esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Simulation and Validation Analysis
### Simulation and Verification of LADR-SMC with Smith Predictor
The LADRC-SMC-SP scheme stands out from the three groups of control schemes based on the literature in the introduction. Under the requirements of anti-interference, delay elimination, and motor saturation prevention in Sections 5.1-5.3, a better force-position tracking performance than other schemes is realized. This has a better effect on these problems than solutions in the literature [7; 8; 9] and creates a new reference for the control strategy of this type of model. In similar application scenarios, the scheme will have strong portability if the appropriate control parameters are selected according to the design principle of the scheme;
* There are limitations of theory and constraints of practice. The theory proposed in this paper is limited by tuning experience and does not employ intelligent parameter optimization methods. Therefore, this issue can be considered a direction for future research. Moreover, although the LADRC-SMC-SP scheme has strong portability, it is necessary to pay attention to various constraints during the foot-ground contact process of legged robots in practical applications. Therefore, the analysis of constraints is crucial to the design of control laws.
## 3 Results and Discussion
### Numerical Model Validation
#### 3.1.1 CFD Fluid Domain Model Validation  
The developed CFD model was further validated quantitatively by comparing the results of the predicted slug characteristics (slug initiation position, slug translational velocity, slug liquid length and slug frequency) for the cases shown in Table 2 with the measured results by [PERSON] [19]. Selected cases (1, 2 and 4) were used to validate qualitatively the present model with the slug contours of the STAR-CCM+ model and slug images from [PERSON] [19].  
Figure 5: Mesh independence study for developed FE model using Altair OptiStruct.  
The results of the qualitative analysis of slug formation and growth and the liquid length of the present model were compared with the slug contours in the experimental work and numerical model of [PERSON] [19] (Figure 6). The captured slugs present different hydrodynamic behaviours and liquid lengths, which change as a function of gas and liquid superficial velocities. The slug flow for \(j_{L}=1\) m/s and \(j_{C}=2.44\) m/s shows the shortest liquid slug which increases in length as the water superficial velocity decreases, as in \(j_{L}=0.86\) m/s and \(j_{L}=0.7\) m/s. As shown in Figure 6, the qualitative results of the developed CFD model are in good agreement with those of the experimental work and CFD model of [PERSON] [19].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Validation Model** & **Air Velocity (\(j_{C}\)) (m/s)** & **Water Velocity (\(j_{L}\)) (m/s)** \\ \hline Case 1 * & 2.44 & 0.70 \\ Case -2 * & 2.44 & 0.86 \\ Case -3 & 2.44 & 0.93 \\ Case -4 * & 2.44 & 1.00 \\ Case -5 & 0.70 & 0.70 \\ Case -6 & 0.70 & 0.86 \\ Case -7 & 0.70 & 0.93 \\ Case -8 & 0.70 & 1.00 \\ \hline \hline \end{tabular}
* Selected cases for validation.  
\end{table}
Table 2: Cases for the quantitative validation of CFD model.  
Figure 6: Qualitative validation of slug contours against the experimental work and numerical model of [PERSON] [19].  
Figure 7 shows the quantitative validation of slug initiation position, slug translational velocity, slug liquid length and slug frequency against those reported by [PERSON] [19]. In general, the slug characteristics are strongly dependent on the water and air superficial velocities. As the water superficial velocity increases, the slug formation tends to occur further downstream from the piping inlet. Moreover, a reduction in the slug liquid length is observed as the water superficial velocity increases. The comparison of the results of the present model with those of [PERSON] [19] shows that the highest deviations recorded for the slug initiation position, slug translational velocity, slug liquid length and slug frequency are 11.8%, 11.5%, 10.8% and 11.8%, respectively. [PERSON] [19] validated his experimental work by using the STAR-CCM+ numerical model [19] and found that the highest deviation recorded for slug length was 17.6% when \(j_{G}\) = 2.44 m/s and \(j_{L}\) = 0.86 m/s. Therefore, relative to the experimental work of [PERSON] [19], the present model achieves a reasonable prediction of slug characteristics.
