esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning
## 1 Introduction  
Roller bearings are critical components of machinery and directly affect the performance of the whole system [1]. However, bearings inevitably degenerate and break down due to an overload of repetitive work. According to statistics, 30 percent of faults in rolling machinery originate from bearing faults; therefore, bearing fault prognostics is an important area of research [2; 3; 4; 5]. Recently, studies on bearing faults prognostics have focused on fault signal feature extraction and fault classification. Commonly used methods for extracting the features of fault signals are Empirical Mode Decomposition (EMD), morphology, wavelet transform, signal value decomposition, and principal component analysis [6; 7; 8; 9; 10]. However, features extracted using the above methods are largely redundant, as bearing fault signals are unstable and nonlinear [11]. Manifold learning, such as Isomap [12], Locally Linear Embedding (LLE) [13], and Laplacian Eigenmap (LE) [14] can be used to extract effective features. Bearing vibration signals produce a great deal of noise and the first two methods referred to are very sensitive to noise. LE has a powerful local keeping ability and is not sensitive to noise [15]. Good results can be obtained with this method to extract features from roller bearings and reduce dimensionality. The local field selection of manifold learning is the key step and greatly affects performance [16]. Therefore, we propose an adaptive local field selection system based on Lalacian mapping to extract features of bearing fault signals. Classifications of fault signals and information fusion approaches such as firefly neural networks [17], recursive complex networks [18], and fuzzy C mean clustering [19] are commonly used. Neural networks have a great ability to fit nonlinear dataand recognize bearing faults. The proposed wavelet neural network with expansion and translation obtains better freedom and has a greater flexible effective function approximation ability than other conventional neural networks. It also learns more quickly and can learn from mutation function and discontinuous signal details.  
From the above analysis, it is clear that LE is suitable for use in the nonlinear descending dimension method. The performance of LE depends greatly on the neighborhoods \(k\). Generally, \(k\) is given according to experience. In this paper, we propose an improved LE that can be changed adaptively for any given \(k\) and obtain good results. We can also attain good characteristic signals using this method. Additionally, we propose a fault diagnosis method based on a wavelet neural network model where the merits of wavelet transform are integrated with that of an artificial neural network. The experimental results prove the effectiveness of the proposed methods.
## 3 Numerical Results
It can be seen in the figure that the SER of the signal received by the USER shows a monotonically increasing trend with either variable of \(H_{UAV}\) or \(\alpha\), that is, if one variable of \(H_{UAV}\) or \(\alpha\) is fixed and the value of the other variable is changed, the SER always increases with the increase in this variable. The coordinate points set at the intersection line represent the critical point for ensuring that the USER can receive good signals. In theory, all of the coordinate points of the UAV on the intersection line can achieve a good cellular data transmission effect. However, in order to achieve the maximum signal coverage area, we extracted coordinate points on the intersection line and performed function fitting in two-dimensional coordinates. The binomial expression obtained by fitting is:  
\[\alpha=p_{1}\cdot H_{UAV}^{2}+p_{2}\cdot H_{UAV}+p_{3}, \tag{24}\]  
where \(p_{1}=-0.0001417\), \(p_{2}=0.01029\), and \(p_{3}=0.8557\). In order to obtain a better signal transmission effect, it is necessary to improve the coverage area as much as possible on the premise of ensuring normal signal reception. It is known that the intersection of the plane, surface, and X-axis is 117, that is, the range of values of \(H_{UAV}\) is 0-117 m.  
Figure 5: Symbol error rate versus \(d2\). Figure 6: Symbol error rate of the USER versus \(\alpha\) and \(H_{UAV}\). After specifying the height of the UAV and the range of variation in the radiation angle, we need to explore the relationship between the above two variables and the coverage area \(S\) because the coverage area \(S\) is directly related to the radius \(R\) of the circle at the bottom of the cone, and \(R\) can be expressed as:  
\[R=\tan\alpha\cdot H_{UAV}. \tag{25}\]  
Therefore, the expression of the coverage area \(S\) is:  
\[S=\pi\cdot\tan^{2}\alpha\cdot H_{UAV}^{2}. \tag{26}\]  
Therefore, the problem of finding the optimal deployment position of the UAV is transformed into the problem of finding the extreme value of Formula (26) under the condition of Formula (24). This is obtained by using the Lagrange multiplier method; when \(H_{UAV}\) = 63 m, \(S\) achieves its maximum value, which is about 23,500 m\({}^{2}\).
