esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Corrosion of RCS
### Prediction of the Service Life of RCS  
Corrosion modeling of reinforcing steel is one of the methods that has been gaining increasing interest in the last two decades [4; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41]. Modeling the corrosion process will allow the assessment of the current state of the existing RCS, improve the stability and help to extend the service life of RCS. Most of the models developed are related to the penetration of Cl\({}^{-}\), therefore with the initiation phase of corrosion [26; 28; 31; 32; 33; 34].  
The analysis of the behavior of RCS after corrosion initiation is important in order to efficiently select the maintenance and repair strategies. Furthermore, for an improved assessment of the conditions of the existing structures, the knowledge of the amount of reinforcing steel lost due to corrosion is crucial. The propagation period depends on the \(i_{corr}\), which in turn depends on the temperature, relative humidity, resistivity of the concrete and the presence of oxygen near the reinforcement. To improve the actual predictive models for the corrosion propagation period, data on \(i_{corr}\) values measured in existing structures are necessary. Therefore, recording changes in \(i_{corr}\) in real structures is an essential aspect to support the formulation and validation of a model for the propagation period. Therefore, it is crucial to develop reliable sensors to embed in existing structures that are able to detect its real state, enable the design of repair and maintenance strategies, and the prediction of the service life of RCS [38; 41; 42; 43; 44].
# Monitoring System of Transmission Line in Mountainous Area Based on LPWAN
## 4 Experiment and Simulation Analysis
### System Life Calculation  
In order to verify that the proposed system has lower energy consumption, we used the LoRawan terminal life calculation tool to calculate the service life of the LoRa terminal in monitoring mode, early warning mode, and maintenance mode, respectively. The experimental results are shown in Table 5.  
According to the above test results, the monitoring mode can effectively reduce the energy consumption of the system. The energy consumption of the early warning mode and the maintenance mode is relatively high, especially in maintenance mode, which consumes 9000 mA battery energy after 142 h of operation. The transmission line online monitoring system proposed in this paper has multiple working modes, and the actual working life of the system can be calculated according to the distribution of the fault rate. Taking the fault trip rate of the transmission line in the southwest mountainous area of the State Grid of China from 2011 to 2013 as an example, the fault rate and line length are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Working Mode**} & **Transmission** & **Received** & \multirow{2}{*}{**Loss Rate**} & **Communication** \\  & **Distance/km** & **Packet** & & **Quality Requirements** \\ \hline \multirow{3}{*}{Monitoring mode} & 1.5 & 940 & \textless{}6\% & \multirow{3}{*}{Low} \\  & 1.5 & 957 & \textless{}5\% & \\  & 1.5 & 959 & \textless{}5\% & \\ \hline \multirow{3}{*}{Early warning mode} & 3 & 982 & \textless{}3\% & \multirow{3}{*}{Normal} \\  & 3 & 985 & \textless{}3\% & \\ \cline{1-1}  & 3 & 980 & \textless{}3\% & \\ \hline \multirow{3}{*}{Maintenance mode} & 5 & 998 & \textless{}1\% & \multirow{3}{*}{High} \\  & 5 & 997 & \textless{}1\% & \\ \cline{1-1}  & 5 & 995 & \textless{}1\% & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Communication test results of three working modes of the system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Reporting Interval/s** & **Packet Size/bit** & **Battery Life/year** \\ \hline Monitoring mode & 300 & 8 & 18.611 \\ Early warning mode & 5 & 24 & 0.090 \\ Maintenance mode & 1.2 & 36 & 0.016 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Service life of three working modes.  
The online monitoring system of the transmission line proposed in this paper can greatly prolong the service life of the system under the monitoring mode. Taking the failure rate of transmission lines in mountainous areas provided in Table 6 as an example, it can be seen that the monitoring system works 82% of the time in a year of daily monitoring mode, which can reduce the energy consumption by 80% compared with the online monitoring system with a single working mode. The simulation results showed that the monitoring system proposed in this paper can have a working life of up to 15.13 years.
