esa-sceva/satcom-chunk-collection huggingface dataset:
# Damage Location Diagnosis of Frame Structure Based on a Novel Convolutional Neural Network
## 3 Fault Diagnosis Process
### Ceemdan  
In the process of wavelet de-noising, because part of the low-frequency feature information was filtered out, it is necessary to use the method of multi-scale modal decomposition to highlight the feature information in the reconstructed signals. EMD is the most commonly used multi-scale decomposition method, but it has the problem of mode aliasing. In order to solve this problem, an EEMD decomposition method was proposed by adding Gaussian white noise with positive distribution to the original signals [36]. Although EEMD can theoretically remove the Gaussian noise signal whose average value is zero, it cannot completely remove the added noise in practice. In order to solve the defects of EEMD and make EEMD adaptive to the noise added in the original data, so as to completely eliminate the added noise, CEEMDAN (Adaptive EEMD) was generated. The algorithm of CEEMDAN is as follows.  
1. Add Gaussian white noise signal obeying the normal distribution to the original signal \[x_{i}(t)=x(t)+\varepsilon_{0}w_{i}(t),\text{ i = 1,2,3\ldots T}\] (1) where \(x(t)\) is the original signal sequence, T is the number of times to add noise, and \(\varepsilon_{0}\) is the standard deviation of noise.
2. Calculate the first natural mode component IMF\({}_{1}(t)\) by using the EEMD algorithm \[\text{IMF}_{1}(t)=\frac{1}{\text{I}}\sum_{i=1}^{1}\text{IMF}_{i1}(t)\] (2)
3. Calculate the first residual margin \(r_{1}(t)\) \[r_{1}(t)=x(t)-\text{IMF}_{1}(t)\] (3)
4. E\({}_{i}(w_{i}(t))\) is defined as the \(i\)-th IMF component after EEMD, \(\varepsilon_{\text{j}}\) is the standard deviation of noise. The second IMF can be obtained by decomposing the sequence \(r_{1}(t)+\varepsilon_{1}\text{E}_{1}(w_{i}(t))\). \[\text{IMF}_{2}(t)=\frac{1}{\text{I}}\sum_{i=1}^{\text{I}}\text{E}_{1}(r_{1}( t)+\varepsilon_{1}\text{E}_{1}(w_{i}(t))\,)\] (4)
5. The calculation formula for the \(k\)-th residual component (\(k\) = 2, 3... K, K is the highest decomposition order) can be written as follows. \[r_{k}(t)=r_{k-1}(t)-\text{IMF}_{k}(t)\] (5) Therefore, the \(k+1\)-th IMF component can be obtained by Equation (6) as follows. \[\text{IMF}_{k+1}(t)=\frac{1}{\text{I}}\sum_{i=1}^{\text{I}}\text{E}_{1}(r_{k} (t)+\varepsilon_{k}\text{E}_{k}(w_{i}(t))\,)\] (6)
6. Repeat steps 4-6 until the residual signal cannot be decomposed and the residual signal \(R(t)\) satisfies the relationship \[R(t)= x(t)-\sum_{k=1}^{\text{K}}IMF_{k}(t)\] (7) So, the original signal can be expressed as \[x(t)= R(t)+\sum_{k=1}^{\text{K}}IMF_{k}(t)\] (8)
# Ferroelectric Materials: A Novel Pathway for Efficient Solar Water Splitting
## 4 Ferroelectric Materials for Photoelectrochemical (PEC) Water-Splitting Devices
### (5) PbTiO\({}_{3}\)  
Although a large number of studies have been performed based on PbTiO\({}_{3}\)-containing materials as photocathodes in PEC cells for hydrogen production, photoanodes composed of PbTiO\({}_{3}\) (PTO) are increasingly of concern. Specifically, there are numerous reports on nanostructured PTO because of their superior PEC properties, which are related to their randomly shaped particles [152; 153]. Moreover, many useful approaches based on heterojunctions from different semiconductors have resulted in improved photoactivity [154; 155; 156; 157; 158].  
