esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Results
#### 4.6.1 Results on IBM Dataset
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Accuracy \%** & **Precision \%** & **Recall \%** & **F1-Score \%** & **AUC \%** & **Time (M:S)** \\ \hline Proposed Model+ GAN & 92.17 & 81.93 & 66.84 & 73.60 & 84.95 & 03:04 \\ Proposed Model + Random & 91.83 & 79.50 & 74.15 & 75.99 & 85.76 & 01:39 \\ Oversampling & 91.93 & 90.91 & 94.11 & 92.36 & 91.92 & 01:40 \\ Proposed Model + ADASYN & 92.11 & 92.33 & 92.19 & 92.24 & 92.11 & 01:51 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of the proposed model with different data augmentation techniques. Figure 8: The ROC-AUC diagrams of the proposed model on IBM dataset. is constructed along the line segment. ADASYN takes difficulty and data density into account while creating synthetic data. In areas with limited minority class examples, more synthetic samples are produced, increasing the adaptability of the class distribution. Figure 8(a) presents the accuracy and validation accuracy curves for the proposed model with GAN-based data augmentation, while Figure 8(b) illustrates the loss and validation loss curves for the same model. Additionally, Figure 10 presents the ROC-AUC (Receiver Operating Characteristic-Area Under the Curve) diagram for the proposed model with GAN-based data augmentation on the IBM dataset. These diagrams show a clear trend of decreasing loss and increasing accuracy compared to the diagrams for the proposed model without data augmentation. Additionally, they exhibit fewer fluctuations than the diagrams for the model without data augmentation, likely due to the more balanced data. Figure 10: The ROC-AUC diagrams of the proposed model with GAN-based data augmentation. Figure 9: Loss and accuracy diagrams of the proposed model with GAN-based data augmentation. To identify the key features that have the greatest impact on employee turnover, the explainable AI (XAI) technique, SHAP (Shapley Additive Explanations), was implemented in the proposed model. This approach helps elucidate the features contributing to the model's predictions, providing deeper insights into employee turnover. Figure 11 illustrates the key features in the IBM dataset that have the greatest impact on the prediction of employee turnover. SHAP (Shapley Additive Explanations) [39] is an XAI technique that uses a mathematical approach to determine a score for each feature in the model. This score indicates the feature's weight in the model output. Its foundation is game theory, which determines how much each player (feature) contributes to the payout (prediction). In order to determine the scores, it takes into account every possible combination of features to account for both the instances in which the model uses all features and a subset of features. SHAP values provide information on which features are essential for predicting [86].
## 7 Conclusions  
This study presents an in-depth analysis of stress wave propagation in a semi-infinite Rayleigh-Love rod system when impacted by a striker rod with varying impedances. The different impedances between the striker and the semi-infinite rod give rise to distinct wave signals, occurring in one cycle or multiple impact cycles. These observations offer significant insights into the effect of impedance contrast on the characteristics of the waves. The influence of lateral inertia and Poisson's ratio on longitudinal waves in rods leads to higher stress results compared to scenarios where these factors are not considered.  
This research applied approximate formulae for numerically inverting Laplace transformations to handle complex equations. This is an effective method for solving the problem of wave propagation in rods with different impedances; it provides a reliable and accurate tool to analyze stress wave behaviors in rods involving interactions with different impedances. The comparison analyses with [PERSON]'s analytical solution with the same impedance [14], [PERSON] and [PERSON]'s solution [11] without effects of lateral inertia and Poisson's ratio, and numerical experiment by finite element analysis affirmed the high accuracy of this numerical inversion Laplace transform approach.  
This study provides examples showing that changing the material parameters of the striker rod results in different waveforms. These findings enable the development of customized experimental models that can precisely achieve goals in materials testing and analysis.  
