esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Introducing the Multi-Linear Model to Advanced Control System Design
### Multi-Model Predictive Control  
The proposed multi-linear model predictive control (MMPC) can be seen as an upgraded version of linear model predictive control (LMPC). In MMPC, there are basically two methods to integrate the multi-linear model with the model predictive control (MPC) algorithm: the controller weighting method and the model weighting method. In the controller weighting method, the multi-linear model is utilized indirectly; multiple LMPCs are designed for each selected operating point and then the control move is calculated by weighting the outputs of the local LMPCs using the scheduling function of the multi-linear model [45]. The advantage of the controller weighting method is that the local controllers can be tuned easily based on the local linear models. In the model weighting method, the multi-linear model is directly used for prediction and only one controller is designed, which can reduce the online computational cost. However, the tuning of such a controller can be difficult, since the multi-linear model is actually time-varying. Hence, complex design algorithms which can guarantee the stability of the model weighting-based MMPC have been studied [49]. The control structures of the two methods are presented in Figure 15.  
Figure 15: \(Cont.\)  
In practice, MMPC is more attractive than nonlinear model predictive control (NMPC), as it can be easily applied by modifying the LMPC algorithm. Moreover, the NMPC has the problem of local minimal solution and heavy online computational cost, which makes it a theoretical concept rather than a practical solution [50].  
In comparison with LMPC, MMPC can significantly improve the flexibility of the CO\({}_{2}\) capture plant in a wide operating range, because it has a much better prediction model.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 4 Experimental Results
### Discussion of Experimental Results  
The experimental results on NSL-KDD, CSE-CIC-IDS2017, and CSE-CIC-IDS2018 demonstrate the advantages of GAN-based data augmentation methods over traditional oversampling techniques, particularly in minority-class detection. While some improvements in F1-score may appear numerically small (e.g., from 0.168 to 0.200), these gains are directionally consistent across datasets and GAN variants and frequently observed in the most underrepresented categories. In practical intrusion detection scenarios, even modest performance gains on rare attack types can significantly reduce the risk of undetected threats.  
The integration of SNNL further improves minority-class performance by enhancing the feature-space alignment between synthetic and real samples. The consistent upward trends across multiple metrics, including macro-F1, indicate that these improvements are not incidental. For instance, WGAN-GP+SNNL achieves macro-F1 gains of 3.3%, 1.0%, and 2.0% over WGAN-GP on NSL-KDD, CSE-CIC-IDS2017, and CSE-CIC-IDS2018, respectively. Notably, in cases where recall differences appear small for a single class (e.g., 0.110 vs. 0.112), performance remains at a comparable level while other categories exhibit clearer improvements--together contributing to a more balanced and reliable classifier overall.  
In the infiltration class of CSE-CIC-IDS2017, traditional methods exhibit slightly higher recall, leading to marginally better F1-scores. However, SNNL-integrated GANs consistently outperform their non-SNNL counterparts on this class, suggesting that even in challenging scenarios, SNNL contributes to performance improvement.  
In terms of computational cost, we conducted three repeated experiments comparing WGAN and WGAN+SNNL on the CSE-CIC-IDS2017 dataset. On average, incorporating SNNL increased training time per epoch from 102.26 s to 128.41 s (a 25.5% overhead), while peak GPU memory usage remained effectively unchanged. These results show that the additional cost is moderate and manageable, particularly in offline training scenarios where detection effectiveness is prioritized over latency.  
Lastly, performance on majority classes remains stable--or even slightly improved--after incorporating SNNL. This confirms that the proposed method improves class-wise detection balance without sacrificing overall accuracy or majority-class performance, underscoring its practicality for real-world NIDS applications with severe class imbalance.
# Optimisation of Energy Transfer in Reluctance Coil Guns: Application to Soccer Ball Launchers
## 5 Results and Discussion
### Discussion on Simulation Results
#### 5.1.3 Impact of a Variation of the Coil Triggering Instants  
Having a powerful coil gun is important, but its behaviour robustness is also a key factor. This is especially true for the triggering instants which are important parameters for optimizing power transmission and have to be tuned carefully.  
