esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Adaptive FCM with Cooperative Multi-Population Differential Evolution Optimization
## 2 Background
### Initialization of Cluster Prototypes  
In most implementations of FCM-AO, the cluster centers are randomly initialized. The random initialization affects the accuracy and running time of the algorithm, especially in datasets where natural clusters overlap. Based on the initial choice of cluster prototype, the algorithm can either quickly converge or get trapped in local minima. A subtractive clustering algorithm has been used to find the initial cluster centers in [27; 28]. However, there are several parameters that need to be set to get the desired outcome, which are not trivial. A cluster prototype initialization based on a density cluster algorithm is presented in [29]. A fuzzy entropy algorithm is proposed with a hybrid FCM algorithm to initialize cluster centers in [30]. An improved FCM algorithm based on morphological reconstruction and affiliation filtering (FR FCM) which can determine the number of clusters while optimizing initial cluster prototypes has been presented in [31].  
Metaheuristic methods such as evolutionary algorithms including genetic algorithms [32; 33], particle swarm optimization, and differential evolution [34] have been used for cluster prototype initialization. A detailed recent review of metaheuristics used to solve the problem of cluster prototype initialization for FCM in the area of image segmentation can be found in [35]. A single population differential evolution algorithm called automatic differential evolution-based fuzzy clustering (ADEFC) was used to optimize initial cluster prototypes and number of clusters in [9]. The multiple population genetic algorithm (MPGA) to optimize cluster prototypes using multiple evolving populations was proposed in [36]. Each subpopulation is allowed to evolve independently; however, a migration operator is used to relate the individually evolving populations. Recently, the derivative multi-population genetic algorithm (DMGA) was proposed in [37] which first initializes the population by using a derivative operator before each subpopulation is evolved using canonical genetic operators. The probabilities of the genetic operators are dynamically selected by an adaptive probability fuzzy control operator. The quality of the initial cluster prototypes is shown to be superior to those found using MPGA. However, these methods are very resource intensive for big datasets.
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 3 Pretreatment of Feedback to Enhance Biogas Production
### Thermal Treatments  
This approach enhances the anaerobic process through hydrolyzing complex organic constituents, where the structure of the materials (mainly the cell membrane) is disintegrated under a certain temperature and pressure. Nowadays, different thermal pretreatment techniques using various temperatures (50-250 \({}^{\circ}\)C) are being studied and employed to improve the hydrolysis rate of AD feedstock. Among them is liquid hot water treatment, where the water is maintained as a liquid at high temperatures (160 to 230 \({}^{\circ}\)C) and under high pressures (\(>\)5 MPa) [55; 62]. Another option is the use of thermal pretreatment as \"an isochoric or constant volume process\", where the material is placed in a sealed container and heated without applying extra external pressure [72]. On the other hand, steam treatment involves exposure of the biomass to high temperatures of up to 240 \({}^{\circ}\)C and pressure for a few minutes, thereby causing disruption in the structure of the material [55; 73]. Another method is autohydrolysis, which is fundamentally based on hydrolyzing hemicelluloses using highly pressurized liquid hot water at 200 \({}^{\circ}\)C.  
Besides high energy demand and operation at high pressure, the main disadvantage of these processes is the potential formation of inhibitors, such as furfural and soluble phenolic compounds, which inhibit the production of methane [55]. Concerning the lignocellulosic substrates, temperatures exceeding 160 \({}^{\circ}\)C cause not only the solubilization of hemicellulose but also the solubilization of lignin. The released compounds are mostly phenolic compounds that are usually inhibitory to anaerobic microbial populations [55]. The formation of inhibitors during liquid hot water pretreatment is relatively low because of the high water input [76]. In order to minimize the formation of these inhibitors, the addition of external compounds is needed to keep the pH in the range of 4 to 7 [55]. Thermal pretreatment at high temperatures (\(>\)170 \({}^{\circ}\)C) might lead to the creation of chemical bonds and result in the agglomeration of the particles [77]. One of the most known phenomena is the Maillard reaction, which occurs between carbohydrates and amino acids, resulting in the formation of complex substrates that are difficult to biodegrade. This reaction can occur in extreme thermal treatment at temperatures exceeding 150 \({}^{\circ}\)C, or with longer treatment times at lower temperatures (\(<\)100 \({}^{\circ}\)C) [1; 53; 54; 78]. \begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline  
The aforementioned technologies have been applied as pretreatment methods in anaerobic digestion, and Table 1 provides some examples where these technologies have been successfully employed. However, it is worth mentioning that a combination of different technologies has been employed in many cases.