## 3 Results and Discussion
### Analysis of Water Retention of Non-Ionic Fluorinated Dust Suppressants  
2.1 Study on the Change Law of Coal Dust Weight after the Action of Anionic Fluorine-Containing Solution  
As shown in Figure 5, the weight of the samples at each concentration demonstrates an overall decreasing trend from 0 h to 60 h. The weight change between the FS-30 solutions at each concentration can be visually compared, where slower weight decrease indicates greater water retention capacity. The FS-30 solution exhibits the highest water retention at a concentration of 0.2%, followed by a concentration of 0.05%. The weight of coal dust varies at each concentration of FS-31, but all show a tendency for the weight of coal dust to decrease with time. The highest initial weight concentration is 0.009% at around 42 g, and the lowest initial weight concentration is 0.04% at around 34 g. The highest weight of coal dust at 60 h is around 33 g (0.03%), and the lowest weight is around 25 g (0.04%). The FS-34 solution shows an overall decreasing trend in sample weight from 0 h to 60 h at all concentrations, with the solution retaining the most water at a concentration of 0.3%, followed by the 0.08% solution. The FS-50 solution demonstrates a decreasing trend in water retention over time at all concentrations. At concentrations of 0.01% to 0.04%, the weight loss of the dust suppression solution roughly show a trend of decreasing, then increasing, and then decreasing again. At concentrations of 0.05% to 0.3%, all show a fluctuating trend of decreasing, then increasing, and then decreasing and increasing again. The arbitrary concentration occurs in 24-36 h, when the weight loss is the largest. Water retention is an important indicator of the performance of dust suppressants. Reasonable deployment of the concentration for the control of water retention solution plays an essential role in achieving water retention by making reasonable use of resources.
## 2 Theoretical Framework and Methods
### Uncertainty Measurement Principle
#### 2.2.1 Uncertainty Set  
The establishment of uncertain sets is aimed at studying uncertain mathematics, inheriting the characteristics of fuzzy sets and gray sets. When \(0\leq a\leq b\leq 1\) is satisfied, there exists a non-negative uncertain number \(F(x)\) less than \(1\) on \([0,1]\), mathematically represented as \(\{[a,b],F(x)\}\). The set composed of all \(F(x)\) is called an uncertain set, denoted as \(H_{[0,1]}\), as shown in Equation (1):  
\[H_{[0,1]}=\Big{\{}\Big{\{}[a,b],F_{(X)}\Big{\}}\Big{|}a\geq 0,a\leq b\leq 1 \Big{\}} \tag{1}\]  
If \(C\) is an uncertain set, \(U\) is the domain, and \(C\) is mapped (membership function) \(\mu:U\to H_{[0,1]},\ u\rightarrow\mu(u)\in H_{[0,1]}\), and \(u\in U\) represent the combination of elements \(u\) in the domain \(U\) with the uncertain numbers in the uncertain set \(H_{[0,1]}\), the membership degree of \(u\) for C is denoted as \(\mu(u)\), and the uncertain set C with \(\mu(u)\) as the membership function is denoted as \(C_{\mu(\mu)}\).
## 3 Uncertainty Quantification Model
### Model Validation by Monte Carlo Simulation (MCS)
First and foremost, the collocation method had demonstrated its effectiveness in addressing uncertain parameters, a critical aspect when dealing with the variability of travel times. This effectiveness lies in its ability to produce precise estimates of travel time variability, even when working with relatively smaller sample sizes. One of the significant advantages of the collocation method was its efficiency in capturing the essence of travel time uncertainty. While the Monte Carlo simulation (MCS) is a valuable technique, it often requires a substantial number of random samples to achieve accurate results. In contrast, the collocation method excels in providing reliable estimates without the need for an extensive sample pool. This efficiency translates  
Figure 4: PDF using the collocation method. Figure 3: PDF using the MCS. into resource savings, making it an attractive choice for practical applications. Moreover, in the context of distribution logistics, where decisions must often be made in real-time, computational efficiency is paramount. The collocation method's computational speed outpaces that of the MCS, allowing for quicker analysis and decision-making. This attribute aligned perfectly with the demands of our study, where timely logistics decisions could significantly impact the efficiency and effectiveness of supply chain operations.