In the case of a two-coil EML, simulations show that the second coil optimal triggering instant is 10 ms after the first coil. Table 3 shows that a variation of this triggering instant of \(\pm\)1 ms has a very limited impact on the ball speed, which is only reduced by less than 2%, leading to a good robustness of this system.  
In the case of a four-coil EML, the fourth coil optimal triggering instant is 15.5 ms after the first coil. Table 4 shows that a variation of this triggering instant of \(\pm\)1 ms has an important impact on the ball speed which is reduced by 11% or more. Consequently, the EML using four coils is far less robust than the EML using two coils in terms of sensitivity to the coil-triggering instants.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Coils** & 1 & 2 & 3 & 4 \\ \hline
**Optimized Ball Speed (m\(\cdot\)s\({}^{-1}\))** & 13.5 & 16 & 16.1 & 16.3 \\ \hline
**Kicking Range (m)** & 18.6 & 26.1 & 26.4 & 27 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimal ball speed depending on the number of coils in the EML.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Triggering Instant (ms)** & 7 & 8 & 9 & 10 & 11 & 12 & 13 \\ \hline
**Optimized Ball Speed (m\(\cdot\)s\({}^{-1}\))** & 14.4 & 15.2 & 15.8 & 16.1 & 16 & 15.8 & 15.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Impact of a variation of the second coil triggering instant in the case of a 2 coils EML.
# Efficient Opportunistic Routing Protocol for Sensor Network in Emergency Applications
## 5 Simulation Model, Setup and Results
### Performance Metrics  
The performance metrics to measure the performance of the proposed ODCR protocol are defined as follows:  
* Energy consumption is the total energy consumed by the sensor nodes in the network over a number of rounds.
* Throughput is the amount of data transmitted by a node per second. We calculate throughput in Gigabytes/second in this paper.
* Packet delivery ratio is the ratio of the total number of packets delivered to the total number of packets transmitted.
* Packet loss ratio is the ratio of the total number of packets lost to the total number of packets transmitted.
## 4 LECP-CP Details
### Cluster Setup Phase
#### 4.1.1 Node Local Energy Consumption Prediction Phase  
When selecting CHs, LECP-CP firstly needs to predict the local energy consumption ratio of a node and then determines whether it has the ability to act as a CH.  
Each node broadcasts a \(Node\_Msg\) within radius \(R_{a}\) with the following two values: the node \(id\) and its current energy \(E_{cur}\). At the same time, it receives the \(Node\_Msgs\) from its neighbor nodes, each node can compute its approximate distance to its neighbor nodes based on the received signal strength; according to which, each node can predict its local energy consumption and calculate its local energy consumption ratio. Depending on the ratio, each node can determine whether it has the ability to act as a CH or not. For any node \(s_{i}\), we define its local energy consumption \(ratio(s_{i})\) as follows:  
\[ratio(s_{i}) = \sum_{i=1}^{n+1}E_{con}(s_{i})/\sum_{i=1}^{n+1}E_{cur}(s_{i}) \tag{3}\] \[= \{6(n+1)E_{elec}+(n+1)\left(E_{sen}+E_{com}\right)\] \[+(21+n)\varepsilon_{fs}R_{a}^{2}\}l/\sum_{i=1}^{n+1}E_{cur}(s_{i}).\]  
where \(E_{cur}(s_{i})\) denotes the current energy of node \(s_{i}\), \(E_{con}(s_{i})\) denotes the energy consumption of node \(s_{i}\) and \(n\) is the number of nodes within radius \(R_{a}\) of \(s_{i}\). The detailed derivation process of the local energy consumption ratio of nodes is offered in Section 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**State** & **Description** \\ \hline \(Candidate\) & Candidate node \\ \(Head\) & Cluster head node \\ \(Plain\) & Plain node \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of node state messages.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Message** & **Description** \\ \hline \(Node\_Msg\) & Tuple(selfid, selfenergy) \\ \(Head\_Msg\) & Tuple(selfid) \\ \(Join\_Msg\) & Tuple(selfid, headid) \\ \(Schedule\_Msg\) & Tuple(schedule, order) \\ \(Route\_Msg\) & Tuple(selfid, selfratio) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of control messages.  
