esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Risk Model
### Support Vector Machine (SVM)  
Support vector machine creates a hyperplane using the supervised learning method of input data and determines the class of data through hyperplane. Suppose that m data \(X=\{X_{1},X_{2},\ldots,X_{m}\}\) for the water supply in a certain area are given, each of which is labeled with a binary class \(Y_{i}\in\{-1,1\}\). If the ground subsidence occurs at the output of the SVM, \(Y_{i}=1\) otherwise \(Y_{i}=-1\). The goal of the SVM is to design a decision hyperplane \(Y_{i}=\mathbf{W}^{T}\mathbf{x}_{i}+w_{0}\) for \(i=1,\ldots,m\) which maximally separates two classes, \(S_{+}=\left\{\mathbf{x}_{j}|Y_{j}=1\right\}\) and \(S_{-}=\left\{\mathbf{x}_{j}|Y_{j}=-1\right\}\) where \(\mathbf{W}\) and \(w_{0}\) are the weight and bias of the decision function, respectively as shown in Figure 4. In other words, SVMs are trained with samples from two classes for finding the maximum-margin (shaded area in Figure 4) hyperplane. Samples on the margin are called the support vectors. Further, the general SVM model can predict which class the data belongs to; however, unlike the logistic regression model, the probability of belonging to each class cannot be calculated. To overcome this problem, a probabilistic SVM [14] model was used to estimate posterior probability using the distance between the test data and the hyperplane.
# Line Scan Spatial Speckle Contrast Imaging and Its Application in Blood Flow Imaging
## 3 Results and Discussion
### Choose the Appropriate Integration Time for Blood Flow Imaging of Click Embryo  
The exposure time is a freely selectable parameter, but the choice of the integration time has a great influence on the calculated contrast values (shown in Figure 4). To choose the proper integration time for imaging the blood flow of chick embryos, multi-exposure laser speckle contrast imaging was studied. Figure 4 shows the temporal speckle contrast images of a chick embryo (NO.1 day 3) obtained with the SI-LSCI at different exposure durations of 0.1, 0.2, 0.5, 1, 2, 4, 6, and 10 ms, respectively. All speckle contrast images in Figure 4 are obtained by imaging the same region of the chick embryo. With a short exposure durations of 0.2 ms (Figure 4b), the laser speckle contrast image shows the blood vessels with fast flow velocity around the heart. Meanwhile, with a shorter exposure duration of 0.1 ms (Figure 4a), the contrast image shows nothing. The vessels are invisible in the image. With a longer exposure duration of 10 ms (Figure 4h), the speckle contrast image highlights the blood vessels with slower flow velocity further away from the heart. The experimental results demonstrated that the smaller vessels, which have a lower flow velocity, become more apparent with the increasing exposure time. A quantitative analysis is provided in Figure 4i, where the relative noise \(\sigma_{K}/\mathrm{K}\) in the speckle contrast images is plotted as a function of the exposure time. It shows that the image noise at the exposure time 4 ms is very close to that of 10 ms. Therefore, 4 ms is a near-optimal compromise between the image quality and the image acquisition speed for LS-TSCI and LS-SSCI.
# Continuous Latent Spaces Sampling for Graph Autoencoder
## 4 Present Work: colaGAE
### Reconstruction Loss  
The reconstruction loss, denoted as \(\mathcal{L}_{\text{RE}}\), measures how well the model can reconstruct the original graph structure in terms of edges. To calculate this loss, we use a graph autoencoder, which has an encoder that maps input features \(\mathcal{X}\) to hidden representations \(\mathcal{Z}\), and a decoder, which maps it back to reconstruct the adjacency matrix \(A\) of the graph. We calculate the reconstruction error using the mean squared error (MSE), denoted as **MSE**, as shown in Equation (4).  
\[\textbf{MSE}=\frac{1}{n^{2}}\sum_{i,j}(Dec(\mathbf{Z}_{i,j})-\mathbf{A}_{i,j} )^{2} \tag{4}\]  
Using the MSE can lead to near-zero values, which is not desirable, as the concept of \"lengths of edges\" is not a topology-based concept. As a solution, we use the binary cross-entropy (BCE), which determines whether there is an edge or not between two nodes, instead. The BCE is used to calculate the reconstruction error; thus, the reconstruction loss is a combination of both MSE and BCE, as shown in Equation (6).  
