esa-sceva/satcom-chunk-collection huggingface dataset:
# Earth Rotation Parameters Estimation Using GPS and SLR Measurements to Multiple LEO Satellites
## 3 ERP Estimation Based on the Single LEO Satellite  
In this section, the quality of the LEO orbit that we calculated is assessed, firstly, by comparison with external products and SLR validation. Then, the influence of LEO orbit improvement on the ERP estimation is assessed. After that, we compare and analyze the ERP solutions based on the different LEO observations, and highlight the differences between these single-LEO solutions.
# A Novel Adaptive Transient Model of Gas Invasion Risk Management While Drilling
## 4 Comparison and Discussion
### Titrotle Pressure Comparison  
Figure 11 shows the variation in throttle pressure with time for three BHP control models in dealing with the gas invasion. At 960 s, a gas invasion warning is issued, and all three controls start regulating the wellbore pressure simultaneously. The PID controller model took a total of 400 s from the start of the gas invasion warning to the end of the gas invasion treatment, and the fuzzy PID control model took a total of 320 s from the gas invasion warning to the end of the gas invasion treatment, while the fuzzy neural network PID control model took only 240 s to handle the gas invasion, which improved the  
Figure 10: Control parameter vs. times for PID, fuzzy PID, and fuzzy neural network PID.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indicators** & **PI** & **PID** & \
\begin{tabular}{c} **Fuzzy** \\ **PID** \\ \end{tabular} &
\begin{tabular}{c} **Fuzzy Neural** \\ **Network PID** \\ \end{tabular} \\ \hline Regulation time (s) & 87 & 53 & 42 & 32 \\ RMSE (MPa) & 0.31 & 0.24 & 0.22 & 0.20 \\ IAE (MPa\(\cdot\)s) & 3250.39 & 1870.05 & 1674.84 & 1487.58 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of the four controllers.  
Figure 9: Effects of various intelligent controllers on BHP.  
regulation efficiency by 40% over the PID control model and 25% over the fuzzy PID control model. Therefore, the fuzzy neural network PID can regulate the throttle valve opening and pressure more efficiently, thus increasing the BHP and reducing the gas invasion rate in a short time until the well is successfully pressurized.
# Design Trends and Comparative Analysis of Lightweight Block Ciphers for IoTs
## 4 Related Work
### Generalized Feistel Network (GFN)
Most notably, its GE count (2180), although small, is higher than some other modern TBCs, such as SKINNY-64-128 (1696 GE) and CRAFT-128 (1193 GE), which were designed from scratch with minimal tweak cost in mind. Additionally, T-TWINE inherits the limitations of TWINE's 64-bit block size, which may not be sufficient for applications requiring larger security margins or higher throughput. The cipher also does not claim security in the chosen tweak and related-key setting, a scenario increasingly relevant in certain side-channel and protocol-driven environments. Therefore, while T-TWINE is excellent for systems needing minimal modification to existing Feistel-based designs, it may not be the best candidate for use cases demanding ultra-low GE or broader adversarial security guarantees.
## 3 Results
### Feature Signal Capture  
According to the proposed signal feature extraction method, a set of fundamental wave energy feature \(E_{pure}\) is generated from normal power signals by Wavelet Transform and Parseval's theorem, in order to highlight the features of power quality disturbances. The power quality disturbance signals are processed by DWT and Parseval's theorem. The feature \(E_{distortion}\) of the instantaneous signal of a PQD is obtained, from which the fundamental wave energy is deducted, the energy difference \(\Delta E\) is obtained, and the mathematical Equation (19), the power quality disturbance signals in Figure 5a-e are calculated by the aforesaid signal characteristic energy. The obtained wavelet energy eigenvalue curve diagram is shown in Figure 6a-e. The feature curve diagram shows different energy distributions of different power quality disturbance signals, proving that the proposed method can effectively extract the power quality disturbance features.  
\[\Delta E=\left[\begin{array}{ccc}E_{distortion}&c_{0}\\ E_{distortion}&d_{1}\\ \vdots\\ E_{distortion}&d_{j-1}\\ \end{array}\right]-\left[\begin{array}{ccc}E_{pure}&c_{0}\\ E_{pure}&d_{1}\\ \vdots\\ E_{pure}&d_{j}\\ \vdots\\ E_{pure}&d_{j-1}\\ \end{array}\right]=\left[\begin{array}{ccc}\Delta E_{c_{0}}\\ \Delta E_{d_{1}}\\ \vdots\\ \Delta E_{d_{j}}\\ \vdots\\ \Delta E_{d_{j-1}}\\ \end{array}\right]. \tag{19}\]  
Figure 5: Power system signal with different disturbances: (**a**) power interruption; (**b**) voltage sag; (**c**) voltage swell; (**d**) voltage flicker; (**e**) power harmonics.  