For each node \(s_{i}\), we give the following formula to calculate its waiting time \(t_{i}\) for broadcasting a \(Head\_Msg\).  
\[t_{i}=ratio(s_{i})\cdot T_{2}\cdot V_{r}. \tag{4}\]  
where \(V_{r}\) is a real value randomly distributed in [0.9, 1], which is introduced to reduce the probability that two nodes send \(Head\_Msgs\) at the same time.  
According to Formulas (3) and (4), we can see that the smaller the local energy consumption ratio of node \(s_{i}\) is, the shorter the waiting time \(t_{i}\) is; thus, it is easier to be selected as the CH.
## 4 Results Analysis  
### Data Description  
Temperature fluctuations can lead to variations in assembly steel strut axial force, particularly under the influence of diurnal temperature differences, causing the axial force to oscillate around its design value to a certain extent. If these fluctuations are not effectively predicted and controlled, they may threaten the stability of the foundation pit support system and even pose safety risks. To analyze the relationship between assembly steel strut axial force and temperature changes in greater detail and to validate the effectiveness of the proposed method, this study uses axial force monitoring data from six assembly steel struts in the second layer of Zone 5 at Sanbao Station as experimental data.  
This study utilizes monitoring data from assembly steel struts ZC2-1 to ZC2-6 collected between 22 October 2019 and 26 December 2019. The data were recorded hourly, capturing daily temperature variations and their corresponding impact on the axial force of assembly steel struts over consecutive days, as illustrated in Figure 8. To ensure the scientific validity of the model training and evaluation, the dataset was divided into three parts: 70% for model training, 10% for validation, and 20% for testing. By modeling and analyzing this dataset, the study uncovers the fluctuation patterns of assembly steel strut axial forces under daily temperature changes. Furthermore, the prediction results provide 4 h forecasts of axial force, offering technical support and data-driven insights for safety monitoring during foundation pit construction and stability assessment of the support system.  
Figure 7: Layout of the second layer of assembly steel struts with servo system in zone 5 of Sanbao Station.
## 3 Variational Autoencoder  
The VAE is an autoencoder variant aiming to reconstruct the input at the output layer [19]. The main difference between the VAE and an ordinary autoencoder is that the former assumes that the observed data \(\mathbf{x}\) is generated from a random latent variable \(\mathbf{z}\) which has a specific prior distribution, such as the standard Gaussian. The VAE is composed of two directed networks: encoder and decoder networks. The encoder network outputs the mean and variance of the posterior distribution \(p(\mathbf{z}|\mathbf{x})\) given an observation \(\mathbf{x}\). Using the latent variable distribution generated by the encoder network, the decoder network tries to reconstruct the input pattern of the VAE at the output layer.  
Given a training sample \(\mathbf{x}\), the VAE aims to maximize the log-likelihood, which can be written as follows [19]:  
\[\text{log}p_{\theta}(\mathbf{x})=D_{\text{KL}}(q_{\phi}(\mathbf{z}|\mathbf{x} )\|p_{\theta}(\mathbf{z}|\mathbf{x}))+\mathcal{L}(\theta,\phi;\mathbf{x}). \tag{9}\]In (9), \(\phi\) denotes the variational parameters and \(\theta\) represents the generative parameters [19]. The first term on the right-hand side (RHS) of (9) means the Kullback-Leibler divergence (KL divergence) between the approximated posterior \(q_{\phi}(\mathbf{z}|\mathbf{x})\) and the true posterior \(p_{\theta}(\mathbf{z}|\mathbf{x})\) of the latent variable, which measures the dissimilarity between these two distributions. Since the KL divergence has a non-negative value, the second term on the RHS of (9) becomes the variational lower bound on the log-likelihood, which can be written as:  
\[\begin{split}\text{log}p_{\theta}(\mathbf{x})\geq& \mathcal{L}(\theta,\phi;\mathbf{x})\\ =&-D_{KL}(q_{\phi}(\mathbf{z}|\mathbf{x})\|p_{ \theta}(\mathbf{z}))\\ &+\mathbb{E}_{q_{\phi}(\mathbf{z}|\mathbf{x})}[\text{log}\,p_{ \theta}(\mathbf{x}|\mathbf{z})],\end{split} \tag{10}\]  
where \(q_{\phi}(\mathbf{z}|\mathbf{x})\) and \(p_{\theta}(\mathbf{x}|\mathbf{z})\) are respectively specified by the encoder and decoder networks of the VAE.  
