esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Integrated Geophysical Exploration Techniques for Earth and Rock Dams
### Principles of Integrated Geophysical Exploration Information Fusion Technology
After the scale unification, it is also necessary to unify them into the same numerical range to eliminate the influence of different data levels on the fusion accuracy. The specific steps are as follows. (1) Scale uniformity  
The data information obtained from the electrical resistivity tomography method is densely distributed and of high resolution within the detection profile, which is sampled using the averaging method. The sounding data of the surface wave and geological radar methods are a series of non-standard intervals of data information, which need to be collected by linear sampling methods. Linear sampling methods require a sampling range to be determined first, and the data information within it to be re-extracted and harmonized to the same resolution. The linear sampling schematic is shown in Figure 15.  
Figure 15: Schematic diagram of linear sampling. As can be seen from the figure, _Cell_ is the geological unit corresponding to the framed sampling range. Since the resolutions of the information obtained by different detection methods are different, it is necessary to obtain detection information with the same resolution by sampling the geological units in a uniform range. Its final value is mainly determined by the detection values of \(S_{i-1}\), \(S_{i}\), and \(S_{i+1}\) sampling points within the sampling range and their corresponding \(W_{i-1}\), \(W_{i}\), and \(W_{i+1}\) weights. The value of _Cell_ unit is shown in Equation (3). \[Cell=\left\{\frac{\sum_{k=0}^{n}W_{i}S_{i}^{-1}}{\sum_{k=0}^{n}W_{i}}\right\}^ {-1} \tag{3}\]  
where \(S_{i-1}\), \(S_{i}\), and \(S_{i+1}\) indicate the detection values of sampling points in the boxed range, \(W_{i-1}\), \(W_{i}\), and \(W_{i+1}\) indicate the corresponding weights of sampling points. (2) Numerical uniformity  
The data information at the same resolution obtained through scale unification can be unified in the same value range by linear transformation. In this paper, taking [0, 100] as an example, the numerical unification formula is shown in Equation (4). \[Normal(i)=100*(S_{i}-S_{\text{min}})/(S_{\text{max}}-S_{\text{min}}) \tag{4}\]  
where \(S_{i}\) indicates the value of the sampling point, \(S_{\text{max}}\) and \(S_{\text{min}}\) indicate the maximum and minimum values of the set of sampling points, and _Normal_ indicates the result of the converted data. Through the scale unification and numerical unification processing in the previous section, standardized geophysical exploration data were obtained. These standardized data can ensure that the original features of the data remain relatively unchanged and meet the requirements for the next step of data fusion processing and analysis. The standardized data is fused and reconstructed by mathematical algorithms to obtain complete hidden information about the geological body, which can be used to locate the leakage channels.
## 3 Results and Discussions  
### FDHs of Mueller Matrix Elements for Tendon Tissues in Different States  
Through the transformation process of 2D Mueller matrix elements images into MMT parameters, some detailed information can be lost that may account for the sample structure features [34]. To this end, we converted 2D Mueller matrix elements images of bovine tendon tissues firstly into FDHs, then calculated their corresponding central moments. Additional information related to the mechanical property may be obtained by analyzing the position, width, and shape of the FDHs of the Mueller matrix elements. Figure 4 shows the FDHs of 3 \(\times\) 3 Mueller matrix elements for tendon tissues in four states: origin, TDO, MDO, and ODO. Here, the FDH curves of tissues in each of the four states are plotted into the same subgraph, where the blue solid lines represent MDO, the orange solid lines represent TDO, the yellow dashed lines represent ODO and the purple dashed lines represent the origin state.  
It can be seen from Figure 4 that: (1) the FDHs of elements M22 and M33 are different for all the stretching states. Taking the sample without stretching (Origin) as an example, the mean values of the M22 and M33 curves are 0.16 and 0.06, respectively, meaning that the tendon tissue is highly anisotropic. In addition, the FDH curves of M33 in all four states are steeper than those of M22. However, the differences between M22 and M33 change as the stretching states change, showing that the applied external force can result in different degrees of anisotropy to the tendon tissue samples. A closer observation of Figure 4 also reveals that the FDH curves of the off-diagonal elements M12, M21, M13 and M31, which mainly present the linear dichroism property induced by fibrous scatterers, have none-zero mean values, especially for the M12 and M21 elements [35]. The none-zero values of origin sample demonstrate the existence of fibers, and the characteristic value changes with different stretchings applied show that the external force information can be extracted from the Mueller matrix elements [18; 36].  
