esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Fault Diagnosis of Rolling Element Bearings Based on Modified AlexNet +
## 3 Experimental Verification Based on Vibration Signals
### Experimental Outcome
#### 3.2.3 Evaluation in a Noisy Situation  
Noise has an impact on signals in real applications in industry. Noise is an additional problem that is created because of changes in the working environment, which decrease the model's performance. In the following section, the effectiveness of the proposed system is investigated in a noisy situation while identifying bearing faults. Before being tested on noisy signals, the proposed model is trained on the original signal. Additive white Gaussian (AWG) noise is added to create a noisy signal by changing the signal-noise ratio (SNR) to raw signals. The SNR is determined as follows:  
\[\text{SNR }=10\log_{10}(\frac{P_{signal}}{P_{noise}}) \tag{13}\]  
The proposed system is validated with noisy signals. Figure 14 depicts the performance of the proposed system in noisy situations with SNR values ranging from \(-10\) dB to \(10\) dB. The red line in Figure 14 shows classification effectiveness during a noisy environment with SNR values between \(-10\) dB and \(10\) dB, while the blue line shows classification performance in the same environment with the same SNR values. However, even in a noisy  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **TP** & **FP** & **FN** & \
\begin{tabular}{c} **Precision** \\ **Rate** \\ \end{tabular} &
\begin{tabular}{c} **Recall** \\ **Rate** \\ \end{tabular} & **F1-Measure** \\ \hline Modified & 185 & 2 & 0 & 98.93\% & 100\% & 99.46\% \\ AlexNet-Softmax & 183 & 3 & 1 & 96.51\% & 98.81\% & 98.92\% \\ \hline Modified & 186 & 3 & 0 & 98.41\% & 100\% & 99.20\% \\ AlexNet-kNN & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation results of the proposed system.  
Figure 13: Evaluation results.  
environment with SNR = \(-10\) dB, the proposed system exhibits a high accuracy of 96.50% on the CWRU dataset and 95.40% on the MFPT dataset. The outcomes of this experiment indicate that the proposed system is strong and capable of handling noisy environments.
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 4 Research on Multi-Branch Line Fault Ranging Technology Based on Fault Branch Judgment Matrix
### Simulation Verification
#### 4.4.2 T-Node Failure  
When a fault occurs at node T2, the time for the line mode components to reach each endpoint is shown in Table 5. A fault branch judgment matrix is then constructed.  
Construct the fault branch judgment matrix according to Equation (11)  
\[\begin{bmatrix}\text{M}_{1}&\text{N}_{1}&\text{N}_{2}&\text{N}_{3}&\text{M}_{2} \\ \text{M}_{1}&\times&0&0&<0&<0\\ \text{N}_{1}&0&\times&0&<0&<0\\ \text{N}_{2}&<0&<0&\times&<0&<0\\ \text{N}_{3}&<0&<0&0&\times&0\\ \text{M}_{2}&<0&<0&0&0&\times\end{bmatrix} \tag{34}\]  
From Equation (36), the row elements corresponding to N2 are all less than zero. The column elements are all equal to zero, consistent with the T-node fault determination proposed above. Thus, the fault point can be identified as the T2 node.  
To calculate the fault distance using the N2 end as the initial end, the following is obtained:  
\[d_{N_{2}F}=\frac{(100.012+100.112+100.136+100.149)}{4}=100.102\ km \tag{35}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measurement Point Name** & **M1** & **N1** & **N2** & **N3** & **M2** \\ \hline \(\alpha\)-mode & 1120 & 1437 & 1273 & 276 & 1405 \\ \(\beta\)-mode & 1023 & 1356 & 1103 & 291 & 1312 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Time for the line modulus component to reach each endpoint.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measurement Point Name** & **M1** & **N1** & **N2** & **N3** & **M2** \\ \hline \(\alpha\)-mode & 720 & 1054 & 903 & 710 & 1056 \\ \(\beta\)-mode & 687 & 984 & 921 & 690 & 1145 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sampling numbers that the initial wave arrives at each measurement when the fault occurs on a T node.  
This demonstrates that the fault point has been accurately determined, with a positioning error is 102 m.
## 6 Discussion
### Strategies and Solutions for Design Implementation Challenges  
The complex challenges identified in the preceding sections necessitate systematic strategies and innovative solutions to ensure the optimal implementation of supplementary damping systems. Research by [PERSON] et al. [176] proposes a multi-objective optimization framework that addresses the damper selection and sizing challenges through a three-phase approach. The first phase involves preliminary sizing based on equivalent linear analysis, followed by refined optimization using non-linear time history analysis, and concluding with practical adjustments considering constructability constraints. This methodology has demonstrated a 15-20% improvement in performance compared to traditional design approaches.  