The encoder and the decoder networks of the VAE can be trained jointly by maximizing the variational lower bound, which is equivalent to minimizing the following objective function [24]:  
\[E_{VAE}(\mathbf{x})= D_{KL}(q_{\phi}(\mathbf{z}|\mathbf{x})\|p_{\theta}( \mathbf{z}))\] \[-\mathbb{E}_{q_{\phi}(\mathbf{z}|\mathbf{x})}[\text{log}\,p_{ \theta}(\mathbf{x}|\mathbf{z})]. \tag{11}\]  
The first term on the RHS of (11) is the KL divergence between the prior distribution and the posterior distribution of the latent variable \(\mathbf{z}\), which regularizes the encoder parameters [19]. On the other hand, the second term can be interpreted as the reconstruction error between the input and output of the VAE. Thus, the VAE is trained not only to minimize the reconstruction error but also to maximize the similarity between the prior and posterior distributions of the latent variable.
## 5 New Challenge: Safety
### Additional Safety Provided by HMIs of AVs
Furthermore, advances in predictive analytics and machine learning enable HMIs to anticipate potential hazards before they escalate into imminent threats. By analyzing patterns in driving behavior and environmental conditions, these systems can provide preemptive warnings or automatically prepare to intervene when necessary. For example, upcoming intersections, sudden braking patterns from other vehicles, or changing weather conditions can trigger specific adjustments in display information or initiate autonomous responses, further enhancing safety.
## 3 Innovation
### Data Innovation
#### 3.2.3 The Combination of Sensors Enhances the Concentration of Information  
The strategy of integrating diverse data sources can significantly enhance the informational dimension, consequently improving the accuracy and efficiency of soil salinization monitoring [27]. Direct measurement of soil spectra during crop growth is often influenced by vegetation cover, which limits the applicability of remote sensing technology in monitoring soil salinity to some extent [72]. [PERSON] et al. [73] addressed issues related to obstacles in plant water uptake and growth inhibition caused by high soil salinity. They proposed a monitoring method that integrates spectral information from the vegetation canopy with crop aboveground growth parameters to more accurately assess the salinization status of soil in the root zone. To achieve the inversion and dynamic monitoring of soil salinity on a regional scale, it is imperative to effectively coordinate the complementarity between images of varying resolutions and to explore their intrinsic connections thoroughly. Focusing on the Yellow River Delta as the study area, [PERSON] et al. [74] constructed conversion coefficients for spectral indicators between Landsat and MODIS images, optimizing them using the mean ratio method. They proposed an upscaling conversion approach based on land use zoning, which not only improved the scale of soil salinization inversion but also enhanced the timeliness of monitoring.
## 3 Method  
### Overview of the Method  
Describing the method implies describing the inputs, outputs, interfaces, and processes necessary to achieve the objective of validating the OF model. An effective way to present this is through the flowchart in Figure 7, which can be further clarified with the following points:  
* The variable to validate is the combined towline tension, defined as the sum of the tensions in each towline, for various speeds through water \(\mathbf{u}_{\text{stw}}\). The span is the distance between the wing's extremities as depicted in Figure 8. The figure also shows the System Length (\(SL\)), towlines, wings, and Retention Zone (RZ). The System Ratio (\(SR^{*}\)) is defined as the ratio between the span and the system length (\(SR^{*}=span/SL\)). Three span values are considered as shown in Table 1 for System 002's length equal to 800 m:
* The OF model validation was performed in three cycles: 1. **First cycle**: Simulation based on the initial estimation of the OF axial drag coefficient \(C^{*}_{D,\text{OF},\text{a}}\). This step is detailed in Section 3.2.