Figure 3: Schematic of the four-layered, a fully connected neural network consisting of 18 computational units used in this study.  
The above conclusion illustrates that the FDHs of the Mueller matrix elements for the bovine tendon tissue samples can be significantly different when stretched by external forces. For more quantitative analysis of the relationship between the structural changes and the stretching by different external forces, we derived the MMT parameters in the next section.
###### Abstract  
Predictive maintenance strategies are established in the industrial context on account of their benefits in terms of costs abatement and machine failures reduction. Among the available techniques, vibration-based condition monitoring (VBCM) has notably been applied in many bearing fault detection problems. The health indicators construction is a central issue for VBCM, since these features provide the necessary information to assess the current machine condition. However, the relation between vibration data and its sources intimately related to bearing damage is not effortlessly definable from a diagnostic perspective. This study discloses a diagnostic investigation performed both on the vibration signal and on the contact pressure signal that is supposed to be one of main forcing terms in the dynamic equilibrium of the damaged bearing. Envelope analysis and spectral kurtosis (SK) are applied to extract and compare diagnostic features from both signals, referring to the Case Western Reserve University (CWRU) case-study. Namely, health indicators are constructed by means of physical considerations based on the effect of faults on the signal power contents. These indicators show to be promising not only for damage detection but, also, for damage severity assessment. Moreover, they provide an invaluable reading key of the link occurring between the contact pressure path and the vibration response.  
condition monitoring; VBCM; health indicators; bearing diagnostics; damage severity; envelope analysis; Spectral Kurtosis; non-Hertzian contact
# Outage Performance of Wireless Powered Decode-and-Forward Relaying Networks in Rician Fading
## 2 System Model  
The considered DF SWIPT-enabled relay network is shown in Figure 1. It includes an energy-rich source node \(S\), an energy limited relay node \(R\), and a destination node \(D\). \(R\) has no available power, so it needs to collect energy from the source node to receive and transmit information in half-duplex mode. Assuming that there is no direct link from \(S\) to \(D\) due the barrier, so \(S\) can only transmit information to \(D\) via \(R\).  
\(S\) first transmits energy and information to \(R\), and \(R\) uses TS (It is the fact that the PS model usually outperforms the TS one [31]. Nevertheless, the TS model is still a very popular SWIPT receiver model, which has been attracting much interest. The reason for us to choose the TS receiver architecture is that compared with the PS model, the TS one is much simpler to be deployed in practice. Therefore, similar to many recent works [32; 33], we consider the TS one in this paper.) receiving architecture to receive energy and information. Then, \(R\) forwards the information to \(D\).  
For clarity, the time framework of energy collection and information transmission of DF relay protocol is shown in Figure 2. The whole time period is divided into two stages. The first one is the energy collection stage, and the second one is the information transmission stage, where the information transmission stage is further divided into two parts. In the first part, \(R\) receives information from \(s\), and in the second part, \(R\) transmits information to \(D\) by DF relaying. \(\rho\) is the time division factor, and \(T\) is the interval of a time period. Assuming that \(R\) has no energy before starting transmission, and the two channels are quasi-static, independent and Rician Fading distributed.
