esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Mixing Rubber Fibers on Shear Strength of Expansive Clay  
Numerous studies have shown that the freeze-thaw cycling process significantly reduces the shear strength of unreinforced soils [29; 30; 31; 32; 33; 34]. Figure 4 shows the relationship between the shear strength of rubber fiber-reinforced expansive clay and the number of freeze-thaw cycles. The shear strength decreases after 15 freeze-thaw cycles. However, the maximum decrease in shear strength was 9.6% for all confining pressures, which was significantly different from the performance of unreinforced expansive clay under freeze-thaw cycles. The changes in shear strength of unreinforced expansive clay under freeze-thaw cycles from previous studies are referenced to show how mixing rubber fibers affects the shear strength, and the test conditions are summarized in Table 2[49; 50]. [PERSON] et al. [51] indicated that the freezing or thawing temperature had almost no effect on shear strength; therefore, the magnitude of freezing or thawing temperatures was not differentiated in this study.  
The normalized shear strength was obtained by dividing the shear strength after freeze-thaw cycles by the shear strength without freeze-thaw cycles. The effect of mixing rubber fibers on the shear strength under freeze-thaw cycles can be determined by comparing the  
Figure 4: Shear strength with different number of freeze–thaw cycles.  
Figure 3: Part of the stress–strain curves. (**a**) \(N=0\); (**b**) \(N=15\).  
normalized shear strength of the rubber fiber-reinforced expansive clay with that of the unreinforced expansive clay. The results are shown in Figure 5.  
Figure 5 shows the variation in the normalized shear strength with the number of freeze-thaw cycles, and the different curves in the same color represent different confining pressures. The normalized shear strength of rubber fiber-reinforced expansive clay changed the least after 15 freeze-thaw cycles. However, the shear strength of unreinforced expansive clay decreased by approximately 34% (average) after 9 freeze-thaw cycles in [PERSON] et al. [49], and by approximately 27% (average) after 10 freeze-thaw cycles in [PERSON] et al. [50]. Unreinforced expansive clay generates cracks under freeze-thaw cycles and the cracks develop with the increasing number of freeze-thaw cycles [52; 53; 54]. The cracks reduce the shear strength of the expansive clay [53]. When cracks are generated in the expansive clay, the rubber fibers produce a tensile force to weaken the development of the cracks, thus reducing the effect of freeze-thaw cycles on the shear strength of the expansive clay [55; 56]. It can be observed from the above discussion that mixing rubber fibers enhances the ability of the expansive clay to resist the effects of freeze-thaw cycles and thus the service life of expansive clay engineering in seasonal frozen regions is prolonged.
# Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer
## 6 Conclusions  
CFRPs play a key role in many industries. CFRP composites are expensive, but valuable in many fields. CFRPs are extensively used in aircrafts. New large aircrafts are designed with composite wing and fuselage structures. Aircraft operators have indicated the demand for product upgrades, and aftermarket providers have provided the solutions for improving the performance and efficiency of legacy aircraft by incorporating composites. Electrical behaviors of CFRP composites largely determine their multi-functional applications under electrical effects and have been widely explored.  
The electrical conductivity of CFRPs is important for monitoring structural health and protecting aircrafts from lightning strike. Recent studies on the electrical conductivity of CFRPs are mainly focused on the electrical conductivity of single carbon fibers. Due to the anisotropic properties of carbon fibers, the electrical conductivity of CFRPs should be anisotropic as well and cannot be fully obtained via bulk measurements of CFRP composites. Related studies are largely restricted because existing experimental techniques cannot be used to examine the anisotropic features of CFRPs. This is an ongoing work and much yet to be explored.  
In the future development of CFRP, more accurate simulation models and theoretical analysis are required to improve the understanding of CFRPs conductive behaviors in different applications. The microstructural study of CFRPs may promote the breakthrough of CFRP development. The CFRPs industry has promising prospects and new innovative products will be developed.  
Conceptualization, Q.Z., K.Z. and R.Z.; methodology, Q.Z., S.Z. and H.X.; validation, D.C.; investigation, L.Z. and W.Y.; resources, H.X.; writing--original draft preparation, Q.Z., K.Z.; writing--review and editing, W.Y.; funding acquisition, Q.Z., D.C. and R.Z.  