To address integration challenges, [PERSON] et al. [177] developed a comprehensive workflow that combines building information modeling (BIM) with structural analysis to identify potential conflicts and optimize damper placement. Their approach includes (1) early-stage coordination between structural engineers and architects to establish spatial constraints, (2) the development of standardized connection details for various damper types, and (3) the implementation of clash-detection algorithms to minimize conflicts. This integrated approach has been successfully implemented in several high-rise projects, reducing design iterations by approximately 30%.  
To overcome code compliance challenges, [PERSON] et al. [178] proposed a performance-based design framework that bridges the gap between various international standards. Their methodology incorporates the development of project-specific acceptance criteria that satisfy multiple code requirements and the implementation of hybrid testing protocols that address both component and system-level performance. This framework has been validated through several case studies, demonstrating its effectiveness in meeting diverse regulatory requirements.  
Regarding cost-effectiveness concerns, [PERSON] et al. [169] presented a life-cycle optimization strategy that balances initial costs with long-term benefits. Their approach includes the development of probabilistic cost-benefit models that account for regional seismic hazard levels and the incorporation of sustainability metrics in the decision-making process. Case studies indicate potential life-cycle cost reductions of 25-35% compared to conventional designs.  
Furthermore, recent advances in artificial intelligence and machine learning have led to innovative solutions for damper design optimization. [PERSON] et al. [82] demonstrated the application of deep learning algorithms for the real-time adjustment of damper properties based on structural response data. Their research shows that these adaptive systems can improve damper efficiency by up to 40% under varying loading conditions.
## 4 Clustering Routing Algorithm Design
### Cluster Head Selection Phase  
CHNs play a very important role in data transmission. The CHNs are responsible for processing the data received from their CMNs, and then forwarding the processed data to the SN. Many algorithms, such as the LEACH algorithm, generate CHNs in a random selection without considering the residual energy of the nodes. If the residual energy of the selected CHNs is too low, the nodes will die too early, which is bad for energy balance and network efficiency. Therefore, when selecting CHNs, the residual energy of the nodes should be considered. If the residual energy of one node is less than the average energy of the nodes in its cluster, it will not be qualified for the selection. In this paper, we consider not only the residual energy of nodes but also the distance factor to select CHNs. Hence, we propose an index for CHN selection as follows:  
\[I_{i}=\frac{\lambda E_{\textit{ires}}}{d_{\textit{is}}d_{\textit{avg}}} \tag{14}\]  
\[d_{\textit{avg}}=\frac{1}{N-1}\sum_{n=1}^{N-1}d_{\textit{in}} \tag{15}\]  
where \(\lambda\) is a constant, \(E_{\textit{ires}}\) is the residual energy of node \(i\), \(d_{\textit{is}}\) is the distance between node \(i\) and the SN, \(d_{\textit{avg}}\) is the average distance between node \(i\) and the other nodes in the cube, \(N\) is the total number of nodes in the cube, and \(d_{\textit{in}}\) is the distance between node \(i\) and node \(n\) in the cube. It can be seen from (14) that it is more likely for a node to become a CHN if it has more residual energy, a shorter distance to the SN, and a shorter average distance to the other nodes in the cube.  
In each cube, every qualified node calculates its value of \(I_{i}\) and broadcasts the message with its ID and \(I_{i}\) value to other nodes in the cube. Through comparisons, the node with the largest value of \(I_{i}\) will become a CHN. Then the CHN broadcasts the CHN message to the other nodes in the cube. After receiving the CHN message, the nodes reply to the CHN with an acknowledgement message and become CMNs. In addition, all the selected CHNs send message packets to the SN and the packets carry information such as the ID, the location, and the residual energy.