Figure 6: \(Cont.\)
# Dye Schedule Optimization: A Case Study in a Textile Industry
## 2 Brazilian Textile Sector
### Manufacture  
The textile chain starts transforming the raw material (natural, synthetic fibres and polymers) into yarns and filaments in the spinning mills. Then there can be two flows, first: warping, dyeing, flat weaving, finishing, and finally the clothing phase, and second: knitting, finishing, and finally the clothing and distribution phase. In Figure 1, we can see the macro flow of the chain. Thus, the result of each step constitutes the primary input of the next one. Each stage has its characteristics, with discontinuity between them, subdividing into several operations and elaborating intermediate products.  
The dyeing sector stands out as a critical process in operation and with many opportunities for gain. Beyond the technical aspect, there have been concerns about minimizing the negative environmental impact of the dyeing industry. For this production, scheduling has been applied [10] in many process industries for reducing pollutions emissions. Denim manufacturing faces an eco-efficiency challenge concerning sustainability. Alternatives have been studied to develop new dyeing processes that are cleaner, more efficient, faster, cheaper, and easier to apply [8].  
The dyeing process of denim type fabrics (used to make jeans) is characterized by dyeing the cotton yarn before weaving [11]. In this process, the most used dye is indigo, whose method remains the same as natural indigo [12].  
In the continuous indigo dyeing process, there are three technologies, which are:  
* Rope dye
* Slasher dye
* Loop dye
## 2 Numerical Experiment
### Experimental Design  
In this study, we perform a 48-h numerical simulation of Lekima from 0000 UTC 6 August to 0000 UTC 8 August, which involved the period of primary intensification and initial maturity of the Typhoon. The model used here is the Advanced Research version of WRF (ARW-WRF) model, version 3.6. The model domain is duplicately nested through two-way nesting, and the outer domain is centered at (20.75\({}^{\circ}\) N, 127.55\({}^{\circ}\) E) and the inner domain is set to move to keep the simulated TC always locating at the center of the domain. The horizontal grid-point number of the outer and inner domain is 601 \(\times\) 601 and 401 \(\times\) 401, with a horizontal resolution of 7.5 and 2.5 km, respectively. Both model domains have 51 vertical levels, with the top at 25 hPa (z = -25 km).  
For both domains, the simulation uses the Thompson microphysics scheme [25], the Yonsei University (YSU) boundary layer scheme [26], and the Rapid Radiative Transfer Model for general circulation models (RRTMG) longwave and shortwave radiation scheme [27]. Rayleigh damping is applied to the vertical velocity in the upper 5 km of the model domains to prevent artificial reflection of gravity waves from the model top [28]. In addition, the Kain-Fritsch cumulus parameterization scheme [29] is used in the outer domain. To provide the initial fields and lateral boundaries for the simulation, the European Center for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis data at a horizontal resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) and a time interval of 6 h are used. All analyses in this study are conducted based on the results from the inner domain, which are output at 1-h interval.
## 4 Experimental Results and Analysis
### Comparisons with Existing Methods
#### 4.4.1 Robustness  
To compare the robustness of ZWNet with [PERSON]'s method, [PERSON]'s method, and [PERSON]'s method, we conducted tests using the same test image, Lena, and subjected them to identical attacks. The results are summarized in Table 4.  
From Table 4, it is evident that under the same attack conditions, ZWNet exhibits higher NC values compared to the other methods in most cases. It excels in robustness, with only a slight decrease in performance under rotation attacks. This suggests that the features extracted by the convolutional layer may not be highly robust when it comes to rotation attacks. However, ZWNet still achieves a substantial NC value greater than 0.9 in this scenario, which is more than adequate for copyright identification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Image** & **Lena** & **Mandril** & **Tree** & **Girl** \\ \hline Lena & 0 & 90 & 88 & 113 \\ [PERSON] & 90 & 0 & 92 & 97 \\ Tree & 88 & 92 & 0 & 91 \\ Girl & 113 & 97 & 91 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hamming distances between the zero-watermarks of four test images.
## 3 Method
### Retriever Consensus Confidence Fusion (RCCF)
#### 3.3.1 Retriever Consensus Measurement  
The foundation of RCCF lies in accurately measuring consensus among different retrievers. For a query \(q\) and document \(d\), given \(n\) retrievers \(R_{1},R_{2},\ldots,R_{n}\), we define comprehensive consensus measures that capture both ranking and scoring agreement patterns.  
