esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Analysis Methods
#### 2.3.3 Model Analysis
Therefore, the nonlinear problem in the original space is transformed into a linear problem in the new space [19]. The principle is as follows:  
\[f(x)=\text{sgn}(w.z+b)=\text{sgn}\left(\sum_{i=1}^{n}\alpha_{i}y_{i}(x_{i}.x) +b\right) \tag{9}\]  
After the nonlinear transformation of the feature x, the new feature is recorded as \(z=\varphi(x)\), then the SVM decision function constructed in the new feature space is:  
\[f(x)=\text{sgn}(w^{\phi}.z+b)=\text{sgn}\left(\sum_{i=1}^{n}\alpha_{i}y_{i}( \phi(x_{i})\,.\,\phi(x))+b\right) \tag{10}\]  
When observing Equations (9) and (10), it is found that the inner product \((x_{i}.\,x_{j})\) in the original space becomes \(\left(\varphi(x_{i})\,.\,\varphi(x_{j})\right)\) in the new space after the transformation. Because the inner product in the new feature space is also a function of the original feature, it is recorded as a kernel function:  
\[K(x_{i},x_{j})\overset{def}{=}\left(\phi(x_{i})\,.\,\phi(x_{j})\right) \tag{11}\]Thus, Equation (10) can be rewritten as:  
\[f(x)=\text{sgn}\left(\sum\limits_{i=1}^{n}\alpha_{i}y_{i}K(x_{i}.\,x)+b\right) \tag{12}\]  
\[ \begin{array}{l}\max\limits_{\alpha}Q(\alpha)=\sum\limits_{i=1}^{n}\alpha_{i}- \frac{1}{2}\sum\limits_{i,j=1}^{n}\alpha_{i}\alpha_{j}y_{i}y_{j}K(x_{i}.\,x)\\ s.\,t.\,\,\sum\limits_{i=1}^{n}y_{i}\alpha_{i}=0\\ 0\leq\alpha_{i}\leq C,\,\,i=1,\dots,n\end{array}  \tag{13}\]  
where \(\alpha\) is the optimal solution to the optimization problem. It is found that a nonlinear SVM can be constructed by selecting a suitable kernel function. Previous studies have shown that the radical basis function (RBF) is better than the polynomial kernel function and sigmoid tanh [41], so the RBF was used to construct a nonlinear SVM in this research. In order to further improve the accuracy of the model, the LDA-SVM model was constructed, combining the advantages of the FLDA and SVM. The original high-dimensional features were reduced by the FLDA algorithm, and the low-dimensional eigenvectors obtained were used as input variables of the SVM model. Figure 4 summarizes the basic steps from hyperspectral signatures to three different models, and then to final identification. Figure 4: General workflow for identification of wheat Fusarium head blight; FLDA (fisher’s linear discriminant analysis); SVM (support vector machine); LDA–SVM (the combination of FLDA and SVM).
## 2 Techniques Used for Adaptive Equalization
### Least-Mean-Squared Error  
LMS is an adaptive filtering algorithm widely used for adaptive equalization [23]. It aims to minimize the mean squared error between the desired signal and the filter output [24]. LMS updates the filter coefficients iteratively based on the instantaneous estimation error and the input signal [25; 26; 27]. In the context of adaptive equalization, LMS is employed to adjust the equalizer's coefficients and compensate for distortions caused by the channel [28]. By continuously adapting the filter coefficients, LMS enables the equalizer to adapt to changing channel conditions and optimize its performance [29]. LMS is known for its simplicity and ease of implementation, making it a popular choice in various communication systems.
## 4 Discussion and Validation  
### Data Collection  
Data in terms of scheduled maintenance plans and experienced corrective maintenance including failure modes, failure rates, and MTTR values were extracted from the notification system of the case company and incorporated into the computational model. In addition, several discussions with engineers have been conducted to attain a correct description and understanding of the case study and its data. In final, to improve the reliability of the simulations, data including failure modes, failure rates, and MTTR values were extracted from the OREDA database and compared with the experienced case study data.