\[BCE=\frac{-1}{N^{2}}\sum A\cdot\log Dec(\mathbf{Z})+(1-A)\cdot(1-\log Dec( \mathbf{Z})) \tag{5}\]  
Using the BCE loss is a way to measure the model's ability in order to predict whether or not there is an edge between two nodes in the graph. This loss treats each entry in the adjacency matrix as a binary classification problem, and encourages the model to learn to predict the correct label for each edge in the graph instead of merely minimizing the difference between predicted and actual edge weights. By combining the MSE and BCE losses, it is possible to train a model that captures the structure of the graph while ensuring the correct presence or absence of edges.  
\[\mathcal{L}_{\text{RE}}=\textbf{MSE}(Dec(\mathbf{Z}),A)+\textbf{BCE}(Dec( \mathbf{Z}),A) \tag{6}\]
# Synchronization of Heterogeneous Multi-Robotic Cell with Emphasis on Low Computing Power
## 4 Validation of the Proposed Solution
### Simulation Validation of Solution Functionality  
The functionality of the proposed solution was validated by simulations using the MATLAB software tool (R2018a, The MathWorks, Inc., Natic, MA, USA, 2018). The designed algorithms were processed by a script and the results were represented in graphical form.  
Duration times \(T_{1,j}\) of master manipulator \(M_{1}\) operations were simulated as generated random values of a vector in the range of 0-5000 ms. Each value \(T_{1,j}\) was modified during its processing with a random element from the range (-2%, +2%), that represents a stochastic process part. Because the synchronization algorithm for all slave manipulators \(MS_{i}\) is identical, the case with one slave manipulator \(MS_{i}\), where \(i\) = 2, was validated by simulation. Operation durations \(T_{i,j}\) of this slave manipulator were simulated on the basis of Equation (6).  
\[T_{i,j}=S_{i,j}/V_{i,j} \tag{6}\]  
In Equation (6) the variable \(S_{i,j}\in S=\{S_{2,1},\,\ldots,\,S_{n,m}\}\) represents the path length of operation \(O_{j}\) performed by manipulator \(MS_{i}\). An idealized kinematic model of a manipulator with omission of the non-linear character of robot arm movement in acceleration and deceleration was used for simulation validation purposes. Elements of the \(S\) set were generated as random values of a vector in the range of 0-1000 mm in the simulation validation process. The default endpoint movement speed \(V_{i,j}\) of the slave manipulator was set to 200 mm/s. In processing of each operation duration \(T_{ij}\), an additional modification with the stochastic part from the interval (-2%, +2%) of \(T_{i,j}\) was also used.
# Matching-Based Resource Allocation for Satellite-Ground Network
## 7 Conclusions  
In this paper, we propose a resource allocation model for the satellite-ground network, where the relationship between GEO satellite, LEO satellite, and user is established. GEO satellites provide data backhaul for user, and LEO satellites provide data transmission services according to users' requests. Therefore, we propose the three-sided cyclic matching algorithm for GEO satellites to solve the above problems. The simulation results verify that the proposed three-sided cyclic matching algorithm is superior to the traditional algorithm in terms of time delay and satellite revenue.  
Conceptualization, H.D., S.M., S.Z. and M.W.; methodology, H.D., M.W., J.L. and P.Q.; software, S.Z. and M.W.; validation, J.L., P.Q. and X.Z.; formal analysis, J.L.; investigation, S.Z.; resources, H.D., S.M. and X.Z.; data curation, J.L. and H.L.; writing--original draft preparation, J.H. and H.L.; writing--review and editing, H.D. and X.Z.; visualization, M.W.; supervision, H.D.; project administration, S.Z.; funding acquisition, H.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the State Grid Corporation of China under the project \"Research on fusion architecture of 5G and Satellite Network for Transmission and Distribution Scenario (No. 5700-202155325A-0-00)\".  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
In the study, a comparison of the different structures was made to differentiate the resonance and bandwidth ranges. In 2021, a triple-hexagonal SRR based on metamaterial sensor was developed for real-time applications to improve sensitivity in fuel adulteration detection [30]. The previous studies already contributed certain advantages to the applications. However, to obtain the desired performance of the metamaterial structure, there are hindrances that the researcher may face during the process, especially in the size of the structure and its electromagnetic properties. This study proposed a new combination of oval- and square-shaped split-ring resonator (SRR)-based metamaterial for satellite communications and radar applications. The metamaterial design was printed on 9 \(\times\) 9 \(\times\) 0.508 mm\({}^{3}\) size of Rogers RO4003C substrate. The substrate was a good dielectric material and smaller in size. The metamaterial structure manifested resonance frequencies at 5.53 (C-band) and 8.81 GHz (X-band). Both resonance frequencies manifested left-handed characteristics. The effective medium ratio (EMR) of the metamaterial structure was 6.03. Thus, the metamaterial structure was compact because the EMR value was higher than the standard value, 4. The proposed design is new and applicable for the dual frequency bands. In this paper, the method used for unit cell design, simulation, measurement, and equivalent circuit are discussed in Section 2. In Section 3, the result of the simulation and experimental are compared to validate the performance of the metamaterial structure. Parametric studies such as type of substrate, the design of the metamaterial structure, and a comparison of the array structure have also been discussed further in Section 4.
