esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analysis of Pre-Tightening Effects on U-Shaped Steel Clamping
### Principle of Yieldable Support  
As indicated by the preceding analysis, applying secondary pre-tensioning to the cable clamp joint can further enhance its slip resistance. Through integrating the ground reaction curve and the support characteristic curves of different structural types (Figure 3), the principle of the secondary pre-tensioning yielding support system for a U-shaped steel arch was analyzed.  
* Figure 3 illustrates all types of arch support systems that can be applied when the surrounding rock deformation reaches u\({}_{1}\). In the figure, curve 2 represents a high-strength non-yield-capacity arch (difficult to yield). Its high slip resistance intersects the ground reaction curve at Point a, requiring greater support force and resulting in higher construction costs. Curve 3 corresponds to a yieldable support without secondary pre-tightening. Its intersection with the ground reaction curve at Point b indicates excessive rock deformation and an insufficient safety margin, leading to inadequately effective support. During the slip phase represented by Curve 4, implementing secondary pre-tensioning when the yielding displacement reaches u\({}_{2}\) enhances slip resistance. The modified curve intersects the ground reaction curve at Point c, indicating moderate deformation of the rock and a high safety margin, thereby exemplifying an optimal configuration for yielding support.
## 4 Development of the Model
### ANN Model  
For ANN modelling, the hidden layers and neurons per layer need to be chosen carefully. Too many hidden layers or neurons can lead to overfitting [83]. In addition, they can affect the processing time of the model. Therefore, ANNs with two or three hidden layers were recommended by [PERSON] et al. [61] for the simple regression problems. In this study, an ANN model with a structure with two hidden layers was selected to estimate the ilmenite content. Its structure includes 5, 16, 10, and 1 neurons, for the input, the first and second hidden, and output layers, respectively (Figure 12). Unlike the previous models, the min-max scale method was applied to normalize the dataset in the range of (\(-\)1 to 1); 50 repetitions were performed to determine the initial weights and biases of the ANN model. Subsequently, the optimal weights and biases of the ANN model were calculated, as shown in Figure 12 through the black and grey lines.  
Figure 11: Performance of the \(k\)-nearest neighbors (kNN) model in this study.
# An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs
## 5 Simulation and Evaluation  
### Simulation Setup  
In this section, we describe our relevant simulation tests, which demonstrate the high efficiency and scalability of the suggested approach. The simulation was performed using the network simulator OMNeT++ for the proposed routing protocol in each case (measuring energy consumption and WSN lifetime) over large scale networks, and python scripts were written to implement the corresponding algorithm of clustering. The simulation was conducted on a Dell Inspiron 155,567 computer with an Intel i7 CPU running at 2.4 GHz, Windows 7 (64-bit), and 8 GB of RAM. We used network settings that are identical to those indicated in Table 4 throughout our simulations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline N & 1000 \\ \hline M & 13 \\ \hline CH & 30 \\ \hline N\({}_{\mathrm{ant}}\) & 500 \\ \hline \(\alpha\) & 0.5 \\ \hline S & 30 m \(\times\) 30 m \\ \hline \(\Delta\alpha\) & 0.05 \\ \hline K\({}_{1}\) & 0.1 \\ \hline K\({}_{2}\) & 0.15 \\ \hline N\({}_{\mathrm{Iteration}}\) & 500 \\ \hline min\_R\({}_{\mathrm{energy}}\) & 0.3 J \\ \hline max\_R\({}_{\mathrm{energy}}\) & 0.9 J \\ \hline E\({}_{\mathrm{R}-\mathrm{elec}}\) & 20 nJ/bit \\ \hline E\({}_{\mathrm{T}-\mathrm{elec}}\) & 45 nJ/bit \\ \hline E\({}_{\mathrm{fs}}\) & 10 Pj/bit/m\({}^{2}\) \\ \hline E\({}_{\mathrm{mp}}\) & 0.0013 Pj/bit/m\({}^{4}\) \\ \hline C\({}_{\mathrm{range}}\) & [10 m, 30 m] \\ \hline D\({}_{\mathrm{threshold}}\) & 30 m \\ \hline Initial\_E\({}_{\mathrm{threshold}}\) & 0.2J \\ \hline \hline \end{tabular}
\end{table}
Table 4: Setting Considered For Our Simulations.  