**Rank Consistency:** The rank consistency measures how similarly different retrievers rank a document:  
\[C_{\text{rank}}(d)=1-\frac{\sigma_{\text{rank}}(d)}{\text{max}(\sigma_{\text{ rank}})}\times\frac{1}{\sqrt{|A(d)|}} \tag{3}\]  
where \(\sigma_{\text{rank}}(d)\) is the standard deviation of document \(d\)'s ranks across retrievers, \(\text{max}(\sigma_{\text{rank}})\) is the maximum rank standard deviation among all candidate documents for normalization, \(|A(d)|\) is the number of retrievers that return document \(d\), and \(\frac{1}{\sqrt{|A(d)|}}\) is a penalty factor that reduces consistency scores when fewer retrievers return the document.  
**Score Consistency:** Due to the significant scale differences between retriever scores, we first normalize scores for each retriever:  
\[s_{\text{norm},i}(d)=\frac{s_{i}(d)-\text{min}(S_{i})}{\text{max}(S_{i})- \text{min}(S_{i})} \tag{4}\]  
where \(s_{i}(d)\) is the original score from retriever \(i\) for document \(d\), and \(S_{i}\) is the set of all scores returned by retriever \(i\). The score consistency is then computed as follows:  
\[C_{\text{score}}(d)=1-\frac{\sigma_{\text{score}}(d)}{1+\sigma_{\text{score }}(d)}\times\frac{1}{\sqrt{|A(d)|}} \tag{5}\]  
where \(\sigma_{\text{score}}(d)\) is the standard deviation of normalized scores for document \(d\).  
**Overall Consensus:** The final consensus measure combines both ranking and scoring consistency:  
\[C(d)=\alpha\cdot C_{\text{rank}}(d)+(1-\alpha)\cdot C_{\text{score}}(d) \tag{6}\]  
where \(\alpha\in[0,1]\) balances the importance of rank versus score consistency.
# Protein Dynamics in F-like Bacterial Conjugation
## 3 Structures Involved in Pilin Processing, Pilus Extension, and Retraction
### Pilus Retraction--TraH and TrbI  
TraH is a cysteine-rich protein unique to F-like systems, with a size of 458 aa that is processed to 433 aa for localization, and contains a C-terminal coiled-coil domain known as a motif for oligomerization or other PPIs [14]. This motif is predicted to mediate the interaction complex between TraF, -W, -U, TrbB and -I; TraH directly interacts with TraF, TrbI and the mating pair stabilization (Mps) protein TraU [85]. TraH also contains three N-terminal hydrophobic domains of approximately 20 aa each (the first 25 aa being the cleaved signal peptide), which aligns with results showing that TraH, -F, -U and -W are bound in the OM in the context of the complex [87]. Disulfide bond formation within TraH performed by DsbA and isomerization by TrbB are important for the proper activity of TraH as shown through mutational assays. Interactions with TrbI occur at conserved sequences in the TraH N-terminus, specifically Gly193-Leu226, which also contains a Walker A motif; TraH exhibits no NTPase activity despite this characteristic motif [85; 87]. As TraH has many interacting partners its role in the T4 SS is difficult to ascertain; the mutation of _traH_ appears to affect both pilus elongation and retraction [99].  
Pilus retraction is still a major point of intrigue as it is hypothesized that it occurs in an energy independent manner [11]. Once initiated, F-pili retraction occurs at an average of 15.8 nm/s, which is less than half the mean extension rate of 39.5 nm/s [70]. Trbl is a bitopic, 128 aa IM protein that plays a role in the retraction process by interacting with the periplasmic OM-associated complex composed of TraF, -H, -U, and -W [55]. Trbl spans the IM by an N-terminal anchor from residues His17\(-\)Val40, with the remaining 88 residues forming a hydrophilic domain in the periplasmic space [85; 96]. Several proteins of the T4 SS that are localized to the periplasmic space express conserved cysteine residues, homologs of TrbI tend to express a single conserved cysteine residue [14]. Mutations in \(trbl\) were not observed to affect pilus production or DNA transfer efficiency; however, it was observed that some mutations will cause abnormally long pili, while excess TrbI has also been observed to have an effect on male-specific phage sensitivity [64]. Both single-stranded DNA and RNA phage infections are inhibited by excess TrbI [96]. Both examples support the hypothesis that TrbI functions solely in the regulation of retraction. As previously noted, TrbI can directly interact with TraH, and this interaction is considered to initiate pilus retraction [85]. If TrbI localizes in the IM and TraH assembles into the OM when in the context of the T4 SS complex, the TrbI:TraH pair could be part of a second envelope-spanning structure analogous to the TraB, -K, -V core complex [87]
# Ellipsoidal K-Means: An Automatic Clustering Approach for Non-Uniform Data Distributions
## 3 Integrating Adaptive Ellipsoidal Clustering with Simulated Annealing Heuristic
### SAELLC Algorithm  
The SAELLC algorithm, which is shown in Algorithm 2, aims to find the optimal centers for a given number of clusters \(K\). It ensures that the epoch length is sufficient to identify the best centers by utilizing split and merge procedures.  