## 6 Discussion
### Adaptability of the Measurement Scale in Diverse Regions and Institutions  
While our study provides a measurement scale for examining government cloud adoption intentions, its broader contribution to IT for development lies in its adaptability across diverse regions and institutional contexts. The scale's psychometric properties suggest that the core constructs--performance expectancy, governance framework, information security, privacy, perceived risk, and government intentions--represent dimensional government cloud adoption decisions in the developmental contexts. However, the successful application of the measurement scale across different regions requires careful consideration of contextual factors that may influence both the relevance of constructs and their measurement properties. The governance framework construct may be perceived differently among federal versus state governments, while cultural dimensions may influence perceptions and prioritise information security and privacy concerns. Additionally, different stages of regional economic development may require contextualisation of performance expectancy items to reflect development priorities.  
Furthermore, the region and institutions' economic development levels and technological infrastructure development may prioritise adaptability and testing to reflect various concerns. For instance, regions with limited cybersecurity capacity may show heightened sensitivity to information security and privacy, while those with more developed digital infrastructures may have different risk perception patterns. Therefore, the regional or institutional adaptation of the measurement scale may consider cultural, economic, government structure, and statistical validation approaches to ensure that the constructs capture how cloud adoption contributes to achieving specific development objectives relevant to each region or institutional context.  
Cloud adoption in developing contexts like Nigeria is shaped not only by technological readiness but also by legal sovereignty. The current reliance on foreign cloud infrastructures requires important issues of jurisdiction and regulatory alignment; therefore, a regional cloud governance framework would be recommended. Additionally, policymakers can integrate the scale into readiness assessments, while development partners and cloud providers may use it to identify trust and security bottlenecks that hinder inclusive cloud deployment and adoption.
# Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM
## 5 Experimental Validation and Analysis
### CNN-LSTM Trajectory Prediction Model Evaluation  
A comprehensive evaluation of the model is performed using the receiver operating characteristic (ROC) curve, which illustrates the model's performance by showing the true positive rate (TPR) and the false positive rate (FPR) at various classification thresholds. This approach enables a comprehensive assessment and comparison of the performance of the CNN-LSTM model with that of the standalone CNN and LSTM models. The ROC curve provides a comparison of the performance differences between models, and the area under the ROC curve (AUC) is calculated to quantify the model's performance. The closer the AUC value is to 1, the better the model's performance is.  
The top-left corner represents the optimal point (Figure 6). The proposed CNN-LSTM model is closer to this point, indicating that it has a better generalization performance than the standalone CNN and LSTM models. Additionally, the AUC values calculated from the ROC curves in Figure 6 are 0.9854 for the CNN-LSTM model, 0.9478 for the CNN model, and 0.9351 for the LSTM model. Therefore, the CNN-LSTM model outperforms both the CNN and LSTM models.  
The proposed CNN-LSTM model hierarchically extracts interactions, making it superior to individual CNN and LSTM models. Figure 7 compares the trajectory prediction results of the models. The scenario shows that changing to the left lane is a reasonable choice: in the current lane, the lead vehicle is decelerating; in the right lane, adjacent vehicles are moving faster than the target vehicle, providing no incentive to change lanes; and, in the left lane, adjacent vehicles are moving slower, with a safe distance sufficient for a lane change. While the LSTM and CNN models are almost incapable of predicting the future trajectory of the target vehicle, the CNN-LSTM model predicts the future trajectory more accurately.  
Figure 6: ROC comparison curve.