Figure 17: Stress wave propagation histories in the striker rod and the semi-infinite rod at various locations (\(Z_{1}=0.5Z_{2}=\)16,043.602 kg/s, \(A_{1}=0.5A_{2}=3.5345\times 10^{-4}\) m\({}^{2}\), \(E_{1}=E_{2}=195\) GPa, \(v_{1}=v_{2}=0.3\), \(\rho_{1}=\rho_{2}=7850\) kg/m\({}^{3}\), \(2v_{0}=11.6\) m/s, \(L=0.2\) m, input energy = 37.34 J).  
An interesting finding from this study is that when \(Z_{1}<Z_{2}\), a positive reflection wave occurs after the first cycle, causing the two rods to separate; thus, there is no need to continue the analysis after Cycle 1. Conversely, when \(Z_{1}>Z_{2}\), multiple cycles of compressive waves occur, leading to the rods adhering together. Notably, with the same impedance, the clarity of the signal is optimal, making it easier to analyze and identify the geometric and material properties of the sample.  
C.-Y.W.: advisor, conceptualization, methodology, writing--review, and editing. N.N.T.: conceptualization, methodology, formal analysis, numerical simulation, validation, data curation, visualization, and writing--original draft. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 5 \"Migrate-Transfer-Control\" Technology of Roadway Surrounding Rock and Its Application Effect  
### \"Migration-Transfer-Control\" Technology of Roadway Surrounding Rock  
After the excavation of the roadway, the original equilibrium state is destroyed, and the surrounding rock stress is redistributed, as shown in Figure 18. During stress redistribution, part of the stress is released along the excavation face in the form of rebound deformation, and the other part is transferred to the deep roadway surrounding rock. Therefore, the stress is distributed in a gradient. From the stress point of view, it can be divided into a stress reduction zone, stress increase zone, and original rock stress zone. Due to the existence of stress gradient, the fracture degree along the radial direction of roadway is different. Finally, the roadway from shallow to deep forms the fracture zone, plastic zone, and elastic zone successively.  
Figure 19 is a simplified mechanical model of a circular roadway. In this model, the vertical stress \(p_{0}\) and the horizontal stress \(\lambda p_{0}\) are not equal. It can be considered as the superposition of uniform stress field and horizontal tension vertical compression stress field.  
According to elastic mechanics, the stress solution of circular roadway is:  
\[\sigma_{r}=\frac{(1+\lambda)p_{0}}{2}(1-\frac{R_{0}^{2}}{r^{2}})-\frac{(1- \lambda)p_{0}}{2}(1-4\frac{R_{0}^{2}}{r^{2}}+3\frac{R_{0}^{4}}{r^{4}})\cos 2\theta \tag{14}\]  
\[\sigma_{\theta}=\frac{(1+\lambda)p_{0}}{2}(1+\frac{R_{0}^{2}}{r^{2}})+\frac{( 1-\lambda)p_{0}}{2}(1+3\frac{R_{0}^{4}}{r^{4}})\cos 2\theta \tag{15}\]  
\[\tau_{r\theta}=\frac{(1-\lambda)p_{0}}{2}(1+2\frac{R_{0}^{2}}{r^{2}}-3\frac{R _{0}^{4}}{r^{4}})\sin 2\theta \tag{16}\]  
where \(\sigma_{r}\), \(\sigma_{\theta}\), and \(\tau_{r\theta}\) are radial stress, tangential stress, and shear stress, MPa; \(\lambda\) is the lateral pressure coefficient; \(p_{0}\) is vertical stress, MPa; \(r_{0}\) is the radius of roadway, m; \(r\) is the distance from any point to the center of the roadway, m; and \(\theta\) is the angle between the line from any point to the center of the roadway and the horizontal x-axis.  
Figure 18: Elastic–plastic deformation zone and stress distribution of surrounding rock of circular roadway [31].  