```
Initialization:  Let variables \(K_{\min}\) and \(K_{\max}\) indicate, respectively, the initial minimum and the maximum number of clusters;  Choose \(K\) randomly from \([K_{\min},K_{\max}]\) clusters as initial centers \(c_{k},k=1,\ldots,K\);  Choose the cooling schedule parameters: initial temperature \(T_{\max}\), final  temperature \(T_{\min}\), cooling rate \(\lambda\in(0,1)\), \(M\) iterations number of epoch length;  Set \(T:=T_{\max}\) and counter \(p:=1\); Main Loop: while\(T>T_{\min}\)do Epoch Loop: for\(p=1\)to\(M\)do  Compute a trial solution of centers as \(\tilde{c}_{k}^{p}=c_{k}^{p}+\Delta c_{k}^{p}\) with the step size \(\Delta\).  Calculate the new objective function value \(f(c_{p}^{p})\) using the _ESF-NN_ function as in Equation (21); if\(f(\tilde{c}_{k}^{p})>f(c_{k}^{p})\)then \(c_{k}^{p}:=\tilde{c}_{k}^{p}\);  end if else  Compute \(\Delta f=f(c_{k}^{p})-f(\tilde{c}_{k}^{p})\);  Generate a random number \(r\in[0,1]\); if\(r<e^{-\frac{\Delta f}{T}}\)then \(c_{k}^{p}:=\tilde{c}_{k}^{p}\);  end if  end while  end while Splitting and Merging: if\(p=1\)then  Randomly apply cluster Merging or Splitting Procedure 1, 2;  end if else  Comparison between results obtained in the previous two main loops;  Let \(f_{v}\) be the best obtained centers, where \(\
u\in(p,p+1)\); if\(K_{v}\) is the largest number of clustersthen  Apply Splitting Procedure 2;  end if else  Merge the smallest number of clusters \(K_{v}\) using Merging Procedure 1;  end if  end Decrease temperature: \(T:=\lambda T\), \(p:=p+1\);  end if Set \(f_{\text{best}},x_{\text{best}}\), and \(K_{\text{best}}\) to the optimal values determined by the algorithm;
```  
**Algorithm 2**SAELLC Algorithm
# The Debris Flow Risk Prediction Model Based on PCA-Elman
## 4 Results
### Generalization Ability  
When developing a predictive model, the objective is to not only generate accurate predictions on existing data but also to ensure the model's reliability when applied to new, unseen data. In this investigation, 26 mudslide samples from Bailong River Basin, as referenced in [PERSON] et al. [28], were utilized to conduct hazard assessments to validate the predictive capacity of PCA-Elman model on unfamiliar data, as illustrated in Table 6. Owing to regional disparities, different evaluation indicators were chosen; consequently, out of the 10 indicators selected for Yunnan Province, 7 were derived from Bailong River Basin, while the remaining 3 indicators (S2, S6, S10) underwent null value treatment.  
From the 26 debris flow samples, a set of 9 samples was selected, including 3 samples from each of the different risk levels, to serve as the model's prediction set. Additionally, to mitigate the potential impact of data imbalance on the model, the training set utilized a dataset expanded with the ADASYN algorithm. The 63 samples were divided into 5 subsets, and 4 of these subsets were selected in rotation to form the training set, ensuring that each subset served as the training set once, with the experiment repeated 5 times. After principal component analysis, the PCA-Elman model, PCA-BP model, PCA-SVM model, and PCA-RF model were established, with their prediction results shown in Table 6. Compared to the PCA-BP, PCA-SVM, and PCA-RF models, the PCA-Elman model demonstrated higher accuracy when dealing with unknown debris flow samples, indicating a stronger generalization capability.  