# Analysis of Six Active Power Control Strategies of Interconnected Grids with VSC-HVDC
###### Abstract  
In this paper, the generator angle stability of several active power control schemes of a voltage-source converter (VSC)-based high-voltage DC (HVDC) is evaluated for two interconnected AC systems. Excluding frequency control, there has been no detailed analysis of interconnected grids depending upon the converter power control, so six different types of active power control of the VSC-HVDC are defined and analyzed in this paper. For each TSO (transmission system operator), the applicable schemes of two kinds of step control and four kinds of ramp-rate control with a droop characteristic are included in this research. Furthermore, in order to effectively evaluate the angle stability, the Generators-VSC Interaction Factor (GVIF) index is newly implemented to distinguish the participating generators (PGs) group which reacts to the converter power change. As a result, the transient stabilities of the two power systems are evaluated and the suitable active power control strategies are determined for two TSOs. Simulation studies are performed using the PSS\({}^{\otimes}\)E program to analyze the power system transient stability and various active power control schemes of the VSC-HVDC. The results provide useful information indicating that the ramp-rate control shows a more stable characteristic than the step-control for interconnected grids; thus, a converter having a certain ramp-rate slope similar to that of the other generator shows more stable results in several cases.  
Keywords:grid-interconnection; active power control strategies; transient stability; GVIF index; angle spread; VSC-HVDC +
Footnote †: journal: Applied Physics Letters
## 1 Introduction
[13; 14] experimentally and numerically studied the behavior of two RC T-beams strengthened in shear with CFRP, it showed that the shear strengthening of RC beams by externally bonded with FRP sheets is effectively improved. Basalt fiber reinforced polymer (BFRP) has been increasingly used in building structures due to its better cost-effectiveness, high ultimate strengthen, and light weight. Mechanical performance of GFRP and structural members reinforced with BFRP were widely studied in recent several decades. [PERSON] et al. [15] reviewed the art concerning BFRP and affirmed the merits of BFRP. [PERSON] et al. [16] experimentally investigated the debonding failure mode of BFRP reinforcements from concrete supports, and the influence of composite thickness, width ratio between the BFRP width and the concrete one, anchor length, on the debonding load was discussed. [PERSON] et al. [17] evaluated the flexure and shear performance of concrete beams reinforced with BFRP bars and stirrups. [PERSON] et al. [18] experimentally investigated the effects of adding different types of fibers to the concrete mixes on the flexural behavior of concrete beams reinforced longitudinally with BFRP bars, it showed that introducing basalt fibers to the concrete increased curvature ductility of the beams and improved the flexural capacities. [PERSON] et al. [19] investigated the shear performance of deep concrete beams reinforced with BFRP bars without web reinforcement, and the influence of parameters including the effective depth and the longitudinal reinforcement ratio on the shear capacity of the beams was studied. [PERSON] et al. [20] investigated the degradation of the tension-tension fatigue behavior of BFRP composites after exposure to a salt solution, the results showed that the static strength of the BFRPhardly degenerated after aging in the salt solution, whereas the detrimental effect of saltwater became apparent under fatigue loading. [PERSON] et al. (2019) proposed a hybridized basalt FRP and steel (BFRP-steel) rebar to overcome the limitation of high initial cost, low elastic modulus, and brittleness of BFRP bars. Based on the existing research literature, when concrete frame structures are reinforced with BFRP bars, they will be more vulnerable to collapse than common RC structures due to weak stiffness of structural members in the scenario of load-bearing columns loss. Collapse performance of concrete structures retrofitted with carbon FRP and glass FRP has been investigated in recent years ([PERSON] et al., 2019; [PERSON] et al., 2020). However, little research is done on the progressive collapse of concrete frames reinforced with BFRP bars. In view of the existing problems, collapse tests, including dynamic collapse tests and static collapse tests, were conducted on six beam-column frame sub-assembages reinforced with BFRP bars in the beams, and collapse responses, as well as collapse patterns of the sub-assembages, were discussed. This paper mainly reports the research results of the static collapse tests.