To develop our contribution, we used data from the Stanford Large Network Dataset Collection's Brightkite dataset, a location-based online social network [65]. There are 5000 nodes in the retrieved dataset, each with its coordinates. We deployed 13 BSs in the network, and we set up the nodes with varied residual energies ranging from 0.3 to 0.9.  
As known in the literature, hierarchical protocols and methods are state-of-the-art and based on clustering algorithms such as LEACH [57], DP clustering [61], and HEMP [39]. We then compared the previous algorithms with our contribution to demonstrate the proposed technique's performance. LEACH randomly selected and updated the CHs after a fixed amount of time. It also managed to send data from a CH to a specific BS in a random way. In the DP Clustering approach, the CHs are chosen based on the coordinate location of these nodes. The residual energy and node degree are considered while choosing the CHs in the HEMP. The shortest path is used in all approaches to send the data from the nodes to a specific BS.
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 3 Case Studies of PDoS Attacks
### StoneDrill  
StoneDrill emerged in 2012. Distinctive for its destructive payload and advanced evasion techniques, StoneDrill infiltrates systems primarily through phishing attacks and exploits zero-day vulnerabilities. Once embedded, it employs a unique in-memory execution style. It injects malicious code directly into the memory of the browser process, making it particularly elusive. The malware's payload is centered on data encryption and deletion, specifically targeting and overwriting a variety of file types. Its design enables it to bypass administrative controls and access core system areas, leading to extensive data loss and system corruption. One of the notable technical feats of StoneDrill is its use of advanced obfuscation techniques, making the analysis and reverse engineering of its code a significant challenge for cybersecurity experts.  
In effect, StoneDrill not only renders infected systems inoperable, but also necessitates comprehensive recovery efforts, often involving complete device replacement or extensive data recovery processes. Its impact is immediate and permanent, placing it among the more severe class of PDoS malwares.
## 4 Results and Discussion
### Pdr  
The PDR is defined as the ratio between the amount of received packets and the number of transmitted packets over the MANET, which is expressed in Equation (18). Table 4 shows the performance analysis of the PDR.  
\[PDR=\frac{Amount\ of\ received\ packets}{Amount\ of\ transmitted\ packets} \tag{18}\]  
Figure 4: Analysis of energy consumption.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Node Count**} & \multicolumn{4}{c}{**Energy Consumption (J)**} \\  & **CEELBRP [24]** & **EECRP-PSO [25]** & **HFF-GSO [28]** & **Proposed ECTSA** \\ \hline
50 & 11.1 & 10 & 9.1 & 7.1 \\
75 & 11 & 8.3 & 8.5 & 6.8 \\
100 & 10.5 & 7.8 & 7.7 & 6.3 \\
125 & 10.3 & 7.0 & 7.1 & 6.0 \\
150 & 10.3 & 7.0 & 6.3 & 5.4 \\ \hline \end{tabular}
\end{table}
Table 3: Performance of Energy Consumption.  
Figure 5 shows the comparison of the PDR for the CEELBRP [24], EECRP-PSO [25], HEFFGSO [28], and ECTSA. The PDR of the ECTSA is varied from 0.94 to 0.99. From the analysis, it is concluded that the ECTSA achieves better data delivery than the CEELBRP [24], EECRP-PSO [25], and HFFGSO [28]. The data delivery of the ECTSA is improved by avoiding node/link failure and selecting the path with a high DSR.
# A Relay Optimization Method for NOMA-Based Power Line Communication Systems
###### Abstract
Combining the predicted CSI with improved simulated annealing algorithm to optimize system power and relay positions. The simulated annealing algorithm is a stochastic optimization algorithm capable of escaping local optima and approaching global optima. By continuously adjusting relay positions and transmission power during the simulated annealing process, the optimal relay positions and minimum power configuration that meet system performance constraints are ultimately obtained. 4. Conducting extensive simulation experiments to verify the effectiveness of the proposed method. The results show that the combination of LSTM prediction and simulated annealing optimization significantly improves system transmission performance, reduces total power consumption, and maintains stable communication quality in complex PLC channel environments. The full-text structure flow chart is shown in Figure 1.  