# Nucleotides as Bacterial Second Messengers
## 4 The cAMP Nucleotide
### cAMP Binds to cAMP Receptor Protein (CRP)  
Signaling by cAMP in bacteria is mainly at the level of regulation of gene expression, whereas in eukaryotic cells, the mechanism is different. The cAMP receptor protein (CRP) family functions as a transcription factor in bacteria, and cAMP binds to CRP directly in order to activate it. These receptors counteract different cellular events directly whereas, in eukaryotic cells, cAMP signaling is first mediated by an intermediate, commonly the protein kinase A (PKA) complex, and, subsequently, the activation of transcription factors [59] and one of the functions of cAMP in eukaryotic cells was in hormonal signal transduction.  
In bacteria, cAMP regulates the glucose response or catabolic repression [60]. Low glucose levels reduce cAMP production, activating the lac operon by binding cAMP to the Crp protein to form the cAMP-Crp complex. In recent years, the study of cAMP in bacteria has discovered other processes mediated by this messenger, such as bacterial virulence, toxin control, and activation of master regulators.
## 3 Application Description and Requirements
### Performance Metrics  
The efficiency of the mentioned system requires quantitative assessment through the use of numerical metrics. Central to the validation of this research are key performance metrics tailored to the outlined scenario.  
The uptime and availability represent the time when the system can deliver the intended behavior, and the time needed to recover from an unexpected series of events with different degrees of disturbance (e.g., partial service failure, multiple services failure) [9; 10; 11; 24].  
Concurrency represents the number of users supported at the same time in a collaboration session. Being a matter of resource management and sharing, this metric aims to provide insights about the number of users able to join a session based on the system's available resources [25].  
Data resolution times the amount of time required to propagate changes in a collaboration session between all users [9; 10].  
Scalability is the ability of the system to adapt to higher workloads [9; 10; 12].  
Resource utilization examines the resources used to provide a working service [9; 10].
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 4 CenTri: Reinforcement Learning Model and Algorithm
### Reinforcement Learning Algorithm
#### 4.2.1 Implementation Requirements and Parameters  
The implementation has eleven USRP/GNU radio units as nodes and BSs. Each of the ten USRP/GNU radio units is connected with a Raspberry Pi3 B+ unit equipped with 30 GB of external memory for storing and running algorithms. The USRP unit, specifically USRP N200, is equipped with the VERT900 antenna, and the GNU radio runs an open-source software-defined radio (SDR). A personal computer, which is equipped with the core i7 processor and 16 GB RAM, serves as MC BS. The D2D nodes have closer proximity among themselves compared to MC BS, so the transmission power is 10 dBm (10 mW) among themselves and 20 dBm (100 mW) with the MC BS.  
Table 2 presents the parameters. In this platform, the user datagram protocol (UDP) is the preferred transport layer protocol for multimedia applications because it is connectionless and it does not perform retransmission during packet loss, which reduces delay at the expense of the acceptable packet loss. Figure 5 shows the platform with USRPs equipped with RP3. Nodes are located in the MC BS proximity and receive route information proactively from the CC via MC BS.