# Analysis and Visualization of Production Bottlenecks as Part of a Digital Twin in Industrial IoT
## 5 Integration of Analysis Results into a Digital Twin
The generated machine leaning models and results are again stored within the knowledge graph. In order to enable a flexible and powerful simulation of the production system behavior, the overall production process is divided into functional mockup units (FMUs) [24]. Each FMU shows an independent dynamic behavior by simply using its input at time \(t\) to calculate the corresponding output at time \(t+1\). In order to model the input-output relation of an FMU, typically a physical mockup, for example, described by a (hybrid) ordinary differential equations system (ODE), is used (e.g., [25]). However, our proposed approach relies on ML-based FMUs (cf. Figure 10) as, for example, proposed by [26; 27]; thus, the dynamic behavior of the FMUs is automatically deduced from the execution and performance data by machine learning approaches. In general, the underlying machine learning models may vary from appropriate deep learning methods to more basic models like decision tree ensembles (Random Forest) (cf. [27]). The overall system behavior then results from the interplay of all FMUs connected via output-input-relationships. Finally, all relevant information, i.e., the FMU definitions and machine learning models, is passed on to the simulation system [24]. While in the DT concept described above, machine learning is used to automatically learn the behavior of single machines (i.e. FMUs), the bottleneck analysis approach can now be used to analyze and optimize the overall system behavior, i.e., the interaction between the FMUs based on its output-input-relationships. As already stated above, the bottleneck analysis is now no longer based on static system behavior, due to pre-assumptions for each machine, but on real system behavior, dynamically learned via machine learning approaches, e.g., the dynamic relationship between a machine's cycle times and output performance based on the increase in machine failure. Consequently, besides analyzing which machine constitutes a bottleneck within the production system, we can now simulate the variance of a machine's bottleneck score depending on changes in its input parameter, based on automatically learned system behavior.
## 2 Background
### Bba  
BBA is a derivative of the ABR algorithm designed on the basis of the correlation between buffer occupancy and the quality of the video to be downloaded next [4]. This algorithm uses the network bandwidth estimated from downloading the previous chunk and the buffer occupancy (the ratio of images currently stored in the buffer) as inputs. Figure 2 illustrates the method used for calculating buffer occupancy in BBA. The total playtimes of the chunks stored in the buffer are converted into seconds, and the buffer occupancy is calculated using Equation (1). The play time of the video chunks currently stored in the buffer is divided by the total play time of the entire buffer.  
\[\text{B(t)}=\frac{\text{play time of stored video}}{\text{total play time of buffer}} \tag{1}\]  
As shown in Figure 3, BBA selects video quality based on C'(t), the predicted network bandwidth, and B(t), the buffer occupancy. The network bandwidth measured during the download of the previous chunk is used as C'(t). The buffer occupancy B(t) calculated using Equation (1) is not directly used to select the video quality of the next chunk, but is adjusted using the adjustment coefficient function F(\(\bullet\)). The BBA operates on the basis of these two values. Even if the predicted network bandwidth is low, indicating that the next chunk may take a long time to download, high video quality is selected if the buffer occupancy is high. Conversely, if the buffer occupancy is low, then low video quality is chosen, even if the predicted network bandwidth is high, to account for potential delays in video downloading. Consequently, BBA improves the user's QoE by reducing the rebuffering rate by 10% to 20% compared with the ABR algorithm.  
Figure 2: Buffer occupancy calculation.  
However, BBA is unsuitable for wireless network environments such as 4G, 5G, and wireless LANs. These environments change rapidly as a result of user mobility and frequent variations in the surrounding environment. For example, if a user is streaming videos when moving on foot, by bus, or by train, the network bandwidth fluctuates because the wireless communication environment between the base station and the user changes under the influence of factors such as communication distance and obstacles. BBA struggles to respond to these changes appropriately, as it assumes that the network conditions during the previous chunk download remain constant. This can lead to issues such as rebuffering or the selection of low video quality. To address these challenges, we use deep learning-based network performance prediction to adapt to rapid changes in wireless network environments.
