esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Simulation Results and Performance Analyses
### The Packet Loss Rate  
This section analyzes the performance of the network with 300 nodes and compares the six protocols by the packet loss rate, which is defined as the rate of the number of data packets sent by CHNs to the number of data packets received by the SN. The network load is defined as the number of data packets sent by every CHN per minute. Figure 13 illustrates the packet loss rate versus the network load for these six protocols, from which we conclude that the packet loss rate rises as the network load increases for these six protocols. However, the EECRP always has the lowest packet loss ratio. For example, when the network load is 3 packets per minute, the packet loss ratio of the EECRP, FCMMFO, FBCPSO, EGRC, LEACH-ERE, and LEACH is 16.8%, 18.1%, 19.8%, 21.6%, 24.6%, and 30.8%, respectively. The LEACH has approximately a 1.8 times higher packet loss rate than the EECRP does, which is because the EECRP employs the BPNN to fuse the data. Furthermore, it uses the improved GA to find the optimal multi-hop transmission paths, which is capable of reducing the risk of packet loss. Figure 14 displays the number of the packets that the SN receives versus the number of rounds for different protocols. The protocol is more effective when more packets are received by the SN. Apparently, the EECRP protocol is the most effective one, the efficiency of which is 86.7%, 18.1%, 31.3%, 46.9%, and 10.1% higher than that of the LEACH, FBCPSO, EGRC, LEACH-ERE, and FCMMFO, respectively, in round 1000.  
Figure 12: The number of rounds when the energy is exhausted under the different number of nodes.  
Figure 13: The packet loss ratio versus network load.
# A Novel Spaceborne Sliding Spotlight Range Sweep Synthetic Aperture Radar: System and Imaging
## 3 Modified Polar Format Algorithm  
The continuous azimuth-range beam steering of the SSS-RSSAR will induce serious 2D signal coupling to the echo, which, as a result, will be hard to focus. If processed by the algorithms such as the RDA, CSA and RMA, the slant range variation that has been removed by the CVPI method should be added back by zero-padding the echo in range as shown in Figure 4, where \(N_{\text{r}}\) and \(N_{\text{m}}\) denote the range sample number before and after the zero padding, respectively. The blue square denotes the valid data received with the CVPI technique and the white region denotes the valid data to which the slant range variation has been added back. It is clear that the processing efficiency would be dramatically decreased due to the increased invalid data marked by the yellow triangles.  
The PFA is the only frequency-domain algorithm that can focus the echo with no necessity of recovering the full range migration and thus, will not induce additional invalid data. However, due to the employed CVPI technique, the azimuth sampling of the SSS-RSSAR is no longer uniform and thus, the traditional PFA cannot be directly applied for the SSS-RSSAR imaging. In this section, a MPFA is proposed with a new 2D dechirp filter and an interpolation-based data correction manipulation based on a new wavenumber depending on the CVPI technique.
## 4 Experimental Results
### Organic Matter Maturity
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Sampling**} & \multirow{2}{*}{**Stratum**} & \multicolumn{5}{c}{**Whole Rock Reflectance/\%**} \\ \cline{4-7}  & & & **Minimum** & **Maximum** & **Average** & **Measured** \\  & & & **Value** & **Values** & **Value** & **Point** \\ \hline Y-3 & 3304 & & 1.91 & 2.32 & 2.13 & 50 \\ Y-5 & 3308.7 & & 2.04 & 2.59 & 2.32 & 43 \\ Y-6 & 3323.38 & & 1.61 & 2.09 & 1.82 & 58 \\ \hline Y-8 & 3333.54 & & 1.67 & 2.26 & 1.94 & 25 \\ Y-11 & 3345.07 & & 2.07 & 2.66 & 2.47 & 68 \\ Y-14 & 3355.47 & & 1.8 & 2.43 & 2.03 & 38 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Whole-rock reflectivity of Permian L-well shale in northeastern Sichuan Province. Raman spectra can reflect the information of atomic and molecular vibrations in the aromatic carbon ring structure of organic matter, i.e., the parameters of the Raman spectral peaks of organic matter respond well to the changes in the chemical structure of organic matter during thermal evolution, and thus characterise the maturity. The peak spacing between the D and G peaks has been shown to be the most reliable indicator of maturity. The G (1580-1600 cm\({}^{-1}\)) and D (1350-1380 cm\({}^{-1}\)) peaks correspond to the E2g stretching vibrational modes of the graphite aromatic layer and the defects of the aromatic lattice, respectively. The current study used Equation [63]:  