# A Reliable Routing Algorithm Based on Path Satisfaction in the Energy Internet
## 3 Network Architecture and System Model
### System Model
#### 3.2.2 Modulation Level
\begin{table}
\begin{tabular}{c l l} \hline
**Types** & **Classical Services** & **Service Characteristics** \\ \hline
1 & Relay protection and control services & High-reliability, dual-route, and millisecond scale \\
2 & Dispatching automation and WAMs & Rigorous latency, dual routes, and high reliability \\
3 & Substation video monitor, lightning positioning monitoring & Broadband, and real-time \\
4 & Protection information management, office automation & Nonreal-time, and narrowband \\ \hline \end{tabular}
\end{table} 
Table 2: Service classification. \[\sum_{b_{0}}\gamma_{r,p}^{b_{0}}=\kappa_{r}\times S_{r},\forall r,p \tag{23}\] \[\sum_{k_{p}\in\mathbb{M}}Z_{r,p}^{k_{p}}=1,\forall r,p\] (24) \[n_{r,p}=\left\lceil N\Big{/}k_{r,p}\right\rceil \tag{25}\]  
where \(\theta_{p(r)}\) denotes path satisfaction of service \(r\) with path \(p\). As mentioned above, it is calculated with formula (17). Constraint (20) guarantees the end-to-end latency and bandwidth with services, where \(C(r)\) describes the bandwidth of service \(r\), and \(B\) is the channel bandwidth. \(X_{r,p}^{b_{0}}\), \(Y_{r,p}^{b_{0}}\), \(Z_{r,p}^{k}\), and \(S_{r}\) are decision variables. \(b_{0}\) signifies the resource block (\(t_{0}\),\(f_{0}\), \(n\)), which is composed of \(n\) adjacent frequency slots with the starting time slice \(t_{0}\) and starting frequency slot \(f_{0}\). If \(b_{0}\) is assigned to the working path \(p\) for service \(r\), then \(X_{r,p}^{b_{0}}=1\), and \(0\) if otherwise. If \(b_{0}\) is assigned to the alternate path \(p\) for service \(r\), then \(Y_{r,p}^{b_{0}}=1\), and \(0\) if otherwise. If the resource requirement of service \(r\) is satisfied, then \(S_{r}=1\), and \(0\) if otherwise. Constraints (22) and (23) allocate unique resource blocks for the services in the working path and alternating path, respectively. Constraint (24) calculates the number of frequency slots on the path for service \(r\) with the highest modulation format, and constraint (25) calculates the number of frequency slots on the path for service \(r\) with the highest modulation format.
# A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm
## 6 Conclusions  
In this study, we investigated the dynamic edge-server placement problem in a mobile edge computing environment. By improving the SO algorithm, we achieved an intelligent edge-server placement strategy to determine the optimal placement locations of edge servers. Experimental results show that our dynamic deployment method reduces latency by 5% to 12% compared to traditional algorithms while decreasing service costs by 20% to 43%. At the same time, the proposed minimum placement-cost algorithm effectively optimizes the deployment expenses of edge servers. Although the current research primarily focuses on optimizing latency and cost, practical applications may require balancing multiple objectives. Future work can incorporate multi-objective optimization, considering factors such as latency, cost, and energy consumption, to create a balance among these goals. It will further enhance the practicality and adaptability of dynamic edge-server placement schemes and provide stronger support for the development of the mobile edge computing field.  
Methodology, P.Y.; Software, X.W.; Writing--original draft, J.L. All authors have read and agreed to the published version of the manuscript.  
This study was funded in part by the National Natural Science Foundation of China (62072159, U1804164 and 61902112).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Designing Automated Logistics Warehouse Stackable Bidirectional Infinite-Loop Modules
## 3 Materials and Methods
### Operation of Stackable Bidirectional Infinite-Loop Module  
The stackable bidirectional infinite-loop module, as shown in Figure 3a, consists of two stacked layers or stacks. Instead of traditional steel racks for inventory storage, this design employs conveyors or similar continuous transport mechanisms. In this study, conveyors served as the fundamental component. The conveyor checks the inventory location and transports it to the lift in the direction closest to the lift, based on the horizontal direction. The lift transports the inventory in the vertical direction up and down and serves as a waiting area for the logistics to be transported to the buffer. Inventory waiting for shipment are stored in the buffer. The inventory that arrives at the buffer is transferred to the worker by the stacker crane in order of arrival. The stacker crane, located on both sides of the rack where the buffer is located (Figure 3b), transfers the inventory that has arrived at the buffer to a worker responsible for receiving incoming and outgoing inventory. An example of the  
Figure 2: (**a**) General form of logistics warehouse; (**b**) structure of a logistics automated warehouse with stackable bidirectional continuous track modules; (**c**) advantages in space and area due to secured space.  
circulation of logistics using the stackable bidirectional infinite-loop module illustrated in Figure 3 can be found in Appendix A.  