Figure 8: Specific electrical resistance longitudinal to the fiberorientation for different (hybrid) composites [206]. C = carbon fiber; V = stainless steel fiber; and B = copper cladded low carbon steel fiber.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 61601260, 61601324), Science and Technology Project of Qufu Normal University (Grant No. xkj201502), Key Research and Development Program of Shandong Province (Grant No. 2017 GSF18116).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Comparing Commit Messages and Source Code Metrics for the Prediction Refactoring Activities
## 4 Experimental Results and Analysis
### RQ1. How Effective Is Text-Based Modeling in Predicting the Type of Refactoring?
#### 4.2.1 Classification Using Random Forest  
We used the inbuilt functions from scikit-learn library of python to build a random forest model and to test it. We followed the below approach to set up this experiment:  
* Imported pandas, numpy, and sklearn library from python;
* Imported dataset as data frame;
* Removed null entries, randomized the dataset, and normalized it with statistical modeling;
* Trained the random forest model and tested it.  
This model produced overall 75% accuracy, and this was the best accuracy we have achieved so far. We used precision, recall, F1-score and support as measuring units to evaluate the model. Precision, recall, and F1-score for this model are shown in Table 7. Precision provides us with the percentage of positive predictions made correctly, whereas recall depicts the percentage of positive instances predicted correctly by the classifier over all positive instances in the dataset. Tje F-1 score is nothing but a harmonic mean of precision and recall.
## 3 Sensitivity Analysis of the TRID/ARID System
### Control Effectiveness under Different Excitation Frequencies  
The numerical analysis models were established using Simulink, according to the motion equations of the TRID and ARID systems. A series of numerical analyses were carried out. The dynamic characteristics of the TRID and ARID systems were compared, according to the numerical analyses results.  
Different sinusoidal excitation frequencies were applied to the suspended structures with the TRID and ARID systems. The numerical analyses parameters of the systems are shown in Table 1. \(\omega\) is defined as the excitation frequency. The structural natural frequency can be obtained by \(\omega_{m}=\sqrt{g/l}\). \(\lambda\) is defined as \(\lambda=\omega/\omega_{m}\). Furthermore, the _Peak_ and _RMS_ of structures under different excitation frequencies were calculated to analyze the control effectiveness of the control systems.  
As shown in Figure 2, the control effectiveness of the TRID systems with different frequency modulation ratios were compared with the ARID system. The frequency modulation ratio \(\gamma\) is defined as \(\gamma=\omega_{t}/\omega_{m}\), where \(\omega_{t}\) is the frequency of the TRID system. The frequency modulation ratio of TRID1, TRID2,..., TRID8 is 0.2, 0.4,..., 1.6, respectively. The structural parameters and the rotary inertia of the TRID and ARID systems are the same. It can be concluded from Figure 2 that the ARID system has a better control effectiveness than the TRID system under different excitation frequencies. Furthermore, the ARID system is less sensitive to the excitation frequency and has a better control robustness compared with the TRID system.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Structural length (\(l\))/(m) & 0.65 \\ Suspended particle mass (\(m\))/(kg) & 0.5 \\ Rotary inertia mass (\(m_{a}\))/(kg) & 0.3 \\ Structural damping coefficient (\(c\))/(N-s/m) & 0.005 \\ Rotary inertia (\(J_{a}\))/(kg\(\cdot\)m\({}^{2}\)) & 0.0015 \\ Radius of rotary inertia mass (\(r\))/(m) & 0.065 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter configuration for the numerical analyses.  
The control effectiveness of the TRID system with different frequency modulation ratios are also analyzed in this part. \(J\) is the rotary inertia of the TRID system. As shown in Figure 3, the rotary inertia gets bigger from \(J_{1}\) to \(J_{5}\). It can be seen that the TRID system has a better control effectiveness when the frequency modulation ratio \(\gamma\) is close to 1. The control effectiveness gets better as the rotary inertia gets bigger. Furthermore, the control effectiveness is sensitive to the frequency modulation ratio \(\gamma\). When \(\gamma\) is changed from 1, the control effectiveness gets much worse. The control robustness of the TRID system is greatly affected by the frequency modulation ratio \(\gamma\).
# Path Planning for Laser Cladding Robot on Artificial Joint Surface Based on Topology Reconstruction
## 4 Generation of Laser Cladding Paths
### Topological Reconstruction of the Artificial Joint CAD Model
```
1/*Initialization*/
2 Create arrays : ListVertex, ListEdge, ListFacet
3/*Read file and create topological relation*/
4 Import the STL file
5 fori = 1:number of the triangular facets
6 forj = 1:3
7 ifVertex[j] not contained in ListVertex then Add Vertex[j] to ListVertex;
8 else record the index of Vertex[j] in ListVertex;
9 endfor
10 fork = 1:3
11 ifEdgr[k] not contained in ListEdge then Add Edge[k] to ListVertex;
12 else record the index of Edge[k] in ListEdge;
13 endfor
14 Add Facet[i] to ListFacet;
15 endfor
16/*Output*/
17 ListVertex, ListEdge and ListFacet are output with topological relation. ```  
**Algorithm 1**Topological reconstruction algorithm. The local relations of a triangular mesh example extracted from the STL file are shown in Figure 6. The topological relation of edges after topological reconstruction and the quantity of the recorded data are listed in Tables 1 and 2, respectively. Comparing Figure 6a with Figure 6b, it was obvious that the data structure was significantly simplified after topological reconstruction. The data of vertices, edges and facets were recorded uniquely after topological reconstruction, which contained no redundant information and provided complete topological relation. The topological relations of the edges are listed in Table 2. According to such information, it was easy to find the two vertices of each edge and the two facets on which the edge is located. This advantage played a huge role in the subsequent path planning method.