\[\text{Ro}_{\text{Rmc}}=0.0537\text{d}(\text{G}-\text{D})-11.21 \tag{1}\]  
The reflectance converted from Raman spectral peaks and the above equation in the current study basically matches the local organic matter maturity characteristics, but it is generally higher than the measured values, which is in line with the previous studies. (Table 6) This converted value is larger than the measured value mainly because the Permian shales in Northeast Sichuan are generally in the high-overmature stage, and most of the organic matter is decomposed into fine particles under thermal maturation, making it difficult to distinguish the lenticular group, the inertial group, and the crustal group under the microscope. Especially for dispersed sedimentary organic matter (mudstone, shale, etc. ), the fine vitrinite fragments and the relatively high content of transition components such as hemimirror and hemifilament in lacustrine sedimentary environments make it difficult to accurately distinguish each microscopic component [64]. Therefore, the problem of identification of microscopic components in the present study may be the main reason for the higher calculated reflectance values of Raman parameters than the measured reflectance values of specular bodies.
# Immobilization of Pectinolytic Enzymes on Nylon 6/6 Carriers
## 3 Results and Discussion
### Pectinase Immobilization Yield  
Pectinase Ultra Tropical preparation was immobilized onto nylon 6/6 threads and pellets using the selected protocol. Carriers were first soaked into DMS at 60 \({}^{\circ}\)C for 2 min, after that, washed with ice-cold methanol and buffer, and kept in 12.5% glutaraldehyde solution 1 h at room temperature. Finally, activated nylon carriers were incubated in 20-fold diluted enzyme solution (89 U/mL) for 24 h at 4 \({}^{\circ}\)C. After careful washing to remove unbound enzyme, we measured the pectinase activity of immobilized enzymes and the immobilization yield was expressed as pectinase activity per g of carrier. Results are presented in Table 4.  
As expected from the comparison of the surface area of thread and pellets, the immobilization yield of pectinase per 1 g carrier was over 40 times higher for threads than pellets, although the pectinase activity per 1 mm\({}^{2}\) surface area was ~2 times higher for pellets (262 and 140 \(\mu\)U/mm\({}^{2}\) for pellets and threads, respectively).  
Up to now, few studies have investigated the immobilization of pectinases on nylon carriers. [PERSON] et al. [40] immobilized purified polygalacturonase from _Aspergillus niger_ (MTCC 3323) on nylon-6 beads and reported an immobilization yield of 17.54 U/g. This immobilization yield is higher than the measured yield obtained in the present study. This apparent lower yield can be related to the use of the commercial enzyme preparation, which contains different enzymes that are potentially immobilized onto the carrier, although only pectinase activity was assessed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Nylon-6,6 Carrier** & **Beads** & **Coiled Thread** \\ \hline Immobilization yield (mU/g) & 27 \(\pm\) 2 * & 1248 \(\pm\) 97 \\ \hline \hline \end{tabular}  
* All values are expressed as means \(\pm\) standard deviation (n = 3).  
\end{table}
Table 4: Pectinase immobilization yield on different nylon 6/6 carriers.  
Figure 3: Nylon 6/6 threads ((a), shot with Olympus CX31) and pellets ((b), shot with UV transilluminator, Cleaver Scientific Ltd., Warwickshire, UK).
# Realtime Tsunami Prediction System Using Ocean Floor Network for Local Regions
## 3 Implementation of the Prediction System
### Visualization  
One of the practical applications of this system is the use of predictions by municipalities to protect coastal residents from disasters by using the system prefecture construct. The most important issue is the ability of an untrained person to visualize the impact of a tsunami on the display. On the map of the inundation area, there is a control window to select the location to be visualized, lists of arrival times and heights, and propagation of the tsunami with bar graphs of real-time pressure gauges and calculated waveforms of the predicted point are shown at the same time. Once the seafloor observatories detect that a tsunami has been triggered, the prediction is started. The inundation area is extended if the tsunami is propagating from a region near the Nankai Trough. The interval between successive predictions on the display depends on the model distribution in the database, but the prediction information itself is produced at an interval of one second.  