# Time-Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks
###### Abstract  
Existing deep learning-based seismic signal denoising methods primarily operate in the time domain. Those methods are ineffective when noise overlaps with the seismic signal in the time domain. Time-frequency domain-based deep learning methods are relatively rare and usually employ single loss function, resulting in suboptimal performance on low SNR signals and potential damage to P wave. This paper proposes a method based on generative adversarial networks (GANs). Compared to convolutional neural networks, the discriminator in GANs helps retain more true signal details by judging denoising performance. Additionally, an attention mechanism is introduced to fully extract signal features, and a perceptual loss is employed to evaluate the difference between the denoised result and the target's high-level features. Experimental results show that this method can effectively improve SNR and ensure that the denoised result is close to the true signal. Furthermore, by comparing DeepDenoiser and ARDU, it is proven that the proposed method achieves better denoising performance, especially for low SNR signals, while causing less damage to the seismic signals.  
seismic signal; noise reduction; generative adversarial network; perceptual loss Article
# Model Checking Resiliency and Sustainability of In-Vehicle Network for Real-Time Authenticity
## 6 Implementation and Experiments  
In this section, the implementation and experimental results of MAuth-CAN are provided to check whether Theorem 1 and 2 proved in the formal analysis are applicable to the CAN testbed considering real CAN traffic with message authentication. In the experiment of MAuth-CAN, we adopt the BLAKE2S algorithm with keyed mode for implementation of message authentication code, which is used to generate authentication tags for CAN messages and report messages. BLAKE2S is a cryptographic hash function which is faster than keccak (SHA3) in software implementations. The security proof of BLAKE2S with keyed mode is referred to [31]. We tested the implemented source codes on the Raspberry Pi 3 Model B and Arduino Zero that are assumed to be the authenticator and the normal ECUs, respectively.
## 3 Proposed Watermarking Framework
### Overall Watermarking Scheme  
Figure 1 shows the overall structure of the proposed digital watermarking scheme: (a) for the embedder, (b) for the extractor, both relatively conventional. Our scheme is designed to process only one channel that Y component after converting RGB image to YCbCr components is used. Before entering it, it is normalized to the range of \([-1,1]\). Meanwhile, the WM data is binary data, and it is scrambled with a key. Both normalized host image data and scrambled WM data are preprocessed, and the results are concatenated. Here, the WM data is multiplied by a strength scaling factor (\(s\)), to adjust the trade-off between invisibility and robustness. The concatenated result is processed in the embedding network to output the watermarked data, which is de-normalized and converted to RGB format with the converted Cb and Cr components to form the watermarked host image.  
The extraction process receives a watermarked and attacked RGB image, which is converted to YCbCr format. Only the Y component is taken and normalized to the \([-1,1]\) ranged data processed in the extractor network. It extracts the WM information as the output, and the result is de-scrambled with the key used in the scrambling process. The de-scrambled data is the final extracted WM.
# A Review of Deep Learning Based Speech Synthesis
## 4 Deep Learning Based Speech Synthesis
### Deep Bidirectional LSTM-Based Speech Synthesis
#### 4.4.2 Deep BLSTM-Based Speech Synthesis  
When using a deep BLSTM-based (DBLSTM) model to predict acoustic parameters, first we need to convert the input text prompt into a feature vector, and then use the DBLSTM model to map theinput feature to acoustic parameters. Finally, the parameter generation algorithm is used to generate the acoustic parameters and a vocoder is utilized to synthesize the corresponding speech. For instance, the authors in [48] proposed a multi-task learning [50,51] of structured output layer (SOL) BLSTM model for speech synthesis, which is capable of balancing the error cost functions associated with spectral feature and pitch parameter targets.