Based on these two interpretations, [PERSON] et al. [159] synthesized vertically aligned core-shell PTO@TiO\({}_{2}\) heterojunction nanotube arrays fabricated on FTO glass by a three-step process that can help suppress the opaque photoanode problem reported in a previous study [91]. As expected, the directional charge transport in one-dimensional (1D) nanostructure and additional heterojunction effects between PTO and TiO\({}_{2}\) favor charge separation and enhanced PEC efficiency in PTO@TiO\({}_{2}\) photoanode-based systems. Recently, there was a report on the synthesis of nanotubular PTO-based photoanodes in PEC devices for the first time [160]. The investigation was performed with two synthetic electrodes of 1D nanotube arrays of PTO (NT-PTO) and Pt-dots@PTO nanotube arrays (PNT-PTO) in comparison with two reference electrodes made of PTO nanotubes (no Pt dots) and PTO powder (no nanotubes). The result presents that the NT-PTO photoanode achieved the highest photocurrent density of 64 \(\mu\)A/cm\({}^{2}\) at 1.05 V versus RHE. In comparison, the PTO powder-based photoelectrode exhibited a meager value of 8 \(\mu\)A/cm\({}^{2}\) at the same RHE potential. The enhanced PEC performance was elucidated by reducing the electron-hole recombination through the isolated oxidation sites at the external surface of the Pt-dots@PTO nanotubular structure.
## 4 Results and Discussion  
This systematic review of the integration of multi-criteria decision analysis (MCDA) and Artificial Intelligence (AI) in reverse supply chains of solid waste (RSCSW) within the circular economy framework reveals significant advancements in operational efficiency, cost reduction, and material recovery. The findings are structured around four main themes derived from the analysis of the 22 articles reviewed:  
1. Integration of MCDA and AI in RSCSW Optimization--Examination of how these methodologies enhance decision making and operational efficiency.
2. Operational Efficiency, Cost Reduction, and Material Recovery--Analysis of how AI-driven and MCDA-based techniques contribute to economic and environmental performance.
3. Framework and Roadmap for Optimizing RSCSW--Development of a structured integration model based on evidence from the reviewed studies.
4. Governance and Policy Barriers--Discussion of the regulatory, financial, and technological constraints affecting implementation.
# Bit Independence Criterion Extended to Stream Ciphers
## 3 BIC Algorithm in Stream Ciphers  
In this section, an algorithm is proposed to extend the bit independence criterion (BIC) to stream ciphers, experimentally confirming its effectiveness. The two main differences that arise in this scenario with respect to its application in S-boxes are discussed.  
Let \(f\) be the function that will be evaluated by the BIC, \(D=\{X_{1},\ldots,X_{l}\}\) the set of \(l\) inputs \(X_{r}\) of \(n\) bits generated randomly and \(m\) the number of bits of the outputs of \(f\), the proposed method consists of the following steps:  
**Step 1.**: Construct the \(n\) SAC \(H^{i}\), \((i=1,\ldots,n)\) matrices of dimension \(l\times m\).  
1. Evaluate \(Y_{r}=f(X_{r}),\ (r=1,\ldots,l)\), and generate the output \(Y_{r}\) of size \(m\).
2. Evaluate \(Y_{r}^{i}=f(X_{r}\oplus e_{i})\), and generate the output \(Y_{r}^{i}\) of size \(m\), where \(e_{i}\) is the canonical vector.
3. Build the avalanche vector \(V_{r}^{i}=Y_{r}\oplus Y_{r}^{i}=\{v_{r1}^{i},\ldots,v_{rm}^{i}\}\) of \(m\) avalanche variables \(v_{j}^{i},\ (j=1,\ldots,m)\).
**Step 2.**: Evaluate the independence between the avalanche variables \(v_{\cdot j}^{i}\) and \(v_{\cdot k}^{i}\).  
1. For each pair \((j,k)\), with \(1\leq j,\ k\leq m\) and \(j\
eq k\), measure the independence between the avalanche variables \(v_{\cdot j}^{i}\), \(v_{\cdot k}^{i}\) by a test statistic.
2. Set a significance level \(\alpha_{1}\) and decide, using a statistical criterion, if the observed value of the test statistic allows to reject or not the hypothesis of independence between \(v_{\cdot j}^{i}\) and \(v_{\cdot k}^{i}\).
3. Count the number \(T^{i}\) of rejections between \(C_{2}^{m}\) pairs of the matrix \(H^{i}\).
**Step 3.**: Decision on whether or not to comply with the BIC criterion:  
1. Count the total number \(T\) of rejections between the \(n\) matrices \(H^{i}\).
2. Set a significance level \(\alpha_{2}\)
3. Decide, using a statistical criterion, whether the observed value of \(T\) allows to reject the BIC compliance.  
The following sections describe each of these steps and end with the proposal of an algorithm to evaluate the BIC in stream ciphers.