The topography, including any construction on land, is represented using data gridded at 10 m intervals, and users can choose to zoom in. The conditions of the evacuation locations and key facilities such as municipalities are confirmed after the inundation. The ability of national roads to play a role as emergency routes can also be confirmed. If such facilities are in the inundation area, they are at risk of being isolated. Even if the depth of inundation is relatively shallow, the facilities may be surrounded by tsunami debris and fires. Prefectures and municipalities can grasp the situation in each area and identify locations that require help and support.
## 3 Experimental Results and Analyses
### Detection Performance of YOLOv3-Lite  
Depthwise separable convolution is used to achieve the goal of the lightweight network. At the same time, the feature pyramid method is used to detect crack defects of different sizes. In order for each layer of the feature pyramid to fuse the information of the high-level and low-level layers, the feature pyramids are concatenated. The experiments show that YOLOv3-Lite can achieve high accuracy and greatly improve the detection speed. The input size of the image for our aircraft structural crack detection network is adjusted to \(416\times 416\) pixels, in order to increase the model performance. The image is detected by YOLOv3-Lite in 0.1s with an AP of 38.7%. Finally, the network outputs the detection result. Our method can quickly and accurately detect cracks in various parts of the aircraft, such as the surface of the aircraft fuselage, engine blades, aircraft tires, wing tail, and other parts. The detection results are shown in Figure 7. Whether it is obvious large-sized cracks or  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Size of Input Images** & **Batch Size** & **Initial Learning Rate** & **Decay** & **Training Steps** \\ \hline \(416\times 416\) & \(10\) & \(0.001\) & \(0.1\) & \(300\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The initial parameters of YOLOv3-Lite in stage 1 of training.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Size of Input Images** & **Batch Size** & **Initial Learning Rate** & **Decay** & **Training Steps** \\ \hline \(416\times 416\) & \(4\) & \(0.0001\) & \(0.1\) & \(50\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The initial parameters of YOLOv3-Lite in stage 2 of training.  
small-sized cracks which are difficult to find with a nosisy background, they can be precisely detected by YOLOv3-Lite.
## 3 Materials and Methods
### Spectral Clustering  
As an efficient method of graph-theoretic clustering, spectral clustering relies on the spectral properties of a graph for effective data grouping [29]. In the encoding part of our model, we particularly emphasize the role of the Laplacian matrix in elucidating the structural characteristics of graphs. The Laplacian filter integrated into the encoder not only smooths the features between adjacent nodes, thereby preserving vital information, but also enhances the representation of inter-node relationships. This process results in graph embeddings that are suitable for spectral clustering, offering a rich feature space. The spectral clustering algorithm follows the following process.  
(1) Construction of the Similarity Matrix:  
We first construct a matrix that reflects the similarity between samples. In this process, for the graph embeddings generated by the graph attention autoencoder, we calculate the similarity between nodes using the Gaussian kernel function, defined as follows:  
\[s_{ij}=exp\left(-\frac{\left|\left|z_{i}-z_{j}\right|\right|^{2}}{2\sigma^{2}}\right) \tag{11}\]  
where \(\sigma\) is the bandwidth parameter of the Gaussian kernel, determining the rate of similarity decay. \(S_{ij}\) denotes an \(n\times n\) similarity matrix, where each element of \(s_{ij}\) represents the similarity between nodes \(i\) and \(j\).  
(2) Computation of the Laplacian Matrix:  
Subsequently, we construct the graph's adjacency matrix \(W_{ij}\), i.e., \(S_{ij}=W_{ij}=\left[s_{ij}\right]_{n\times n}\), and calculate the degree \(d_{i}=\sum_{j=1}^{N}s_{ij}\) of each sample point, forming the degree matrix \(D=diag(d_{1},...d_{n})\). The Laplacian matrix is defined consistent with the method using the Laplacian filter described earlier:  
\[L=D-W \tag{12}\]  
(3) Computation of Eigenvectors and \"Soft-Cutting\":  
To partition the data into \(k\) clusters, we compute the eigenvectors corresponding to the \(k\) smallest eigenvalues of the Laplacian matrix \(L\) and combine them into the featurematrix \(H^{\prime}=\left[{{{h^{\prime}}_{ij}}}\right]_{n\times k^{\prime}}\). This step is seen as \"soft-cutting\" the graph, providing appropriate feature representation for subsequent clustering.  
