esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid NHPSO-JTVAC-SVM Model to Predict Production Lead Time
## 2 Prediction Model
### Svm
#### 2.2.2 NHPSO-JTVAC Algorithm  
The PSO algorithm has a fast convergence speed, but it sometimes falls into a local optimum, and there is no guarantee that it can search for the optimal solution [34].  
To solve the above problems, HPSO-TVAC was proposed by [PERSON] et al. [35] as an efficient improved algorithm of the classic PSO algorithm. [PERSON] et al. [36] proposed an enhanced version of HPSO-TVAC called NHPSO-JTVAC, which has better performance than the original HPSO-TVAC algorithm. To avoid particles falling into local optima, they are afforded the ability to suddenly jump out during the algorithm iteration according to Equations (13)-(15):  
\[c^{Iter}=\left(c_{f}-c_{i}\right)\times\frac{Iter}{Iter_{max}}+c_{i} \tag{13}\]  
\[c_{1}^{Iter}=|w|^{({{\color[rgb]{0,0,0}{{{c}^{Iter}}}\times w})}} \tag{14}\]  
\[c_{2}^  
* Preprocess the data, and then split the dataset randomly into a training and test set (8:2).
* Randomly initialize the velocity and position of the particles, where the position vector (3-dimensional) represents the three parameters (\(C\), \(\varepsilon\), \(\sigma\)) of the SVM.
* Calculate the fitness value of each particle and determine the current p-best and g-best positions. The fitness function selected in this study was the mean absolute percentage error (MAPE) function (Equation (17)). Figure 3 illustrates the concept of k-fold cross-validation (CV). To prevent the model from overfitting, a 5-fold CV method was adopted in this study [37]. \[fitness(MAPE_{CV})=\frac{100\%}{m}\sum_{i=1}^{m}\left|\frac{y_{i}-\hat{y}_{i}}{ y_{i}}\right|\] (17) where \(m\) is the number of training samples, \(y_{i}\) is the actual value, and \(\hat{y}_{i}\) is the predicted value.
* For each particle, compare its fitness value with the p-best position it has experienced. If this is better, use it as the current p-best position.  
Figure 3: Concept of k-fold cross-validation (k = 5).  
Figure 2: Flow chart of SVM based on NHPSO-JTVAC.  
* For each particle, compare its fitness value with the g-best position. If this is better, replace its fitness value with the g-best.
* Calculate and update the velocity and position of each particle.
* If the termination condition is not satisfied, return (b); otherwise, the optimal solution is obtained, and the algorithm ends.
## 4 Influencing Factors and Error Analysis
### Effect of Image Center on Correction Error  
To analyze the influence of image center on the correction error, the center detection results were adjusted based on the above experiments. Then, the same method was used for correction and statistical measurement error. The image center of the \(\varphi\)12 mm hole endoscopic image was adjusted to (295, 386) and (315, 386), while that of the \(\varphi\)28 mm hole endoscopic image was adjusted to (318, 368) and (318, 388). The correction results are shown in Figure 12 where (a) and (b) represent \(\varphi\)12 mm holes, while (c) and (d) represent \(\varphi\)28 mm holes.  
Figure 11: Correction process and results. (**a**) \(\varphi\)12 endoscopic image (**b**) \(\varphi\)12 image center (**c**) \(\varphi\)12 circumferential correction (**d**) \(\varphi\)12 axial correction (**e**) \(\varphi\)12 final correction result (**f**) \(\varphi\)28 endoscopic image (**g**) \(\varphi\)28 image center (**h**) \(\varphi\)28 circumferential correction (**i**) \(\varphi\)28 axial correction (**j**) \(\varphi\)28 final correction result.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Pixel Distance** & **Mean** & **Standard** & **Unit Pixel** & **Maximum** & **Maximum** & **Deviation** \\  & **/Pixel** & **/Pixel** & **Deviation** & **Size/mm2** & **Deviation** & **Deviation** & **Deviation** & **Range** \\ \hline \(\varphi\)12 & 494.96; 513.58; 496.19; 502.56; 499.14 & 501.29 & 6.69 & 0.0233 \(\times\) 0.0233 & 2.45\% & 1.26\% & 3.71\% \\ \(\varphi\)28 & 471.88; 490.67; 487.20; 492.49; 490.03 & 482.45 & 8.46 & 0.0513 \(\times\) 0.0513 & 2.08\% & 2.19\% & 4.27\% \\ \hline \end{tabular}
\end{table}
Table 3: Pixel position and measurement error statistics.  