## 4 Result and Discussion
### Latency Characterization
Figure 9a, depicts the packet interarrival time for control signals and Figure 9b illustrates for the force feedback signals.
# Conceptual and Technical Design Aspects of Accelerators for External Injection in LWFA
## 5 Challenges for Beam Stability, Control and Diagnostics
### Position and Pointing Jitter  
In a plasma, a mismatch \(\Delta x\) of the transverse position between the external beam and the drive laser, which creates the plasma channel, will introduce a rapid projected emittance growth [27; 29]  
\[\frac{\Delta\varepsilon}{\varepsilon_{0}}\propto\left(\frac{\Delta x}{ \sigma_{x_{0}}}\delta\right)^{2} \tag{17}\]  
due to the large focusing strength. In the equation above, \(\sigma_{x0}\) is the beam size at the entrance of the plasma chamber, \(\varepsilon_{0}\) and \(\delta\) are the beam emittance and energy spread. Therefore, the ratio between the beam centroid displacement and the beam size is important. A mismatch of the divergence between the external beam and the drive laser can also result in a transverse position mismatch, which sets an limit on the length of the plasma channel. For instance, a beam will have a transverse displacement of 10 \(\upmu\)m after traveling for 10 cm with an initial divergence of 100 \(\upmu\)m. For the strongly focusing optics upstream of the plasma, a divergence of 100 \(\upmu\)m can easily occur. The divergence of a beam after a focusing thin lens with a focal length \(f\) would then become  
\[<x^{\prime}>=-<x_{0}>/f+<x_{0}^{\prime}>, \tag{18}\]  
and the pointing jitter of the beam at the focal point is given by  
\[\sigma_{<x^{\prime}>}=\sqrt{\sigma_{<x_{0}>}^{2}/f^{2}+\sigma_{<x_{0}^{\prime} >}^{2}}\approx\sigma_{<x_{0}>}/f, \tag{19}\]  
assuming that the initial position and pointing jitter are uncorrelated and the latter is small. For the ARES linac, the focal length of the PMQ triplet is on the order of 10 cm [78], and so a 10 \(\upmu\)m position jitter upstream of the PMQ will introduce about 100 \(\mu\)rad pointing jitter at the focal point.  
A major source of position and pointing jitter of the beam is the position jitter of the photocathode laser. Start-to-end simulations have been carried out for WP2 and WP4 of the ARES linac [77] and the results are shown in Figure 13. In this study, the beam emitted from the photocathode was assumed to have a displacement in the transverse or vertical plane and then tracked up to the focal point. Both space-charge and CSR effects were included. Simulations show that, generally, the final displacement and divergence are linear functions of the initial beam displacement on the photocathode. The displacements at the focal point are smaller than the rms beam sizes (\(\sim\)2 \(\upmu\)m) for an initial beam displacement of 20 \(\upmu\)m. However, the overall divergence (the combination of the horizontal and vertical planes) at the focal point is well above 100 \(\upmu\)rad for only an initial 10 \(\upmu\)m beam displacement on the photocathode. The final displacement and divergence are also affected by the beam optics and the slit collimator. In general, the slit collimator helps to reduce the displacement and divergence in the corresponding plane (the horizontal plane here).
## 2 Materials and Methods
### Methods
#### 2.3.4 Theil-Sen Slope Analysis and Mann-Kendall Trend Test  
The Theil-Sen Median method is a robust non-parametric statistical trend calculation method. This method has high computational efficiency and is not sensitive to measurement errors and outlier data, making it suitable for long-term time-series data trend analysis, and it has been widely used in scientific research [54; 55; 56]. In this article, a Theil-Sen median estimation trend analysis was combined with a Mann-Kendall test for time-series analysis.  
(1) Theil-Sen median estimator  
\[\beta=Median\left(\frac{x_{j}-x_{i}}{j-i}\right),\left(1\leq i<j\leq k\right) \tag{15}\]  
where \(\beta\) denotes the slope of changes involved in the time series under calculation, with \(k\) = 18 representing the length of the time series. \(J\) and \(i\) represent the years. When \(\beta\) > 0, it indicates an upward trend in the time series; when \(\beta\) < 0, it suggests a downward trend in the series.  