K-means Clustering and Mapping Back to the Original Dataset:  
Finally, we perform K-means clustering on the feature matrix \(H^{\prime}\) to obtain the final clustering results. These results represent the clustering of the original sample space and provide the possibility for further analysis of EEG background waves across different clusters.
###### Abstract  
In this paper, a new method for fault diagnosability research based on information geometry is proposed. The problem of the diagnosability evaluation of dynamic system faults is transformed into a distance calculation problem on a manifold. The Fisher information distance is used to realize a quantitative judgment of diagnosability, and a quantitative evaluation index of the fault diagnosability of a satellite attitude determination system is designed. This includes a fault detectability index and a fault isolability index. The validity and superiority of the new indexes are verified through a mathematical simulation. In addition, the fault information is visually presented by the geodesics of the fault manifold, and the properties and behavior of the fault are mined and analyzed on the fault information manifold, which lays a foundation for further exploration of fault information through geometric methods.  
Keywords:diagnosability; evaluation indexes; satellite attitude determination system; fault information manifold; Fisher information distance (FID) +
Footnote †: journal: Applied
# Thermal Analysis of Cornea Heated with Terahertz Radiation
## 3 Results and Discussion
### Cornea-Strinkage-Region Estimation  
Since the temperature in the cornea can rise to a certain range for collagen shrinkage, terahertz radiation can be used as a novel corneal thermal therapy. Here, we estimate the effective region of cornea shrinkage based on the above spatial and temporal temperature distributions under THz heating with a beam radius of \(w_{0}=3\) mm and power density of \(P_{\mathrm{d}}=0.6\) W/cm\({}^{2}\). As we discussed earlier, 55\(\sim\)75 \({}^{\circ}\)C is the safe shrinkage temperature range. Therefore, we can define the region within this temperature range as the shrinkage region. According to the spatial and temporal temperature distributions, we obtained the time-dependent evolution of the corresponding collagen shrinkage regions, as shown in Figure 5. Results show that the tomography of the collagen-shrinkage region  
Figure 4: Time-dependent temperature evolution of at the top center of the cornea layer under different power densities (0.2\(\sim\)1 W/cm\({}^{2}\) as marked in the figure) and terahertz beam radius of (**a**) \(w_{0}=1.5\) mm; (**b**) \(w_{0}=2.0\) mm; (**c**) \(w_{0}=2.5\) mm; (**d**) \(w_{0}=3.0\) mm; (**e**) \(w_{0}=3.5\) mm; and (**f**) \(w_{0}=4.0\) mm.  
Figure 3: Temperature distribution across the surface beneath the THz beam (beam radius \(w_{0}=3\) mm, power density \(P_{\mathrm{d}}=0.6\) W/cm\({}^{2}\), and exposure time 1200 s).  
evolves from a funnel-like shape into the cylindrical profile. After temperature saturation, the diameter of the collagen-shrinkage region is about 3 mm, which is comparable to that of the microwave-heating approach (diameter is about 1.3\(\sim\)2.8 mm [5]).  
Given the similarity of heating with THz and infrared (IR) lasers (near-surface heating in both cases), we compared our results with typical IR modeling studies and experiments in the literature [7; 23]. The comparison of heating the cornea with THz and typical IR lasers is summarized in Table 2. For IR laser-based heating, the shrinkage effect is not deep enough because of the rather limited heating depth of the laser into the cornea (only \(\sim\)50%). Therefore, laser irradiation using THz wavelengths may heat deeper into the cornea than IR wavelengths.  
Although reshaping the cornea using IR lasers is well-established, there still exist some problems for IR-heating techniques. According to the comparison summarized in Table 2, IR heating techniques cannot produce enough heating depth (only \(\sim\)50%) and a good profile of the shrinkage effect (wedge), which are very important for cornea reshaping based on thermal techniques. However, the THz heating technique can produce a significantly deeper region and a better shrinkage profile. Therefore, we expect this result can clarify the potential benefits of using THz radiation compared with using IR lasers in cornea reshaping in ophthalmology.
