection and discrimination by combining the process with the PCA-LDA-based SVM, supporting DCDRS being a potential method for the diagnosis of glaucoma in the future.  
Raman spectroscopy; glaucoma; tear; principle component analysis (PCA) 2023  
T. May 2023
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Model Interpretability
#### 5.4.3 Evaluation of the Existing Solutions  
LIME helps users understand which features affect individual predictions, aiding in troubleshooting and model improvement. However, its focus on local approximations can miss broader patterns and may be sensitive to the choice of local models, potentially leading to inaccurate interpretations [118]. SHAP offers a detailed and consistent view of feature importance, improving model transparency. Nonetheless, it can be computationally expensive, especially for complex models and large datasets [119]. Grad-CAM is useful for visualizing relevant areas in medical imaging, highlighting influential regions of an image. However, it is limited to convolutional neural networks and does not provide a complete explanation of the decision process [120]. t-SNE helps in understanding data structure and relationships through visualization, but it can be computationally intensive and may distort global data structures, leading to potential misinterpretations [121]. In conclusion, LIME is suitable for local interpretability but may overlook global patterns, while SHAP provides a comprehensive view of feature importance at the expense of computational efficiency. Grad-CAM is effective in visual domains but model-specific, and t-SNE is beneficial for data visualization but may distort global data structures. Combining multiple XAI techniques can offer a more complete understanding of model behavior, addressing the limitations of individual methods and enhancing decision-making processes.
## 3 Results and Analysis
### Impact of Ice Content on Deposition Characteristics  
Under identical initial accumulation forms, debris flows with varying ice contents display distinct accumulation patterns after scraping the same base material. As the ice content increases, the deposition length on the flat panel lengthens and the accumulation area expands (Figure 7). This trend indicates that higher ice content enhances the rock-ice avalanches capacity to cover a larger area and achieve greater accumulation lengths following the scraping process.  
Figure 6: Schematic diagram of the relationship between scraping characteristics and three initial ice accumulation forms under varying ice contents: (**a**) relationship between scraping length and initial ice accumulation form; (**b**) relationship between maximum scraping depth and initial ice accumulation form.  
Figure 7: Final accumulation patterns.  
The maximum deposition length and thickness of rock-ice avalanches increase progressively with ice content. For the pure ice group (100% ice content), the maximum deposition length is approximately 4.15 times greater than that of the pure rock group (0% ice content). The increase in deposition thickness is even more pronounced, with the pure ice group exhibiting an 8.2-fold increase compared to the pure rock group (Figure 8).  
Analysis of these results indicates that during the scraping process, a portion of the rock-ice mixture scrapes the base material and continues to move, eventually depositing on the plate. The rock-ice mixture located above the scraping zone bypasses this area and accumulates directly on the plate. Given that ice has a lower density than rock, and the total mass remains constant, a higher ice content leads to a larger relative volume of ice in the initiating material. Consequently, as ice content increases, the volume of the mixture that flows directly to the plate also increases, resulting in a progressive rise in both maximum deposition length and thickness.  
Observations of the deposition plate revealed that the deposition primarily consists of ice blocks. During retrieval, it was noted that the upper layer of the deposited material is composed mainly of ice blocks, while the lower layer contains some particles from the scraped base material and rock particles from the initial mixture. A distinct particle segregation phenomenon was observed, with particles separating based on size, density, and shape during the flow process [26]. This segregation effect results in the deposition patterns shown in the figure, demonstrating how the lower density of ice compared to rock influences particle distribution during the sscraping process.
# Towards Efficient Sink Mobility in Underwater Wireless Sensor Networks
## 6 Conclusions  
Results in this paper illustrate that the efficient and optimum utilization of mobile sinks play a crucial role in improving throughput and network lifetime of the routing protocol. Moreover, monitoring the utilization of the resources that are used in the protocol helps in improving the results of any routing scheme. In the proposed scheme, MUR is calculated in each round, which indicates the utilization of mobile sinks. The mobility pattern of the mobile sinks kept the MUR higher and thus the proposed scheme results in better throughput with balanced energy utilization.  
The use of mobile sinks can result in high end-to-end delays. Consequently, delay in each round for the proposed scheme should be evaluated in future works. Additionally, a rigorous evaluation of MUR in various UWSN scenarios will be performed in the future. Additionally, evaluation of the proposed scheme in the 3D plane can be further investigated in the future. Moreover, the holding time mechanism can be applied to decrease the packet drop ratio.  
Figure 6: Mobile sink utility ratio.  
Figure 7: Packet drop ratio.  
* [PERSON] proposed and the main idea under the supervision of [PERSON] and [PERSON]. A. A. A. and S.S.S. organized the flow and refined the manuscript. [PERSON] reviewed the manuscript.  
The authors declare no conflict of interest.
# Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation
## 2 Preliminaries
### Unscented Kalman Filter
#### 2.2.2 Update  
Similarly, a set of \(2n+1\) sigma points are computed by the predicted state mean and covariance matrix  
\[\begin{split}\chi^{0}(k|k-1)&=\widehat{\mathbf{x}} (k|k-1),\\ \chi^{i}(k|k-1)&=\widehat{\mathbf{x}}(k|k-1)\\ &\quad+\left(\sqrt{(n+\lambda)\mathbf{P}(k|k-1)}\right)_{i},i=1, \ldots,n,\\ \chi^{i}(k|k-1)&=\widehat{\mathbf{x}}(k|k-1)\\ &\quad-\left(\sqrt{(n+\lambda)\mathbf{P}(k|k-1)}\right)_{i-n},i= n+1,\ldots,2n.\end{split} \tag{16}\]  
The transformed set is given through the measurement model  
\[\gamma^{i}(k)=\mathrm{h}(k,\chi^{i}(k|k-1)),i=0,\ldots,2n. \tag{17}\]  
Then, the predicted measurement mean and covariance matrix are calculated by  
\[\widehat{\mathbf{y}}(k)=\sum_{i=0}^{2n}w_{m}^{i}\gamma^{i}(k), \tag{18}\]\[\mathbf{P_{yy}}=\sum_{i=0}^{2n}w_{c}^{i}\left[\gamma^{i}(k)-\widehat{\mathbf{y}}(k )\right]\left[\gamma^{i}(k)-\widehat{\mathbf{y}}(k)\right]^{T}+\mathbf{R}(k). \tag{19}\]  
In addition, the state-measurement cross-covariance matrix is computed by  
\[\mathbf{P_{xy}}(k)=\sum_{i=0}^{2n}w_{c}^{i}\left[\chi^{i}(k|k-1)-\widehat{ \mathbf{x}}(k|k-1)\right]\left[\gamma^{i}(k)-\widehat{\mathbf{y}}(k)\right]^{ T}. \tag{20}\]  
Next, the Kalman gain is given as  
\[\mathbf{K}(k)=\mathbf{P_{xy}}\mathbf{P_{yy}^{-1}}. \tag{21}\]  
Finally, the filter state in this case is obtained by  
\[\widehat{\mathbf{x}}(k|k)=\widehat{\mathbf{x}}(k|k-1)+\mathbf{K}(k)\left( \mathbf{y}(k)-\widehat{\mathbf{y}}(k)\right). \tag{22}\]  
Additionally, the filter covariance is recursively updated as  
\[\mathbf{P}(k|k)=\mathbf{P}(k|k-1)-\mathbf{K}(k)\mathbf{P_{yy}}\mathbf{K}^{T}(k). \tag{23}\]
# Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar
## 5 Conclusions  
Through the stationary or scanning operating mode of the mono-static UWB life-detection radar, the two different methods proposed can deal with the cases when there exist quasi-static trapped human beings. The stationary operating mode can enhance and extract components with similar echo characteristics in the measured area by cross-correlation in the slow-time domain, achieving the accurate detection of the non-periodic but non-weak micro-motion targets. The scanning operating mode can enhance and extract components with similar echo characteristics in the measured area by distributed radar deployment, achieving the quasi-periodic but weak micro-motion targets' accurate detection. A set of experiments show that the system has excellent performance in terms of detection range, scene adaptability and target distribution. The author made an interesting attempt in the actual rescue scene, indicating that the UWB radar system has better scene adaptability.  
Experimental results demonstrate the effectiveness of the proposed methods for detecting the weakly or quasi-/non-periodic echo responses of the trapped human beings.  
The research was performed by the authors as follows: Conceptualization, K.Y.; methodology, K.Y. and S.W.; software, K.Y. and S.W.; validation, K.Y.; formal analysis, S.W.; investigation, K.Y. and G.F.; resources, G.F.; data curation, K.Y.; writing--original draft preparation, K.Y.; writing--review and editing, S.W.; visualization, K.Y.; supervision, G.F.; project administration, S.W.; funding acquisition, G.F. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by the National Science Foundation of China under Grants 61971397 and 61501424. The authors wish to express their gratitude to the editor and the anonymous reviewers.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Performance Analysis
### Spectral Efficiency Computation
#### 3.2.2 Spectral Efficiency of NU  
As discussed in Section 2, NU directly communicates with the BS via the direct link in \(P_{1}\). However, NU keeps silent in \(P_{2}\). The overall SE of NU in \(S_{1}\) and \(S_{2}\) is therefore obtained respectively as  
\[\begin{array}{rl}R_{\text{SE,NU}}^{S_{1}}=&\frac{1}{2}Q_{1}\bigg{\{}1+P_{e, \text{NU}}^{A,P_{1}}\log_{2}(P_{e,\text{NU}}^{A,P_{1}})\\ &+(1-P_{e,\text{NU}}^{A,P_{1}})\log_{2}(1-P_{e,\text{NU}}^{A,P_{1}}))\bigg{\}}, \\ R_{\text{SE,NU}}^{S_{2}}=&\frac{1}{2}Q_{1}\bigg{\{}1+P_{e,\text{NU}}^{S,P_{1} }\log_{2}(P_{e,\text{NU}}^{S,P_{1}})\\ &+(1-P_{e,\text{NU}}^{S,P_{1}})\log_{2}(1-P_{e,\text{NU}}^{S,P_{1}}))\bigg{\}}. \end{array} \tag{9}\]  
where \(Q_{1}=\log_{2}(A_{\text{BS}})\) in \(S_{1}\) and \(Q_{1}=\log_{2}(M_{1})\) in \(S_{2}\). In (9) and (10), the scaling factor \(\frac{1}{2}\) is used to denote that NU is unable to receive data from BS in \(P_{2}\).
# Assessing Cyber Challenges of Maritime Navigation
## 3 Findings
### Cyber Trends and Challenges
#### 3.3.9 Environmental Pollution  
According to research, the side effects of disasters caused by a hacked port system or deluded onboard ship system could include environmental threats. The key to the security of the waterways is agility and constant paradigm-shifting to out-manoeuvre those who want to damage or disrupt the maritime transportation system (MTS) [69; 131]. One might expect that cybercriminals, terrorists,and rogue states will, at some point, begin holding the environment to ransom--cyber-induced environmental pollution [2]. It is a possible scenario that hackers would over-ride ship systems in ports, open valves and initiate leaks, or dump hazardous materials, ballast water, fuel, or oil.