esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Experiment
### Bolt Preload  
Bolt preload is considered a significant factor in joints because if the preload of the bolted joint is not high enough, it can be easily loosened and lead the joint to fail. Furthermore, if the preload is also more than high enough, the bolt may fail in this condition. Considering these concepts, a test was conducted to determine the optimal initial preload for three types of bolt materials because of the lack of information about nominal bolt preloads for 3D-printed bolts. For this, several PLA, ABS-2, and glass bolts and nuts were printed by using two printing methods (vertical printing and horizontal printing) in a Zortrax M200 3D printer, as shown in Figure 1. The difference between both printing methods is only the bolts' position as the bolt is placed horizontally Figure 1a and vertically Figure 1b. The rest of the printing parameters, such as printing speed, layer thickness, printing time is default and the same. After that, a clamped part and a load cell (manufacturer: Radian QBIO, Seoul, South Korea, model: RCWW-1T, nonlinearity: 1%, hysteresis: 0.5%) were tightened by the bolts and nuts made with three types of materials. The clamp force of the joint was increased by a wrench slowly until the bolt failed, while measurement of the load cell was recorded simultaneously. The point that the bolt failed was considered as a maximum point of a bolt preload for this study. The test was performed three times for all printing methods and materials in order to confirm the results.  
Figure 2 shows the maximum preload for the bolts made with the three different materials. All bolts had a maximum preload lower than 1000 N with the vertical printing method, while the maximum preload of the bolts was more than 1500 N with the horizontal printing method. The PLA bolt had the highest preload of more than 3000 N, and the ABS-2 bolt had the lowest preload in both printing methods. According to the result, the horizontal printing method showed better performance than that of the vertical printing method. The maximum preload of the bolts printed in the horizontal printing method was about three times higher than that of the vertical printing method. For that reason, the printing direction of the Zortrax M200 3D printer was perpendicular to the axis of the bolt in the horizontal printing method, while the printing direction was in the same direction of the bolt axis. By considering these concepts, a horizontal printing method is strongly recommended in printing threaded fasteners. Therefore, the bolts and nuts were printed in a horizontal printing method, and the initial bolt preload was selected as 1000 N.  
Figure 1: Printing method: (**a**) horizontal printing, (**b**) vertical printing.
# Practical Performance Analysis of Interference in DSS System
## 5 Field Test and Performance Analysis
### Neighbor Cell Interference Test
#### 5.2.3 LTE Uplink Interference From NR Terminal to DSS Cells  
At the same point, use the NR and LTE test terminals to perform uplink ping, respectively, and observe the background noise. When there are no terminal upload data, the LTE background noise of the tested DSS cell is \(-120\) dB. When NR and LTE terminals upload data, the test results are shown in Figure 16.  
Figure 16: NR terminal to LTE uplink interference test results.  
Figure 15: NR to LTE downlink interference test results.  
As can be seen from Figure 16, the impact of the NR terminal on the LTE background noise increase in the first circle neighbor DSS cell is basically the same as that of the LTE terminal, and the difference in the background noise increase is about 1.11 dB. In the first circle of DSS cells, the LTE uplink background noise increased by about 10%. The impact of the NR terminal on the LTE background noise increase in the second circle neighbor DSS cell is basically the same as that of the LTE terminal, and the difference in the background noise increase is about 3.95 dB. In the second circle of DSS cells, the LTE uplink background noise increased by about 10%. The terminal has no effect on the rise of the background noise of the third circle. It can be seen that the LTE uplink interference from the NR terminal to the DSS cell can be neglected compared to the NR downlink interference from the LTE to the DSS test cell in the neighbor cell.