# Dynamic Response of Electro-Mechanical Properties of Cement-Based Piezoelectric Composites
## 1 Introduction
The existing research on the influence of quasi-static and dynamic impact loads on the electro-mechanical response characteristics of cement-based piezoelectric composites is mostly focused on type 0-3 and type 1-3, and there is a lack of comparative research on the electro-mechanical properties of cement-based piezoelectric composites with different structural types. In this study, quasi-static and dynamic impact compression tests were performed for type 1-3 and type 2-2 cement-based piezoelectric composites to study the electro-mechanical response characteristics under different load conditions. Then, the applicability and stability of sensor sensitivity were compared to provide theoretical guidance for the application of cement-based piezoelectric sensors in practical engineering.
## 1 Introduction
* _Experimental result and discussion using real data set:_ The performance of the proposed strategy is verified using the real data set measured in Korea. The experimental results show that when applying the proposed dynamic VESS operation strategy, participants can achieve greater benefit than the existing VESS operation strategy and have a cost benefit of more than double when compared with that of using the existing VESS operation. Using the results, we carefully discuss how the proposed VESS operation strategy achieves these additional benefits with varying the VESS service price and electricity tariff. Moreover, the future research directions of the VESS for SECs are also suggested. The rest of this paper is organized as follows: In Section 2, the VESS for the SEC model is described, and in Section 3, the design method of the proposed VESS operation strategy is discussed. In Section 4, measurement studies applied to the proposed strategy are presented. In Section 5, lessons and future research directions are discussed. Finally, the conclusions of the study are presented in Section 6.
###### Abstract  
Industry standards pertaining to Human-Robot Collaboration (HRC) impose strict safety requirements to protect human operators from danger. When a robot is equipped with dangerous tools, moves at a high speed or carries heavy loads, the current safety legislation requires the continuous on-line monitoring of the robot's speed and a suitable separation distance from human workers. The present paper proposes to make a virtue out of necessity by extending the scope of on-line monitoring to predicting failures and safe stops. This has been done by implementing a platform, based on open access tools and technologies, to monitor the parameters of a robot during the execution of collaborative tasks. An automatic machine learning (ML) tool on the edge of the network can help to perform the on-line predictions of possible outages of collaborative robots, especially as a consequence of human-robot interactions. By exploiting the on-line monitoring system, it is possible to increase the reliability of collaborative work, by eliminating any unplanned downtimes during execution of the tasks, by maximising trust in safe interactions and by increasing the robot's lifetime. The proposed framework demonstrates a data management technique in industrial robots considered as a physical cyber-system. Using an assembly case study, the parameters of a robot have been collected and fed to an automatic ML model in order to identify the most significant reliability factors and to predict the necessity of safe stops of the robot. Moreover, the data acquired from the case study have been used to monitor the manipulator' joints; to predict robot autonomy and to provide predictive maintenance notifications and alerts to the end-users and vendors.  
on-line monitoring; collaborative robots; human robot collaboration; machine learning +
Footnote †: journal: Applied Sciences
## 2 Methods for Removing Rain Streaks Based on Single-Frame Images  
Current methods on rain streaks' removal from single-frame images are two main categories. One comprises methods based on image decomposition that involve using a model to describe the rain streaks, background images, and an optimization algorithm. The other comprises single-frame image-based methods that use deep learning, which solves the problem by constructing and training a network.
## 3 Proposed Methodology
### Data Preprocessing
#### 3.3.2 Data Normalization  
As seen in Equation (1), the raw sensor data are normalized to the range 0 to 1. This procedure assists in resolving the model learning issue by ensuring that all data values are within a close range. Consequently, gradient descents may achieve a higher rate of convergence.  
\[X_{i}^{norm}=\frac{X_{i}-x_{i}^{min}}{x_{i}^{max}-x_{i}^{min}},i=1,2,\ldots. \tag{1}\]  
where \(X_{i}^{norm}\) denote the normalized data, \(n\) denoted the number of channels, \(x_{i}^{max}\) and \(x_{i}^{min}\) are the maximum and minimum values of the \(i\)-th channel, respectively.