Figure 12 shows that the center detection results have a relatively significant impact on the image correction results. When the image center shifts horizontally, there will be convex and concave deformation in the correction results, while vertical shifts will cause tilt deformation. The measurement error of the pentagonal side length is calculated after adjusting for image center, and the results are presented in Table 4.  
Table 5 shows that the standard deviation and deviation range of the side length increase significantly when adjusting the image center horizontally, but even more so when adjusted vertically. This indicates that the accuracy of correction is greatly affected by the detection result of the image center. As most imaging planes have an aspect ratio greater than 1, it is recommended that internal surface defects are captured in a vertical direction by rotating the endoscopic probe during shooting, which can reduce correction errors and improve contrast group results.
# Efficient Machine Learning-Based Prediction of Solar Irradiance Using Multi-Site Data
## 3 Results and Discussion  
The dataset was obtained from six different stations. After applying preprocessing steps and conducting exploratory data analysis, we gained insights into impactful features and existing correlations. This analysis also identified possible historical steps (time steps) that could be used to obtain the most accurate GHI values. The dataset was then passed to a windowing function, allowing the use of the current and historical 30 min of data to  
Figure 8: Feature importance for the different stations: (**a**) NELHA, (**b**) ULL, (**c**) HSU, (**d**) UNLV, (**e**) RAZON, and (**f**) NWTC.  
predict the next 30 min of GHI. Training and testing sets were created in a ratio of 80% to 20%, respectively. The full training was performed in Google Colab, a free Python (version 3.11.13) development and data analysis framework provided by Google. Scikit-learn was primarily used for loading the ML models [26], except for the MLP, which utilized the Keras and TensorFlow libraries. Primarily, tree-based algorithms were utilized, and ensemble learners were created using different tree-based frameworks and a simple MLP architecture. This approach aimed to provide a lightweight solution that could be generalized to various sites simultaneously. The composition of the ensembles and the individual learners is detailed in the aforementioned sections. The results, considering the 30 min-lagged version of the features and results as references, are provided below in the following order:  
* Results from each station: NELHA, ULL, HSU, UNLV, NWTC, and RAZON;
* Ensemble learners in different stations;
* Hourly GHI prediction and link with time-lagged versions;
* Sensitivity analysis through time shifting;
* UNLV use case.
# Energy Efficient Access Point Placement for Distributed Massive MIMO
## 3 Different Channel Models
### Graph-Based Models
#### 3.2.3 Two Graph-Based Models  
Based on Algorithm A1, two models are introduced.  
* Shortest path-based model: this model does not consider the pathloss due to angular changes. Hence, \(\mathsf{penalty}(.)\) in Algorithm A1 is fixed to one.
* Angular penalty-based model: this model takes into account the pathloss, e.g., due to diffraction. Hence, \(\mathsf{penalty}(.)\) is calculated via Algorithm A3.