# Optimal Prediction of Individual Vessel Trajectories Based on Sparse Gaussian Processes
## 2 Related Work  
Prior research examined the forecasting of vessel trajectories. The ship's trajectory is forecasted by utilizing a dynamic ship model that takes into account the ship's current condition, propulsion, and external factors [13; 14; 15]. Furthermore, the application of an extended Kalman filter [16; 17; 18] and particle filters [19] was used to enhance the prediction results of the ship dynamic model by incorporating observed position data. Extrapolation [2; 14; 20; 21; 22; 23] is a straightforward technique that predicts the future position of a vessel by assuming that the observed state data at the most recent time will remain constant. Ship dynamics models, filter-based models, and extrapolations are appropriate for short-term trajectory prediction that necessitates real-time observations because they do not account for future nonlinear changes in input values. The neural network (NN) is an artificial intelligence model that mimics the behavior of neurons in the human brain. It has been employed in various studies [24; 25; 26; 27; 28; 29] to forecast ship trajectory. A neural network (NN) utilizes observed navigation data to undergo a learning process, thereby enhancing its performance through a deep structure. Nevertheless, it necessitates a substantial quantity of training data and is susceptible to data overfitting. [PERSON] et al. [30] introduced a technique that utilizes AIS data and Gaussian processes [7] to forecast the uncertainty of vessels' future trajectories. Given that the majority of data are subject to noise, it is crucial to anticipate and account for these uncertainties. The work presented in [31] focuses on the application of GP for probabilistic travel-time prediction, discussing computational issues in GP, with Cholesky factorization proposed as a solution. However, the cubic complexity of matrix operations during training limits GP to small datasets. For larger datasets, approximation methods are recommended to reduce computational costs. The SGP utilizes approximation methods such as SoD, SoR, and FIC to enhance computational efficiency while preserving the benefits of GP. This use of approximation methods allows for more practical applications in real-world scenarios, making SGP a preferred choice for this study.
## 2 Shear Spring Model  
### Analytical Model Types  
The nonlinear numerical models for RC members can be broadly classified into two main categories, as illustrated in Figure 1[26; 27]. The concentrated plasticity model simplifies the nonlinear behavior by lumping it at specific locations like springs or hinges. These hinges are calibrated based on the nonlinear moment-curvature relationship, typically derived from cross-sectional analysis or experimental results of RC members. In contrast, the distributed plasticity model offers a more detailed representation by spreading the nonlinearity throughout the member. This category further branches into three sub-types: finite-length hinge models, fiber-section models, and finite element models [27].  
The fiber-section model depicts the cross-section of the RC member using a fiber model with uniaxial material properties. Unlike the concentrated plasticity spring model, it does not necessitate the calibration of moment-curvature relationships, and it can promptly calculate the response based on axial force and moment. The finite-length model applies to a nonlinear model in specific regions, such as plastic hinge lengths, while retaining elastic behavior elsewhere. The most sophisticated approach is the finite element model. Thismodel can systematically capture shear deformation and failure but requires significant computational resources.  
While the fiber-section model is effective for capturing flexural behavior, it falls short in representing the shear-critical behavior of RC members, especially in cases where shear deformation significantly influences the response. Therefore, this study employs a model that incorporates a translational shear spring into the fiber-section-based element. The details of the model are presented in Section 3.
## 3 The Proposed Scheme
### Task Accomplished Phase
The client selects a random \(r_{10}\), and computes the following parameter:  
Figure 9:9  
The timestamp \(T_{17}\) will be appended to the event message:  
\[M_{Event_{6}}=(ID_{Event}||ID_{SC}||ID_{SCC}||T_{13}||Seq_{i}) \tag{56}\]0  
The client uses the hash function to compute the parameter \(h_{C_{1}}\):  
\[M_{Event_{6}}=(ID_{Event}||ID_{SC}||ID_{SCC}||T_{13}||Seq_{i}) \tag{56}\]1  
The client uses the private key \(Prk_{C}\) and \(M_{Event_{8}}\) to compute the signatures as follows:  
\[M_{Event_{6}}=(ID_{Event}||ID_{SC}||ID_{SCC}||T_{13}||Seq_{i}) \tag{56}\]2  
