esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
First of all, for data preprocessing, we need to remove the bad datum and abnormal values before attempting to calculate, then normalize the data to eliminate the effects of sample spans, and smooth the training process. The data is subjected to a fivefold cross validation for a more stable data model. Due to the particularity of the precipitation datum, the regional precipitation datum are distributed unevenly in time and space. The days of precipitation are obviously shorter than that of the total sample. Although the datum are pretreated, they still cannot completely remove the datum on the impact of the forecast results, and the degree of volatility is greater when the number of samples is small.  
We analyze the results of the optimization parameters \(C\) and \(g\) with the four methods. The PSO-SVM method based on the Particle Swarm Optimization algorithm has better experimental results, with a higher classification rate among the four parameters' optimization methods, which are the traditional M-SVM and the GA-SVM based on the Genetic algorithm, and the AC-SVM based on the Ant Colony algorithm. This indicates that the Particle Swarm Optimization algorithm has a strong ability to optimize the parameters of the Support Vector Machine \(C\) and \(g\), and the results are more accurate.  
In order to further analyze the performance problem of PSO-SVM based on the Particle Swarm Optimization algorithm and the algorithms based on bionic intelligence GA-SVM and AC-SVM, we use the data set whose sample size is 1000 as an example, and the generalization ability of these four algorithms is shown in Figure 5. In Figure 5, the abscissa is the FPRate (False positive rate) and the ordinate is the TPRate (true positive rate). The curve in Figure 5 is the ROC (Receiver Operating Characteristic) curve drawn from the wired sample. The two learners are judged by comparing the area under the curve, that is, the AUC (Area Under Curve). Secondly, we use MSE (Mean Square Error) to evaluate the stability of the four methods. It can be seen from the experimental results in Table 2 that the four methods are different for different samples of MSE, but on the whole, the SVM model parameters are selected by using the Particle Swarm Optimization algorithm, which is much more stable than the Genetic algorithm and the Ant Colony algorithm. With small samples, the accuracy of the PSO-SVM method and the GA-SVM method are considerable, but the PSO-SVM is more efficient, and, with the increase in the number of samples, PSO-SVM will be more stable and efficient. Compared with AC-SVM, PSO-SVM has higher classification accuracy; however, the computation time of the PSO-SVM model is slightly longer with the large data set. These four algorithms are better for large data classification. It can be seen from Table 2 and Figures 4 and 5 that the SVM based on Particle Swarm Optimization algorithm has better learning performance. Experiments show that SVM model parameter selection based on the Particle Swarm Optimization is effective and feasible.
## 2 Theoretical Background
### Multiple Regression Analysis and Cross-Validation  
MLR is a statistical technique used to identify and model the relationship between two or more independent variables and a dependent variable by fitting a linear equation to the observed data. If the data are in a linear relationship, the MLR of the observations for the independent variables (\(x_{i}\) for \(i=1\), 2,..., \(n\)) can be expressed as  
\[y=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\cdots+\beta_{n}x_{n}+\varepsilon \tag{2}\]  
where \(\varepsilon\) and \(\beta_{0}\) are the error of prediction and intercept, respectively, and \(\beta_{1}\), \(\beta_{2}\),..., \(\beta_{n}\) are the regression coefficients, which are the slopes in the MLR equation. In MLR, if \(x_{0}\) is set to 1, considering the constant term of the MLR, Equation (2) can be simply expressed using a matrix as follows:  
\[[X][\beta]=[Y] \tag{3}\]  
where \([X]\) is the matrix for the independent variables with a size of \(m\) by \((n+1)\), \([\beta]\) is the matrix of regression coefficients with a size of \((n+1)\) by 1, and \([Y]\) is a matrix for the dependent variables with a size of \(m\) by 1. Typically, the regression coefficients of an MLR are estimated by the least squares method, and can be obtained by calculating the matrix as  
\[[\beta]=\left([X]^{T}[X]\right)^{-1}[X]^{T}[Y] \tag{4}\]  
Although there can be various independent variables for a prediction, the MLR is complicated to account for all the independent variables, and some independent variables may not have a substantial effect on the outcome prediction (described in more detail below).
## 3 Results
### Spatio-Temporal Variation of Atmospheric Oxygen Emissions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**City**} & \multirow{2}{*}{**GPP (10\({}^{5}\) t C)**} & \multirow{2}{*}{**NPP (10\({}^{5}\) t C)**} & \multirow{2}{*}{**NEP (10\({}^{5}\) t C)**} & \multirow{2}{*}{**Oxygen Emission (10\({}^{5}\) t O)**} & \multirow{2}{*}{**Proportion of Total Oxygen**} \\  & & & & & ** Emission (\%) * \\ \hline Guangzhou & 60.052 & 41.651 & 22.561 & 60.169 & 10.10 \\ Shenzhen & 28.889 & 19.969 & 10.768 & 28.717 & 4.82 \\ Zhuhai & 3.737 & 2.509 & 1.388 & 3.702 & 0.62 \\ Foshan & 17.759 & 12.321 & 6.660 & 17.762 & 2.98 \\ Jiangmen & 97.635 & 67.541 & 37.157 & 99.097 & 16.64 \\ Dongguan & 7.688 & 5.365 & 2.915 & 7.773 & 1.31 \\ Zhongshan & 5.961 & 4.168 & 2.302 & 6.139 & 1.03 \\ Huizhou & 165.326 & 114.825 & 63.023 & 168.083 & 28.22 \\ Zhaoqing & 213.615 & 146.092 & 76.560 & 204.184 & 34.28 \\ PRD & 600.662 & 414.441 & 223.334 & 595.626 & 100.00 \\ \hline \hline \end{tabular} Note: * Proportion of total oxygen emission = oxygen emission of the city/oxygen emission of the PRD.  
