esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Local Pre-Rerouting Algorithm
### Collection of Routing Information  
When sun outage occurs, the traffic in the failed link is redistributed to other paths so that this traffic can arrive with the minimum delay and does not exceed the carrying capacity of the surrounding links. Since sun outage is predictable, the routing algorithm can perform route calculations before sun outage occurs, so the algorithm can tolerate a certain calculation time. Our algorithm is shown in Figure 7. The link that is about to fail is \(l_{1,2}\). The area where link load needs to be collected is limited to \(n\) hops, and this area is called a limited area. The value of \(n\) is determined by the number of satellites in the constellation and their business requirements. The specific setting of this parameter will be explained in Section 7.1. \"Simulation design\". During a period of time \(t_{c}\) before sun outage occurs, the satellite connected to the link \(e\) in the limited area counts the total number of data packets \(p_{e}\) forwarded through the link during this period, and sends the link information to the satellite (node 1) that is directly connected to the link that is about to fail, and the latter uses this to estimate the average load of the link  
\[l_{e}=\frac{p_{e}\cdot P_{avg}}{t_{c}}. \tag{9}\]  
At the same time, the satellite directly connected to the link that is about to fail counts the traffic information within the link, which is called link-failure traffic. What needs to be collected is the source address, destination address, required bandwidth and path of the services in the traffic. The service shown in Figure 7 is part of the link-failure traffic. Its source address is node s, the destination address is node d and the path before sun outage  
Figure 7: Limited area and alternate paths of the algorithm.  
is s-1-2-3-4-d. Assume that the total number of data packets forwarded by service \(d\) via link \(l_{1,2}\) during \(t_{c}\) is \(p^{d}\), then the traffic volume of this service is  
\[w^{d}=\frac{p^{d}.P_{avg}}{t_{c}}. \tag{10}\]
# Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function
## 2 Materials and Methods
### Proposed Methodology
#### 2.5.1 Proposed Model  
The proposed model mainly consists of a convolutional layer, maximum pooling layer, improved residual block, AAP layer, and FC layer. The network structure of the proposed model is shown in Figure 4.  
Residual networks deepen the number of network layers through residual learning to obtain deep features [31; 35]. In this paper, we use modified residual blocks to extract deeper features for fault diagnosis.  
The construction of a suitable first feature extraction layer in CNN plays a vital role in the performance of the whole model [18; 20; 30]. In this paper, we use a wider convolutional kernel to enhance the perceptual field in the first layer of the model to reduce the influence of noise on the model feature extraction in a small area and provide assurance for the subsequent extraction of deeper key features.  
By dynamically learning the parameters of the ACON activation function and designing different forms of activation for each neuron, the feature representation capability of the model can be improved, thus making the model more generalizable [36]. In this paper, a convolutional layer based on the meta ACON-C activation function is designed between a wide kernel convolutional layer and an improved residual block. After extracting local features, it is used to improve the generalization performance of the model by exploiting the adaptive activation properties of the ACON activation function.  
CBAM, as an attention mechanism, captures both channel features and spatial features, and has good embeddable ability [37], which first achieves control of global information by establishing the connection between feature channels and then finding important information in the channel axis space to enhance the sensitivity of the model to key features, raises the model performance. In this paper, CBAM is embedded into the residual blocks to obtain more feature information on channels and spaces within the residual connections by improving the blocks of the residual network. The CBAM_ResNet is constructed, and the model has deep key feature extraction ability, which can effectively suppress redundant information interference and solve the problems of gradient dispersion and network degradation at the same time.
# Fault Diagnosis of Wind Turbine Gearbox Based on Mel Spectrogram and Improved ResNeXt50 Model
## 4 Experiment
### Data Processing  
The signal of the wind turbine is extracted through the Mel spectrogram, and finally a 256 \(\times\) 256 feature map is generated. Figure 7 shows the Mel spectrograms of different gear faults. Four fault features and healthy conditions each generate 2000 samples, and then the training set and test set are divided according to a ratio of 8:2. The specific fault sample distribution is shown in Table 1.  
\begin{table}
\begin{tabular}{l l l} \hline
**Fault Label** & **Training Set** & **Test Set** \\ \hline Healthy working state & 1600 & 400 \\ Chipped tooth & 1600 & 400 \\ Missing tooth & 1600 & 400 \\ Root fault & 1600 & 400 \\ Source fault & 1600 & 400 \\ \hline \end{tabular}
\end{table}
Table 1: Division of fault sample datasets.  