Figure 20 is a schematic diagram of the \"Migrate-Transfer-Control\" technical system in the deep roadway. After roadway excavation, the initial equilibrium state of roadway is broken, the overburden rock moves, the stress is transferred and adjusted, and, finally, the fracture zone, plastic zone, and elastic zone are formed. The strength in fracture zone and plastic zone is greatly lower than that in initial stress state. Therefore, it is crucial to strengthen the control of the fracture and plastic zones. Under normal circumstances, stability needs to be controlled by means of support so that the roadway stress can be restored to the state before excavation as far as possible and the roadway can also reach a safe state.
# FEA for Optimizing Design and Fabrication of Frame Structure of Elevating Work Platforms
## 2 Materials and Methods
### Analysis Procedure  
The analysis was conducted in ANSYS with the following steps:  
1. Static Structural Analysis: For each material, a static structural analysis was performed to calculate stress distribution, deformation, and factor of safety (FOS) under static and dynamic loading scenarios. The analysis focused on identifying maximum von Mises stress locations and comparing them to each material's yield strength to assess safety.
2. Modal Analysis: A modal analysis was conducted to determine the natural frequencies and mode shapes of the frame. This analysis helps to identify any resonance issues when subjected to dynamic loads, ensuring that the selected material does not lead to unwanted vibrations or structural instability.
3. Fatigue Analysis: Using the fatigue module in ANSYS, the frame's durability under cyclic loading was evaluated for each material. The number of load cycles to failure was estimated to assess each material's resistance to fatigue and predict the frame's operational lifespan under repeated loads.
4. Deformation and Deflection Analysis: Deformation under loading was calculated to evaluate stiffness and resistance to bending. Excessive deflection was considered a potential failure criterion, as it could impact platform stability and operational safety. In addition to the magnitude of deformation, the direction of deformation vectors was analyzed to assess the risk of instability, tipping, or excessive lateral deflection. This directional analysis helps identify critical points in the structure that may compromise safety or maneuverability during agricultural operations.
# Timing and Frequency Synchronization Using CAZAC Sequences for OFDM Systems
## 5 Results and Discussion
### Comparison of the Proposed Algorithms with Previous Algorithms  
The performance of our two proposed algorithms is simulated and compared with previous algorithms, including [PERSON]'s algorithm [8], [PERSON]'s algorithm [9], [PERSON]'s algorithm [10], and [PERSON]'s algorithm [16]. The preambles of the previous algorithms and those of the proposed algorithms are summarized in Table 2. The simulation results are shown in Figure 14.  
Figure 13: Correct timing probability comparison of the CCPD algorithm and the ACPD algorithm.  
As can be seen in Figure 14, the correct timing probability of [PERSON]'s algorithm reaches \(100\%\) when the SNR is greater than \(-7\) dB, while the CCPD algorithm reaches \(100\%\) when the SNR is greater than \(-11\) dB. This is a performance improvement of \(4\) dB. Furthermore, the ACPD algorithm reaches \(100\%\) at \(-14\) dB, which is \(7\) dB better than [PERSON]'s algorithm. Therefore, both the CCPD and ACPD algorithms showed a great improvement in performance when compared with the previous algorithms.
## 4 Material Characterization
### Adhesive Properties  
In this comprehensive experimental program, two adhesives, representative for the construction market, were used for the anchor tests but also fully characterized as materials. The latter is essential to develop a thorough understanding of the relevant mechanisms and to develop reliable numerical models. Product 1 (later P1) is an epoxy-based mortar based on a bisphenol-A/F epoxy resin with an amine hardener, while Product 2 (P2) is vinyl-ester based with a dibenzoyl peroxide hardener.  
Figure 2: Experimentally obtained (**a**) drying creep and (**b**) drying shrinkage measurements.  
Both products contain inorganic filler materials (quartz, cement) with a relatively high amount (20-50 vol%) [47].  
The still ongoing experimental characterization includes the short-term elastic and fracture-mechanical properties as well as long-term (creep, and time-to-failure) tests for both products. The goal of this investigation is not only to characterize the materials at one age, but also to provide insights into the time-dependent changes of mechanical properties for different curing conditions.  