\end{table} 
Table 1: Accumulated atmospheric oxygen emission in each city of the PRD in 2011.
## 3 Results and Discussion  
### Model Performance Evaluation  
The specific results of the experiment are shown in Table 3. Due to the unbalanced dataset, positive texts are more easily recognized compared to negative texts, and the Recall is generally higher than the Precision. In the model without Ernie-Tiny, both BiLSTM and GRU outperformed the CNN model in all metrics, with the \(F1\) of BiLSTM being 4% higher than that of CNN, which proves that considering contextual information can improve the model's textual emotion recognition ability. The overall effect of BiLSTM and GRU is not much different. Because the GRU network's complexity is lower than that of BiLSTM and the \(F1\) of GRU is only 0.27% lower than that of BiLSTM, the GRU cost performance is relatively higher, so the model proposed in this study uses BiGRU. Compared with the Ernie-Tiny series models and other basic models, the Ernie-Tiny series models score relatively higher in each evaluation metric. The specific performance is that the Accuracy, _Precision_, _Recall_ and \(F1\) are increased by at least 4.02%, 3.28%, 0.65%, and 3.42%, respectively. This shows that the Ernie-Tiny pre-trained model can extract the semantic features of Chinese text more effectively than the collocation word vector. A comparison of each model's performance is shown in Figure 6.  
Comparison among the models using Ernie-Tiny: ET_p is slightly better compared to ET_s, with an \(F1\) value improvement of 0.47%, indicating that for the dataset used in this paper, the sentence vector representing the semantic features of whole sentences in Ernie-Tiny is more representative of the overall features of the text than the averaged sequence vector and is more suitable for classification. The ET_s_BiGRU model has the highest accuracy, the smallest gap between accuracy and recall, and the best balance, with 0.69% improvement in accuracy and 0.19% improvement in F1 value compared to ET_s, which proves the benefit of using BiGRU. ET_s_BG+p achieves the highest optimal accuracy and \(F1\) value in the whole experiment, integrating the advantages of ET_p and ET_s_BiGRU, and proves the value and significance of the model proposed in this paper. The model effect difference comparison is shown in Figure 7.  
In addition, comparing the running time of each model, the running time of the Ernie-Tiny series models is shorter than that of other basic models, which shows that the pre-trained model is efficient. The running time within the Ernie-Tiny series of models is positively correlated with the complexity of the model, but there is no significant difference.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 4 Common Attacks within MANETs
### Wormhole Attacks  
Wormhole attacks, also referred to as tunnel attacks, exploit a vulnerability similar to that in sinkhole attacks. The attack is initiated by having two malicious nodes establish a covert tunnel (wormhole) among them. The attack is then launched by capturing packets at one end of the wormhole, transmitting them through the out-of-band connection, and re-injecting them at the other end. This can trick legitimate nodes into believing the wormhole route is shorter, leading to disrupted traffic flow, potential data manipulation, and degraded network performance [34].
## 4 Methodology  
To address resource competition issues, the low allocation efficiency in satellite networks, and the lack of adaptability and real-time performance in dynamic environments, this paper proposes the Coding Multi-Path Theory (CMPT) method. CMPT employs a game-theoretic model with Nash bargaining mechanisms to simulate resource competition among satellites and users, ensuring efficient and fair resource distribution. It leverages the Lagrangian multiplier method to optimize resource allocation, while satisfying multiple constraints such as bandwidth, power, and interference, thereby achieving multi-path load balancing. Additionally, CMPT incorporates a multi-path scheduling strategy based on real-time network state monitoring to dynamically select optimal transmission paths, reducing packet loss and latency, and enhancing network reliability. By integrating Random Linear Network Coding (RLNC) and Software-Defined Networking (SDN)-based domain clustering, CMPT ensures robust data transmission and minimizes network control overhead. The proposed CMPT framework is illustrated in Figure 1, outlining the key steps, from dynamic network state monitoring, game-theoretic resource allocation and load balancing, and multi-path scheduling based on real-time conditions, to the execution of optimized scheduling and resource allocation decisions.