# Crop Disease Diagnosis with Deep Learning-Based Image Captioning and Object Detection
## 3 Materials and Methods
### Structure of the Crop Disease Diagnosis Solution
#### Transformer  
Transformer is a model that adopts the Attention mechanism from seq2 seq's Encoder-Decoder structure. This model shows better performance than RNN without using RNN. RNN-based seq2 seq models squeeze the input sequence into a single context vector, and the decoder generates an output sequence through this context vector. However, this structure loses some information of the input sequence while the encoder compresses the input sequence into a context vector. Therefore, Attention was used to correct it [30]. Encoder-Decoder structure with Attention allows Transformer to achieve better parallel processing and thus make the model faster than RNN [31].  
The Transformer model obtains the positional information of the input sequence through positional encoding. Existing RNNs allow input values to be entered in sequential order, but Transformers use a positive encoding technique and, therefore, cannot be entered in sequential order. As shown in Figure 7, Transformer also utilizes three attentions, which are Self-Attention in encoders, Masked Self-Attention in decoders, and a vanilla Attention layer.  
In this study, the feature points of the image extracted through the InceptionV3 model can also be used as an input value of the transformer.
## 4 Establishment of Double-Coupled Duffing Weak Signal Detection System
### Variable Scale and Phase Shift Processing of Double-Coupled Duffing System  
In order to facilitate the detection of any frequency signal in engineering applications, the time variable \(\tau\) is introduced to rewrite the mathematical model of a Duffing system. The time scale is transformed as follows [26]:  
Define \(t=\omega\tau\), where \(\tau\) is a generalized time variable. Then,  
\[\dot{x_{1}}=\frac{dx}{dt}=\frac{1}{\omega}\frac{dx}{d\tau}\quad  
Make the total driving force greater than the chaos threshold. Because \(A_{0}\gg A\), when \(\varphi\) is in the range of \((-\pi/2,\pi/2)\), the amplitude of the total driving force of the system is greater than the chaos threshold. At this time, it can be detected correctly. Otherwise, the same frequency signal will reduce the amplitude of the total driving force, causing false detection.  
According to the above analysis, a single calculation cannot detect the signal to be measured at any initial phase angle, and multiple calculations are required. In order to eliminate the detection blind area so that multiple calculations can detect the full range of phase angles, different initial phase angles can be used, or the signal to be measured can be phase-shifting. Since two calculations can cover all phase angles, this paper uses the method of two calculations to shift the phase of the signal to be measured. In the first calculation, the signal to be measured is not processed. In the second calculation, the signal is inversed as a whole, which is equivalent to adding a phase angle of \(\pi\) to the signal. In this way, the second calculation can detect the detection blind area in the first calculation, thus avoiding misjudgement.  
If the same frequency signal is included, one of the two calculations will make the state of the double-coupled Duffing system jump. Therefore, after the two variance jump rates are calculated, the maximum variance jump rate is retained. If the maximum variance jump rate is greater than the jump rate threshold, it is proved that the same frequency signal is detected.
## 3 Results
### Shrinkage Reduction Mechanism of DE  
According to the above experiments, replacing part of the cement with the same mass of DE can significantly reduce the autogenous shrinkage of cement mortar while maintaining the 28 d compressive strength, thereby improving the early crack resistance of cement mortar. The mechanism analysis is as follows.  
1. SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) in DE can undergo secondary hydration reactions with Ca(OH)\({}_{2}\) to produce hydration products such as C-S-H and expansion-type AFt, compensating for some of the shrinkage. SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) in DE have high contents and can react with Ca(OH)\({}_{2}\) to produce ettrigite, which prolongs the secondary expansion period of mortar autogenous shrinkage.