Mechanical properties of two used adhesive materials were characterized by [PERSON] et al. [47] considering different storage time and post-curing temperatures. The obtained material properties are listed in Table 3 for both investigated products.  
All experimentally observed mechanical properties for other temperature cases are in detail reported in [47], showing the substantial changes of properties in course of time while the curing related volume changes are reported in [48]. Mode I fracture properties are reported in [PERSON] et al. [49].  
Additionally, 50-year master creep curves have been constructed for tensile and shear modulus using the time-temperature shift methodology as reported in [50,51], for both investigated mortars albeit only at a fully cured state.
# Neural Network-Assisted Interferogram Analysis Using Cylindrical and Flat Reference Beams
## 3 Results
### Numerical Simulation
In turn, unambiguously can help reduce errors in recognizing aberrations of the analyzed wavefront from interferograms with machine learning methods and neural networks.
## 3 Results
Summary plots for both median and 95 th percentiles are available in the Appendix A. Table 2 shows the effect of the level of indoor coverage on peak SAR exposure (95 th percentile) for non-users and heavy users. From the data, it is obvious that heavy users can benefit from indoor coverage, as it reduces their dose by more than 20 dB for full indoor coverage. There is a consistent trend that, with increasing N, the dose of heavy users decreases for all geographical locations and cell types. On the other hand, non-users are subjected to more exposure, as more indoor coverage generally results in stronger incident fields. This effect is the largest in urban microcells because RSRP levels there are much lower than than the ones measured from indoor base stations. For rural areas, more indoor coverage can decrease the exposure due to lower RSRP levels from indoor cells than from rural macrocells. In comparison, it should be noted that the absolute peak exposure of non-users is a factor of 10 to 1000 lower than for active users of any type. The same effect can be observed upon shrinking the cell sizes by 6 and 12 dB. The higher signal strengths and lower path losses in the cells result in lower doses for active users. For non-users, the dose is increased. Figure 3: Histograms and cumulative distribution functions for the rural cell scenario as a function of usage (increasing left to right), with constant network topology. Statistic summaries of all simulated scenarios are presented in the Appendix A. Table 3 depicts the total dose changes for varied bystander distances and data usages relative to non-user dose. Only the urban exposure scenarios were analyzed, given that, for suburban and rural scenarios, bystanders are unlikely to be very close by. The results show that a close bystander dominates the exposure from a nearby base station antenna for non-users. As soon as a user generates even small amounts of uplink data traffic (simulated by 100 MB/d), the effect of the bystander becomes negligible (\(<\)0.2 dB). The effect is the largest in macrocells (\(>\)5 dB for a bystander at 1 m versus 5 m), where the largest path losses are to be expected, and this is expected to decrease for mini and microcells (1 dB for a bystander at 1 m versus 5 m). Table 4 summarizes the 95 th percentile values that reflect the effect of a person's own data usage on the total dose. The data reveal that even small amounts of data usage from a person's own mobile phone dominates the total dose. A user with low data usage requirements experiences an increase in dose by 6-11 dB compared to a non-user. Moderate and heavy users increase their total dose by 6-25 dB and 25-40 dB, respectively, compared to non-users. The effect is within the same order of magnitude over all cell types and locations. This is most likely a result of the network architecture. Better indoor coverage can consistently lower the dose of data users, except for the urban microcell and no or low data usage. This effect can be attributed to the generally higher RSRP from the urban microcell than from indoor cells.