## 7 Experiments
### Test 1: From Near to Far Zone of PRN 4  
In Test 1, the user moves from the near to the far zone of PRN 4. Figure 15 shows the test environment and the user trajectory.  
Figure 14: Test environment.  
Figure 15: Test 1: Test setup and user trajectory.  
Figure 16 shows the tracking results for the PRN 4 signal, comparing the ITL and VTL. After about 15 s, the user has moved away from PL PRN 4 into the far zone. The correlation power of ITL starts to decrease and then the user loses its tracking lock on PRN 4. Diverging pseudorange and range-rate measurements identify the loss of the lock.  
Compared with ITL, VTL tracks the signal continuously, even though the tracked signal power decreases while the user stays in the far zone. The pseudorange and range-rate measurements are estimated without diverging. When the user returns to the near zone after 60 s, correlation power was recovered without a reacquisition process and measurements are collected continuously. Thus, VTL estimation was successful and effective in tracking the signal affected by the near-far problem.
###### Abstract  
During the construction of tunnels using tunnel-boring machines (TBMs), the wear of disc cutters is an unavoidable process. The timely replacement of worm disc cutters can have a positive effect on construction time and therefore on costs. The wear of disc cutters can be assessed using various measurement methods. The aim of this article is to show different methods of measuring and evaluating the wear of disc cutters and to present their main advantages and disadvantages. In this study, four different wear-measuring methods were used and applied to a worn double disc cutter: a macroscopic visual inspection, profile measurement with a caliper, weighing, and close-range photogrammetry. The results of the measurements showed that the worn disc cutter was subject to normal abrasive wear, with local steel chipping occurring. Based on the close-range photogrammetry measurements, a profile of the worn disc cutter was also created and compared with the original profile. It was found that the best results for the wear assessment of a disc cutter can be achieved by using several measurement methods simultaneously. The integration of different measurement and evaluation methods is therefore recommended for a comprehensive understanding of disc cutter wear.  
TBM; tunneling; disc cutter; wear Article Investigation of Disc Cutter Wear in Tunnel-Boring Machines (TBMs): Integration of Photogrammetry, Measurement with a Caliper, Weighing, and Macroscopic Visual Inspection.
## 2 Related Works
### Deep Learning Models
#### 2.1.3 ResU-Net  
The ResU-net [16] architecture combines elements of the ResNet and U-Net architectures to improve semantic segmentation performance. ResU-Net has gained popularity as a basic model for semantic segmentation in various domains.  
The ResU-Net architecture consists of two main parts: the encoder and the decoder [2; 17; 20; 21; 22; 23]. The encoder part utilizes the ResNet architecture for feature extraction. ResNet's residual blocks can alleviate the vanishing gradient problem and enable more effective feature learning by incorporating skip connections. By using ResNet as the backbone in the encoder, ResU-Net improves the overall accuracy and feature representation capabilities of the model.  
In the decoder part, the architecture adopts the U-Net framework for feature fusion at different scales, as shown in Figure 1. The feature maps extracted from the end of the encoder are progressively fused with the corresponding feature maps from the previous layers in the decoder. This hierarchical fusion allows the model to combine both high-level and low-level features, capturing both global and local information. The fusion process improves the spatial precision and context understanding of the model. The lowest-order feature map is then subjected to further feature fusion, and the final prediction is obtained using a softmax function. This final prediction output represents the segmented regions in the input image.  
ResU-Net uses ResNet for feature extraction to capture more intricate and detailed features, enhancing segmentation accuracy, and the U-Net-based decoder for feature fusion and contextual understanding, leading to improved segmentation results. ResU-Net has demonstrated better performance compared to the original U-Net architecture in various semantic segmentation tasks across several fields, which has made it a widely adopted and effective model for semantic segmentation.
# Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture
###### Abstract  
In microseismic monitoring, the reconstruction of a reliable velocity model is essential for precise seismic source localization and subsurface imaging. However, traditional methods for microseismic velocity inversion face challenges in terms of precision and computational efficiency. In this paper, we use deep learning (DL) algorithms to achieve precise and efficient real-time microseismic velocity modeling, which holds significant importance for ensuring engineering safety and preventing geological disasters in microseismic monitoring. Given that this task was approached as a non-linear regression problem, we adopted and modified the Attention U-Net network for inversion. Depending on the degree of coupling among microseismic events, we trained the network using both single-event and multi-event simulation records as feature datasets. This approach can achieve velocity modeling when dealing with inseparable microseismic records. Numerical tests demonstrate that the Attention U-Net can automatically uncover latent features and patterns between microseismic records and velocity models. It performs effectively in real time and achieves high precision in velocity modeling for Tilted Transverse Isotropy (TTI) velocity structures such as anticlines, synclines, and anomalous velocity models. Furthermore, it can provide reliable initial models for traditional methods.  
microseismic; velocity model; deep learning; Attention U-Net Article
## 2 Materials and Methods
### Overall Signal Processing Scheme of RF Signals  
In the proposed architecture, one set of RF signals scanned from a single patient generates four parametric images, namely B-mode, entropy, phase, and attenuation images, representing distinct tissue characteristics in either the time or frequency domains. Figure 4 shows the overall scheme of the multiple parametric images generated from the raw RF signals. The B-mode image is the most common image that provides the echogenicity of the scanned region of the soft tissue and is obtained directly from a set of envelope signals. While the entropy image expresses the local texture patterns related to the microstructural information of the tissue, the phase image shows the morphological information about the anatomical structure of the scanned area. The attenuation image is an example of the Q-mode image used in this work and represents the spectral property of tissue that is not easily visible in the time-domain representation. All the parametric images generated from RF signals represent complementary tissue information and provide rich information to the CNN-based deep learning network in terms of image enhancement and data augmentation.
## 3 Methodology
### Feature Extraction and Selection
This process reduced noise, improved the signal-to-noise ratio (SNR), and preserved essential diagnostic features. The results show that the denoised signals were diagnostically useful. This filtered signal was further processed to separate harmonic and percussive components with margin 4, enhancing the SNR and accuracy of subsequent analyses and diagnoses. For better classification between abnormal and normal PCGs, extracting features that contain the essence of the unsegmented audio is very important. Therefore, for this work, the PCG audio files (Table 6) were processed to extract features using Librosa music and audio analysis API [48]. The feature extraction required three steps, starting with preprocessing each file for basic noise removal using the DWT, and then the denoised signal was decomposed further for HPSS with margin 4. The HPSS margin value selection was carried out through an iterative process based on preliminary experiments and performance observations. The margin value of 4 was found to provide a balanced separation between harmonic and percussive components. Figures 6 and 7 demonstrate the comparison of time-frequency signal representation for abnormal VS normal patients. Additionally, Figures 8 and 9 demonstrate the waveplot comparison of abnormal patient HPSS original VS noise reduced PCG signal. Whereas Figures 10 and 11 demonstrate the waveplot comparison of normal patients. Following this, both harmonics and percussive signals were separately processed to extract the Chroma STFT (pitch), Chroma CENS (tempo), MFCC, Chroma CQT (frequency domain), root mean square energy (signal magnitude), spectral centroid (weighted mean on the spectrum), spectral bandwidth (variance in spectral centroid), spectral roll-off, zero crossing rate (change in signal), and applicable statistical features.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 2 Background
### Energy Readrage Techniques
#### 2.3.3 Wireless Recharging  
In this method, there is no need for the UAV to descend to the GCS to recharge its batteries, as various wireless power transfer techniques can be used, classified according to their transmission range. These techniques fall into two main categories: (i) near-field transmission and (ii) far-field transmission [20].  
For distances of less than one meter, near-field methods such as capacitive power transfer (CPT), inductive power transfer (IPT), and magnetic resonance coupling (MRC) are used. CPT is based on the flow of an electric field between two parallel plates separated by a dielectric material, which produces low power losses in metallic environments and is suitable for low-power applications. IPT, on the other hand, works on the weakly coupled transformer principle, using coils for transmission and reception and power electronic converters at both ends. This technology operates at KHz frequencies. MRC, an improvement on IPT, introduces an intermediate coil between the transmit and receive coils, minimizing losses and allowing operation at MHz frequencies. In addition, MRC is ideal for applications where multiple loads need to be powered simultaneously, such as charging multiple UAVs [20].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristics** & **Ni-Cd** & **Ni-Mh** & **LiPo** & **Li-S** \\ \hline
**Specific energy (Wh/kg)** & 40 & 80 & 180 & 350 \\ \hline
**Energy density (Wh/L)** & 100 & 300 & 300 & 350 \\ \hline
**Specific power (W/kg)** & 300 & 900 & 2800 & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different batteries [20].  
