esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Prediction Methods for Ultra-Low Cycle Fatigue Damage of Steel Piers Under Earthquakes
## 2 Research Status and Development Trend at Home and Abroad
### Continuum Damage Mechanics (CDM) Model
#### 2.4.4 Advantages and Disadvantages of Prediction Methods  
Advantages and disadvantages of prediction methods is shown as Table 1. The research indicates that the traditional CDM model is an effective method for studying ultra-low cycle fatigue damage in steel and steel structures. Compared to empirical formulas and the CVGM, the CDM model offers several advantages:Despite the promising development potential of traditional CDM models, they also have the following shortcomings. (1) The existing models do not consider the effect of equivalent plastic strain on damage increment. (2) There is a lack of numerical simulation methods grounded in the CDM model to predict the ultra-low cycle fatigue crack propagation in welded joints. (3) Predicting fatigue crack propagation requires a combination of solid elements for accurate results, resulting in increased computational demands. Furthermore, there is an absence of mixed-element division criteria for steel bridge piers that take into account ultra-low cycle fatigue damage.
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
## 5 Ongoing R&D  
In recent years, a number of adaptations and enhancements to the existing specifications have been proposed in order to maximize the spectral efficiency of the RA E-SSA-based scheme, and to adapt it to different utilization scenarios. In particular, some of the current R&D activities are related to the possibility of operating a massive number of low-cost IoT terminals also using non-geostationary satellite orbit (NGSO) satellites and smaller channelization. The main examples of the current R&D tracks are illustrated in the following sub-sections.
# Detection and Localization of Failures in Hybrid Fiber-Coaxial Network Using Big Data Platform
## 3 HFC Network Architecture  
In this section, we give a brief description of HFC network architecture. The purpose of this section is to present typical devices in HFC networks and determine which of them cannot be monitored directly.  
Figure 1 shows a typical segment of an HFC network covered by one CMTS (Cable Modern Termination System). The CMTS distributes services, such as TV, voice, and data, in the downstream direction using optics. Downstream optical signal from CMTS reaches ON (Optical Node) via Hub. Optical signal is converted to an electrical signal in ON. The services, received from CMTS, are then distributed as electrical signals in the downstream direction to the end users. AMPs (Amplifiers) are passive devices that perform non-linear RF (Radio Frequency) amplification. They are needed to cope with coaxial cable attenuation. At the end, the signal reaches CPE via an Access Point (AP). CPEs are typically cable modems or set-top boxes. In the upstream direction, signals from CPEs are frequency multiplexed and sent in the opposite direction through the cable network to an optical node that performs electrical to optical conversion of the signal. Finally, the upstream signal reaches the CMTS via Hub.  
HFC network operators can collect data from the HFC network devices. However, not all devices support data collection. AMPs, APs, and ONs are not capable of supporting data collection. On the other hand, CMTSs and CPEs do support data collection. Depending on the supported protocols, there are several ways to collect data from CMTSs and CPEs. For example, SNMP (Simple Network Management Protocol), IPDR (Internet Protocol Detail Record), and FTP (File Transfer Protocol) can be used for data collection purposes on CMTSs and CPEs.  
Figure 1: HFC network. CMTS: Cable Modern Termination System, ON: Optical Node, AP: Access Point, CPE: Customer Premises Equipment, AMP: Amplifiers.  
Figure 1 shows that the cable network part has a tree topology. This means that from each user there is exactly one path to the CMTS. This information can be used to indirectly estimate the health of other devices in the tree, such as ONs, AMPs, and APs. For example, if the data collection process shows that all CPEs under some APs are not available, most likely that AP is down.