# Feasible Energy Density Pushes of Li-Metal vs. Li-Ion Cells
## 6 Liquid Electrolytes vs. Solid Electrolytes for LMBs
After inorganic solid electrolytes, crystalline sulfide electrolytes such as Li\({}_{10}\)GeP\({}_{2}\) S\({}_{12}\) (LGPS) and argyrodite-type crystallines such as Li\({}_{6}\)PS\({}_{5}\)X (X = Cl, Br, I) (LPS) are the most popular with their ionic conductivity of 1.9 \(\times\) 10\({}^{-3}\) S/cm and 6.8 \(\times\) 10\({}^{-3}\) S/cm, (X = Cl and Br, respectively [45; 46]. However, unlike the polymer electrolytes, they do not have flexible structure and are expensive (-LGPS: 6958/10 g) [19]. Solid polymer electrolytes can be processed more easily than crystalline sulfide electrolytes thanks to their softness. This flexibility provides low-cost production. Nevertheless, they suffer from good ionic conductivities at room temperature and the requirement of high operational temperature limits their use in EV applications. In order to overcome many challenges related to SEI, dendrite formation, and low ionic conductivity, \"solid\" composite electrolytes may be the alternative solution. The reduced contact area of the liquid electrolyte part with a lithium metal anode can be benefitted by preparation of gel polymer electrolytes. Gel polymer electrolytes are prepared with a low crystalline polymer such as poly(vinylidene fluoride)-co-hexafluoropropylene (PVdF-HFP) and an organic liquid electrolyte such as LiPF\({}_{6}\) in EC-DMC) in the polymer matrix. Despite their good ionic conductivities (up to 6 \(\times\) 10\({}^{-3}\) S/cm) [47], they suffer from lower mechanical properties and electrode compatibilities [48]. The addition of crystalline sulfide electrolytes to the composition would solve the low mechanical characteristics of the resulting electrolyte.
## 5 Comparative Analysis
### Comparative Analysis of Backscatter Intensity  
The computation of average backscatter values from two SAR images enabled the creation of backscatter intensity maps for LT-1 and Sentinel-1 satellite data over a shared area. These maps, analyzed alongside Google Earth imagery, illustrate variations in typical ground object reflection intensities attributable to different bands and polarization modes, as demonstrated in Figure 8. The brightness of these images corresponds to backscatter intensity levels.  
Figure 8: (**d,e**) Backscatter intensity maps of LT-1 and Sentinel-1 satellite data, respectively; (a-c,f-h) enlarged views of LT-1 satellite data, Sentinel-1 satellite data, and Google optical images.  
In Figure 8a-c, the red-framed areas highlight clusters of buildings. LT-1 satellite data distinctly preserve the clear boundaries of these ground objects. This clarity results from the multi-looking process applied to LT-1, involving four looks in both the range and azimuth directions. This process yields pixel spacings of 6.66 m in range and 7.88 m in azimuth for LT-1 data. In contrast, Sentinel-1's multi-looking process, comprising four looks in the range direction and one in the azimuth, results in larger pixel spacings of 9.32 m in range and 13.93 m in azimuth. This comparison underscores LT-1's superior resolution, leading to a denser array of results.  
A quantitative analysis of backscatter intensities for both SAR datasets focused on red-framed areas within buildings (high reflectors) and grassland (low reflectors). Figure 8a-c shows the average intensity values in the building area, where LT-1 and Sentinel-1 data register 36.8 dB and 33.6 dB, respectively, a difference of 1.1 times. Conversely, Figure 8f-h reveals that in the grassland area, the average intensities are 24.4 dB for LT-1 and 1.4 dB for Sentinel-1, a significant 17.4-fold difference.  
Despite the disparate polarization modes of LT-1 and Sentinel-1 data, LT-1 exhibits approximately 1.1 times the reflection intensity of Sentinel-1 in high-reflectivity areas like buildings. In low-reflectivity zones such as vegetation, the superior penetration capability of LT-1's L-band results in a reflection intensity markedly greater than Sentinel-1's, approximately 17.4 times higher. Consequently, selecting LT-1 satellite data as the SAR data source substantially enhances the spatial distribution and density of scattering points.