Figure 3: Simulated NPP (**a**) and product NPP (**b**) compared with field-derived NPP.  
Mann-Kendall trend test:  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}sgn\big{(}x_{j}-x_{i}\big{)} \tag{16}\]  
\[sgn\big{(}x_{j}-x_{i}\big{)}=\begin{cases}+1&x_{j}-x_{i}>0\\ 0&x_{j}-x_{i}=0\\ -1&x_{j}-x_{i}<0\end{cases} \tag{17}\]  
\[Z=\begin{cases}\frac{S}{\sqrt{Var(S)}}&(S>0)\\ 0&(S=0)\\ \frac{S}{\sqrt{Var(S)}}&(S<0)\end{cases} \tag{18}\]  
\[Var(S)=\frac{n(n-1)(2n+5)-\sum_{m=1}^{n}t_{m}(m-1)(2m+5)}{18} \tag{19}\]  
where \(x_{j}\) and \(x_{i}\) represent the observed values corresponding to the time series \(i\) and \(j\) (\(i<j\)), \(sgn()\) is the sign function, and \(Z\) signifies the standardized test statistic. \(n\) is the number of entities in the time series (\(n>10\)), and \(t_{m}\) indicates the range for any given mean m.  
When the absolute value of \(Z\) is greater than or equal to 1.96 (or 2.32), it denotes that the trend in the time series has been confirmed at a confidence level of 95% (or 99%). The experimental results passed a two-tailed significance test with a 95% confidence interval (confidence level, \(\alpha=0.05\)).
# Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots
## 7 Obstacle-Avoidance
### Returning Main Trajectory by Reshaping Curve Algorithm  
After the robot no longer see the observed obstacle, the robot will go back to the main trajectory. For easily getting back to the main trajectory, we will interpolate a retuning point on the path that has fixed distance \(\Delta L\) from it to the current position of the robot. Let \(L(u_{i}\),\(i_{R})\) be the total distance which the robot has moved, \(L(u_{b}\), \(i_{b})\) is the arc length of curve from the initial point to the returning point that defined by Equation (35).  
\[L(u_{b},i_{b})=L(u_{R},i_{R})+\Delta L \tag{35}\]  
By the approximating table and the Equations (26)-(28) we obtain the returning point position. In the current example, Figure 15 illustrates the re-path algorithm (illustrated in Figure 8) implementation when the robot exceeds the allowed position error.
###### Abstract  
The surrounding environment of a GNSS observation station is changed during a flood, and this results in a more serious multipath than in a normal environment. Considering that the multipath error is largely related to the pseudo-range multipath and CNR (Carrier-to-noise ratio) of the GNSS signal, the influence of floods on a pseudo-range multipath and CNR is analyzed in theory and through experiment. To ensure the accuracy of the analysis results, the ground track repeat period of GPS, GLONASS, and BDS satellites is investigated from the perspective of theory and skyplots. Two real cases study collected in Zhengzhou and Xinxiang, China, in 2021, are used to demonstrate the influence of floods on a pseudo-range multipath and CNR in detail. Experimental results show that the pseudo-range multipath of a GPS satellite performs more seriously during a flood. The maximum RMS increase rate is approximately 17.85%, and the average of all other satellites with a whole arc is approximately 6.55%. In addition, the CNR of three GNSS systems performs a decrease during a flood. For GPS and GLONASS satellites, the decrease performs more seriously at a high elevation angle than that at a low elevation angle. The maximum decrease is approximately 5 dB-Hz for the GPS satellite and approximately 7 dB-Hz for the GLONASS satellite. In terms of the BDS system, the CNR of all three orbital type satellites decreases during a flood. The average decrease is approximately 2 dB-Hz for BDS MEO and GEO satellites, and about 1 dB-Hz for the BDS IGSO satellite.  