Furthermore, according to the research findings in reference [29], spatial variability is present during the study of debris flow risk. When dealing with debris flow samples from two different regions, the differences in geological conditions such as topography, geomorphology, and meteorological hydrology between these areas may impact the model's predictive  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ModelNumber** & **1** & **2** & **3** & **4** & **5** & **Average Accuracy** \\ \hline PCA-Elman & 66.67\% & 55.56\% & 44.44\% & 55.56\% & 33.33\% & 51.11\% \\ PCA-BP & 33.33\% & 44.44\% & 44.44\% & 33.33\% & 33.33\% & 37.77\% \\ PCA-SVM & 22.22\% & 22.22\% & 33.33\% & 33.33\% & 11.11\% & 24.42\% \\ PCA-RF & 44.44\% & 33.33\% & 33.33\% & 44.44\% & 44.44\% & 39.99\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model prediction accuracy on unknown data.  
accuracy. To address this, the remaining 17 debris flow samples from the Bailongjiang River basin were evenly distributed into 5 subsets, and cross-validation was used again to predict the 9 Bailongjiang River debris flow samples. The results are shown in Table 7. Compared to the results in Table 6, it was found that incorporating a certain amount of data from the Bailongjiang River basin into the training samples can reduce spatial variability and significantly improve the model's predictive accuracy for unknown regions.
# Photo-Thermoelectric Conversion of Plasmonic Nanohole Array
## 3 Results and Discussion
### Quantification of Plasmonic Local Heating
reported that temperature increases for periodic plasmonic nanoparticles cannot be estimated by considering the nanoparticles individually [26]. They showed that a temperature shift originating from a two-dimensional array of 16 gold nanoparticles was enhanced more than four times with respect to that of a single nanoparticle, because the heating effect can be enhanced as a result of the accumulation effects and inter-particle coulombic interactions [27]. We calculated the local heating temperatures produced upon changing the unit numbers of nanoholes from 1 to 49. As the unit number increases, the amount of plasmonic local heating increases because of a rise in the external temperature generated by the neighboring nanoholes [28; 29]. Figure 4 shows a logarithmic fit of plasmonic local temperature versus the number of nanohole units in the array. The fit to the data, also shown in in Figure 4, is expressed analytical by Equation (3). \[y=0.9171\mathrm{ln}(x)+1.239 \tag{3}\]  
As the number of nanoholes included in an illumination spot is \(4.27\times 10^{4}\), the estimated plasmonic local heating temperature at the illumination spot is 11.0 K, which is more than 2.4 times higher than that estimated from the experimental data. A possible reason for this difference between the temperatures estimated experimentally and numerically, is the discrepancy between the optical power used in the simulation and the actual power of the illuminated light. Our laser beam has a Gaussian profile, whereas we selected a uniform light spot for the illumination calculations. However, the actual light power illuminating each nanohole is different. Nonetheless, it should be noted that our calculation technique generated a relatively good estimation for the nanoholes of the temperature of the plasmonic local heating, which contributes to the thermoelectric conversion process. In other words, this calculation technique is valid for plasmonic local heating and should facilitate the optimization of configurations for plasmonic local heating. Figure 3: Electric field enhancement produced by plasmonic local heating. Cross-sectional view of nanohole: (**a**) scanning electron microscopy (SEM) image; and (**b**) electric field distribution.
# FBG-Based Monitoring of Geohazards: Current Status and Trends
## 4 Current Status of Applying FBG Systems for Geohazard Monitoring
### Debris Flow Monitoring  
The conventional instruments for debris flow monitoring include photocells, geophones, seismometers, and wire, ultrasonic, laser and radar sensors [73]. The key problem here is how to detect the initiation of the debris flow. Monitoring and warning of debris flows using FBG inclinometers and column-nets was reported by [PERSON] et al. [49]. The inclinometer can perform real-time measurement of borehole displacements, as seen in Figure 9. The FBG column-net, with FBG sensors glued on the bottom of its steel pipes, was installed in the debris flow direction and functioned by detecting the strain at the bottom, which was affected by impact forces when debris flows pass by. In Weijiagou Valley, southwest China, the FBG sensors were used to detect the initiation of debris flow process. Once the impact force rises up to a certain value, the column-net will break. The researchers, however, did not propose such a threshold that would give suitable warning of a debris flow. The interpretation and correlation of measured data is still an unsolved problem.  
[PERSON] et al. pointed out that FBG-based ground vibration and noise monitoring can be an effective approach for detecting the occurrence of landslide and debris flows [64]. The Mach-Zehnder and Sagnac hybrid interferometer were developed to establish the sensing system. The benefit of this approach is that the FBG signals can be transmitted to longer distance without affecting the sensitivity than conventional systems such as geophones. They conducted a field trial in a debris flow site in  
Figure 9: Borehole displacements at the lower part of the debris flow site [49].  
Taiwan. The fiber optic vibration sensing system is found to be effective for sensing ground vibration between 10 and 200 Hz frequency range.