## 4 Experimental Validation
On applied 70% of UCM datasets as training datasets, the SIDTLD-AIC model has resulted in average \(accu_{y}\), \(prec_{n}\), \(rec_{d}\), \(F_{score}\), and \(G_{mean}\) of 96.60%, 95.89%, 95.86%, 95.85%, and 97.80%, respectively. Likewise, on applied 30% of UCM datasets as testing datasets, the SIDTLD-AIC model has resulted in average \(accu_{y}\), \(prec_{n}\), \(rec_{d}\), \(F_{score}\), and \(G_{mean}\) of 99.50%, 94.93%, 94.67%, 94.70%, and 97.15%, respectively. Figure 11 illustrates a comparative \(accu_{y}\) examination of the SIDTLD-AIC model with recent models. The experimental values implied that the DL-PlacesNet and DL-VGG-VD19 models have shown lower values of \(accu_{y}\). Moreover, the DL-VGG-VD16, DL-VGG-M, DL-VGG-F, DL-CaffeNet, and DL-AlexNet models have resulted to closer \(accu_{y}\) values. Then, the DL-VGG-S and DL based multiobjective PSO (DL-MOPSO) techniques have reached reasonable \(accu_{y}\) values of 95.24% and 95.81%. Though the DL-C-PTRN model has resulted in considerable \(accu_{y}\) of 98.96%, the SIDTLD and SIDTLD+SSA models have accomplished near optimal \(accu_{y}\) of 98.98% and 99.01%. However, the SIDTLD-AIC model has accomplished superior outcome with maximum \(accu_{y}\) of 99.50%. Figure 9: ROC curve analysis of SIDTLD-AIC technique on UCM dataset. Figure 11: Accuracy analysis of SIDTLD-AIC technique with existing methods. Figure 10: Average analysis of SIDTLD-AIC technique with various measures. Finally, a computation time (CT) assessment of the SIDTLD-AIC model with recent models is carried out in Figure 6:0 The experimental values implied that the DL-PlacesNet, DL-VGG-VD-19, DL-VGG-VD-16, and DL-VGG-S approaches have obtained increased CT values. Followed by, the DL-VGG-F, DL-CaffeNet, and DL-AlexNet models have reached moderately reduced CT values. The DL-MOPSO and DL-C-PTRN models have accomplished reasonable CT of 135s and 95s, respectively. Meanwhile, the SIDTLD and SIDTLD+SSA models have attained CT of 67s and 54s, respectively. Finally, the SIDTLD-AIC model has outperformed other methods with minimal CT of 40s. The results implied that the SIDTLD-AIC model has gained enhanced classification performance due to the inclusion of SSA and SOA based hyperparameter optimizers. From the above results and discussion, it can be stated that the SIDTLD-AIC model has accomplished enhanced image classification results on the UAV networks.
## 3 Results  
### Methodology of Measurement and Evaluation of Measured Data  
The measurements were carried out in the receptor/family house situated in the nearby environment of Trebejov Quarry and simultaneously in the indoor environment of the housing according to the standards. The transmitting rock environment was carbonate rocks--dolomites. Vibrographs placed on the sites were calibrated before the measurement, and their sensitivity was checked. The graphical course of the individual seismic wave components at the measured firing No. 693-698 is demonstrated. The individual graphical records are four seconds long. The vibrographs were placed on the measuring points (see Figure 7) in order to assess the impact of the technical seismicity on the assessed objects (Table 5).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Baset No.**} & \multirow{2}{*}{**Profile**} & \multirow{2}{*}{**Position of the Measurement**} & \(\mathbf{v_{x}}\) & \(\mathbf{v_{y}}\) & \(\mathbf{v_{z}}\) \\  & & & **[mm.s\({}^{-1}\)]** & **[mm.s\({}^{-1}\)]** \\ \hline
693. & - & S1/Minimate & 0.81 & 1.15 & 0.81 \\
694. & - & S1/Minimate & 1.46 & 1.55 & 1.26 \\
695. & - & S1/Minimate & 1.59 & 1.13 & 0.87 \\
696. & - & S1/Minimate & 1.23 & 1.63 & 0.81 \\
697. & - & S1/Minimate & 1.2 & 1.62 & 0.82 \\
698. & - & S1/Minimate & 0.98 & 1.58 & 0.58 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Measured values of peak particle velocity in blasts (source: author).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Description of the Protected Room in Buildings**} & \multirow{2}{*}{**Reference Time Interval**} & \
\begin{tabular}{c} **Continuous or Intermittent,** \\ **Periodic or Steady-State** \\ **Random Vibration** \\ \end{tabular} &
\begin{tabular}{c} **Shocks and Vibrations with** \\ **Large Dynamics Occurring** \\ **Several Times a Day** \\ \end{tabular} \\ \cline{3-4}  & & \(a_{weq}\) & \(a_{weq}\) \\  & & [**\(m\)\(\ast s^{-2}\)] & [**\(m\)\(\ast s^{-2}\)] \\ \hline \multirow{2}{*}{**Enhanced areas (such as hospital rooms, spa patients)**} & Time of occurrence for day, \\  & evening and night & 0.004 & 0.008 \\ \hline \multirow{2}{*}{**Residential rooms, dormitories, retirement homes**} & Time of occurrence for day & 0.008 & 0.11 \\  & evening & 0.008 & 0.11 \\  & night & 0.005 & 0.05 \\ \hline \multirow{2}{*}{**Nurgeries, schools and libraries**} & Time of occurrence while & \multirow{2}{*}{0.008} & \multirow{2}{*}{0.11} \\  & using the room & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Allowed values of vibration quantities in the indoor environment of buildings [58, 59].  