# A Novel Real-Time Match-Moving Method with HoloLens
## 4 Experimental Results  
### Camera Trajectory  
Once the video recording process is completed, it is used to extract the camera trajectory of RGB camera, HoloLens and Oculus Rift. Figure 9a,c shows the camera trajectory extracted from HoloLens in comparison with the ground truth for the first and second half of the video, respectively, whereas Figure 9b,d illustrates the camera trajectory of the RGB camera for the first and second half of the video, respectively. The distance deviations calculated for both HoloLens and Boujou methods in comparison with the ground truth are indicated in Table 1. From Figure 9 and Table 1, it can be seen that the camera trajectory extracted from the HoloLens is almost perfectly aligned with the ground truth. On the other hand, the camera trajectory extracted from the RGB camera using the Boujou method is highly affected by the occlusion recorded in the second half of the video while the first half has only minor differences when compared with the ground truth.  
From the experiments, it is clear that occlusion has a great effect in case of traditional methods such as Boujou, whereas the proposed method has no direct effect of occlusion because it only records/stores the position information of the RGB camera and not the video. It implies that the proposed method is better at finding the camera trajectory as compared to the Boujou method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Position \(X\) (cm)** & **Position \(Y\) (cm)** & **Position \(Z\) (cm)** \\ \hline Ground truth-HoloLens (1–400 Frame) & 0.64 & 0.57 & 0.97 \\ Ground truth-Boujou (1–400 Frame) & 1.86 & 1.98 & 0.98 \\ Ground truth-HoloLens (401–800 Frame) & 0.63 & 0.28 & 1.03 \\ Ground truth-Boujou (401–800 Frame) & 6.85 & 12.46 & 49.26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Standard deviation of the comparison of ground truth, HoloLens and Boujou.  
Figure 8: Experimental setup for comparing match-moving results.
## 3 Result Analysis  
### Dataset and Experimental Working Conditions  
To evaluate the proposed method, the publicly available seeded fault bearing dataset by Case Western Reserve University (CWRU) Bearing Data Center [41] is used. The data were accumulated by using a 2-horsepower (hp) Reliance Electric motor with a torque transducer and a dynamometer to apply different loads, ranging from 0 hp to 3 hp. Rotation velocities of the motor also varied from 1797 rpm to 1730 rpm. Drive end bearings were seeded with defects on the inner raceways, outer raceways, and rolling elements with the assistance of an electro-discharge machine, shown in Figure 6. The dataset consists of ratings of healthy condition (HC), inner raceway fault (IRF), ball fault (BF), and outer raceway fault (ORF) signals under the considered working conditions. The ORF has three variants: ORF at the center, ORF at the orthogonal, and ORF at the opposite positions. In this study, the variable length vibration acceleration signals were recorded at 12,000 samples/second (Hz) for the drive-end bearings.  
To segment the signals by an adjustable sliding window, the proposed method considers 1024 data points for every frame. After that, the invariant conditions are established through vibration imagining via DOST-based stacking. As discussed earlier, after stacking, the dimension of an individual sample becomes \(8~{}\times~{}1024\). To evaluate the performance of this speed-invariant experiment, four different working conditions are considered. Under each set of working conditions, six different health types  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage** & **Stage Specification** & **Layer No.** & **Layer Name in Network** & **Transfer** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Training stage} & 01 & Convolutional & Yes \\  & & 02 & Maxpooling & Yes \\  & & 03 & Dropout & Yes \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{Training stage} & 04 & Convolutional & Yes \\  & & 05 & Maxpooling & Yes \\  & & 06 & Fully connected & Yes \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{Training stage} & 07 & Dropout & Yes \\  & & 08 & Fully connected & Yes \\ \hline
4 & Output stage & 09 & Fully connected & No \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proposed architecture of the CNN for transfer specification for target network.  
Figure 6: An overview of the experimental setup.  
are included. Table 2 presents the details of the working conditions. In total, 300 fine-tuned epochs are revolved around the network for performance assessment.