## 2 Sensitivity Analysis of Chaotic Systems Using MSS
\begin{bmatrix} \mathbf{v}_{0}\\ \mathbf{v}_{1}\\ \vdots\\ \mathbf{v}_{K}\end{bmatrix} \ \ \ \underline{\mathbf{b}}= \begin{bmatrix} \mathbf{b}_{1}\\ \mathbf{b}_{2}\\ \vdots\\ \mathbf{b}_{K}\end{bmatrix} , \tag{8}\]  
and \(\Phi_{i+1}=P_{t_{i+1}}\phi^{t_{i}t_{i+1}}\), \(\mathbf{b}_{i+1}=P_{t_{i+1}}\int_{t_{i}}^{t_{i+1}}(\phi^{\tau,t})\frac{ \partial\mathbf{f}}{\partial\mathbf{s}}\,d\tau\). Equation (8) is obtained by writing the state vector \(\mathbf{v}(t)\) in \(t_{i}\leq t<t_{i+1}\) as  
\[\mathbf{v}(t)=P_{t}\left(\phi^{t,\tau}\mathbf{v}(t_{i})+\int_{t_{i}}^{t}\phi^{ t,\tau}\frac{\partial\mathbf{f}}{\partial\mathbf{s}}\,d\tau\right), \tag{9}\]where \(\phi\) denotes the state transition matrix satisfying  
\[\frac{d\phi^{t,\tau}}{d\tau}=\left(\frac{\partial f}{\partial u}\bigg{|}_{\tau} \right)\phi^{t,\tau}, \tag{10}\]  
and \(P_{t}\) is the projection operator which eliminates \(\eta\) from Equation (5)  
\[P_{t}=I-\frac{\mathbf{f}(t)\mathbf{f}(t)^{T}}{\mathbf{f}(t)^{T}\mathbf{f}(t)}. \tag{11}\]  
Problem (7) is a standard minimization problem in linear algebra. It can be put into a Karush Kuhn Tucker (KKT) form:  
\[ \begin{bmatrix}-I&A^{T}\\ A&0\end{bmatrix}\begin{bmatrix}\mathbf{y}\\ \underline{\mathbf{w}}\end{bmatrix} = \begin{bmatrix}0\\ \underline{\mathbf{b}}\end{bmatrix} . \tag{12}\]  
Introducing the Schur complement \(S=AA^{T}\), the above system takes the form:  
\[S_{\underline{\mathbf{w}}}= \begin{bmatrix}\Phi_{1}\Phi_{1}^{T}+I&-\Phi_{2}^{ T}\\ -\Phi_{2}&\Phi_{2}\Phi_{2}^{T}+I&-\Phi_{3}^{T}\\ &\ddots&\ddots\\ &&-\Phi_{K}&\Phi_{K}\Phi_{K}^{T}+I\end{bmatrix}\begin{bmatrix}\mathbf{w}_{1} \\ \mathbf{w}_{2}\\ \vdots\\ \mathbf{w}_{K}\end{bmatrix} =\underline{\mathbf{b}}. \tag{13}\]  
System (13) is solved with a Krylov solver, such as GMRES, by supplying matrix vector products \(S_{\underline{\mathbf{w}}}\). The system is usually ill-conditioned, and the Krylov solver requires a preconditioner to accelerate its convergence. In this work, we use the preconditioner developed in Reference [21], which results in  
\[\left(\gamma I+\Gamma S\right)\underline{\mathbf{w}}=\Gamma\mathbf{b}. \tag{14}\]  
The introduction of matrix \(\Gamma\) and the regularization parameter \(\gamma\) reduces the condition number of \(S\) by orders of magnitude and accelerates the convergence of the iterative Krylov solver. After solving System (14), the sensitivity of \(\overline{G_{\infty}}(s)\) is given by  
\[\frac{d\overline{G_{\infty}}(s)}{ds}=\frac{1}{T}\sum_{i=0}^{K-1}\int_{t_{i}}^{ t_{i+1}}\left\langle\frac{\partial G}{\partial u}\bigg{|}_{t},\underline{\mathbf{y}} \right\rangle dt+\frac{1}{T}\sum_{i=0}^{K-1}\frac{\langle\mathbf{f}_{i+1}, \underline{\mathbf{y}}(t_{i+1})\rangle}{\|\mathbf{f}_{i+1}\|_{2}^{2}}( \overline{C}-G_{i+1})+\frac{\partial\overline{J}}{\partial s}. \tag{15}\]  
This approach to sensitivity analysis in chaotic systems has been shown to produce sensitivities that are representative of the physical properties of the underlying system and are in good agreement with finite differences. All gradients in this paper will be evaluated using the aforementioned algorithm.
## 2 Literature Survey
### Background, Definitions, and Cryptographic Algorithms  
The Internet of Things (IoT) has emerged as one of the most innovative technologies for connecting virtual and physical objects over the Internet, thus opening up a slew of new possibilities in a variety of fields. The unexpected growth of the Internet of Things system has opened up new opportunities in a variety of fields. However, there are still some issues with the IoT that act as a roadblock to the promised widespread adoption of IoT devices. The lack of trust and confidence is one of these issues. In the current IoT centralized model, a third-party central authority has complete control over data collection and processing from various IoT objects, with no clear restrictions on how the data are used. It is not easy to integrate blockchain into an IoT system. The first and most crucial step is to decide which blockchain platform will be used to combine IoT and blockchain technology. The most commonly used platforms for implementing blockchain with IoT are Ethereum, Hyperledger, and IOTA. They are not only open-source platforms, but they can also provide key capabilities such as block connectivity, and the cryptographically secure hashing of different transactions in single blocks, advanced marks, consensus, and smart contracts.  