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 4 Discussion
As shown in Table 19, which presents a comparison of a 20 \(\Omega\) fault at bus 2, all the algorithms meet the DC protection requirements, except for OC, which fails to provide the necessary sensitivity and response time for this fault condition. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fault at Bus 2**} & \multicolumn{2}{c}{**Detection Time at R21 (ms)**} & \multicolumn{2}{c}{**Detection Time at R24 (ms)**} \\ \cline{2-5}  & **P2P** & **P2G+** & **P2P** & **P2G+** \\ \hline OC & R = 20 \(\Omega\) & 3.000 & - & 6.295 & - \\ \hline UV & R = 20 \(\Omega\) & 0.065 & 0.140 & 0.065 & 0.140 \\ \hline ROCOC & R = 20 \(\Omega\) & 0.020 & 0.030 & 0.020 & 0.030 \\ \hline ROCOV & R = 20 \(\Omega\) & 0.01 & 0.01 & 0.01 & 0.01 \\ \hline DIFFERENTIAL & R = 20 \(\Omega\) & 0.01 (P2P) & 0.01 (P2G+) \\ \hline LIVRD & R = 20 \(\Omega\) & 0.01 & 0.01 & 0.01 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Fault at bus 2, detection times for all the algorithms.
# Prognostics of Electromechanical Actuator with Partial Time Scaling Invariant Temporal Alignment
###### Abstract  
In today's industrial environment, effectively monitoring assets throughout their entire lifetime is essential. Prognostic and Health Management (PHM) is a powerful tool that enables users to achieve this goal. Recently, sensor-equipped actuator electrification has been introduced to capture intrinsic key system variables as time series. This data flow has opened up new possibilities for extracting essential maintenance information. To leverage the full potential of these data, we have developed a novel algorithm for time series registration, which serves as the core of a new similarity-based prognostic method in a PHM context: Partial Time Scaling Invariant Temporal Alignment for Remaining Useful Life Estimation (PARTITA-RULE). Our algorithm transforms acceleration signals into a subset of descriptors for a new actuator, creating a time series. We can extract valuable maintenance information by aligning this time series with the one already labeled from past behaviors of the same actuator's family of heterogeneous sizes and robust scaling factors. The unique aspect of our method is that we do not need to inject prior knowledge for registration intervals at this stage. Once the unknown series is aligned with all possible candidates, we create a weighting scheme to assign a relevance score with an uncertainty measurement for each aligned pair. Finally, we compute interpolants on the Wasserstein space to obtain the asset's Remaining Useful Life (RUL). It is important to note that a relevant result in a PHM context requires a database filled with different labeled system behaviors. To test the effectiveness of our method, we use an industrial data set of vibration signals captured on an aeronautical electric actuator. Our method shows promising Remaining Useful Life (RUL) estimation results even with incomplete time segments.  
Prognostic and Health Management (PHM); electrical actuator; time series registration; similarity measurement; Remaining Useful Life (RUL); prognostic +
Footnote †: journal:  
procedures for airlines. Moreover, when integrated into the aircraft's electrical harness, this actuation is conducive to implementing sensors and monitoring circuits. However, electromechanical systems are susceptible to potential issues not encountered in current pneumatic or hydraulic systems, particularly issues related to jamming. For a primary (or secondary) flight control actuator, this dreaded event must be avoided at all costs. Therefore, it is necessary to establish monitoring strategies to address the potential occurrence of these defects. These monitoring strategies are part of the overall Prognostics and Health Management (PHM) context; see Figure 1. Our work is part of the prognosis approach.
## 4 Methods
### PAR and APAR
For obtaining the solar radiance at the top of the atmosphere this algorithm uses the following variables, Earth-Sun distance, solar inclination, the angle between the Earth's orbital and equatorial planes, solar elevation angle, geographical position, and day of year [45]. The variables of surface elevation, day length and cloud cover information at localities were used to compute optical depth of the atmosphere and to estimate solar irradiance reaching the ground. Spatial distribution of the radiation reaching the ground strongly depends on the geometry of terrain (relief slope, exposition, aspect). These variables were obtained from a digital terrain model and inputted into the equation given by [46]. The incoming solar energy is reduced to the photosynthetically active radiation by a factor of 0.48 [18]. Amount of the absorbed photosynthetically active radiation (APAR) was calculated through multiplication of PAR with fPAR.