Figure 7: Mel spectrograms generated by different fault features.  
A wind turbine (mainly including gearbox, blade, bearing, etc.) is usually installed in the high-altitude environment in the field. Long-term continuous operation bears dynamic heavy loads, and the operating conditions are complex and changeable. The gearbox has a high probability of failure in the entire power transmission chain, so it is the research object of this paper.  
Under laboratory conditions, four types of faults (chipped tooth, missing tooth, root fault, source fault) are artificially implanted in the wind turbine gearbox, and then the audio signal is collected under different fault conditions.  
A chipped tooth removes small pieces of material by milling the tooth surface to simulate a tooth surface defect caused by impact. A missing tooth is completely removed by mechanical processing to simulate severe fracture failure. A root fault simulates fatigue cracking at the filled of the tooth root due to mechanical impact. A source fault creates local wear in the bearing raceway via chemical etching to simulate early surface degradation.  
Spectrum distribution is uniform in a healthy state, intermittent impulse noise appears in the chipped tooth state, strong periodic peaks appear in the missing tooth state, continuous harmonic components appear in the root fault state, and early weak clutter appears in the source fault state.  
There are 1600 samples for each state for training, and the manner of labeling is as follows: health 0001-1600, chipped 0001-1600, missing 0001-1600, root 0001-1600, and source 0001-1600.
# Thermal and Mechanical Assessment of PLA-SEBS and PLA-SEBS-CNT Biopolymer Blends for 3D Printing
## 1 Introduction
Among the nanomaterials, carbon nanotube has been selected because it increases mechanical strength, thermal stability, electrical conductivity, and also promotes crystallization [21; 22; 23; 24; 25]. As 3D printing is still an evolving science today, the 3D printing of nanocomposites is receiving even more attention, and more and more researchers are starting to research this area [26; 27; 28]. Thanks to nanomaterials, the field of application of 3D-printed products can be further expanded. In this study, the mechanical and thermal properties of 3D-printed PLA-SEBS blends and PLA-SEBS-CNTs nanocomposites were investigated. Next to the mechanical properties, it is important to know the crystallization process because the added materials affect the crystallization of PLA, and the size and morphology of the crystals greatly influence the processability and mechanical strength [5; 10; 29; 30; 31; 32; 33; 34; 35; 36; 37].
# Multi-Satellite Observation-Relay Transmission-Downloading Coupling Scheduling Method
## 2 Problem Description
### Rule-Based Dijkstra Data Transmission Path Scheduling Method
#### 3.4.2 State-Strategy Select Rules
Then the strategy is selected according to the selection rules:  
**IF**\(c_{E}>c_{D}\)  
**THEN**\(Rule=Rule_{minTime}\)  
**ELSE**  
**THEN**\(Rule=Rule_{minNode}\)  
The rule signifies that when \(c_{E}\) is greater than \(c_{D}\), it indicates that the satellite cluster's memory resources and available satellite-ground data transmission time window are insufficient. Therefore, the selected strategy should prioritize the earliest data download time. Conversely, if the power supply is relatively limited, the strategy chosen should minimize the number of intermediary nodes, favoring the power supply subsystem.
# Improved Safety Analysis Integration in a Systems Engineering Approach
###### Abstract  
The goal of the paper is the integration of safety analysis in a model-based systems engineering approach to ensure consistency between system design and safety artifacts. This integration permits the continuous improvement of the structure and behavior of the system. It also reduces system development time and prevents late detection of errors. To reach this purpose, the SafeSysE methodology is extended. In SafeSysE, a preliminary Failure Mode and Effects Analysis (FMEA) is automatically generated from a SysML model, and this FMEA is then completed by the safety expert but no further development was proposed. The contribution of this paper is to suggest recommendations based on the FMEA analysis in order to enhance the system design and make it comply with safety requirements. First, an updated system structure that may contain redundancy is proposed. Then, a redundancy profile is used to enrich the system model with redundancy information, which will allow the generation of a dynamic fault tree considering the system behavior. Finally, the generated dynamic fault tree should be analyzed in order to create a state machine diagram that describes the behavior of the system. The created state machine with an internal block diagram will help the system designers to better understand the system dysfunctions by simulating the system. The proposed methodology is applied to an Electro-Mechanical Actuator system which is used in the aeronautics domain.  