## 3 Test Results
### Lateral Displacement of Soil
The difference in the maximum horizontal displacement of the soil between driven piles and cast-in-place piles at typical depths is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Pile Type**} & \multirow{2}{*}{**Depth (m)**} & \multicolumn{2}{c}{**Lateral Displacement/mm at \(r=3.3\) m (mm)**} & \multicolumn{2}{c}{**Lateral Displacement/mm at \(r=4.2\) m (mm)**} \\ \cline{3-8}  & & **Min** & **Max** & **\(\Delta\)** & **Min** & **Max** & **\(\Delta\)** \\ \hline \multirow{4}{*}{Driven pile} & 10 & \(-\)0.2 & 25.1 & 25.3 & \(-\)0.5 & 8.3 & 8.8 \\  & 20 & 0.0 & 23.6 & 23.6 & \(-\)1.8 & 12.1 & 13.9 \\  & 30 & \(-\)0.8 & 19.1 & 19.9 & \(-\)1.1 & 13.0 & 14.2 \\  & 40 & 0.0 & 8.2 & 8.2 & \(-\)0.2 & 7.9 & 8.2 \\ \hline \multirow{4}{*}{PGP pile} & 10 & \(-\)5.1 & 8.6 & 13.6 & \(-\)5.0 & 4.9 & 9.9 \\  & 20 & \(-\)5.4 & 5.1 & 10.5 & \(-\)3.8 & 5.8 & 9.6 \\ \cline{1-1}  & 30 & \(-\)4.5 & 5.6 & 10.1 & \(-\)2.7 & 1.1 & 3.8 \\ \cline{1-1}  & 40 & \(-\)3.8 & 8.2 & 11.9 Table 3 outlines soil's extreme displacement around the test pile. Driven piles show the greatest displacement away from the pile. For PGP piles, there is an initial displacement toward the pile, then away. After a one-day break, drilled and grouted piles rebound faster than driven piles. Contractions are observed at 4.2 m depth for drilled and grouted piles. During the construction of hammer piles, the soil is squeezed by the pile body and will only move away from the pile, and finally there will be a part rebound due to the dissipation of pore pressure and the consolidation of the soil. The maximum displacement of PGP piles is generally 50.7-53.8% of that of driven piles, but the displacement at the bottom of the pile is greater than 45.1% of that of driven piles.
## 5 SCODE_plus_: Enhanced Secure Routing and Key Management Scheme
### Key Management
#### 5.2.2 Direct Key Establish Phase  
After deployment, every sensor node discovers the sharing key-space with its neighbors. Assume that node \(N_{A}\) with three space IDs \(f_{i}\), \(f_{j}\), \(f_{k}\) needs to discover shared key-space with its neighbors. It broadcasts a 1-hop discovery message _Key-Space Discovery Message_ (KSDM) containing the following information:  
\[N_{A},\textit{nounc}_{A},H(f_{i}\oplus\textit{nounc}_{A}),H(f_{j}\oplus \textit{nounc}_{A}),H(f_{k}\oplus\textit{nounc}_{A})\]  
where \(H\) is the hashing function and \(\oplus\) is the XOR operation.  
When a neighbor of \(A\), say \(B\), receives this message, it finds out that it could share three, one or no common key-space with \(A\). Similarly, node \(A\) also receives \(B\)'s KSDM message and finds out common key-spaces. If the sharing is at least one common key-space, the pair-wise key between \(B\) and \(A\) is calculated at B as follows:  
\[K_{B_{A}}=f(N_{B}\oplus\textit{nounc}_{B},N_{A}\oplus\textit{nounc}_{A}) \tag{2}\]  
After getting \(K_{B_{A}}\), node \(B\) deletes value \(\textit{nonce}_{A}\) from its memory. The process of computing pair-wise key at \(A\) is similar. Because of the symmetric property of bivariate polynomials, \(K_{AB}=K_{B_{A}}\). After this phase, every node stores a list of pair-wise keys with its neighbors, beside the key-space information and a random value in previous phase.
# Testing Accuracy and Repeatability of UAV Blocks Oriented with GNSS-Supported Aerial Triangulation
## Results and Discussion
### Influence of the Standard Deviation Assigned to the Observations
Simulations with CALGE show that only the weighted average of the residuals of the least squares adjustment, sigma zero, is affected to some extent as, unlike the reprojection error, it accounts for all observations. To the best of our knowledge, none of the software packages coming from Computer Vision provides this information, however. If available, the estimates of camera precision provided by the RTK data processing should be used, perhaps with a penalty factor as they are generally too optimistic. For periodic surveys of targets, a sensitivity analysis is advisable, looking at the changes of the ground coordinates for different standard deviations. As far as the four flights are considered, with a penalty factor of 2 applied to the nominal precision of the camera position encoded in the image EXIF (1 to 1.5 cm in X and Y and from 1.8 to 2.5 cm in Z), all of the software packages produce satisfactory, although not necessarily optimal, results in most cases. The results of the second set of test on the accuracy of block orientation as a function of different standard deviations assigned to tie points and camera stations are summarized in Figure A1 in Appendix A, where the RMSE in elevation obtained by PhotoScan is plotted for RTK1 and RTK3 (notice that the curves in Figure 2 represent a section of the surface plotted). As long as the ratio between the two standard deviations is constant, the least squares solution should be unchanged and so the RMSE. Therefore, one would expect that the RMSE remain constant over an angle with vertex on the origin. This is indeed roughly the case for RTK1, where it seems that the tie point precision is less critical than the camera station precision as far as accuracy in concerned. However, with RTK3 the pattern looks quite different, as the best accuracy is found far from the \"reasonable\" set of tie point and camera station precision. The reason why this happens needs to be further investigated.