Asymmetric-key algorithms and hash functions are the two types of cryptographic algorithms used in blockchains. Hash functions are employed to give each participant the capability of a single view of the blockchain. The SHA-256 hashing method is commonly used as the hash function in blockchains. Each transaction is hashed before being bundled into a block on a blockchain. By storing a hash of the data in the previous block, hash pointers connect each block to the one before it. Data on the blockchain are immutable because each block is linked to the one before it. Because of the hashing function, any modification in a transaction generates a completely different hash, which affects the hashes of all the following blocks [10].
## 2 Material and Method
### k-Nearest Neighbor (k-NN)  
The results showed that k-NN had the highest accuracy among algorithms for categorizing and comparing k-NN patterns, based on their accuracy [48]. The k-NN method was used for data processing [49]. The distance between two points in feature space can be calculated using various distance metrics, such as Euclidean distance, Manhattan distance, Minkowski distance, or other distance metrics, of which the first is the most commonly used. The distance between two points \(A\) and \(B\) in dimension \(n\) is  
\[(A,B)=\sqrt{\sum_{i=1}^{n}(A_{i}-B_{i})^{2}} \tag{1}\]  
where \(A_{i}-B_{i}\) is the \(i\) component of the two vectors to be compared and \(n\) is the number of dimensions (features) of each vector.  
After finding the \(k\)-nearest neighbor of the point to be classified, the majority class of the \(k\) neighbor is taken as the class prediction. If \(k=1\), then the predicted class will be the class of the nearest neighbor. If \(k>1\), then class predictions are usually taken based on the majority of the class of the \(k\)-nearest neighbor.  
The distance between each data point in the training dataset and the data point to be predicted was measured as part of the k-NN computation. The following are the main steps in k-NN calculations:  
1. The distance metric used to calculate the proximity between data points must be selected using the Euclidean distance.  
Using the Euclidean distance metric, the distance to each data point in the training dataset and each testing data point is to be determined.
3. After calculating the distance, the next step is to find the k-NN and look at the k-NN of the test data points based on the most negligible distance value. Thereafter, the calculated distances are to be sorted and the lowest K value selected.
4. The next step is to predict the class (classification) as a class prediction for the test data points; if the task involves classification, it should be ensured that the majority class is from the k-NN.  
Figure 2 below shows the stages of the classification model:
## 4 Validation of The Proposed Approach
### Experiment Setup  
The experiment was conducted on a virtual tele-abrasive system, shown in Figure 5a [32]. The surface to be blasted was created virtually with a size of 275 \(\times\) 25 mm and was placed horizontally along the \(X\)-axis (Figure 6); the path was 10 mm wide, and the target line was in the middle of the path (Figure 6).  
Figure 5: (a) A virtual tele-abrasive workstation. (b) An example of the blasting operation.  
The guideline for training was designed to have an amplitude, H, of 10 mm; the wavelength, W, was 6 mm. The amplitude and wavelength were each set according to the numbers obtained from our preliminary experiment in order to find values that our participants were comfortable with in terms of the movements of their wrists and fingers during the operation. The guideline is shown in Figure 7.  
The parameters of the state space model for stage 1 were set as follows:  
* \(B_{k}^{i}u_{k}^{i}\) and \(m_{k}^{i}\) were assumed to be very small (very close to zero).
* \(C_{k}^{i}\) was set as 1.  
The parameters for the Kalman filter were set as follows:  
* D\({}^{\text{I}}\) was set at 1, as the target line position was constant on the \(Y\)-axis (horizontally straight).
* The term E\({}^{\text{I}}\)s\({}^{\text{I}}\) was zero, as there was no control input.
* W\({}^{\text{t}}\) was set as equal to the MSE of the reference guideline, which was 8.324 for this experiment.
* C\({}^{\text{I}}\) was set at 1.  
In this experiment, a power analysis was used to identify the number of participants. In a small previous experiment, the standard deviation was 0.801. The confidence level was set at 95%. The power of testing was set to at least 90%. We expected that the proposed approach would help keep the average nozzle position closer to the target line by at least 1 mm. Minitab was used to calculate the number of participants, and it suggested that at least nine participants are required; therefore, we decided to have 10 participants for the experiment.  
The participants were trained to use the conventional approach (a straight movement) to control the master controller to blast a surface covered by unwanted particles following a given path, but without a target line. They then performed this operation twice. In performing the task, the participants needed to visually judge the appropriate velocity such that the given path would be cleanly blasted. Then, they were trained to blast the surface using the proposed arm movement pattern with the guideline until they had become  
Figure 6: Surface to be blasted in the experiment.  