# A Blockchain Network Communication Architecture Based on Information-Centric Networking
## 5 Method Analysis
### Domain Gateway Node Selection Algorithm
#### 5.2.2 Security Analysis  
In terms of security, we consider two types of attack approaches in the process of evaluating the local betweenness centrality between nodes, and analyze the defensive ability of our local betweenness centrality evaluation protocol against these two attack approaches.  
The first type is a Sybil attack (witch attack), where malicious nodes intercept the heartbeat information of other nodes and forge their own upstream identity to obtain higher local betweenness centrality scores, thus affecting the election results of the domain gateway nodes. In our algorithm, downstream nodes can verify the identity of upstream nodes based on the corresponding private key signature information in the gateway request message, by carrying the negotiated signature information between nodes in the heartbeat message. Since malicious nodes cannot forge the signature information of other nodes, they cannot forge the upstream identity of other nodes, thus they cannot influence the election results of domain gateway nodes through Sybil attacks.  
The second type is an Eclipse attack, where malicious nodes intercept the heartbeat information containing public key information of the targeted nodes, making the nodes upstream of the targeted nodes unable to match the heartbeat information, thus affecting the election results of the domain gateway nodes. In our algorithm, by randomly including public key information in the heartbeat message, only a part of the heartbeat message can be intercepted, which does not affect the privacy security between nodes. Moreover, the heartbeat message itself does not contain block and transaction information, so malicious nodes cannot obtain more valid information. The targeted nodes can judge whether they are under eclipse attacks based on the reply messages of the heartbeat messages. If no reply message is received, it can be considered that they are under eclipse attacks. At this time, the targeted nodes can choose to re-elect domain gateway nodes or choose not to elect domain gateway nodes until the attack is over. It should be noted that it is meaningless for malicious nodes to intercept all packet messages of the targeted nodes, because this can only block the targeted nodes from sending and receiving messages, but cannot control the access the targeted nodes have to the information.
# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
## 2 Materials and Methods
### Viewshed Analysis  
A viewshed is defined as the ensemble of points or areas that are visible from a certain location. A viewshed analysis is thereby crucial in UAV relay planning to determine the regions where a UAV relay simultaneously maintains direct line of sight to the areas of interest and cell-towers (see Figure 3).  
The results of the viewshed analysis are represented as binary raster data through a visibility/blockage map having the same dimensions as the geographic region under consideration. Specifically, a 1 refers to a point within the mutual viewshed, while a 0 refers to a point outside the mutual viewshed. For a successful UAV relay, a simultaneous link between the transmitter and receiver is required, where successful communication is assumed to be equivalent to a line-of-sight connection. Consequently, the blockage area at a specific UAV relay height above ground is defined as the ensemble of points where:  
* There is no line of sight between the transmitter and receiver;
* Only the transmitter has line of sight;
* Only the receiver has line of sight.
# Finite Element Simulation of the Flat Crush Behavior of Corrugated Packages
## 4 Conclusions  
Corrugated paperboard is a representative of eco-friendly packaging material, which has a high strength-to-weight and stiffness-to-weight ratio compared to polymer-based cushioning materials. Therefore, the demand for corrugated paperboard-based packages as a substitute for polymer-based cushioning materials is expected to increase.  
In this study, we analyzed the flat crush behavior of corrugated paperboard by flute type using FEA, as well as the similarity with the experimental results. Our results can be summarized as follows:  
1. The experimental results confirmed that when the SW corrugated paperboard was flat-compressed, the shape of the flute changed in the order of half-sine wave, trapezoid, square, and character 'ohm' shape.
2. In both the experimental and FEA results of the flat crush behavior of the DW-AB/F corrugated paperboard, we found that A/F deformed more and faster than B/F to reach a state of collapse, regardless of the compression direction. Therefore, the effect of A/F on the resistance to flat crushing and deformation of the DW-AB/F corrugated paperboard was significant.