This paper is organized as follows: Section 2 presents the multi-user relay NOMA PLC system model, including the noise model and signal model, and adopts frequency division multiplexing to improve system efficiency. Section 3 details the proposed optimization method combining deep learning and improved simulated annealing algorithms and its performance analysis. Section 4 discusses the simulation results. Section 5 concludes the paper.
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 4 Analysis of the Performance
### Analysis of the Network Performance  
#### 4.2.1 Network Load Distribution with Different Buffer Size  
First, we compare the distributions of the network load with different buffer sizes in nodes. Data packages are sent every five minutes and the sink node is at the center of the network. The residual energy is shown in the following figures. The node cache size of Figure 7a is 30 bytes. The node cache size of Figure 8a is 50 bytes. From the figures we can see that sensor nodes that are closer to the sink always have a heavier workload than edge nodes, which is consistent with the characteristics of sensor network convergence transmission, because all traffic flows to the sink node. The comparison between Figures 7a and 8a also found that there is an obvious peak in the central portion when the node has a smaller buffer size. This is because the possibility of retransmission rises due to the loss of data. The smaller the cache is, the easier for it to be full for the nodes closed to the center, which causes the loss of data. Figure 7a,c and 8a,c compare the energy consumption between Algorithms 2 and 4. From the figures we can see that the network lifetime will be longer if we use the energy balance to make the distribution of the energy more uniform. There is no need to consider the energy consumption of the sink node, since the sink node usually uses the mains-powered system instead of the battery-powered system. We can also find from the figures that Algorithm 2 has the best result. That is because it first considered the energy balance without end-to-end delay as the main factor. At the same time, the curve of this test is smooth. It shows that the load of nodes is almost the same if they are of the same distance to the sink node. However, RPL cannot ensure this. As a result, our algorithm can efficiently balance the load of the packets which are sent by the network. It can better ensure the load balance if there are less nodes. In the actual situation, as time goes, there will be some bottleneck. Their processing speed will be slowing down obviously, resulting in the cache of the surrounding nodes to drop. Using the algorithm we discussed in this essay will effectively remit the tension level of bottleneck nodes and the surrounding nodes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes & At most 89 \\ Area & 100 \(\times\) 100 m \\ communication radius & 10–20 m \\ Flow patterns and rates & CBR, 5 pkt/min \\ The size of packet & 126 bytes \\ Duration time & 1 h \\ Number of bottleneck & 5 \\ Minimum step size of RPL & MinHopRankIncrease = 128 \\ Trickle & Imin = 2\({}^{7}\) ms, Imax = 16 ms, k = 10 \\ MAC & IEEE 802.15.4 \\ Energy consumption model & According to CC2530 data sheet \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter setting.
# Advancing Spanish Speech Emotion Recognition: A Comprehensive Benchmark of Pre-Trained Models
## 5 Discussion and Comparative Analysis
### Fine-Tuning and Practical Applications  
Recent research indicates that fully fine-tuning large PTMs is not always essential to achieve competitive performance in classification tasks, even beyond speech-related domains [88]. In our experiments, the direct approach of freezing Spanish PTMs and using them solely as feature extractors already surpasses or matches existing SER benchmarks across multiple datasets. This observation aligns with the findings of [88], which indicate that freezing BERT can outperform fine-tuning it for classification, and that the best-performing models can be trained by freezing the main model, substantially reducing training time. Given the considerable resources required for large-scale fine-tuning, our results suggest that, particularly for Spanish SER tasks, the additional cost of exhaustive model adaptation is not strictly necessary to achieve state-of-the-art performance.  