Figure 7: Guideline for the experiment.  
familiar with the pattern and velocity. Next, they performed the task using the proposed arm movement pattern without a guideline twice. We recorded the position of the master controller and the operating time for each operation.
## 4 Multicast Routing Approaches in DTN
### Social-Aware Multicast Routing in DTN  
Social-based multicast routing approaches exploit social behaviors of DTN nodes to select relays for forwarding messages. Social characteristics between mobile users in the DTN are stable due to their long-term social relationships and behaviors. Whereas positive social characteristics (e.g., centrality and community) facilitate the routing process, negative characteristics (e.g., selfishness) usually degrades routing performance. We further classify the surveyed social-aware multicast routing protocols regarding contact probability (based on encounter history) and profile-based interests.  
Figure 4: Classification of DTN multicast routing strategies.
# Novel Methodology for Condition Monitoring of
## 2 Theoretical Considerations
### Artificial Neural Network (ANN)  
One of the popular artificial neural networks for pattern classification is the multilayered perceptron neural network (MLP). To define an ANN, it is necessary to establish parameters such as the connections, the number of layers, the activation functions, the propagation rules, etc. [32]. In the case of the MLP, it needs to consider its two different stages: the learning stage and the prediction process. In the case of the MLP, the propagation rule is the weighted sum, and it is defined according to (5).  
\[\sum_{i=1}^{n}w_{ij}x_{i}(t) \tag{5}\]  
where \(w_{ij}\) is the weight that connects neuron \(i\) in the input layer with neuron \(j\) in the hidden layer, \(x_{i}\) is the output from neuron \(i\) in the input layer, \(n\) is the number of neurons in the input layers, and \(t\) is the pattern [36].
# Heat Transfer Performance and Flow Characteristics of Helical Baffle-Corrugated Tube Heat Exchanger
## 4 Conclusions  
In this paper, the enhanced heat transfer and flow characteristics of continuous helical baffle-corrugated tube heat exchangers with different structures are investigated by numerical simulation and experimental research, and the main conclusions are as follows:  
1. The results of the study show that when the total heat transfer coefficients of the heat exchangers are similar, the helical baffle heat exchanger with a larger helix angle has a more obvious advantage in the overall heat transfer performance. Therefore, in industrial applications, a helical baffle heat exchanger with a large helix angle with a large shell volume flow rate should be selected as often as possible, so that the heat exchanger has sufficient heat transfer performance while having a small pressure loss.
2. The efficiency evaluation coefficient (EEC) results show that the helix angle of the helical baffle is the most influential factor. And the optimum levels are **A3**, **B1**, and **C3** in the case of the low pipe flow rate. The section shape of the helical groove is semicircle, the helix angle of the corrugated tube equals 37.07\({}^{\circ}\), and the helix angle of the helical baffle equals 51.07\({}^{\circ}\). The optimum levels are **A3**, **B3**, and **C3** in the large pipe flow rate condition. The section shape of the helical groove is semicircle, the helix angle of the corrugated tube equals 67.69\({}^{\circ}\), and the helix angle of helical baffle equals 51.07\({}^{\circ}\).  
Methodology, J.C. and W.L.; Software, J.C.; Validation, W.C.; Formal analysis, W.L.; Resources, J.Y.; Data curation, W.C.; Writing--original draft, J.C.; Writing--review & editing, W.C. and J.Y.; Supervision, J.Y.; Project administration, J.Y.; Funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by Hubei Key R&D Projects in 2020 (2020 BAB30).  
Figure 10: Level of influence of each factor for _EEC_.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.  
**Conflicts of Interest:** Author [PERSON] was employed by the company CEWUD GROUP COMPANY LIMITED. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods  
### SEM-EDS Measurement  
The collected samples were analyzed by a scanning electron microscope (Quanta 650, FEI, USA) coupled with an EDS (energy-dispersive X-ray spectrometer) with an SDD (silicon drift detector) (EDAX, Octane plus detector). The optimal experimental conditions of the SEM/EDS method presented in [12] were applied in this study: accelerating voltage 25 kV, working distance 10 mm, spot size 5, minimal size of particles 0.5 \(\upmu\)m, area of scanned frame 8 mm\({}^{2}\), image resolution (scan speed 3 \(\upmu\)s per pixel) 1536 \(\times\) 1024 pixels. Secondary electron and backscattered electron images were observed. The identification of GSR was performed in an automatic mode using the building software TEAMTM Basic V4.4.1. The automatic scanning mode was used to study each sample. Each positive GSR sample was confirmed by manually scanning. X-ray signals were collected from 0 to 20 KeV at 20 eV per channel. The live time of spectra acquisition was 5.0 or 10 s., the detector resolution was set at (128 \(\pm\) 15) eV, and the dead time was \(\leq\)30%. The SEM-EDS spectra were acquired in the energy range in which the SEM/EDS system detected chemical elements. A particle was identified as GSR if it possessed a spheroidal/noncrystalline morphology and contained Pb, Sb, and Ba [9].