# Mapping with Pleiades--End-to-End Workflow
## 5 Results
### Digital Terrain Model Generation
#### 5.3.1 Qualitative Evaluation  
For visual comparison, the results of several ground pixel filtering methods are given in Figure 26 for an area of \(721\times 361\) m\({}^{2}\), showing a residential area at a hillside. The figure shows (a) the pansharpened true-ortho image (c) along with the input DSM. Further, it shows (b,d,e) the ground masks as resulting from DSM filtering by (b) using only two directions (i.e., left and right, which corresponds to the method in Reference [93]), (d) using 8 directions and (e) using 8 directions as well as slope dependent processing. It is clearly visible that due to the hillside location the first two methods filter too many points due to the negligence of the local terrain slope, whereas our advanced MSD method clearly yields a highly plausible and reliable segmentation. Forest areas and houses are removed while many ground points are correctly detected. Figure 27 shows a comparison of (a) 3D views of the input DSM and (b) the resulting DTM, which makes the removal of trees and houses very well visible. Figure 28 additionally shows detailed views of an urban and a suburban region.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 3 Proposed Methodology
### Hybrid Fusion Mechanism
#### 3.2.4 Cafm  
To further enhance feature representation, we introduce the CAFM. This hybrid architecture synergistically combines spatial convolution operations with channel-aware attention mechanisms, enabling simultaneous capture of localized patterns and global contextual relationships. As illustrated in Figure 4, the CAFM consists of two complementary branches to handle both local and global information.  
The local feature branch focuses on fine-grained spatial interactions through multistage convolution operations. The computation flow comprises three key steps:  
\[F_{\text{conv}}=\mathcal{W}_{3\times 3\times 3}(\phi_{\text{CS}}(\mathcal{W}_{1 \times 1}(Y))) \tag{10}\]  
where \(\mathcal{W}_{1\times 1}\) and \(\mathcal{W}_{3\times 3\times 3}\) denote \(1\times 1\) and \(3\times 3\times 3\) convolutional layers, respectively. The channel shuffle operation \(\phi_{\text{CS}}\) implements cross-channel communication by dividing the input tensor \(Y\in\mathbb{R}^{C\times H\times W}\) into \(g\) groups along the channel dimension, applying depth-wise separable convolution within each group, and finally concatenating and rearranging channels to preserve topological structure.  
Meanwhile, the global attention branch establishes long-range dependencies through compressed channel attention:  
\[Q,K,V =\mathcal{W}_{Q}(Y),\mathcal{W}_{K}(Y),\mathcal{W}_{V}(Y) \tag{11}\] \[A =\text{Softmax}\Bigg{(}\frac{\mathcal{R}(Q)\mathcal{R}(K)^{\top}}{ \alpha}\Bigg{)}\] (12) \[F_{\text{att}} =\mathcal{W}_{1\times 1}(A\mathcal{R}(V))+Y \tag{13}\]  
Here, \(\mathcal{R}(\cdot)\) reshapes tensors to \(\mathbb{R}^{\hat{C}\times\hat{H}\hat{W}}\), and \(\alpha\) is a learnable temperature parameter controlling the sharpness of the attention distribution. Finally, the two branches are fused through a residual connection, yielding the following CAFM output:  
\[F_{\text{out}}=F_{\text{conv}}\oplus F_{\text{att}} \tag{14}\]  
By integrating the CAFM, the model efficiently combines local and global features, significantly improving multiscale target detection and adapting to complex backgrounds.  
Figure 4: Structure of CAFM.
# Blockchain-Enabled Privacy-Preserving Ecosystem for DNA Sequence Sharing
## 3 System Design and Implementation
### System Implementation
#### 3.3.2 DNA Sharing Chain  
* Registration of Access Control Objects: The registration process requires users to upload a CSR and private key. Patients and research institutes select appropriate access control object types and sign a message to initiate registration. The Ministry of Health and Welfare verifies these details before generating access control objects. Automated tools streamline the process, except for MetaMask-triggered signing.
* Updating Access Levels: As shown in Figure 10, patients manage access levels for DNA segments through a table interface. Offline signing secures updates by signing and submitting transaction proposals. The private key, downloaded from the identity contract, ensures secure authentication. Changes are reflected on the DNA sharing chain upon submission.
* Uploading and Downloading DNA Sequences: DNA sequence uploading involves two steps. Patients provide unique IDs to hospitals, which store sequences in local off-chain databases using variant call format (VCF) for efficiency, optionally tagging  
Figure 10: Updating access levels. Green bars: Represent the currently assigned access levels for specific DNA segments. Gray bars: Represent unselected access levels that can be chosen or updated by the user.  
Figure 9: MetaMask extension.  
data with attributes like gender or medical history. Encrypted access tickets are then uploaded to the patient's access control object on the blockchain. Downloading DNA sequences involves querying, downloading access tickets, and requesting data. Research institutes submit signed queries with optional tags. The DNA sharing chain evaluates permissions and returns authorized tickets and URLs, enabling institutes to securely retrieve data from hospital databases.  