Figure 7: Measuring points S1 and S2 ([60], (source: author)).
## 4 Discussion  
### Advantages of Data Preprocessing  
The study area for this experiment encompasses a wide geographical expanse, with an east-west longitude difference of 7\({}^{\circ}\), necessitating approximately three image strips from Sentinel-2 satellites to comprehensively cover the area. However, due to cloud cover effects, the intervals between usable image acquisitions from different strips may exceed ten days [50]. Optical imagery remains the primary reference for classification in this experiment, necessitating a hierarchical processing approach. Despite compiling time series data based on monthly mean values, anomalies persist in many pixel values [51]. To address this, harmonic analysis was employed to identify anomalies in the vegetation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Region** & **GFI** & **UA** & **Example** & **Example Coordinate** \\ \hline Jinxiang & 0.1175 & 91.77 & & \(116^{\circ}23^{\prime}03^{\prime\prime}\) E \(34^{\circ}57^{\prime}36^{\prime\prime}\) N \\ Laiwu & 0.8261 & 87.26 & & \(117^{\circ}28^{\prime}57^{\prime\prime}\) E \(34^{\circ}16^{\prime}40^{\prime\prime}\) N \\ Lanling & 0.1001 & 92.82 & & \(118^{\circ}05^{\prime}41^{\prime\prime}\) E \(34^{\circ}45^{\prime}36^{\prime\prime}\) N \\ Pizhou & 0.3378 & 94.41 & & \(117^{\circ}46^{\prime}38^{\prime\prime}\) E \(34^{\circ}57^{\prime}36^{\prime\prime}\) N \\ Qixian & 0.1691 & 92.28 & & \(114^{\circ}53^{\prime}37^{\prime\prime}\) E \(34^{\circ}32^{\prime}05^{\prime\prime}\) N \\ Zhongmou & 0.1180 & 96.39 & & \(113^{\circ}57^{\prime}22^{\prime\prime}\) E \(34^{\circ}50^{\prime}24^{\prime\prime}\) N \\ \hline \hline \end{tabular}
\end{table}
Table 7: Table of garlic crushing by county.  
index relative to the previous two years' data and rectify them, thereby reducing the impact of cloud or other noise artifacts and improving the reliability of the time series data. Experimental results confirm the efficacy of these methods in providing more dependable data for garlic classification.
# Practical Use of Starlink Downlink Tones for Positioning
###### Abstract
In [25], the authors have identified the structure of the Starlink downlink signal, which should enable advanced measurements such as pseudo-distance on broadband Starlink signals. In this paper, since we are targeting small platform localization with low power consumption, we will use Starlink downlink tones when received without an antenna parabolic reflector. The tones allow the carrier frequency to be tracked easily, which is useful for Doppler shift-based positioning. The relatively narrow bandwidth occupied by the tones (\(\sim\)1 MHz) can be handled by low-cost software defined radios (SDRs) and CPUs. Moreover, the signals in the 10-12 GHz band are in general captured with a parabolic reflector and a low-noise block down-converter (LNB). The parabolic reflector concentrates the downlink beam coming from a very narrow direction toward the LNB, and hence increases the signal power. However, it is cumbersome in terms of space for vehicles such as drones, and has a selective radiation pattern that prevents the acquisition of multiple satellites simultaneously. For these considerations, we will study tones tracking without a parabolic reflector. In this case, the signal will be much weaker than if it had been captured with a parabolic reflector, but the tone frequency measurements do not need as much power as those required, for instance, to decode useful data. This paper aims to contribute to the current literature of LEO-SOP by taking a step forward in the potential use of Starlink satellite signals for opportunistic navigation. Although Starlink signal processing has been the focus of a few recent papers, many questions remain unanswered regarding the quality of the signal received on the ground, the quality of the measurements, and the number of satellites that can be tracked simultaneously. In this paper, we intend to address these questions, especially when using a universal down-converter without a reflector antenna. Therefore, in this paper, we present the processing and characterization of Starlink downlink tones and the feasibility of their use without a parabolic reflector for positioning purposes. After the introductory section (Section 1), Section 2 describes the detection and tracking of real-life collected tones. Section 3 performs the aggregation of multi-tone measurements leading to a useful frequency shift model. Section 4 presents a practical use of tone frequency shifts in multi-epoch positioning. Section 5 is the conclusion.