Furthermore, our study highlights the remarkable performance of TRILLSson in speaker-independent contexts (LOSO), consistent with previous conclusions about its robustness in capturing paralinguistic cues [52]. TRILLSson's versatility has also been demonstrated in various clinical and diagnostic applications, such as cognitive impairment detection [89], clinical speech AI [90], and [PERSON]'s disease detection [91]. This capability to extract nuanced emotional information is equally beneficial for real-world SER applications. For instance, emotion recognition can be integrated into call centers to automate customer support or implemented in healthcare systems to monitor the psychological state of patients over time and facilitate the early detection of mood disorders.
# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
## 3 Challenges and Problem Statement
### Faults in WSNs
#### 3.1.4 Spike Fault  
The rate of data sensing and processing of the sensor nodes varies abruptly with the actual data rate of sensing and processing.  
The Spike fault is mentioned in Equation (5).  
\[\frac{\left|f(t)-f_{p}(t)\right|}{t}>\lambda \tag{5}\]  
where:  
\(f(t)\)--The actual sensed data.  
\(fp(t)\)--The predicted data at time \(t\).  
While \(\lambda\)--The sensed data changing speed with time \(t\).
## 5 Discussion
### Landsat Radiative Normalization  
As shown in Figure 8, the reflectance values of PIFs from the Landsat images of 2008 and 2012 were compared from band 1 to band 6. The linear regression models were built for each band, and the correlation coefficients were 76%, 79%, 86%, 84%, 87% and 84%, respectively. The significant correlation between the reflectance values of the same band in different time confirmed that the radiative correction and radiation normalization for Landsat images were done correctly and successfully. It provided a solid basis on which to apply the empirical model developed with the forest field AGB and the spectral information of the ETM+ image in 2012 to the same area with same forest types in 2008. However, because the spectral response of TM/ETM+ images were not linear with the increasing of forest AGB, we recommend incorporating other information, such as forest age, to further improve the accuracy of forest AGB estimation.  
Figure 7: The validation of BEPS-based forest AGB in 2012 using the results obtained from forest field-based AGB (**a**), and ALS-based AGB (**b**) at the plot level.
## 1 Historical Overview of Antibiotic Use
### Carbapenem-Resistant Enterobacteriaceae (CRE)  
Antibiotic-resistant bacteria are facing a more recent challenge with the presence of carbapenemases. Resistance to carbapenem antibiotics is commonly caused by \(\beta\)-lactamases carried on plasmids. _OXA65_, _NDM_, _IMP_, _VIM_, and _KPC_ are the most prevalent variants, with minimal occurrence in Western Europe [21; 22]. Hospital admissions in countries like India and Pakistan, where carbapenemases are prevalent, are often associated with infections from these isolates. The increasing occurrence of carbapenemases is a cause for concern because of the limited availability of other treatment options and the near absence of new antibiotics in development. An increase in the prevalence of isolates that produce carbapenemase could result in a situation comparable to the era before antibiotics, similar to the time prior to the discovery of penicillin [23; 24].  
Despite this, resistance to carbapenems has been associated with specific ESBLs such as _OXA-40_, _OXA-48_, and _OXA-23_. In the decade from 2000 to 2010, there was a 3% increase in Carbapenem-resistant _Enterobacteriaceae_ (CRE) infections in the USA, resulting in mortality rates reaching up to 48% because of limited treatment choices [25]. The clinical significance of mobile carbapenemases lies in their unique capacity to break down the \(\beta\)-lactam ring in carbapenems and their tendency to disseminate, making carbapenem resistance a crucial issue. In these instances, colistin may be used as a treatment, although resistance to this medication is spreading rapidly [26]. Previously, it was assumed that colistin resistance was exclusively associated with alterations in chromosomal DNA. That being said, it was in 2015 that the _mcr-1_ gene, associated with colistin resistance, was first recognized. In China, _E. coli_ isolates revealed the presence of _MCR-1_, an enzyme that modifies the colistin target, causing a reduction in drug efficacy [27]. There are currently eight known mcr variations, with at least two having a worldwide presence. Consequently, the range of available treatments is greatly restricted, and the increasing resistance to colistin in infections that are resistant to carbapenem is a major worry for worldwide health. The development and international spread of mobile carbapenemases and _mcr_ genes in clinical pathogen samples emphasize the scale and severity of the horizontal transfer of antibiotic resistance genes [28; 29; 30].