Figure 2: Swapping (conventional) or hot-swapping method, based on [20].  
Regarding far-field transmission, distributed laser charging facilitates the transfer of energy using a laser beam. This technique can deliver up to 2 W of power over a range of 5 m. However, it requires a clear line of sight between the transmitter and receiver, as any obstruction will significantly affect the energy transfer [20]. The process involves a generator emitting a laser beam with a specific frequency and wavelength, which is directed at a photovoltaic cell installed on the UAV. This cell converts the laser beam into electrical energy to recharge the batteries. It is essential that the UAV is at a suitable altitude and that a device capable of identifying the point of maximum laser power is used to optimize the energy transfer, as shown in Figure 3[19].
## 3 Results
### Evolution of Algorithm Types over Time  
Figure 5 presents the yearly distribution of algorithm types used in the reviewed articles from 2015 to 2024. A notable trend can be observed: traditional algorithmic approaches dominated the earlier years, while machine learning and deep learning methods gained prominence after 2019, peaking in 2023.  
The adoption of algorithmic methods for fault detection and diagnosis in power plant systems has evolved significantly over the last decade. Figure 5 illustrates the distribution of algorithm types across the publication years from 2015 to 2024.  
In the early years (2015-2018), research was dominated by traditional statistical techniques such as Principal Component Analysis (PCA), Fault Detection and Isolation (FDI) schemes, and rule-based logic systems. These methods, while robust and explainable, were limited in handling nonlinear behaviors and complex multivariate interactions commonly present in large-scale energy systems.  
From 2019 onward, there was a marked increase in the use of machine learning (ML) and deep learning (DL) models. Articles published during this period increasingly incorporated classifiers like Support Vector Machines (SVM), Random Forests, and neural networks, reflecting both the growing availability of data and the accessibility of ML frameworks. This trend coincides with the broader digital transformation in industry and academia, where intelligent monitoring became a priority.  
Figure 5: Evolution of algorithm types over time.  
Notably, the years 2022-2024 saw an explosion in the use of deep learning architectures--including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). These models were frequently used in settings where sensor data, vibration signatures, thermal imagery, or multivariate time series needed to be analyzed in real time.  
While still less common, hybrid methods (combining physics-based models with data-driven approaches) began to emerge in the last three years. These aim to leverage the explanatory power of mechanistic models with the adaptability of learning systems, especially in safety-critical environments like nuclear or high-efficiency thermal power plants.  
Overall, this temporal analysis reveals a clear shift from interpretable, deterministic models to flexible, high-performance learning systems, signaling a maturation of the field and a growing alignment between industrial diagnostic needs and algorithmic sophistication.
# Crude Oil Price Forecasting Model Based on Neural Networks and Error Correction
## 5 Conclusions and Future Researches  
### Conclusions  
Efficient and accurate crude oil price forecasting contributes to global economic development. A new model using ensemble deep reinforcement learning with error correction is proposed in this article. The article's conclusions are as follows. First, deep neural networks with multiple hidden layers effectively handle non-stationary data and extract complex information. Among the base predictors, TCN, GRU, and ESN achieved superior forecasting accuracy. Second, the ensemble method capitalized on the complementary strengths of the base predictors, improving overall forecasting performance. The RL agent learned the optimal strategy and automatically adjusted it according to the objective, enabling the system to maximize returns or fulfill predefined goals. Hence, RL could autonomously learn and make decisions compared with heuristic algorithms. Additionally, the decomposition method effectively mitigated the nonlinearity of the original series. EWT adaptively partitions the raw data into multiple subsequences, enhancing the forecasting accuracy of the SARSA-TCN-GRU-ESN model. Furthermore, employing the ECM enhanced forecasting performance by extracting predictable components from the ensemble model's residuals, thus reducing prediction errors. Lastly, the proposed model exhibited robust forecasting performance during emergencies. Compared with three state-of-the-art models, it more precisely captured volatility patterns at mutation points, underscoring its promising potential in crude oil price forecasting.