This implementation integrates blockchain-based security and access controls to ensure the robust and efficient management of sensitive DNA data.
# The Genetic and Imaging Key to Understanding Bicuspid Aortic Valve Disease
## 4 Linking Genetics and Imaging
### Role of Genetic Mutations in Imaging Findings  
Genetic mutations can lead to specific phenotypic manifestations observable through imaging. For instance, NOTCH1 mutations have been linked to early valve calcification and abnormal valve morphology. Echocardiography can reveal thickened and calcified valve leaflets, while CMR can provide detailed images of the calcification extent and associated aortopathy [37].  
GATA family genes, particularly GATA4 and GATA5, are crucial in cardiac development and can influence valve formation. Mutations in these genes might lead to valve dysfunctions detectable through echocardiography, such as bicuspid valves with abnormal cusp morphology or associated aortic dilatation [38]. SMAD6 mutations, which affect the TGF-beta signaling pathway, are associated with aortic aneurysms and valve abnormalities that can be precisely measured using CMR to monitor aortic dimensions and detect aneurysm formation [39].
## 5 Simulation Results
### Rates for Different UPA Configurations at the BS  
The multiplexing capabilities of the antenna array at the BS does not depend only on the number of antenna elements, but also on its geometrical configuration. In case of an UPA array, different configurations correspond to different number of columns and rows, changing respectively the number of elements along the \(y\)-axis \(N_{ty}\) and along the \(z\)-axis \(N_{tz}\). In order to evaluate the potential benefits of adopting one configuration rather than another, we simulate the same scenario with 72 antennas at the transmitter \(N_{t}\), but  
Figure 8: UPA radiation pattern with SLNR precoder \(\mathbf{w}_{k}\) targeted for UT \(k\) (\(\theta_{LOS,ZOD}=96.46^{\circ}\), \(\phi_{LOS,AOD}=30^{\circ}\)) with two interfering users, \(N_{ty}=16\), \(N_{tz}=8\), \(N_{r}=1\) for all users.  
Figure 7: Channel frequency response along 1024 subbands, with 16 transmit antennas at the BS. \(N_{ty}=8\), \(N_{tz}=2\), \(N_{r}=N_{ry}=2\). Single polarized.  
spanning three possible antenna arrays configurations \(N_{hy}\times N_{tz}\): \([36\times 2]\), \([24\times 3]\) and \([18\times 4]\). Moreover, the same configurations are compared both for the case of single and dual polarized elements. Given a traffic density of 2500 users/\(\mathrm{km}^{2}\), we expect, on average, 65 UTs in the cell, equally divided among the three sectors. Of course, the rates could be significantly lower depending on the particular channel realizations and interference. A UT is in outage if its SINR is lower than 0 dB. The six CDF curves obtained in this test are displayed in Figure 9. In the case of dual polarized elements, whereas for \([36\times 2]\) and \([24\times 3]\) configurations the curves are very close, the curve for \([18\times 4]\) configuration is subject to a noticeable rate loss. For single polarized elements, the \([36\times 2]\) configuration performs better. This could be explained by the higher resolution required along the azimuth angle \(\phi\) rather than the zenith angle \(\theta\), considering the possible user positions. However, each cell sector permits to serve the major percentage of the UTs simultaneously, achieving rates up to 800 Mbps for the single polarization and almost twice as much for the dual polarization.