## 3 Proposed Scheme
### Correctness Analysis
#### 3.3.1 Spatial Aggregation  
\[\begin{array}{ll}AS_{d}&=\sum\limits_{i=1}^{n}LS(i,d)\bmod M\\ &=\sum\limits_{i=1}^{n}\sum\limits_{j\in ind_{i}[s]}(x_{(i,d)}^{j}+r_{(i,d)}^{j} )\bmod M\ (1\leq s\leq k)\\ &=(\sum\limits_{i=1}^{n}\sum\limits_{j\in ind_{i}[s]}x_{(i,d)}^{j}+\sum \limits_{i=1}^{n}\sum\limits_{j\in ind_{i}[s]}r_{(i,d)}^{j})\bmod M\\ &=(\sum\limits_{j=1}^{n}\sum\limits_{i\in ind_{i}[s]}x_{(i,d)}^{j}+\frac{1}{2} (\sum\limits_{i=1}^{n}\sum\limits_{j\in ind_{i}[s]}r_{(i,d)}^{j}+\sum\limits_{ j=1}^{n}\sum\limits_{i\in ind_{j}[s]}r_{(i,d)}^{j})\bmod M\\ &=(\sum\limits_{j=1}^{n}x_{d}^{j}+\frac{1}{2}(\sum\limits_{i=1}^{n}\sum \limits_{j\in ind_{i}[s]}r_{(i,d)}^{j}-\sum\limits_{i=1}^{n}\sum\limits_{j\in ind _{i}[s]}r_{(i,d)}^{j})\bmod M\\ &=\sum\limits_{j=1}^{n}x_{d}^{j}\bmod M\end{array} \tag{11}\]  
We prove the correctness of our spatial aggregation by permuting the row and column of data matrix formed as Figure 2. Equation (11) shows that the spatial aggregation in a neighborhood equals to the sum of locally spatial aggregation, i.e., the sum of individual data.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 2 Basic Principle and Functionality
### Instrument Configuration and Signal Detection
#### 2.1.1 Transmitter Subsystem
of laser beams [-]: The number of laser beams affects swath width, spatial resolution, and data redundancy. While early missions such as LITE and GLAS employed single-beam configurations, more recent systems such as ATLAS and GEDI utilised multiple beams--typically six or eight--to enhance coverage and efficiency.
## 3 Results
### Evaluation of Near Infrared Spectroscopy (NIRS)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Spectral Pre-Processing** & **LV** & \multicolumn{2}{c}{**Calibration**} & \multicolumn{2}{c}{**External Validation**} \\ \hline  & & \(\mathbf{R^{2}}\) & **RMSE** & \(\mathbf{R^{2}}\) & **RMSE** & **Bias** & **RPD** \\ \hline CP & & & & & & & & \\ Raw spectra * & 5 & 0.874 & 1.882 & 0.844 & 1.622 & 0.057 & 4 \\ SNV & 4 & 0.866 & 1.894 & 0.653 & 2.473 & \(-0.877\) & 3 \\ Normalization & 4 & 0.837 & 1.973 & 0.817 & 1.978 & 0.586 & 3.4 \\ Normalization and SNV & 5 & 0.902 & 1.632 & 0.753 & 2.16 & \(-0.421\) & 3.1 \\ \hline NDF & & & & & & & \\ Raw spectra & 7 & 0.618 & 6.261 & 0.607 & 6.979 & 4.453 & 1.9 \\ SNV & 7 & 0.834 & 4.061 & 0.802 & 4.868 & 0.426 & 2.1 \\ Normalization & 7 & 0.807 & 4.446 & 0.818 & 4.742 & 2.015 & 2.4 \\Figure 4: Optimized NIR spectra in the wavenumber region 3594 to 12,358 cm\({}^{-1}\) (2782 to 809 nm) for (**a**) CP, (**b**) NDF, and (**c**) the PQI. Figure 5: Plot of reference values versus predicted values for calibration and validation phases: (**a**) CP, (**b**) NDF, and (**c**) the PQI.