GNSS; ground track repeat period; pseudo-range multipath; carrier-to-noise ratio; flood
# Spatiotemporal Analysis of Evapotranspiration and Effects of Water and Heat on Water Use Efficiency
## 3 Results and Discussion
### Hurst Index  
The Hurst index method is used to analyze the sustainability of the time series of \(E\) and \(Ep\) in Xinjiang from 2000 to 2014 (Figure 5a). It can be seen that the regions with an \(E\) Hurst index less than 0.5 are mainly distributed in the northern Junggar Basin region, which shows that the future \(E\) variations in this region are contrary to the past. As northern Xinjiang is mainly an area with more agricultural land and greater land use change, the future trend of evapotranspiration in this region is opposite to that in the past. Most of the rest of the areas have a Hurst index of more than 0.5, indicating that the regional distribution of \(E\) in the vast majority of regions in Xinjiang is consistent with the trend  
Figure 4: Spatial distribution of variation coefficient of E and Ep in Xinjiang. (a) \(E\); (b) \(Ep\).  
of the past. The change of \(E\) Hurst index from 2000 to 2014 shows continuity. The most persistence is near the Tianshan Mountains, and the \(E\) Hurst index in this region is mostly above 0.8 from 2000 to 2014.  
The region with an \(Ep\) Hurst index less than or equal to 0.5 is mainly distributed in the Yili River valley in northern Xinjiang, indicating that the future \(Ep\) changes in this area are contrary to the past. The areas with a Hurst index greater than 0.5 for \(Ep\) are mainly distributed in the Junggar Basin, the nearby Tianshan Mountains, and near Tarim River Basin in northern Xinjiang. The Hurst index indicates that the \(Ep\) in these regions is consistent with past trends, and the entire process is continuous. The Tianshan Mountains and the Junggar Basin have the most robust persistence, and the Hurst index in the eastern part of the Tianshan Mountains is above 0.9. In conclusion, \(E\) and \(Ep\) in Xinjiang will continue for a period of time in line with the past trend with no turning point, and the volatility is relatively unstable.
# FPGA-Based Frequency Tracking Strategy with High-Accuracy for Wireless Power Transmission Systems
## 3 Frequency-Tracking Control System
### Simulation Analysis  
The control chip of this system adopts FPGA, whose main advantage is that it is stable and has very low latency. It can quickly respond to a change in phase difference and precisely realize control of the phase difference. First, the analysis and design of each module of the frequency-tracking control method is based on Section 3.1. Then, the corresponding code is written in the Verilog language. Finally, the RTL top-level view of the frequency-tracking control system of this paper is compiled in Quartus-II and shown in Figure 8.  
The digital phase-locked loop is simulated in ModelSim software. When the system clock is at 50 MHz, and the input signal frequency of the digital phase-locked loop is 48 kHz, the simulation waveform of the digital phase-locked loop is shown in Figure 9. The simulation shows that when the input signal frequency is 48 kHz, the output signal can follow the input signal after 13 cycles, and the phase locking is achieved.  
Figure 10 shows the waveform of the input signal frequency of the digital phase-locked loop. In the figure, after the input signal frequency jumps from 48 kHz to 30 kHz, the output signal of the digital phase-locked loop will follow the input signal again in the seventh input cycle to achieve phase locking. The simulation results show that the all-digital phase-locked loop designed in this paper meets the expected goals and has the advantages of a fast phase-locking speed and high precision.  
Figure 11 is the simulation waveform of the frequency-tracking control experiment, where u_in is the voltage waveform in the WPT system, u_i is the current waveform, and u_out is the waveform output by the frequency-tracking control circuit. The phase difference of the voltage and the current in the first two cycles has been locked, the voltage is ahead of the current, and the system is inductive. In the third cycle, the phase difference jump causes the impedance angle to decrease to close to 0\({}^{\circ}\). After 12 cycles, the system impedance angle will be locked again to the previous degree. The simulation results show that the impedance angle of the frequency-tracking control method proposed in this paper is fixed, and the system is always under the ZVS state.  
Figure 8: RTL top-level view of the frequency-tracking control system.  
Figure 10: Input signal frequency hopping.  
Figure 9: Function simulation diagram of digital phase-locked loop.
## 1 Introduction
Figure 1: Components of the TEResNet-based fiber optic sensing external intrusion detection system. A new dataset comprising 6948 signal segments was collected in the construction environment of the Guangzhou Metro, which has been made publicly available to facilitate further research. 3. Experiments show that with only 48,009 learnable parameters, TEResNet achieves an accuracy of 97.12% and an F1 score of 96.15%, which is a superior performance compared to existing methods and advanced neural networks. The remainder of this paper is organized as follows: Section 2 details the data acquisition process and dataset preprocessing methods. Section 3 introduces the principles of the proposed approach, with a particular focus on temporal convolution, along with signal characterization and the network architecture. Section 4 outlines the evaluation metrics, presents the results of ablation studies, and discusses the performance of the model in comparative experiments. Finally, Section 5 concludes the paper and offers insights into potential directions for future research.