## 4 Materials and Methods
### Acoustic Parameters Calculation  
The main acoustic parameters involved in this investigation are the following:  
* Early Decay Time (EDT), which is computed by considering the direct sound for the decay occurring between 0 dB and \(-\)10 dB after the interruption of the sound source [15].
* Reverberation time (T\({}_{20}\)), which is the time it takes the sound pressure level to drop over 20 dB after the sound source has been switched off. This parameter is a function of the room volume and the quantity of absorption in the room [16].
* Clarity index, which is calculated by an early-to-late energy ratio based on a time subdivision of the impulse response. The referent clarity index for music is C\({}_{80}\), while C\({}_{50}\) is involved to evaluate speech performance [17].
* Definition (D\({}_{50}\)) is used in rooms to assess the quality of speech perception. It is represented as the ratio of sound energy reaching the receiver in the first 50 milliseconds over the total sound energy until it is completely dissipated in the room [18].
* Speech Transmission Index (STI) is used to evaluate the distortion of the speech signal that can be affected by an excess of reverberation, high level of background noise and undesirable echoes. It is represented by a pure number ranging between 0 and 1, indicating very poor and optimal speech comprehension, respectively.  
During data post-processing, IRs were obtained by convolving the recorded signals with the inverse filter of the ESS signal used during the measurements [19]. Once the IRs have been obtained, the values of the main acoustic parameters can be calculated using the Aurora plugin in Audition 3.0 software [20]. Aurora is a plugin suite created to perform the analysis of acoustic parameters other than for auralization. Two Aurora plug-ins were used: one for the convolution (i.e., \"Convolve with clipboard v4.4b\") and the other for the acoustic parameters (i.e., \"Acoustical Parameters 4.3\").
# Methodology for the Efficient Progressive Distribution and Visualization of 3D Building Objects
## 3 Methodology
### Visualization Frameworks for the BuildingTree  
For the flying through application, the computation for visualization is heavy as the city scene changes rapidly. To support this, a preloaded BuildingTree is necessary, while for ground users such as road navigation, the proposed BuildingTree structure can be loaded online which may save quite a bit of storage on local devices. The main challenge with the visualization framework is to provide the user with a proper view of the buildings (without abrupt changes in the geometric representations) with a minimum of data transfer.  
Figure 8 is an Unified Modeling Language (UML) sequence diagram to illustrate the interaction between user client and 3D model server. The blank box represents a delay or the processing time on the client side. The visualization rule should be defined according to the specific application requirements. The basic rules proposed in this paper are summarized from the visualization aspect in which visual similarity is the main consideration. In this framework, the client first requests a 3D city model, then a BuildingTree without 3D geometry data related to the selected city area will be supplied. Based on this BuildingTree, the client can calculate which buildings are necessary for the visualization. In this framework, users have more flexibility to adjust the 3D visualization strategy. In other words, 3D rendering is determined more by the user side than the server side. The BuildingTree allows the user to decide which buildings and which levels should be transformed from the server, according to the bandwidth, memory size or current CPU status.  
The following are the basic rules for the user by which to select the 3D models that should be transferred.  
* The Level 1 and Level 2 prototypes which should be displayed are only determined by a distance threshold from the user's position.  
Figure 8: Interaction between client and server of BuildingTree visualization.  
* For the Level 3 (and 4) prototypes, only the features that are potentially seen from the user's position are shown. These features are ordered by importance (as defined by the size of the features) and the most important feature is transferred first.
## 2 OTFS Modulation
### Channel Model  
The transmitted signal passes through the channel that in the DD domain is modeled as  
\[h(\tau,\
u)=\sum_{l=1}^{P}g_{l}\delta(\tau-\tau_{l})\delta(\
u-v_{i}). \tag{4}\]  
where \(P\) is the number of propagation paths and \(g_{i}\), \(\tau_{i}\), and \(v_{i}\) are the complex channel gain, the propagation delay, and the Doppler shift of the \(i\)-th path, respectively.
# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## 2 Related Work
### The Distinctiveness of This Study  
Previous studies on bearing fault detection have utilized various techniques for analyzing time-series data, often incorporating Gaussian noise to evaluate the robustness of their models. However, these approaches face limitations when translating to real-world industrial environments, where noise characteristics differ significantly from Gaussian assumptions.  
This study addresses this gap by introducing three novel-noise-modeling techniques that better represent the noise encountered in industrial scenarios. Furthermore, it proposes a new deep learning model designed specifically for robust performance under these realistic noise conditions. By bridging the gap between theoretical noise assumptions and practical industrial challenges, this study aims to advance the reliability and applicability of bearing fault detection systems.