# Data-Aided and Non-Data-Aided SNR Estimators for CPM Signals in Ka-Band Satellite Communications
## 1 Introduction
The accuracy of channel state estimation is one of the main factors that affect the performance of ACM, and the SNR is used to characterize channel state information. The SNR estimation method consists of time domain and frequency domain methods. The time domain method can be divided into data-aided (DA) and non-data aided (NDA) methods [18]. The DA method has higher estimation accuracy than NDA method but involves the insertion of a periodic pilot sequence, which is inefficient. A significant number of studies have been conducted on SNR estimation technology in recent years. The DA and NDA ML SNR estimators for CPM signals were studied by [PERSON], and the effects of the frequency and phase shifts were also considered. However, the simulation is based on the additive Gaussian white noise (AWGN) channel [19]. [PERSON] studied the SNR estimation method for MPSK signals in Nakagami fading channels and proposed a joint estimation algorithm for SNR and fading factor; however, the algorithm is suitable only for MPSK [20]. Based on the second generation digital satellite TV broadcasting standard (DVB-S2), [PERSON] proposed an SNR estimation algorithm based on eigenvalue decomposition in the AWGN channel; however, it is not used in the Ka band [21]. [PERSON] proposed the ML SNR estimation algorithm for M-APSK signals in slow time-varying fading channels, which improve the estimation accuracy under low SNR, but the CPM signal is not introduced [22]. The SNR estimation algorithm in Ka-band satellite communication is based mostly on PSK modulation, and no relevant literature applies the CPM signal to the ACM technology in Ka-band satellite communication. This study analyzes the channel model in Ka-band satellite communication and focuses on studies on SNR estimation algorithm for CPM signals in the Ka-band fading channel. DA ML and M\({}_{2}\)M\({}_{4}\) estimators are derived and analyzed. The SNR fitting curve, normalized mean square error (NMSE), and CRLB are used to analyze the performance of the estimation algorithm, and the effects of several parameters on SNR estimation are discussed. The paper is organized as follows. Section 2 establishes the channel model of Ka-band satellite communications. Section 3 presents the schematic of the system and the expression of the received signal. Section 4 develops and analyzes two SNR estimators and provides the expression of CRLB. Section 5 presents the simulation results. Section 6 gives the conclusions.
## 3 Data and Methods
### Sub-Watershed Division and HRU  
The division of sub-watersheds involves partitioning a big watershed into multiple smaller sub-basins, which significantly influences the runoff simulation outcomes of the SWAT model (version10.6). Various sub-watershed delineation techniques will yield significant discrepancies in the final simulation outcomes. This article thoroughly examines the geography, water systems, land use, hydrological processes, data availability, and management requirements. The research region was segmented into 29 sub-basins [27], as shown in Figure 3. In the classification of Hydrologic Response Units (HRUs) based on land use, soil type, and slope, sub-basins exhibiting identical combinations are categorized as the same HRU type, with the assumption that they have analogous hydrological behavior across sub-basins. The Land Use/Soils/Slope Definition tool can import the land use and soil datasets of the watershed and its sub-watersheds, analyze the combination and distribution of land use, soil, and slope types, and ultimately partition the study area into 116 Hydrologic Response Units (HRUs).
# Music Similarity Detection Through Comparative Imagery Data
## 3 Experimental Results: Validating the Framework
### Melodic Features  
In the literature, melodic features have been used to detect similarity. Our experiments confirmed, largely, the usefulness of the melodic feature. Figure 5 shows six heatmaps illustrating the use of the _melodic interval histogram_ to compare \"Happy Birthday\" with itself (a), four variations (b, c, d, e), and \"Jingle Bells\" \(\blacktriangleright\) ([[https://github.com/asaner7/imagery-music-similarity/blob/main/songs-in-paper/JingleBells.mp3](https://github.com/asaner7/imagery-music-similarity/blob/main/songs-in-paper/JingleBells.mp3)]([https://github.com/asaner7/imagery-music-similarity/blob/main/songs-in-paper/JingleBells.mp3](https://github.com/asaner7/imagery-music-similarity/blob/main/songs-in-paper/JingleBells.mp3)), accessed on 30 May 2025) (f). There were also comparisons with other types of variations and many other songs, and these six heatmaps are examples demonstrating the indicative power of the melodic feature.  
As shown in Figure 5a, a self-similarity heatmap of this feature usually has a diagonal line of bright pixels, which serves as a baseline reference for observing dissimilar patterns in other heatmaps. The transpose manipulation maintains such a pattern as shown in Figure 5b; hence, this type of variation will be considered as similar to the feature of the melodic interval histogram.  