# Automated Tunnel Point Cloud Segmentation and Extraction Method
## 4 Discussion
In contrast, traditional design axis extraction methods heavily depend on the precision of the design axis, often resulting in extracted tunnel planes that are not perfectly perpendicular to the tunnel alignment. The cross-section extraction results show that, compared to the standard data provided by the total station, the proposed method achieves smaller errors and outperforms traditional centerline-based extraction methods, providing a more accurate representation of the actual tunnel cross-sectional structure. Despite the promising performance of the proposed algorithms in tunnel point cloud segmentation, certain challenges remain. For instance, the regularization procedure in the TMatch algorithm relies on a complete tunnel point cloud, and when there are significant data gaps, its segmentation accuracy may degrade substantially. Additionally, the ring plane fitting technology may struggle to accurately extract regular cross-sections when tunnel damage is present, potentially affecting structural analysis reliability. Future research will focus on optimizing algorithm performance to further enhance the accuracy and robustness of tunnel point cloud processing. On one hand, deep learning models will be explored to improve segmentation accuracy using data-driven approaches, further enhancing the feasibility of fully automated tunnel processing. Additionally, multi-source sensor data fusion will be integrated to strengthen tunnel structure recognition robustness. Computational optimizations for large-scale point cloud data will also be developed to enable efficient real-time processing, ultimately providing more precise and intelligent technical support for tunnel health monitoring and deformation analysis.
## 3 Data and Methodology
### Laboratory Testing of Rock Masses
#### 3.4.4 SEM Analysis  
SEM bridges the gap between transmission electron microscopy and optical microscopy. This method involves scanning the sample with a high-voltage electron beam generated by an electron gun in a specific spatial and temporal order to stimulate various physical information. The information is then collected, processed, and imaged for the purpose of characterizing microscopic morphological features of samples [30; 31]. The experiment utilized a Hitachi SU8010 Scanning Electron Microscope as the testing instrument in this study. Examining the microstructural characteristics of the observed samples can aid in the analysis of the factors and mechanisms contributing to pit slope deformation and damage.
## 4 Performance Evaluation
### Subjective Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Listener**Variable (cm)** & \(\mathbf{x_{1}}\) & \(\mathbf{x_{2}}\) & \(\mathbf{x_{3}}\) & \(\mathbf{x_{4}}\) & \(\mathbf{x_{5}}\) & \(\mathbf{x_{6}}\) & \(\mathbf{x_{7}}\) & \(\mathbf{x_{8}}\) & \(\mathbf{x_{9}}\) \\ \hline M1 & 17.8 & 23.7 & 18.8 & 1.8 & 4.0 & 11.4 & 9.2 & 10.9 & 33.1 \\ M2 & 16.8 & 25.1 & 19.6 & 2.5 & 3.4 & 11.7 & 7.4 & 11.7 & 42.2 \\ M3 & 17.6 & 23.4 & 18.2 & 2.8 & 4.7 & 12.4 & 8.4 & 13.4 & 34.6 \\ F1 & 13.4 & 21.5 & 17.3 & 2.3 & 2.6 & 8.4 & 4.5 & 8.7 & 26.3 \\ F2 & 14.6 & 21.3 & 17.6 & 2.2 & 2.7 & 9.7 & 5.2 & 9.2 & 37.3 \\ \hline
**Listener** & \(\mathbf{x_{10}}\) & \(\mathbf{x_{11}}\) & \(\mathbf{x_{12}}\) & \(\mathbf{x_{13}}\) & \(\mathbf{x_{14}}\) & \(\mathbf{x_{15}}\) & \(\mathbf{x_{16}}\) & \(\mathbf{x_{17}}\) & \\ \hline M1 & 15.3 & 21.7 & 45.3 & 3.4 & 183.5 & 96.0 & 60.0 & 113.6 & \\ M2 & 12.6 & 22.7 & 52.1 & 1.4 & 184.2 & 90.0 & 62.4 & 130.4 & \\ M3 & 13.8 & 25.9 & 49.8 & 1.3 & 175.1 & 89.5 & 61.0 & 123.3 & \\ F1 & 9.6 & 78.9 & 38.7 & 2.0 & 161.0 & 80.3 & 58.0 & 90.4 & \\ F2 & 7.8 & 28.0 & 44.7 & 2.2 & 160.2 & 77.2 & 55.5 & 106.0 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Measured anthropometric data of five listeners. Figure 8: Results of the localization test according to the different HRTF estimation methods: (**a**) average HRTF, (**b**) 17 head and torso measurements for HRTF (DNN(17) HRTF), and (**c**) CNN–DNN HRTF (proposed method).