Keywords:MBSE; SA; FMEA; dynamic fault trees; redundancy; redundancy profile; system description +
Footnote †: journal: Applied Physics Letters
# Well-Distributed Feature Extraction for Image Registration Using Histogram Matching
## 4 Results
### Quantitative Analysis  
The numbers of correct corresponding control points affect the accuracy of the image registration. In our experiments, the numbers of correct control points were measured through the control point ratio (\(CPR\)). It is calculated by using the ratio between the initial numbers of control points and the number of refined control points.  
\[CPR=\frac{\text{\emph{numbers of refined CPs}}}{\text{\emph{number of initial CPs}}}. \tag{12}\]  
The \(CPR\) measure provides values in the range [0, 1], and higher values indicate better distributions of the features. The second quantitative measure, root mean square error (RMSE), was used to evaluate the accuracy of the proposed method. RMSE is defined as  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left\|\hat{C_{i}}-\hat{C_{i}}\right\|^{2}}, \tag{13}\]  
where \(\hat{C_{i}}\) are control points in the reference image, while \(\hat{C_{i}}\) are the estimated control points at the transformation model. The smaller value indicates better accuracy.  
Table 1 shows the number of initial CPs, corresponding CPs and refined CPs for different feature-based and area-based methods. The refined CPs were obtained using random sample consensus (RANSAC) [2]. In the table, it is clear that feature-based methods (SIFT and SURF) extracted more features than area-based methods (SSD, NCC, and FZNCC). However, feature-based methods provided fewer refined CPs than the area-based methods. It means that the features extracted through the feature based methods are not well-distributed. It can also be observed that the proposed method (FZNCC) provided the most refined control points among the area-based methods. Thus, the proposed method is more effective and is likely to provide better accuracy.  
Table 2 shows the comparison of the _CPR_ measure of the previous methods and the proposed FZNCC method. Conventional feature-based methods (SIFT and SURF) provided the lowest CPR values in the comparison. On the contrary, area-based methods (SSD, NCC, and FZNCC) provided better distributions. The proposed method had certain advantages in getting additional control points with better distribution. Consequently, better image registration accuracy was achieved.  
Table 3 shows the RMSE values computed for different methods for accuracy comparison. It is clear that conventional area-based methods extracted more refined CPs than feature-based methods. However, the RMSE values for SSD and NCC are higher than SIFT and SURF. It indicates that SSD and NCC provided less accurate results than SIFT and SURF. However, the proposed method provided the best RMSE values among all area-based and feature-based methods.
## 3 Feature-Selection Method and Application
### Application of FCBF-PSO  
PSO is a type of macro-heuristic algorithm proposed by [PERSON] and [PERSON] in 1995. This algorithm was developed from bird watching and foraging behaviors. The principle is that multiple randomly distributed particles in space represent individuals in the bird population, and the position of each particle is assumed to be the best potential solution in the optimization problem. Since each particle is a feasible solution, the particle will have a fitness value after the operation. Based on the particle's own experience and the group's experience, the flight speed and direction are updated again and iteratively repeated to make all particles converge to the best solution. As shown in (8) and (9) [29]. Among them, this study combines the feature-selection method of FCBF to optimize the weight of the selected features. The acceleration factors \(C_{1}\) and \(C_{2}\) are linearly reduced from 2.5 to 0.5. The weight \(\omega\) is arbitrarily selected from 0.5 to 1 as in (10). Steps of FCBF-PSO is listed as follows:  
\[\begin{split} V_{i}(t+1)&=\omega\times V_{i}(t)+C_{ 1}(t)r_{1}(t)(P_{best}-X_{i})\\ +C_{2}(t)r_{2}(t)(G_{best}-X_{i})\\ \\ X_{i}(t+1)&=X_{i}(t)+V_{i}(t+1)\\ \\ \omega=0.5+rand/2\end{split} \tag{8}\]  
**Step 1.**: Initially, in the \(d\)-dimensional space, parameters including particle number, number of iteration \(T\), acceleration factors \(C_{1}\), \(C_{2}\) and its own weight \(\omega\) are set to form a particle population;  
Figure 5: Flowchart of FCBF feature selection.  