\[M_{Event_{6}}=(ID_{Event}||ID_{SC}||ID_{SCC}||T_{13}||Seq_{i}) \tag{56}\]3  
After that, the client uses the \(SCC\)'s public key \(P{u}{k}_{SCC}\) to encrypt the message into a ciphertext \(C_{C_{1}}\):  
\[M_{Event_{6}}=(ID_{Event}||ID_{SC}||ID_{SCC}||T_{13}||Seq_{i}) \tag{56}\]4  
Then, the \(C\) sends \((ID_{C},ID_{SCC},C_{C_{1}})\) to the \(SCC\). Step 6. Once receiving the cipher message \(C_{C_{1}}\) at \(T_{18}\), the SCC uses its private key \(Prk_{SCC}\) to decrypt the message:  
\[M_{Event_{6}}=(ID_{Event}||ID_{SC}||ID_{SCC}||T_{13}||Seq_{i}) \tag{56}\]5  
Then, the \(SCC\) checks the validity of the timestamp:  
\[M_{Event_{6}}=(ID_{Event}||ID_{SC}||ID_{SCC}||T_{13}||Seq_{i}) \tag{56}\]6  
If the timestamp interval is valid, then the client needs to verify the client's hash parameter:  
\[M_{Event_{6}}=(ID_{Event}||ID_{SC}||ID_{SCC}||T_{13}||Seq_{i}) \tag{56}\]7  
If Equation (81) holds, the latest message will be sent and chained \((R_{C_{1}},h_{C_{1}},Sig_{C_{1}},\)\(Sig_{C_{2}},M_{Event_{8}})\) to the \(BCC\). Step 7. Then, the \(SCC\) selects a random number \(r_{11}\), and computes the following parameter:  
\[M_{Event_{6}}=(ID_{Event}||ID_{SC}||ID_{SCC}||T_{13}||Seq_{i}) \tag{56}\]8  
The _event ID_, client's _ID_, and \(SCC\)'s staff _ID_ will be written to the event message with a timestamp \(T_{19}\):  
\[M_{Event_{6}}=(ID_{Event}||ID_{SC}||ID_{SCC}||T_{13}||Seq_{i}) \tag{56}\]9  
The \(SCC\) uses the hash function to compute the parameter \(h_{SCC_{6}}\):  
\[h_{SG_{2}}=H(R_{SG_{2}}{}^{r_{8}}\cdot Puk_{SG}(M_{Event_{6}}+Prk_{SG})||H(M_{ Event_{6}})) \tag{57}\]0  
The \(SCC\) uses its private key \(Prk_{SCC}\) and \(M_{Event_{9}}\) to compute the signatures as follows:  
\[h_{SG_{2}}=H(R_{SG_{2}}{}^{r_{8}}\cdot Puk_{SG}(M_{Event_{6}}+Prk_{SG})||H(M_{ Event_{6}})) \tag{57}\]1  
\[h_{SG_{2}}=H(R_{SG_{2}}{}^{r_{8}}\cdot Puk_{SG}(M_{Event_{6}}+Prk_{SG})||H(M_{ Event_{6}})) \tag{57}\]2  
Then, the \(SCC\) sends and chains \((R_{SCC_{6}},h_{SCC_{6}},Sig_{SCC_{11}},Sig_{SCC_{12}},M_{Event_{9}})\) to the \(BCC\).
## 3 Examples
### Synthetic Example  
The generated synthetic seismic data result from convolving a 20 Hz Ricker wavelet with the reflection coefficient model (Figure 3a) and introducing 15% random noise (i.e., the S/N is 15 dB). Figure 3b shows the synthetic seismic data, including 1400 traces, each of which has a 2440 time sampling number. The trace located at common middle point (CDP) 700 is selected as a pseudo well, whose true reflection coefficient is already known, to calculate the statistical distribution and randomly generate multiple reflection coefficient models. The 20 Hz Ricker wavelet is used as a statistical wavelet to build the wavelet library, and the 50 Hz Ricker wavelet is convolved with the reflection model (Figure 4a) to generate the true high-resolution data (Figure 3d). Figure 3a-c show three high-resolution results using SSI, the DL-based method, and the proposed method, respectively. From the results, it is evident that the high-resolution estimate in Figure 3c reveals weaker reflectivity and exhibits greater resemblance to the actual high-resolution data (Figure 3d) compared with other methods. Figure 5a-c illustrates the respective reconstruction errors between the high-resolution results and the true data. Analyzing the reconstruction error reveals that, in most instances, the high-resolution data obtained using the proposed method (Figure 5c) exhibit lower errors compared with other methods.  
Additionally, Figure 5d depicts the normalized amplitude spectrum (AMP) of synthetic data, SSI, the DL-based method, the proposed method, and the true high-resolution data. Clearly, the outcome achieved by the proposed method (indicated by the black line) aligns more closely with the true high-resolution data (represented by the cyan line).  
Figure 3: Contrasts among the inferred high-resolution results are illustrated in (**a–c**) for SSI, the DL-based method, and the proposed method, respectively. (**d**) Represents the true high-resolution data.
# Comparative Analysis of Discrete Subtraction and Cross-Correlation for Subpixel Object Tracking
## 2 Materials and Methods  
### Tracking Methods  
As mentioned in the introduction, the objective of this manuscript is to compare the performance of the DSM method with the correlation method for target tracking with subpixel accuracy.  
The cross-correlation method is widely recognized in the community. In [9], the authors extensively discuss the cross-correlation method with subpixel refinement, which involves locally fitting the correlation peak neighborhood. Additionally, specific algorithms in Matlab are provided in the referenced work. Therefore, no further discussion will be added here.  