# Robust Transceiver Design for IRS-Assisted Cascaded MIMO Communication Systems
## 3 Achievable Rate (Full-Duplex Relay)
### Algorithm Description
In Proceedings of the European Wireless 2017, 23 th European Wireless Conference, Dresden, Germany, 17-19 May 2017; VDE: Merianstrasse, Germany, 2017. * (29) [PERSON]; [PERSON] Sum-rate performance and impact of self-interference cancellation on full-duplex wireless systems. In Proceedings of the 2013 IEEE 24 th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), London, UK, 8-11 September 2013. * (30) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Capacity of the Gaussian two-hop full-duplex relay channel with residual self-interference. _IEEE Trans. Commun._**2017**, _65_, 1005-1021. [CrossRef]
* (31) [PERSON]; [PERSON]; [PERSON] Hybrid full-duplex/half-duplex relaying with transmit power adaptation. _IEEE Trans. Wirel. Commun._**2011**, _10_, 3074-3085. [CrossRef]
* (32) [PERSON]; [PERSON] Robust multi-user decode-and-forward relaying with full-duplex operation. In Proceedings of the 11 th International Symposium on Wireless Communications Systems (ISWCS), Barcelona, Spain, 26-29 August 2014. * (33) [PERSON]; [PERSON]; [PERSON]; [PERSON] Robust transceiver design for full duplex multiuser MIMO systems. _IEEE Wirel. Commun. Lett._**2016**, \(5\), 260-263. [CrossRef]
* (34) [PERSON]; [PERSON]; [PERSON] Robust transceiver design for full-duplex decode-and-forward relay-assisted MIMO systems. In Proceedings of the 54 th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 1-5 November 2020. * (35) [PERSON]; [PERSON] Distance-aware bandwidth-adaptive resource allocation for wireless systems in the terahertz band. _IEEE Trans. Terahertz Sci. Technol._**2016**, \(6\), 541-553. [CrossRef]
* (36) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Wireless communications with reconfigurable intelligent surface: Path loss modeling and experimental measurement. _IEEE Trans. Wirel. Commun._**2020**, _20_, 421-439. [CrossRef]
* (37) [PERSON]; [PERSON]; [PERSON] On the capacity of MIMO relay channels. _IEEE Trans. Inf. Theory_**2005**, _51_, 29-43. [CrossRef]
* (38) [PERSON]; [PERSON]; [PERSON]; [PERSON] Robust MIMO precoding for several classes of channel uncertainty. _IEEE Trans. Signal Process._**2013**, _61_, 3056-3070. [CrossRef]
* (39) [PERSON]; [PERSON]; [PERSON]; [PERSON] Robust optimization for amplify-and-forward MIMO relaying from a worst-case perspective. _IEEE Trans. Signal Process._**2013**, _61_, 5458-5471. [CrossRef]
* (40) [PERSON] Bounds for the determinant of the sum of hermitian matrices. _Proc. Am. Math. Soc._**1971**, _30_, 27-31. [CrossRef]
* (41) [PERSON]; [PERSON]; [PERSON] _Inequalities: Theory of Majorization and Its Applications_; Academic Press: New York, NY, USA, 1979; Volume 143.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{\begin{tabular}{c} **Sample** \\ **Pretreatment** \\ **Technique** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Determination** \\ **Technique** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Time of Sample** \\ **Pretreatment** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Time of** \\ **Determination** \\ \end{tabular} } & \multirow{2}{*}{**LOD**} & \multirow{2}{*}{**LOD**} & \multirow{2}{*}{**Log(ng/L)**} & \multirow{2}{*}{**Rrel\%**} & \multirow{2}{*}{**Reference**} \\ \cline{2-2} \cline{6-9}  & & & & & & & \\ \hline ALP & SPE & UPLC-MS/MS & \(-\)40 min & 6.5 min & 20 & 50 & 97 & [24] \\
17 BZs (ALP + FLT) & Single use PP Tubes & LC-MS/MS & \(-\)14 h & 29 min & 1, 3 & 3, 10 & 87–117 & [25] \\
22 BZs (ALP + FLT) & SPE & UPLC-MS/MS & N/R 1 & 20 min & 0.25, 2 & 1, 4 & 80–120 & [26] \\ ALP & N/R & PD-AdS/SV 2 & N/R & 120 s & 100 & 400 & 93–120 & [27] \\
9 BZs (ALP) & On-line SPE & LC-MS/MS & Some minutes & 47 min & 0.6 & 1.9 & 80–120 & [28] \\
8 BZs (ALP) & SPE & UHPLC- & \(-\)40 min & 6.5 min & N/R & 0.9 & 65–134 & [29] \\ ALP + FLT & SB-MSPE & HPLC-TDA & 50 min & 10 min & 3000 & 10,000 & 93.6–112.9 & This study \\ \hline \hline \end{tabular}  
* Not Reported. 2 Differential Pulse Adsorptive Cathodic Stripping Voltammetry.  
\end{table} 
Table 5: Comparison of method’s characteristics with other studies in literature. compounds and metal ions, thus purifying wastewater samples. Hence, the material can be both used as a filtration media and as an adsorbent in analytical chemistry. The only disadvantage this method faced was the higher LODs and LOQs from the other methods reported in the literature, possibly due to the use of a PDA detector while most of the other authors used an MS/MS, increasing the sensitivity and decreasing the limits. However, the simplicity, the swiftness, the high accuracy and precision of the SB-MSPE-HPLC-PDA proposed method offers more advantages to the scientific area.