**Step 2**.: In space, assume that each feature is the coordinate of each particle \(X_{i}{=(X_{i1},X_{i2},\ldots,\ldots,X_{ij})}\) and the flying speed of each particle is \(V_{i}{=(V_{i1},V_{i2},\ldots\ldots,V_{ij})}\);
**Step 3**.: Use FCBF feature-selection method to output important features \(F_{j}\);
**Step 4**.: Bring the coordinates of the particles into the features \(F_{i}{=(X_{i1}\times F_{1},X_{i2}\times F_{2},\ldots\ldots,X_{ij}\times F_{j})}\) to obtain the individual best solution \(P_{best}\) and the group best solution \(G_{best}\);
**Step 5**.: Use \(P_{best}\) and \(G_{best}\) to modify the particle's flight speed \(V_{i,new}\) as shown in (10);
**Step 6**.: Correct the position \(X_{i,new}\) of the particle with the updated flight speed \(V_{i,new}\) to find a new position and speed;
**Step 7**.: If it meets the set number of iterations \(T_{Max}\), it will stop, otherwise repeat _Step 3_ to _Step 5_, usually the termination condition is to reach the best solution or to reach the number of iterations set by yourself;
**Step 8**.: All particles converge to obtain the best solution;
**Step 9**.: Finally, after the optimization process, a set of solutions with the optimal particle coordinates \(X_{best}\) can be obtained, which is the optimized feature weight.  
Among them, the method of optimizing feature weights of FCBF-PSO retains the important features of FCBF feature-selection method and optimizes the weights. The flowchart of the method proposed by this institute is shown in Figure 6.
# A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud-Fog Computing
## 7 Evaluation
### Modeling for Penalty Cost \(\mathcal{C}\) of QoS  
The bandwidth penalty mean at the fog layer is set for \(\mu_{\Gamma}=1.0\), which makes the cost \(\Lambda_{i}\) of bandwidth usage per data unit of a fog job \(J_{i}\) to be \(\Lambda_{i}=\exp(\mu_{\Gamma}=1.0)\), according to Equation (13). At the cloud layer, the waiting penalty mean is to be \(\mu_{\omega}=1.0\), and the execution penalty mean is to be \(\mu_{\mathcal{E}}=1.0\), which respectively produce a waiting cost \(\psi_{i}=\exp(\mu_{\omega}=1.0)\) per time unit of waiting using Equation (15) and a service cost \(\xi_{i}=\exp(\mu_{\mathcal{E}}=1.0)\) per time unit of execution according to Equation (17). An SLA penalty mean is similarly set for \(\mu_{a}=1.0\), which, as a result, incurs an SLA cost \(\zeta_{i}=\exp(\mu_{a}=1.0)\) per time unit of service violation as in Equation (19).
## Appendix B NRMSE  
### Box Plot  
Figure 14 summarizes the model's performance in the box plot using the NRMSE metric (the range value is between 0, which indicates minimum error rate, and 1 indicating maximum error rate). Again, like previous metrics, no model outperforms others; the models score differently based on the datasets. When MA and SA models were compared for the SIJ dataset, the MA model overtook the SA model; this conclusion is also correct for the GDC and NMI metrics. Oppositely, the SA model scored better for the Lung dataset with a lower median and less diverse IQR. Yet, the plot shows many outliers in both datasets. For the remaining datasets, both MA and SA models almost perform equally with the higher IQR for the MA in the Knee and HHS datasets. Furthermore, comparing the metrics shows some disagreement between the GDC and NRMSE metrics. For example, for the HHS dataset, the MA model achieved around 5% higher GDC median, yet the SA model scored a lower NRMSE median and narrower IQR for the same dataset. Similarly, when comparing the MC dropout models again, there is no obvious indication of disparity. There is a fluctuation among all the datasets. For example, the Knee dataset with 20% and 40% dropout rates scores equally, yet for the 60% and 80% dropout rates, both median and IQR scores worsen. However, for the SIJ and HLS datasets, the MC models perform equally except for an 80% dropout rate, in which the performance declines. For the Lung dataset, all MC models perform equally, and the MC model with a 20% dropout rate achieves the lowest median for the HHS dataset. Moreover, MC models perform better (yet with many outliers) than non-MC models only in the Lung dataset; in the remaining datasets, the box plot does not show any clear conclusion and shows high instability between the results. Finally, the regression model scores a high NRMSE median for the Knee and a very big IQR for Lung datasets but scores competitive results for other datasets. For example, the HHS dataset scored the minimum median and smallest IQR. Moreover, the GDC metric supported this result in which the regression model scored a high GDC median for the Heart dataset and scored a very big IQR for the Lung dataset (yet 90% GDC median).