In a logistics replenishment process, the vacant spaces created by outgoing shipments are refilled with other inventory items through a function known as reload. Reload refers to the process of filling the empty spaces left by dispatched logistics with different inventory items. In conventional warehouses, this task is typically performed by handling equipment such as forklifts, lifts, and stacker cranes. Reload enhances spatial efficiency [49]. However, in this study, by employing a bidirectional infinite-loop module using a stacking method, conveyors perform the roles of lifts, stacker cranes, and racks.  
Once the inventory logistics are dispatched via handling devices and buffers, a repetitive process ensues where the next set of inventory logistics to be dispatched is located and positioned within the buffer. This operation, named the pre-transfer system (or free-transfer system), takes place independent of the stacker crane's activities, occurring whenever both the buffer and lift positions are unoccupied. Figure 3a illustrates the concept of the bidirectional infinite-loop module, while Figure 3b indicates the movement directions of the logistics (arrows represent buffer directions) and the conveying devices, including conveyors and lifts.
## 3 Results
### Biosynthesis of Differolide  
The biosynthetic gene cluster of (\(\pm\))-differolide was confirmed by comparing genomes between _S. qaidamensis_ (CP015098.1) and _S. surantiogriseus_ TU3149 (GCA_014649375.1), another _Streplomyces_ strain capable of synthesizing (\(\pm\))-differolide [20]. Genome comparison showed that eight pairs of gene clusters were similar. Predicted synthetic products of each gene cluster are shown in Table 2.  
After analyzing the BGCs, we concluded that C15 BGC in _S. qaidamensis_, a PKS-type cluster, was most likely responsible for (\(\pm\))-differolide biosynthesis. To test this hypothesis, the core gene 5627 in the C15 gene cluster of _S. qaidamensis_ was knocked out (Figure 2a), and the \(\Delta\)C15 mutant was constructed.  
To explore whether the yield of (\(\pm\))-differolide in the \(\Delta\)C15 mutant strain is different from that of WT, both were fermented and extracted as above (in triplicate). The production of (\(\pm\))-differolide was detected using HR-LC-MS, and the total ion chromatogram (TIC) spectrum of two samples is shown in Figure 2b. The diagonal line in the figure is the peak of the target compound. The results showed that the ion signal of (\(\pm\))-differolide is much lower in the \(\Delta\)C15 mutant strain, than that in WT. Comparison of (\(\pm\))-differolide production in \(\Delta\)C15 mutant and WT, suggested the yield of the compound in \(\Delta\)C15 mutant was significantly lower than WT (Figure 2c), indicating that our conclusion is valid, and the C15 BGC is responsible for the biosynthesis of (\(\pm\))-differolide.  
Figure 1: The structure and DPPH radical scavenging assay of (\(\pm\))-differolide. (**a**): Structure of (\(\pm\))-differolide; (**b**): X-ray crystal diffraction of (\(\pm\))-differolide; (**c**): The m/z of (\(\pm\))-differolide; (**d**): The rate of DPPH radical scavenging of (\(\pm\))-differolide (in triplicate).  
Figure 2: Biosynthesis of (\(\pm\))-differolide. (**a**): The biosynthesis gene cluster of (\(\pm\))-differolide (The function of genes was distinguished with a different color. Red: core biosynthetic genes; pink: additional biosynthetic genes; blue: transport-related genes; green: regulatory genes; grey: other genes;  
yellow: gene 5627, which was knocked out). (**b**): The TIC spectrum of HR-LC-HS between \(\Delta\)C15 and WT. (**c**): The yield of (\(\pm\))-differolide difference between \(\Delta\)C15 and WT (*: The result of significant test showed the R\({}^{2}\) < 0.05).
# Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
## 4 Proposed Methods
### Calculation of Threshold
#### 4.3.3 Calculation of Threshold  
We employ Equation (3) to calculate the Hamming distance for a specific window. In the Equation, variable \(p_{i}\) represents the Hamming distance between the current test set and the \(i\)th window, calculated using Equation (2). Variable \(N\) represents the number of recorded windows. By computing the means of the Hamming distances between the window and all benchmarks, we obtain the final Hamming distance for that window. The value \(\alpha_{i}\) represents the final Hamming distance of the \(i\)th window. To calculate the threshold using the 3-sigma method [50], we need to find the value of \(avg\) using Equation (4) and the standard deviation \(\sigma\) using Equation (5). In these equations, \(S\) represents the total number of windows used in calculating the threshold.  
\[\alpha_{i}=\frac{p_{1}+p_{2}+\cdots+p_{N}}{N} \tag{3}\]  
\[avg=\frac{\alpha_{1}+\alpha_{2}+\cdots+\alpha_{S}}{S} \tag{4}\]  
\[\sigma=\sqrt{\frac{1}{S}\sum_{i=1}^{S}(\alpha_{i}-avg)^{2}} \tag{5}\]  
By using Equations (6) and (7), we can determine the upper and lower bounds of the threshold. According to the 3-sigma method, the value of \(\rho\) is typically set to 3. However, in specific models and datasets, the value of \(\rho\) can be adjusted according to the specific requirements.  
\[\theta_{up}=avg+\rho\sigma \tag{6}\]  
\[\theta_{down}=avg-\rho\sigma \tag{7}\]  
To illustrate the algorithm flow for threshold calculation, we provide Algorithm 1 below.  