# Towards Efficient Sink Mobility in Underwater Wireless Sensor Networks
## 4 Proposed Scheme
### Network Configuration  
After the deployment of network, the first phase is to search sparse and dense regions. The sparse and dense regions are searched using sparsity search algorithm (SSA) and density search algorithm (DSA) [11]. The sparse region is searched for the initial placement of MS1 and MS2 in the network field. Dense regions are searched for clustering. MS1 is moved to the topmost sparse region from the set of left-5 regions while MS2 is moved to the top sparsest region from the set of right-5 regions. In Figure 1, it can be seen that the set of the left five regions is from \(R_{1}\) to \(R_{5}\) and the set of the right five regions is from \(R_{6}\) to \(R_{10}\). In case, no sparse region is found in any one set of regions then the sink is moved randomly to specific set region irrespective of density or cluster. Mobile sinks are placed at the midpoints of the region. Midpoints of any region can be calculated by Equation (4):  
\[M_{R_{n}}=\Bigg{(}\frac{(\mathbf{x}_{1_{R_{n}}}+\mathbf{x}_{2_{R_{n}}})}{2},\frac{(\bm {y}_{1_{R_{n}}}+\mathbf{y}_{2_{R_{n}}})}{2}\Bigg{)}. \tag{4}\]  
In the proposed scheme, MS1 is restricted to the left set and MS2 is restricted to the right set. Mobile sinks in every round move to the alternate region of their respective set. For example, if MS1 was in \(R_{3}\) initially, then, in the next round, it will be moved to R5. The movement of mobile sinks is shown in  
Figure 1: Network model.  
Figure 1. The mathematical model for the movement of MS1 and MS2 is given in Equations (5) and (6), respectively:  
\[\textbf{MS1 Region}=\begin{cases}(\textbf{MS1 Region}\mod 5)+2\\ 2&\text{if }\textbf{MS1 Region}==5,\\ 1&\text{if }\textbf{MS1 Region}==6,\end{cases} \tag{5}\]  
\[\textbf{MS2 Region}=\begin{cases}(\textbf{MS2 Region}\mod 10)+2\\ 6&\text{if }\textbf{MS2 Region}==11,\\ 7&\text{if }\textbf{MS2 Region}==10.\end{cases} \tag{6}\]  
Clustering in the dense regions is same as it was in SEEC [11]. In dense regions, sensor nodes forward data to the selected CH. CH then forwards the received data either to static sink directly or to the MS. If CH fails to approach the static sink or MS, then the neighbor node is searched that has either of the sinks in its range.  
For the selection of neighbor node, distance between CH and sensor node should be less than transmission range and greater than threshold range. Distance is calculated using Equation (7):  
\[d=\sqrt{((\textbf{x}_{2}-\textbf{x}_{1})^{2}+(\textbf{y}_{2}-\textbf{y}_{1})^ {2})}. \tag{7}\]
## 4 DPI Applied to Honeynet Traffic and Attacks
### Layer 5 to 7 Payload Analysis
#### 4.4.2 Evaluating Mirai Details  
To analyze and check the modus operandi of the data regarding Mirai botnet towards the Honeynet, a social analysis network as a graph visualization was created as seen in Figure 11.  
In the corresponding graph, the green nodes represent IP addresses while the orange nodes represent the contents of Telnet _data_ field. The relationship between two nodes indicates which data each IP transmitted and the thicker the edge, the more packets with this data value were transmitted by this IP.  
Figure 10: Users (**a**) and passwords (**b**) guessed by the Mirai botnet.  
Although not all IP addresses and Telnet _data_ appear in Figure 11 due to the limited number of nodes and relationships, it may be inferred that some IP addresses transmitted many times _enable_, _system_, _shell_ and _sh_ commands and this kind of data corresponds to the behavior of Mirai, thus providing evidence that this malware is widely available on the Internet with compromised devices.  
Though botnets are not easily detected, as they continuously evolve and adapt to detection techniques [73], Mirai transmits specific and identifiable commands, such as /bin/busybox MIRAI. Therefore, DPI, and other devices deployed to improve the network security, may be configured to drop packets containing these suspicious strings. In addition, as Mirai has hardcoded credentials to be guessed, the appearance of many packets transmitted with these usernames and passwords provide  
Figure 11: Evidenced _modus operandi_ of Mirai botnet.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Username Guessed** & **Number of Packets** & **Password Guessed** & **Number of Packets** \\ \hline root & 257,044 & root & 104,522 \\ admin & 91,141 & admin & 50,649 \\ support & 16,825 & xc3511 & 32,434 \\ user & 11,272 & vizv & 19,825 \\
88888 & 8761 & password & 15,087 \\ guest & 8076 & 12345 & 14,762 \\
666666 & 5160 & 123456 & 12,620 \\ service & 3513 & support & 11,493 \\ ubnt & 3501 & juantech & 10,897 \\ supervisor & 3402 & xmhdipc & 10,880 \\ tech & 3360 & default & 10,827 \\ Administrator & 2562 & 888888 & 10,689 \\ admin1 & 2488 & admin1 & 10,678 \\ administrator & 2403 & 1111 & 8673 \\ mother & 1320 & pass & 8578 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The fifteen most guessed usernames and passwords by the Mirai botnet.  