The Discrete Subtraction method was recently presented in [13]. Briefly, it considers the difference between an image \(I_{0}\) and its shifted version, \(I_{gx}\):  
\[D=I_{0}-I_{gx} \tag{1}\]  
From this value, the sum of the values in the \(i\)th column \(C_{i}\) and the \(j\)th row \(R_{j}\) is computed:  
\[C_{i}=\sum\limits_{j=1}^{N}D_{i,j};\quad R_{j}=\sum\limits_{i=1}^{N}D_{i,j} \tag{2}\]  
where \(1\leq(i,j)\leq N\). Next, the absolute sums of the \(C_{i}\) and \(R_{j}\) values are calculated as:  
\[T_{C}=\sum\limits_{i}|C_{i}|;\quad T_{R}=\sum\limits_{j}|R_{j}|; \tag{3}\]  
In [17], it is shown that, under the assumption of very small displacements, the parameters \(T_{C}\) and \(T_{R}\) are directly proportional to the horizontal and vertical shifts, respectively. However, these parameters need to be pre-calibrated with the movement sequence of the target to determine the proportionality coefficient.
# Directed Network Disassembly Method Based on Non-Backtracking Matrix
## 3 Preliminaries
### Model  
As shown in Figure 1, by applying the DIR method to disassemble the directed network in the figure, according to the spectral characteristics of the non-backtracking matrix, the edge of the directed network is divided into two different red and blue modules; overlapping nodes 5 and 6 that connect these two different edge modules were found. By removing nodes 5 and 6 and disconnecting the edges connected to them, the directed network can be divided into two disconnected sub-parts. Compared with the previous disassembly method, this disassembly method for removing overlapping nodes of edge modules requires fewer disassembly steps, does not need to find the minimum node cover age set, and the corresponding disassembly cost is lower (the nodes found by the traditional decomposition method are 5, 6, 7, 12), which is more suitable for directed networks.
## 6 Discussion
### Validity of the Data Preprocessing Technique  
The irregularity of the raw wind speed series often makes the input data contain high noise and fluctuation. Therefore, removing the noise from the original data is important to obtain a better forecasting accuracy. To test the validity of this data preprocessing technique, the results of the proposed model for four datasets using original and preprocessed data are listed together for comparison. From Tables 2 and 3, the MAPE values of the proposed model decrease by 5.88%, 5.56%, 2.39%, and 7.36% in four wind turbines compared with the CSO-SVM model without the EEMD process. The MAE values also decreases by 0.1160, 0.0086, 0.2031, and 0.3109 in four wind turbines, respectively. The WI value increases by 0.0686, 0.0723, 0.0251, and 0.1565, respectively. The results indicate that the EEMD effectively improves the accuracy of the forecasting. As Tables 2 and 3 show, all six metrics changed positively, and the results demonstrate that after reducing the noise of the raw data, the performance of the hybrid model improved significantly.
# Dynamic Damage Mechanism and Seismic Fragility Analysis of an Aqueduct Structure
###### Abstract  
The Concrete Damaged Plasticity (CDP) constitutive is introduced to study the dynamic failure mechanism and the law of damage development to the aqueduct structure during the seismic duration using a large-scale aqueduct structure from the South-to-North Water Division Project (SNWDP) as a research object. Incremental dynamic analysis (IDA) and multiple stripe analysis (MSA) seismic fragility methods are introduced. The spectral acceleration is used as the scale of ground motion record intensity measure (IM), and the aqueduct pier top offset ratio quantifies the limit of structural damage measure (DM). The aqueduct structure's seismic fragility evaluation curves are constructed with indicators of different seismic intensity measures to depict the damage characteristics of aqueduct structures under different seismic intensities through probability. The results show that penetrating damage is most likely to occur on both sides of the pier cap and around the pier shaft in the event of a rare earthquake, followed by the top of the aqueduct body, which requires the greatest care during an earthquake. The results of two fragility analysis methodologies reveal that the fragility curves are very similar. The aqueduct structure's first limit state level (LS1) is quite steep and near the vertical line, indicating that maintaining the excellent condition without damage in the seismic analysis will be challenging. Except for individual results, the overall fragility results are in good agreement, and the curve change rule is the same. The exceedance probability in the case of any ground motion record IM may be estimated using only two factors when using the MSA approach, and the computation efficiency is higher. The study of seismic fragility analysis methods in this paper can provide a reference for the seismic safety evaluation of aqueducs and similar structures.  
a +
Footnote †: journal: Applied Sciences
## 2 Short-Term and Long-Term Collision Probability Calculation Models
### Short-Term Collision Probability Calculation Model
#### 2.1.4 Relative Position Filtering  
When the space target is in the same volume element, it is judged whether a collision occurs between satellites by the approach distance. Given that the attitude of the space target is unknown, the space target is calculated as an envelope sphere. When the distance between the centers of mass of the two targets is less than the joint radius R, the two targets are considered to have collided immediately.  