In Algorithm 1, Lines 2-8 represent the stage of establishing the benchmark partition window. In Lines 10-14, we iterate through each benchmark window, calculate the Hamming distance using Equation (3), and store the final Hamming distance values in list \(\alpha_{s}\). This list contains the final Hamming distance values for all windows used for training. Lines 16-17 depict the calculation process where we compute the average Hamming distance \(avg\) and standard deviation \(std\). In the final stage, we calculate the upper  
```
0:TrainDataset
0:\(windows,Threshold_{up},Threshold_{down}\)
1:\(windows\leftarrow[]\)
2:for packets in TrainDataset do
3: Combine \(K\) consecutive packets into a windows \(W\);
4:\(windows.append(W)\);
5:if The number of windows is \(N\)then
6:\(break\);
7:endif
8:endfor
9:\(as\leftarrow[]\)
10:for packets in TrainDataset do
11: Combine \(K\) consecutive packets into a windows \(W\);
12:\(Hs\gets getHammingDistanceList(W,windows)\)
13:\(\alpha\gets getArg(Hs)\)
14:\(as.append(\alpha)\);
15:endfor
16:\(avg\gets getArg(\alpha s)\)
17:\(std\gets getStd(\alpha s)\)
18:\(Threshold_{up}\gets avg+\rho\times\sigma\)
19:\(Threshold_{down}\gets avg-\rho\times\sigma\)
20:return\(windows\),\(Threshold_{up}\),\(Threshold_{down}\)
```  
**Algorithm 1** Calculate Threshold
## 3 Results and Discussion
### Temperature Influence  
Table 1 shows the effect of temperature on absorbance decay percentage and TOC removal percentage during the treatment of TA by UV/PS and photo-Fenton processes within 2 h UV irradiation. Temperature was examined in the range of 20-35 \({}^{\circ}\)C. The work of this range of temperature does not cause any risk for either UV lamp or the photo-reactor. Results indicated that the increase of temperature from 20 \({}^{\circ}\)C to 25 \({}^{\circ}\)C increases the percentage of absorbance and TOC removals at 120 min of the treatment. However, the increase of this temperature from 25 \({}^{\circ}\)C to 35 \({}^{\circ}\)C does not have any significant effect on aromatics and TOC removals during the treatment of aqueous solutions of TA by the UV/PS and photo-Fenton processes.  
Thus, the optimum temperature deduced in this study is 25 \({}^{\circ}\)C for the treatment of this effluent, so less heating energy should be consumed. This phenomenon can be attributed to increasing the temperature higher than the optimum value (25 \({}^{\circ}\)C) accelerates the rate of competitive reactions which consumes sulfates and hydroxyl radicals. Besides, for a low temperature, the sulfates and hydroxyl radicals generated in the reaction medium are not able to degrade TA. Many reports discussed the temperature influence and have found similar results [74; 78]. From these results, it can be concluded that UV/persulfate process cannot be a viable option for the treatment of TA in water taking into account its low mineralization yield (54.41%). Further investigations are needed to enhance the performance of UV/PS in the mineralization of organic matter. The photo-Fenton (UV/H\({}_{2}\)O\({}_{2}\)/Fe\({}^{2+}\)) process achieved more than 99% of aromatics removal and more than 94% of TOC removal. The combination of UV/PS and photo-Fenton can be a good cost-effective method that benefits from the advantages of persulfates (low cost) and the high efficiency of photo-Fenton (high mineralization).
# Safety of Nuclear Power Plants with Respect to the Fault Displacement Hazard
## 2 Baseline Information for the Definition of Design Basis PGD
### Regulations Applicable for the Definition of PGD Design Basis  
The IAEA SSG-9 [2] provides instructions for the scope of investigations and criteria for the definition of the capable fault. The SSG-9 advises defining the annual probability of exceedance for PGD, \(\lambda(\text{D}>\text{d})\). However, the SSG-9 [2] does not define the annual rate for the design basis PGA.  