2. **Second cycle**: Triggered by discrepancies between the model and the GPGP data, especially for a narrow span, we used the AS model of a 1 m \(\times\) 1 m piece of the system's net at multiple \(\theta\). Then, the difference between the drag forces on a 1 m \(\times\) 1 m section of the net from OrcaFlex and AquaSim was calculated and written as: \[\Delta\mathbf{f}_{D}=\mathbf{f}_{D,\text{OF}}-\mathbf{f}_{D,\text{AS}}\] (20) Then, the Root Mean Square (RMS) of the set of differences for a group of \(\theta\), corresponding to a certain span, can be calculated: \[RMS=\sqrt{\frac{1}{N_{\theta}^{*}}\sum\limits_{j=1}^{N_{\theta}^{*}}|\Delta\mathbf{ f}_{D}|_{j}^{2}}\] (21) where \(N_{\theta}^{*}\) is the total number of angles of attack for the optimization case. The optimization objective is to obtain the \(C^{*}_{D,\text{OF},\text{a}}\) value that produces the minimum RMS for the specific optimization case. In that way, the OF drag force will be close to the AS drag force. With this optimization, the accuracy of a twin-by-twine calculation and the shielding effect can be included in OF, leading to more-accurate load estimations. Further details about the OrcaFlex modeling are seen in Section 3.2.  
3. **Third cycle**: Triggered by the necessity to verify the AS results, using a CFD model, a two dimensional piece of the net was simulated for various \(\theta\) to quantify the effect of the vortex shedding on the twines in tandem on the average drag coefficient. This gives a piecewise definition of the average drag coefficients of the net. They are given as a function of \(u_{\text{stw}}\) and \(\theta\) and used in the AS drag force Equation (28), replacing the AS drag coefficients. Then, the same optimization (Equations (20) and (21)) can be carried out one more time.  
Figure 7: Method flowchart.
# Research on Sentiment Analysis of Online Public Opinion Based on RoBERTa-BiLSTM-Attention Model
## 5 Results
### Sentiment Focus Analysis  
In order to explore the focus of the public's negative and positive sentiments, keyword frequency counts of comments on negative and positive sentiments were conducted next, with a view to finding enlightening results.  
The analysis of the frequency of keywords for positive emotions showed that the focus of the public's positive emotion was on the truth, expert identification, investigation team identification, disposal methods, and so on. Keywords such as 'hope, good, like, haha' indicated that the public had positive emotions and attitudes towards the investigation results; keywords such as'really, truth, facts' indicated that the public was seeking and emphasising the authenticity of foreign objects; 'official, Government, department' and other keywords may have been related to the actions or statements of the investigation team; \"expert, professional\" and other keywords indicated that the public attached importance to the opinions of authoritative experts. Keywords such as 'investigation, testing, appraisal' were related to the investigation and analysis of the truth of the incident; keywords such as 'credibility, regulation' may have been related to the public's trust in the government or organisations and their ability to regulate; keywords such as'support, solution' indicated the public's support for the investigation team's actions or statements. Keywords such as  
Figure 4: Emotional evolutionary trends.  
'support, solution' indicated the public's supportive attitude towards finding a solution to the incident.  
Analysis of the frequency of keywords for negative sentiment showed that the public's negative sentiment focused on food safety, inadequate supervision, and leadership responsibility. Keywords such as 'rat's head, duck's head, foreign substance' were the abnormal ingredients related to the incident; keywords such as'school canteen, university canteen, food safety, hygiene' indicated that the public was concerned about food safety in school canteen. Keywords such as'making a deer out be a horse, imitate the rat as a duck, Zhao Gao' were new words derived from the incident, indicating the public's negative sentiment towards the act of covering up the truth; keywords such as 'leadership, director, headmaster, supervision, oversight, corruption' were related to the ineffective supervisory behaviour or decision-making of the departments and personnel involved in the incident, indicating the public's concern about the incident. The terms 'evidence, appraisal' were related to the evidence and appraisal process of the incident. Keywords such as 'public opinion, pressure, threat, punishment, dismissal' indicated the seriousness of the incident and the public's concern about the subsequent outcome.