3. In the case of DW-BB/F, the phase difference between the upper and lower flutes had a significant effect on the variation in the flute shape. In the case of a phase difference of 180\({}^{\circ}\), wherein the top and bottom of the upper and lower flutes coincide, the effect of the middle liner on the change of the flute shape was smaller than that in the case of other phase differences. This is because the two flutes changed symmetrically, and the resistance to flat crush was significant. In addition, differences in the SS curve according to the phase difference between the upper and lower flutes were observed. For a phase difference of 180\({}^{\circ}\), the slope of the curve was larger within 50% of the compressive strain, compared to other phase differences.
4. The slope of the SS curve for flat crushing of the corrugated paperboard by flute type through FEA was significantly larger in B/F, followed by BB/F, A/F, and AB/F; moreover, the difference between A/F and AB/F was minimal. This tendency was also confirmed in the experimental results. However, there was an absolute difference in the SS curves obtained by FEA and experiment. The difference could be significantly reduced by applying the viscoelastic properties of the paperboard in the FEA.
5. If the material properties of the corrugated paperboard components and its modeling methods are further studied, the FE-based simulation technique will be a useful alternative tool that can replace the flat crush test.  
Data curation, J.-M.P. and H.-M.J.; Formal analysis, J.-M.P. and J.-M.S.; Investigation, H.-M.J.; Software, J.-M.S. and H.-M.J.; Validation, J.-M.S.; Writing-original draft, J.-M.P.; Writing-review and editing, H.-M.J.; Supervision, H.-M.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**References**
# Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
## 4 Proposed Method: Adopting a Signal-Processing Technique
### Frequency  
Signal data are also commonly converted from the times series into a frequency domain through Fourier Transform. Further analysis of the frequency domain will give some other information that is particularly related to the resolution and thickness of the targeted object. The Fourier transform transforms the time series signal into a one-dimensional (1D) signaland is also applicable for various data in lateral distribution, such as the two-dimensional (2D) image. Fourier transforms will capture the global frequency for the entire signal. Consequently, if the local frequencies are needed for analysis, this technique may not capture them. As an alternative, wavelet transforms may be a suitable choice. Wavelet transforms are able to decompose the signal into a localized area.  
A mathematical expression of the discrete Fourier transform (DFT) is presented in Equation (3) to show how to transform the image into the frequency domain. Application of DFT in image processing can show the most frequent pixels in one corner and the least frequent in the other corner. Additionally, by transforming the image into a frequency domain, a particular filter based on frequency can be applied.  
The DFT of the image with the size McN is formulated as follows:  
\[F(k,l)=\sum_{i=1}^{M-1}\sum_{j=0}^{N-1}f(i,j)e^{-L2\pi(\frac{ik}{N}+\frac{il}{N})} \tag{3}\]  
where _f(i,j)_ is the image in the spatial domain, and _F(k,l)_ is the Fourier transform pair in Fourier space. The maximum frequency is defined by  
\[f_{max}=\frac{1}{2\ pixels} \tag{4}\]  
The frequency attribute extracted from signal data is dependent on the thickness or geometry of the medium, and has limitations due to tuning and the quality of the signal-to-noise ratio. The high frequency of signal sometimes comes from noise, but is usually handled during the data processing. In medical imaging, images are regarded as a signal whereby the frequency component is related to the quality of the signal and is also handled during image processing. The component of a low-frequency image will be related to the high peak signal-to-noise ratio. Laterally, the frequency in the image is related to the distribution of the object, which is also related to the artifact due to scattering when the X-ray signal enters very hard tissue.
# A Comparison of Pooling Methods for Convolutional Neural Networks
## 3 Novel Pooling Methods
### Spectral Pooling  
[PERSON] et al. [54] proposed a novel pooling approach that included the concept of dimension reduction by shrinking the frequency domain representation of the data. Let h*w be the appropriate output feature map parameters and let x Rm*m be the given input map. The given input map is first treated with a discrete Fourier transform (DFT) after during which a frequencies representation submatrix of h*w size is eliminated from the center. Finally, inverse DFT is used to convert the h*w submatrix back into image pixels. By implementing a threshold-based filtering methodology, spectral pooling retains more information over max pooling for the very same output dimension. It fixes the problem of the output map's dimensions being reduced significantly.