The true position of the space target has a random deviation, so the collision between the two space targets is a probabilistic event. In accordance with the \"\(3\sigma\)\" rule, the true position of the space target has a high probability of being within three standard deviations of the average position. Therefore, we can eliminate the targets that may collide for the  
Figure 4: Schematic of space discrete volume element.  
next calculation in accordance with the relative position of the two space targets and the \"3\(\sigma\)\" rule.  
Assuming that the position error square matrix of the two space targets in the orbital coordinate system is  
\[\text{var}(r_{1})\begin{bmatrix}\sigma_{N_{1}}^{2}&\\ &\sigma_{T_{1}}^{2}&\\ &&\sigma_{W_{1}}^{2}\end{bmatrix},\text{var}(r_{2})\begin{bmatrix}\sigma_{N_{2 }}^{2}&\\ &\sigma_{T_{2}}^{2}&\\ &&\sigma_{W_{2}}^{2}\end{bmatrix} \tag{10}\]  
the variance matrix of the relative position vectors of the two targets in the Earth inertial coordinate system is  
\[\text{var}(r_{ECI})=\text{var}(r_{1}-r_{2})=M_{NTW\to ECI}(\text{var}(r_{1})+ \text{var}(r_{2}))M_{NTW\to ECI}^{T} \tag{11}\]  
In accordance with the \"3\(\sigma\)\" rule, when two space targets may collide, the relative distance between the two targets satisfies the following conditions:  
\[\left|\overset{\rightarrow}{r}_{2}-\overset{\rightarrow}{r}_{1}\right|<3| \sigma_{r_{ECI}}|+R \tag{12}\]  
where \(r_{2}\) and \(r_{1}\) are the position vectors of the space target in the Earth inertial coordinate system. \(\sigma_{r_{ECI}}\) is the joint error distance in the Earth inertial coordinate system.
# Research on a Dynamic Calibration Method for Photogrammetry Based on Rotary Motion
## 2 The Calibration Principle  
The core principle of calibrating the dynamic parameters of a photogrammetry system is to provide a dynamic moving target with stable parameters and a unique spatial geometric conversion relationship. By comparing the measurement data of the photogrammetry system with the standard dynamic target design's motion parameters, the dynamic parameter calibration of the photogrammetry system is realized. Based on the above principles, Figure 1 shows the calibration device. The calibration system proposed in this paper consists of a control field, a circular trajectory standard device, and a binocular camera. The calibration field provides the standard spatial geometric relationship of a target point. Since internal parameters, such as the focal length of the camera, will change during the dynamic measurement process, a control field is introduced into the calibration system as a static reference plane for the dynamic calibration process. The stability of the control device is very important [24; 25]. The circular track-generating device in this study can generate a uniform circular motion of 0-1000 rpm, in which the circular grating can provide the standard value of the speed for the standard device. Two targets with coding points are installed on a fixed position of the rotating rod. During the rotation process, the relative distance between the two coding points does not change, that is, the distance can be used as the standard value L during the experiment. Therefore, in the process of uniform rotation of the original trajectory-generating device, the binocular camera obtains the spatial coordinates of the two coding points in real time, calculates the distance L' between the two coding points, and then compares it with the standard distance. The dynamic performance of the camera is then evaluated.  
The control field consists of several photographic control points. These control points are the subjects of the digital photogrammetry system under test and are statically calibrated in advance to provide position criteria. These photographic control points are arranged on the machine and the wall around the turntable, which are relatively fixed and can be set as the fixed points in the calibration system. These photographic control points can also be used as a basis for calibration. According to the measuring range, these control points are often arranged, and the interval is generally between 200 mm and 500 mm.  
The use of circular trajectories enables motion outputs with a large dynamic range. Even if the rotational speed does not change, by changing the radius, the linear speed of the dynamic target changes linearly. In addition, the stable control of speed is also a form of high-precision movement that can be achieved at present. The application of circular motion trajectory is not only a relatively optimized form to achieve the continuous and stable output of dynamic targets, but it is also the most convenient form of measurement value traceability.