evidence of malicious activity. This analysis raises awareness regarding the importance of changing default credentials and having strong passwords.
## 2 Materials and Methods
### Numerical Investigation
#### 2.2.3 Analysis Procedure  
In the field situations where a small deformation is considered to indicate failure, a standard finite element analysis was used for onshore foundations and excavation problems based on serviceability limits. For this kind of behavior, a conventional Eulerian finite element approach is used to solve the small deformations for a constant volume. Lagrangian approaches are typically used in other applications where large or substantial deformations are expected, such as reinforced embankments (e.g., using geosynthetics) or offshore applications, and in particular for the failure situations addressed here. The Lagrangian formulation is accessible in Plaxis 2018 by enabling the updated mesh option, and this was the method used in this investigation. Figure 5a,b show an example of the differences between computed results with and without an updated mesh for uplift and lateral capacity of the helical pile type SHP-4-8.75, respectively. For uplift loading, small difference is observed, and for lateral loading, the result starts to diverge at large deformation. Since the failure criteria affects the interpreted capacity from the numerical analysis. The updated mesh analysis is carried out in this work assuming the system undergoes large deformation under failure conditions.  
Figure 4: Generated Mesh with mesh dimensions.  
Figure 5: Conventional analysis (Eulerian) vs. Update mesh analysis (Lagrangian) under (**a**) Uplift loading; and (**b**) Lateral loading.  
For the numerical simulations, an elastic perfectly plastic constitutive soil model was utilized in conjunction with Tresca yielding criteria. This model was chosen because of its simplicity, ability to address yielding around the anchor, and limited computational complexity, as well as interaction between the shaft and plates. Thus, lateral and pullout anchor capacities, which are reliant on deformation are somewhat dependent on the simulated elastic stiffness also [34].
# A Simple Estimation Method of Weibull Modulus and Verification with Strength Data
## 4 Material Strength Data
### Composites
test & D & [97] \\ CFRP unidirectional & 1660.0 & 6.17 & 7.04 & 6.79 & 78 & 1.037 & RA resin & V & [97] \\ CFRP unidirectional & 2428.0 & 5.46 & 6.48 & 6.01 & 52 & 1.078 & Eproxy resin & V & [97] \\ CFRP unidirectional & 496.0 & 31.9 & 15.57 & 17.44 & 17.12 & 19 & 1.018 & Fiber fraction 0.28 & V & [98] \\ Woven CFRP & 246.0 & 7.94 & 9.34 & 8.73 & 15 & 1.070 & PM6 resin & V & [99] \\ Woven CFRP & 316.4 & 9.80 & 11.70 & 10.78 & 15 & 1.085 & Dispersion treated & V & [99] \\ GFRP unidirectional & 528.7 & 39.0 & 13.56 & 13.90 & 14.91 & 10 & 0.932 & Strain rate 0.0017/s & D & [100] \\ GFRP unidirectional & 541.6 & 56.9 & 9.52 & 9.53 & 10.47 & 10 & 0.910 & Strain rate 25/s & D & [100] \\ GFRP unidirectional & 585.0 & 33.9 & 17.26 & 16.30 & 18.98 & 9 & 0.859 & Strain rate 50/s & D & [100] \\ GFRP unidirectional & 633.7 & 50.5 & 12.55 & 11.95 & 13.80 & 9 & 0.866 & Strain rate 100/s & D & [100] \\ GFRP unidirectional & 740.6 & 78.0 & 9.49 & 9.54 & 10.44 & 9.0 & 0.913 & Strain rate 200/s & D & [100] \\ GFRP reinforcing bar & 1818.0 & 47.0 & 38.68 & 40.00 & 42.55 & 5 & 0.940 & 14-mm diameter & V & [101] \\ GFRP reinforcing bar & 1653.0 & 46.0 & 35.93 & 36.00 & 39.53 & 5 & 0.911 & 18-mm diameter & V & [101] \\ GFRP reinforcing bar & 2010.0 & 11.10 & 18.11 & 21.00 & 19.92 & 5 & 1.054 & 12-mm diameter & V & [101] \\ GFRP reinforcing bar & 1927.0 & 91.0 & 21.18 & 24.00 & 23.29 & 5 & 1.030 & 16-mm diameter & V & [101] \\ GFRP short fiber & 257.0 & 31.1 & 8.26 & 9.24 & 9.09 & 20 & 1.016 & Sheet molding compound & D & [102] \\ ZrO\textgreater{}{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}{}_{}_{}{}_{}_{}_{}{}_{}_{}_{}{}_{}_{}{}_{}_{}_{}{}_{}_{}_{}{}_{}_{}_{}}{ _{}_{}_{}{}_{}{}_{}_{}{}_{}{}_{}_{}{}_{}}{}_{}_{}{}_{}_{}{}_{}{}_{}_{}{}_{}{} \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Composites** & **<\textgreater{}** & **S** & **<\textgreater{}**/SD** & **m\({}_{\text{obs}}\)** & **m\({}_{\text{test}}\)** & **N** & **m\({}_{\text{obs}}\)/m\({}_{\text{test}}\)** & **Note** & **D/V** & **Ref** \\ \hline Zirconia 10\%–TiO\({}_{2}\) & 455.7 & 48.4 & 9.42 & 10.50 & 10.36 & 30 & 1.014 & with 2.7\% Y\({}_{2}\)O\({}_{3}\) & V & [104] \\ Zirconia 10\%–TiO\({}_{2}\) & 439.4 & 65.4 & 6.72 & 8.70 & 7.39 & 30 & 1.177 & with 2.7\% Y\({}_{2}\)O\({}_{3}\) & V & [104] \\ Zirconia 30\%–TiO\({}_{2}\) & 336.0 & 38.7 & 8.68 & 11.70 & 9.55 & 30 & 1.225 & with 2.1\% Y\({}_{2}\)O\({}_{3}\) & V & [104] \\ Zirconia 30\%–TiO\({}_{2}\) & 334.2 & 43.6 & 7.67 & 9.90 & 8.43 & 30 & 1.174 & with 2.1\% Y\({}_{2}\)O\({}_{3}\) & V & [104] \\ SiC/SS composite & 597.0 & 70.0 & 8.53 & 10.20 & 9.38 & 34 & 1.087 & Flexure strength & D & [105] \\ C/SiC composite & 101.8 & 11.9 & 8.56 & 9.00 & 9.42 & 11 & 0.956 & Tensile strength & D & [106] \\ \hline \hline \end{tabular} D/V stands for data type of D = digitized and V = Values from the literature. Ref = reference number. CFRP and CFRP stand for carbon fiber and glass fiber reinforced plastics.  
\end{table} 
Table 6: _Cont._
## 3 Results and Discussions
### Shear Stress Ratio  
In the context of earthquakes, the cyclic shear stresses induced in soil layers during seismic activity are of critical concern. This section focuses on the depth-wise variation in the maximum shear stress ratio (the maximum shear stresses experienced by the soil layers divided by the vertical effective stress acting on them) for all the considered site profiles and input motions. Figure 8 depicts the average maximum shear stress ratio response (red solid line) with the depth in each plot for all six site profiles considered in this study. The black dashed lines in the plots illustrate the shear strength ratio of the site profiles.  