From Figure 5c, we can see that the addition of ornaments to each bar of the song did not change the heatmap drastically, and substantial similarity is still obvious. In Figure 5d, it is clear that the addition of chords to each bar affected the heatmap more in comparison with 5c; the overall melodic similarity has still been captured, but the changes in the bars at the end were not detected, which suggests some weakness of the feature.  
Figure 5e indicates a major weakness of the feature of the melodic interval histogram in detecting genre changes, as there are no bright patterns at all. This suggests that genre changes might fool an ML model that relies too heavily on this feature. However, this observation may be biased by visual inspection. An ML model can still potentially learn to recognize other subtle patterns. Nevertheless, the risk should not be dismissed.  
In Figure 5f, we can observe some bright pixels, suggesting melodic similarity in the bars concerned. These bright patterns appear in different regions of the heatmap and do not form a diagonal line or a block of pixels. This suggests that the similarity is either in an isolated location (e.g., location [\(x=7,y=1\)]) or related to a single bar (e.g., [y = 4]).  
The other melodic features in Table 1 did not result in heatmaps as effective as the melodic interval histogram. The resultant heatmaps were either completely bright or there was almost no difference between similar cases (i.e., variations of \"Happy Birthday\" ) and dissimilar cases (i.e., \"Jingle Bells\" and some other songs). We therefore prioritize the melodic interval histogram for our data collection and ML development effort.
# Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
## 5 Results
### Estimated Coconut Damages  
Using Equation (5), we estimate how many trees were damaged between the two considered time-steps. The estimated number of healthy coconut tree canopies in our study area was 58.4 M before and 44.3 M after the passage of the typhoon, respectively. Meaning  
\begin{table}
\begin{tabular}{l r r} \hline \hline \(\tau\) & **Recall (\%)** & **MAE** \\ \hline
0.10 & 46.68 & 1.08 \\
0.20 & 64.92 & 2.16 \\
0.40 & 95.50 & 5.33 \\
**0.45** & **98.20** & **5.91** \\
0.50 & 99.39 & 6.26 \\
2.00 & 100.00 & 6.50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean absolute error and recall for different levels of \(\tau\) in the validation areas. Marked in **bold** is the selected \(\tau\) level.  
Figure 4: Qualitative example of coconut density estimation. Ground Truth (GT) in top row, prediction of our model in bottom row. Numbers in parentheses represent the sum of (intact) coconut trees in the area. Areas without GT (shown as gray) are excluded from both counts.  
that 14.1 M coconut trees were affected by the typhoon. We aggregated our results per state and observed the largest changes in Camarines Sur, with 3.5 M coconut trees estimated to have been damaged. Cataduanes, the point of first landfall, does not have a significant amount of coconut trees, see Figure 5. Notably, we observe a clear correlation between the level of damage and the proximity to the storm track as well as the temporal order along the track.  
Table 2 shows the top-15 provinces in terms of overall coconut crop yield (before the passage of Goni). In our change analysis we do also predict a few localised areas with new coconut plantings. In 31 of the 47 provinces in our region of interest the estimated changes lie within \(\pm\)0.1 million trees. In certain areas on Masbate island (which represents 6% of the coconut planted area), we observe regions with an apparent increase of coconut trees. These areas however appear to largely be false negatives in the _before_-map, where there are only few cloud free images.
# Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure
## 2 Data Collection and Methodology
The training process was carried out in WEKA software, which is an applicable tool for classification utilization and data mining. Note that many scholars have employed WEKA before for various simulating objectives [36,37]. To evaluate the efficiency of the implemented model, five statistical indices, namely the coefficient of determination (R\({}^{2}\)), mean absolute error (_MAE_), root mean square error (_RMSE_), relative absolute error (_MAE_ in %), and root relative squared error (_RRSE_ in %), were used to develop a color intensity ranking and present a visualized comparison of the results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class Number** & **Stability Description** & **FOS Range** & **Class Value** \\ \hline
1 & Unstable & (FOS \(<\) 1) & 1 \\
2 & Low & (1 \(<\) FOS \(<\) 1.5) & 2 \\
3 & Moderate & (1.5 \(<\) FOS \(<\) 2) & 3 \\
4 & High & (2 \(<\) FOS \(<\) 4) & 4 \\
5 & Very high & (4 \(<\) FOS) & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of stability classification. Figure 3: The k-fold cross-validation process. It should be noted that these criteria have been broadly used in earlier studies [38]. Equations (1)-(5) describe the formulation of R\({}^{2}\), _MAE_, _RMSE_, _RAE_, and _RRSE_, respectively. \[R^{2}=1-\frac{\sum_{i=1}^{s}{\left(Y_{i_{\textit{practical}}}-Y_{i_{\textit{ observed}}}\right)^{2}}}{\sum_{i=1}^{s}{\left  
The main task of MLR is to forecast the unknown terms (i.e., \(\rho_{0}\), \(\rho_{1}\),..., \(\rho_{\rm s}\)) of Equation (6). By applying the least-squares technique, the practical form of the statistical regression method is represented as follows [40]:  
\[y=p_{0}+p_{1}x_{1}+p_{2}x_{2}+\ldots+p_{\rm s}x_{\rm s}+\mu \tag{7}\]  
In the above equation \(p_{0}\), \(p_{1}\),..., \(p_{\rm s}\) are the approximated regression coefficients of \(\rho_{0}\), \(\rho_{1}\),..., \(\rho_{\rm s}\), respectively. Also, the term \(\mu\) gives the estimated error for the sample. Assuming the \(\mu\) is the difference between the real and modelled \(y\), the estimation of \(y\) is performed as follows  
\[\hat{y}=p_{0}+p_{1}x_{1}+p_{2}x_{2}+\ldots+p_{\rm s}x_{\rm s} \tag{8}\]
Note that, in Figure 1b, the parameter \(h_{j}\) is calculated as follows:  
\[h_{j}=\sum_{i=1}^{s}W_{ji}X_{i}+b_{j} \tag{9}\]  
where the terms \(W\), \(X\), and \(b\) stand for the weight, input, and bias of the utilized neuron. Also, the function\(f\) denotes the activation function of the neuron that is applied to produce the output.
## 5 Simulation Results and Analysis
### Comparison of Exploration Strategy in the Training Phase  
Figure 6 depicts the convergence performance of our proposed routing protocol that adopted different exploration strategies during the training phase. We compared the convergence rate of the \(\varepsilon\)-greedy and the IUCB exploration strategy. As shown, the variance in the PDR for the IUCB exploration strategy was approximately 30% smaller than that for the \(\varepsilon\)-greedy strategy. When exploration had been executed for a period of time, all the actions were nearly explored; then, the IUCB policy tended to greedily select the actions with the maximum Q-value, so the performance improved. This was in contrast to the \(\varepsilon\)-greedy strategy, which always randomly selected the next-hop node with a certain probability, resulting in a performance degradation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Movable region & 2 km \(\times\) 2 km \\ The number of UAVs & 40\(\sim\)140 \\ Communication radius & 350 m \\ Hello interval & 0.5 s \\ Packet sending rate & 1 Hz\(\sim\)5 Hz \\ Hello packet transmission power & 1 W \\ Data packet size & 1 kb \\ Wireless technology & IEEE 802.11 DCF \\ Maximum UAV speed & 5\(\sim\)25 m/s \\ Modulation & Modulation\_bpsk \\ Fading model & Nakagami\_m \\ Carrier frequency & 30 GHZ \\ Carrier wavelength & 0.01 m \\ Electromagnetic wave speed & 3 \(\times\) 10\({}^{8}\) m/s \\ Traffic type & Constant bit rate (CBR) \\ \(\mu\) & 0.6 \\ \(\
u\) & 0.4 \\ \(\delta\) & \(-\)0.5 \\ \(\varphi\) & \(-\)0.5 \\ Buffer size & 32 KB \\ Experience relay pool size & 2000 \\ Adam learning rate & 0.001 \\ \(\tau\) & 0.05 \\ \(\gamma\) & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.