The 10 CFR 100 Appendix A [26] provides criteria for the capability of the fault, i.e., for the possibility of permanent ground displacement. According to this, the fault is capable if a movement at or near the ground surface can be identified at least once within the past 35,000 years or movement of a recurring nature within the past 500,000 years. Essentially, these criteria could be used for the definition of the design basis PGD hazard level. Let us assume that the events are Poisson distributed in time, and, applying the Chi-square test, the rate \(\theta\) could not be less than:  
\[\theta=\frac{\chi^{2}_{(1-\alpha),\ 2(r+1)}}{2T} \tag{1}\]  
where \((1-\alpha)\) is the confidence level, r is the number of the events during time interval, \(T\).  
Thus, if there is one near surface movement recognized with a 95% confidence level during the past 35,000 years, the rate \(\theta\) is greater or equal to \(1.36\times 10^{-4}/a\). This is significantly higher than the empirical rate \(\lambda=1/35,000\) years \(=2.86\times 10^{-5}/a\). To the contrary, if no PGD event is identified during the past 35,000 years, the estimated rate for non-zero PGD is not less than \(8.56\times 10^{-5}/a\approx 10^{-4}/a\). Extending criterial time \(T\) to the past 50,000 years and assuming one event happened, the rate for non-zero PGD will be \(\approx\)\(10^{-4}/a\). If there is no event identified during the past 50,000 years, \(\theta\) should not be less than \(6\times 10^{-5}/a\). The condition in 10 CFR 100 Appendix A regarding the recurring nature of events during the past 500,000 years is rather controversial. Recurring nature means a minimum of two events during the past 500,000 years. Assuming two events, the rate \(\theta\) will be equal to \(1.26\times 10^{-5}/\text{year}\). This rate would suggest screening out the hazard from design basis. Consequently, the fact that one movement can be identified during past 35,000 years (or even 50,000 years) is adequately indicating the need for further analysis and the characterization of the hazard and \"no event\" during the past 35,000 years (or 50,000 years) is not sufficient to screen out the hazard with high level of confidence.
# A Fiber Vibration Signal Recognition Method Based on CNN-CBAM-LSTM
## 2 Convolutional Neural Networks  
CNNs convolve the local area of the input signal with the filter kernel and generate output features under the action of the activation function. Since each filter extracts features using the same kernel, the connection between the network layers can be reduced, along with the danger of overfitting. The following is a description of the convolution process:  
\[y_{i+1,\ m}(n)=w_{i,\ m}x_{i}(n)+b_{i,\ m} \tag{1}\]  
Among them, \(x_{i}(n)\) displays the n-th area of the i-th layer; \(y_{i+1,\ m}(n)\) represents the output of the m-th filter in the \((i+1)\)-th layer after convolution; the weight matrix and bias of the i-th layer's m-th filter are denoted by W and B, respectively.  
After convolution, the linear features are obtained under the action of the activation function, which improves the model's capacity for expressing features. The pooling operation can effectively optimize the feature space and network parameters after convolution. This study uses the maximum pooling approach, which can achieve the purpose of reducing parameters and reducing data dimensions [28]. The following is a description of max-pooling:  
\[P_{i+1,\ m}(n)=\max_{(n-1)H+1\ \leq\ t\ \leq\ nH}\left\{q_{i,\ m}(t)\right\} \tag{2}\]  
Among them, p denotes the value associated with the \((i+1)\)-th layer following pooling; q denotes the value of the t-th neuron in the i-th layer in the m frame; \((n\ -\ 1)H+1\ \leq\ t\ \leq\ nH\), where H represents the pooling region's width.
## 4 Enabling Technologies for FSO Communication
### Soft-Switching Configurations
An alternative approach to joint FSO/RF channel coding using a practical implementation of rateless Fountain codes is presented in [434]. The scheme belongs to the category of hybrid automatic repeat request (HARQ) that utilizes incremental redundancy coding, and as such does not require rate adjustment prior to transmission. This is a clear distinction relative to rate-adaptive coding techniques like the ones proposed in [429; 443]. A bit-interleaved CM scheme for hybrid systems is introduced in [431]. Additionally, ref. [432] proposes the use of short-length Raptor codes. The link availability of hybrid FSO/RF systems is explored from an information theory perspective in [444]. Meanwhile, ref. [435] presents diversity combining for parallel FSO and RF channels, assuming both links transmit identical information simultaneously. The performance of a similar hybrid FSO/RF system subject to non-Gaussian noise is analyzed in [445].