Figure 8: Depth-wise variation in average maximum shear stress ratio (red solid line) and shear strength ratio (black dashed line) for site profiles S-1 to S-6. The plots show a decreasing shear stress ratio with depth due to increasing vertical effective stress, with elevated values at soft soil layers.  
The results revealed a depth-wise decreasing trend in the average shear stress ratio values for all the considered site profiles and applied ground motions. The average shear stress ratio values at the surface for S-1, S-2, S-3, S-4, S-5, and S-6 were found to be 0.55, 1.29, 0.75, 0.54, 0.23, and 0.25, respectively. However, the average shear stress ratio values for the same sites decreased to 0.29 (9.906 m), 0.68 (4.80 m), 0.63 (11 m), 0.33 (17.526 m), 0.19 (32 m), and 0.20 (35.25 m), respectively. This decreasing trend can be attributed to the increase in vertical effective stresses (\(\sigma\)) with depth due to the rise in overburden pressure. Deeper soil layers tend to be more consolidated and compacted due to the greater confining pressure exerted by the weight of the soil above. Furthermore, shear stresses induced by seismic shaking are generally higher near the surface and decrease with depth due to the attenuation of seismic waves. Therefore, while the absolute shear stress may increase with depth due to the weight of the overlying soil layers, the shear stress ratio (\(\tau/\sigma\)) typically decreases with depth because the increase in vertical effective stress outpaces the increase in shear stress values.  
The results also indicated higher shear stress ratio values at certain depths relative to the overlying layers, along with a slow rate of decrease in the shear stress ratio values with depth. Such unconventional trends can be attributed to the presence of soft soils, and higher cyclic stresses induced in the soil layer. Additionally, upon reaching the soil's maximum shear strength ratio (indicated by the black dashed lines), the rate at which the maximum shear stress ratio increased diminished. This trend arose because the shear modulus reduction curves were calibrated to asymptotically approach the soil's shear strength at high shear strains. Consequently, the shear stress ratio also gradually approached the shear strength ratio lines, resulting in a decrease in the rate of increase in the shear stress ratio. The findings from [72] corroborate the results of the current study.
## 3 Applications
### Disaster Management  
Emergency handlers handle disasters as recurring events. Disasters are likely to happen every single year, resulting in a follow-up process. The emergency handlers handle a disaster in four phases: preparedness, response, mitigation, and recovery. Disasters happening in water bodies are highly dangerous. This may lead to the loss of human lives and other living organisms in the water [76]. IoUT applications play an important role in all four phases of such emergencies. The IoUT has the ability to sense locations in underwater circumstances. It can process the data locally and transmit the same to the base station via an underwater communication facility available in the IoUT [77]. Seismic sensors can be deployed underwater to detect ground motion when it is shaken by any perturbation. The information received from the underwater sensors should be reliable enough for further analysis and mitigation activities. All course of actions in a disaster rely on the information communicated by the sensor. The IoUT uses acoustic signals for underwater communication. In natural environments and circumstances, the sound emitted becomes affected by the underwater temperature, the density of vegetation, humidity, and various other sounds emitted by living organisms underwater. Besides the above factors, human-involved activities such as industrialization and sounds from ships can also affect the signal. This results in the failure of communication or the failure in anticipating a disaster. Hence, this communication needs to be secured enough wherein the information communicated by the devices reaches the base station on time without unwanted modification. Some factors relevant to acoustic communication were addressed in [78]. Blockchain technology can be used in this regard to protect underwater acoustic communication. Due to the complexity of computation and the underwater scenario, the authors in [79] suggested the use of basic blockchain functions such as adding blocks, block validations, and the implementation of the longest chain rule. Each chain created with blockchain consists of a ledger system to store the information. The ledger is an append-only block structure, which does not allow anyone to remove or modify the block, once it is added to the chain. Through blockchain, communication underwater can be made reliable and instant actions can be taken to prevent disasters. The integrity of information generated by the underwater sensors can thus be preserved using the blockchain-based solutions. This will further aid in taking the right course of action by predicting the disasters that can occur in the oceanic environment.