# Rolling Bearing Incipient Fault Detection Based on a Multi-Resolution Singular Value Decomposition
###### Abstract  
The periodic impulse characteristics caused by rolling bearing damage are weak in the incipient failure stage. Thus, these characteristics are always drowned out by background noise and other harmonic interference. A novel approach based on multi-resolution singular value decomposition (MRSVD) is proposed in order to extract the periodic impulse characteristics for incipient fault detection. With the MRSVD method, the vibration signal is first decomposed to obtain a group of approximate signals and detailed signals with different resolutions. The first detail signal is mainly composed of noise and the last approximate signal is mainly composed of harmonic interference. Combined with the kurtosis index, the hidden periodic impulse signal will be extracted from the detail signals (in addition to the first detail signal). Thus, the incipient fault detection of a rolling bearing can be fulfilled according to the envelope demodulation spectrum of the extracted periodic impulse signal. The effectiveness of the proposed method has been demonstrated with both simulation and experimental analyses.  
Keywords:multi-resolution singular value decomposition; rolling bearing; fault detection; demodulation analysis; signal to noise ratio +
Footnote †: journal: Applied Sciences
## 2 Overview
### Neutron Extraction
Those relaxations of the exact requirement, the slightly sharper inclined mirror as well as the larger coverage of the neutron guide cross section benefit the function of rejecting low wavelengths efficiently and ensuring that the line of sight is covered. The same setup that is rotated by \(180^{\circ}\) is used at the exit end of the neutron extraction, since the neutrons have to be reflected into the horizontal plane again. This means the middle section is 5 m long and the inclination of that middle section should be twice the inclination of the entrance and exit mirrors in order to transport the neutrons with as few reflections as possible. Thus, the central section is a 5 m neutron guide, being inclined by \(1.8^{\circ}\). Figure 4 depicts a drawing of the cross section. The entry and exit part mainly function as an inclined mirror to deflect the desired neutrons by the necessary amount. This functions much like a periscope. The total deflection of the beam that is achieved by this setup is 22 cm, which fulfills the requirement of more than 20 cm. This double reflection should reduce the production of secondary particles in the second footprint of the beam in the supermirror to suppress background. Moreover, the primary hadronic particle shower of the target onto the bunker wall is calculated to be approximately \(20\times 20\) cm\({}^{2}\), i.e., 10 cm in vertical direction from the center of the beam. Offsetting by 22 cm should also reliably suppress that source of background. Figure 4: Sketch drawing of the deflector setup. This shows a vertical section through the deflector. Red lines designate m = 4 supermirrors, inclined by 0.9\({}^{\circ}\), blue lines indicate m = 4 supermirrors inclined by 1.8\({}^{\circ}\). The sides are coated with m = 1 supermirrors, since no vertical displacement of the beam center in necessary. The dashed lines at the beginning and end outline the transported divergence for the shortest collimation with the widest opening (3 cm beam cross section over 4 m collimation length). The central neutron beam is indicated in green. Figure 3: (**a**) Spectra and (**b**) brilliance transfer curves for different solutions of neutron extraction in the case of SKADI. Brilliance transfer was considered for the wavelength shown at divergencies up to 0.0286\({}^{\circ}\), which corresponds to a 1 cm aperture at a collimation length of 20 m. Because SKADI is a small-angle instrument, performance at the lowest angles is important.