## 2 Materials and Methods
### Experimental Set-Up
#### 2.1.3 Marine Oil Properties  
According to the specifications of the main diesel engine in the fishing vessel's engine room, marine diesel was selected as the type of leakage oil in this experiment. The main diesel engine in the fishing vessel's engine room is an in-line four-stroke main engine, which adopts the form of closed cooling. The main diesel engine has 6 cylinders and uses 0# diesel. The marine oil is collectively referred to as marine diesel oil and heavy fuel oil. For an accidental scenario of main diesel engine failure and oil leakage, light marinediesel oil was used in this experiment, whose chemical determination was tested. The flash point temperature of the marine diesel oil used in the experiment is about 63 \({}^{\circ}\)C, which indicates that the flash of disappearance of the liquid surface occurs when the oil contacts with the flash point temperature under normal conditions. The initial boiling temperature is 170 \({}^{\circ}\)C, and the vapor pressure at 20 \({}^{\circ}\)C is 2.0 hPa. The marine diesel has a low/high flammability of around 1% to 6%, which is also an important flammability indicator. The other chemical properties of the marine diesel used in this experiment are shown in Table 1. In this experiment, the external oil tank was connected with a peristaltic pump, and the amount of leaked marine oil on the heated-surface simulator was controlled by a flow controller. The experimental volume ranged from 10 mL to 20 mL. According to the typical heated-surface temperature in the fishing vessel's engine room, a total of 30 tests were conducted based on each adjusted heated-surface temperature. Therefore, the number of tests can ensure a comprehensive analysis of marine oil ignition characteristics by a heated surface in a fishing vessel's engine room.
# An LSTM-Based Deep Learning Approach for Classifying Malicious Traffic at the Packet Level
## 3 Methodology
### Classification
```
Data: Sequence of raw packets from network Result: Accuracy, precision, recall, f1-score, FAR, loss
1:\(dictionary=array()\) % Create the mapping word to the index array
2:\(translatedWords=\) null; %variable to store the values of words to converted to integer format;
3:while true do
4: Parse the packet;
5: Extract 54-byte from each packet
6:if packet length < 54 then
7: Pad zeros;
8:endif
9: Parse each byte of the extracted data as a word; words defines the words extracted from the packet;
10:for\(\{i=1;i<\text{count}(words);i++\}\)do
11:if Word in dictionary, \(dictionary(words[i])\)then
12:\(index=dictionary(words[i])\) % Pulling index of the words
13:else
14:\(dictionary[]=words[i]\); %Update the dictionary with the new word;
15:\(index=dictionary(words[i])\)
16:endif
17:\(concat(translatedWords,index)\)
18:endfor
19:endwhile% return dataset
20: Split Training and Test based on 90/10 (TrainingData, TestingData)
21: Train and Validate (TrainingData, TestingData)
22: Reshaping \(translatedWords\) to an output of 64-dimensional word vector;
23: Input the 64-dimensional word vector to the first layer LSTM;
24: Dropout;
25: Feedforward to the second layer LSTM;
26: Dropout;
27: Feedforward to the third layer LSTM;
28: Dropout;
29: Prepare input for mini-batch (100 packets);
30: Apply RMSProp optimizer;
31: Use softmax to output 0 or 1 to the model;
32: Use binary cross entropy as loss function
33:for\(epoch=1;epoch<200;epoch++\)do
34: Evaluate Loss, Validation Loss
35: Evaluate Accuracy and Validation Accuracy
36:endfor
```  
**Algorithm 1**Algorithm for packet-based traffic classification Training and validation often cost significant time regarding the system evaluation. In fact, the dataset size after converting (transferring to integer format) can be smaller in storage than that of the original text/string as in the packets, since the integer format may cost few spaces than the text data type. However, the dimension unlikely reduces. As a result, selecting word size may dramatically affect the training time. Smaller word size reduces the size of dictionary, but also causes more training data and running time. In this research, we set the maximum word size to 2 bytes based on the consideration of the length of most fields in the packet while limiting the number of words in the dictionary to be less than 65,536.