# PD Detection and Analysis Triggered by Metal Protrusion in GIS Through Various Methods
## 4 Discussion and Analysis
As the voltage increases, the maximum amplitude increases slightly due to the effects of space charges. Finally, leader discharge occurs, and breakdown also occurs. At this time, there is some extremely high amplitude discharge at the peak of the voltage. Therefore, the maximum amplitude increases fast. The PD of the protrusion on the enclosure first occurs at the positive half-cycle of the voltage since the protrusion is grounded. After that, the development of the PD is similar to that of the protrusion on the conductor. Partial discharge causes charge exchange, leading to the apparent charge on the test sample. The pulse current forms due to the apparent charge though the test circuit. The pulse current method detects the PD by measuring the pulse current. Therefore, the result of the pulse current method has a significant relationship with the circuit parameters, and the results are easily interfered with by noise. The results have shown that the sensitivity of the pulse current method is the lowest among the three methods. The reason for this is that the PD of the protrusion is low, while the noise of the circuit is high. The PD of the 12 mm protrusion on the conductor is detected by the pulse current method at a voltage of 235.12 kV, and the maximum magnitude is about 15 pC, as is shown in Figure 18. It can be seen from Figure 17 that the magnitude of the noise is nearly 10 pC. The ultrasonic signal of PD is caused by the charge neutralization, which produces the pulse current. The current makes the PD area expand due to the high temperature, which is similar to an explosion. After the discharge, the expansion area return to the original volume. The volume change causes the medium density to change, which forms the ultrasonic signal. The ultrasonic signal is only related to the discharge energy [12]. The UHF signal of PD is also caused by the pulse current. The rise in the current is at an ns level,; therefore, it radiate electromagnetic waves with a frequency of 0.3-3 GHz, which is the UHF signal. Because the electric field intensity of the radiated electromagnetic is positively related to the frequency of the pulse current, the UHF signal of PD in GIS is positively related to the rising gradient of the pulse current [22]. Figure 27: Leader discharge of protrusion defects.
## Pressure Model of Control Valve Based on LS-SVM with the Fruit Fly Algorithm  
**[PERSON] 1,2,3,* and [PERSON] 1,3**  
1 College of Mechanical Engineering, Shandong University, Jinan 250013, China;  
E-Mail: [EMAIL_ADDRESS]  
2 Mechanical & Electrical Department, Binzhou University, Binzhou 256600, China  
3 Key Laboratory of High-efficiency and Clean Mechanical Manufacture, Ministry of Education, Shandong University, Jinan 250013, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-543-319-0392.  
_Received: 18 May 2014; in revised form: 1 July 2014 / Accepted: 7 July 2014 / Published: 11 July 2014_  
**Abstract:** Control valve is a kind of essential terminal control component which is hard to model by traditional methodologies because of its complexity and nonlinearity. This paper proposes a new modeling method for the upstream pressure of control valve using the least squares support vector machine (LS-SVM), which has been successfully used to identify nonlinear system. In order to improve the modeling performance, the fruit fly optimization algorithm (FOA) is used to optimize two critical parameters of LS-SVM. As an example, a set of actual production data from a controlling system of chlorine in a salt chemistry industry is applied. The validity of LS-SVM modeling method using FOA is verified by comparing the predicted results with the actual data with a value of MSE \(2.474\times 10^{-3}\). Moreover, it is demonstrated that the initial position of FOA does not affect its optimal ability. By comparison, simulation experiments based on PSO algorithm and the grid search method are also carried out. The results show that LS-SVM based on FOA has equal performance in prediction accuracy. However, from the respect of calculation time, FOA has a significant advantage and is more suitable for the online prediction.  
**Keywords:** fruit fly algorithm; LS-SVM; control valve; parameter optimization; identification