2. Replacing part of the cement with DE reduces the activity of the cementing material, thus reducing autogenous shrinkage from the source. Autogenous shrinkage is caused by the hydration of cementing materials such as cement. Although the main component of DE is silicon dioxide, its limited activity allows it to participate in some hydration reactions.
3. The slow release of water in DE increases the relative humidity inside the cement mortar and reduces the driving force of autogenous shrinkage. DE contains a large number of tiny pores and has good water absorption and release properties, which enable it to slowly release water during the hardening process of cement mortar, thereby increasing the internal relative humidity of cement mortar (see Figure 4) and reducing the driving force of autogenous shrinkage.
# SMC-CPHD Filter with Adaptive Survival Probability for Multiple Frequency Tracking
###### Abstract  
We propose a sequential Monte Carlo-based cardinalized probability hypothesis density (SMC-CPHD) filter with adaptive survival probability for multiple frequency tracking to enhance the tracking performance. The survival probability of the particles in the filter is adjusted using the pre-designed exponential function related to the distribution of the estimated particle points. In order to ensure whether the proposed survival probability affects the stability of the filter, the error bounds in the prediction process are analyzed. Moreover, an inverse covariance intersection-based compensation method is added to enhance cardinality tracking performance by integrating two types of cardinality information from the CPHD filter and data clustering process. To evaluate the proposed method's performance, MATLAB-based simulations are performed. As a result, the tracking performance of the multiple frequencies has been confirmed, and the accuracy of cardinality estimates are improved compared to the existing filters.  
multiple frequency tracking; SMC-CPHD filter; probability of survival; cardinality compensation; inverse covariance intersection +
Footnote †: journal: Applied sciences
## 3 Simulation Signal Analysis  
### The Role of Permutation Entropy on Signal Randomness Detection  
The embedding dimension \(m\) and delay time \(\tau\) should be determined in permutation entropy calculation firstly. [PERSON] (1976) recommended that the embedding dimension \(m\) should be in the range of 3-7. If \(m\) is too small, the reconstructed vector contains very little state and the algorithm loses its meaning. If \(m\) is too large, the reconstruction phase space will homogenize the time series, which will not only extend the computation time, but will also be unable to reflect subtle changes in sequence. Thus, the parameter \(m=6\) is selected in this paper. Due to the fact that delay time \(\tau\) has little impact on the operation sequence, \(\tau=1\) is chosen here. To validate the effect of permutation entropy on signal random detection, the harmonic signal, modulation signal and Gaussian white noise signal were analyzed, respectively. The expressions of harmonic signal and modulation signal are listed as follows:  
\[s_{1}=\sin(2\pi f_{1}t) \tag{19}\]  
\[s_{2}=\left[1+0.5\text{sin}(2\pi f_{2}t)\right]\sin(2\pi f_{3}t) \tag{20}\]where \(N=2048\), \(f_{1}=500\) Hz, \(f_{2}=5\) Hz, \(f_{3}=10\) Hz. \(s_{3}\) is the standard Gaussian white noise. According to the definition of arrangement entropy, the values of three simulated signals are chosen as 1.4429, 1.1938 and 6.3648, respectively. The result indicates that there is great difference among the permutation entropy values of noise signal, harmonic and modulation signal. Generally, the permutation entropy value of the noise signal is much larger than harmonic signal and modulated signal. Therefore, the permutation entropy can be used to effectively evaluate the actual denoising.
## 4 Methodology  
As illustrated in Figure 1, our framework consists of three components, the THLR memory prediction module, a simulation environment, and a DRL-based spaced repetition algorithm. The THLR memory prediction module is used for estimating the recall probability of learning items, taking into account the probability history, recall history, and interval history. The simulation environment mimics a learner's daily review process, considering both inter-day and intra-day dynamics. The DRL-based spaced repetition optimization utilizes a DQN with an LSTM mechanism to learn the optimal review intervals for effective long-term memory retention. The problem formulation and detailed descriptions of the three components are given below.