## 6 Conclusions  
Compressive Sensing overcomes the limitation of traditional sampling and provides a method to recover the original signal from a handful of non-adaptive nonlinear measurements. The measurement matrix is the key to this acquisition of data. As it is difficult to engineer a random measurement matrix, owing to the large storage space and complex computation required, and the deterministic measurement matrix is less satisfactory in terms of reconstruction accuracy, a measurement matrix constructed by the cyclic direct product and QR decomposition is proposed here. We tested its performance using simulated data and echoes from a scaled submarine target in a freshwater lake, at frequencies of 40 kHz-80 kHz. Gaussian matrix, Bernoulli matrix, partial Hadamard matrix, Toeplitz matrix and the measurement matrix proposed here are compared and analyzed in terms of their matching rate, SNR enhancement, and construction time, via signal compression and reconstruction. The measurement matrix based on the cyclic direct product and QR decomposition yields higher reconstruction accuracy than the others, especially as the compression ratio increases. In terms of SNR enhancement, performance is nearly the same as the other measurement matrices when the compression ratio is small. As the compression ratio increases, our method shows greater improvement in the SNR. At a compression ratio of above 0.7, the SNR of this matrix is approximately 2 dB higher than those provided by other measurement matrices. The proposed measurement matrix also requires significantly less construction time compared with the others, as it only needs one two-dimensional matrix to be stored during construction, i.e., four elements. The storage space required is less, as it is independent from the number of measurements made and only depends on the signal length. The proposed measurement matrix thus saves storage space and computation time.  
Conceptualization, Methodology, Validation, Writing--Original Draft Preparation, T.S. and H.C.; Writing--Review and Editing, P.B.; Formal Analysis, Y.G. and H.S.; Funding Acquisition, T.S., Y.G. and H.S. This research was funded by the pre-research field fund project from Chinese Military Equipment Development Department, Grant No. 6140243010116 DZ04001, the Extension Fund from Underwater Test and Control Technology Key Laboratory, Grant No. YS24071802 and Chinese National Natural Science Foundation, Grant Nos. 61871166 and 61703128. This article uses experimental data collected at Dalian Test and Control Institute (China), and we gratefully acknowledge our colleagues for their experimental skills. [PERSON] worked on this article as a Visiting Research Fellow to the University of Bath (UK), thanks to the Chinese Scholarship Council (State Scholarship Fund No. 201608330338). The authors declare no conflict of interest.  
Figure 9: Change in SNR with the compression ratio. (**a**) echo with incident angle of 30\({}^{\circ}\); (**b**) echo with incident angle of 45\({}^{\circ}\).
# Enhancing User Experience in Virtual Museums: Impact of Finger Vibrotactile Feedback
## 3 Methodology
### Result Collection and Data Analysis  
In this study, quantitative data were collected using structured questionnaires after participants interacted with virtual museum artifacts in three settings: no haptic feedback, standard controller feedback, and vibrotactile glove feedback. Participants performed tasks involving the identification and manipulation of virtual artifacts, with metrics such as task completion time and accuracy recorded to assess performance differences across settings.  
After the participants' interaction in each setting, they completed a presence questionnaire [35] using a Likert scale to measure subjective experiences of presence and immersion [36]. After all interactions in all settings were completed, a comparison questionnaire [35] was administered to gather participant preferences and overall satisfaction with each interaction method. Thereafter, they filled out a supplementary glove questionnaire evaluating the glove's comfort and usability [37].  
Figure 4: Experimental setup with Oculus Quest 2 and vibrotactile glove components.  
Data analysis focused on quantitative methods to evaluate the effectiveness of glove haptics. Responses from the presence questionnaire were analyzed using paired-sample \(t\)-tests to compare mean scores for task performance and user satisfaction across the three settings. Descriptive statistics, including means and standard deviations, were calculated for task metrics. Paired-sample \(t\)-tests identified significant differences among settings, and graphical representations illustrated comparative results of performance and satisfaction levels, highlighting the impact of advanced haptic feedback. The data analysis was conducted using the SPSS software (version 27) by IBM [38].  
Although qualitative data collection was not a primary focus, open-ended responses in the comparison questionnaire provided insights into user experiences and preferences. These responses were categorized and summarized to identify common themes related to haptic feedback intensity, timing, and realism. This feedback contextualized quantitative findings, offering a deeper understanding of user experiences and highlighting areas for improvement in haptic technology.  
This comprehensive approach ensured that both objective performance metrics and subjective user experiences were considered, providing a robust evaluation of the vibrotactile glove's effectiveness. The final analysis validated the hypothesis that glove haptics significantly enhance user performance and satisfaction in virtual environments, supporting the development of more immersive and interactive haptic technologies for various applications.
## 2 Related Work and Security Landscape in mHealth  
Platforms such as mHealth have evolved into complex ecosystems comprising cloud-based servers, mobile devices, and wearable sensors. These systems are expected to deliver continuous, personalized healthcare services. However, their multi-layered design is rarely protected by consistent cybersecurity measures. The heterogeneous nature of these technologies, which range from Android-based smartphones and Wear OS devices to containerized microservice backends, makes it challenging to maintain a consistent and secure baseline. This heterogeneity also has the potential to leak or alter sensitive data or records, such as PHRs, sensor streams, and authentication tokens.  
Wearable devices are a growing attack vector due to their direct interaction with users' physiological signals and their tendency to operate autonomously in low-power states. Several studies have shown that even popular wearables transmit biological data, such as heart rate and sleep patterns, over unencrypted BLE channels. These channels are vulnerable to man-in-the-middle and metadata inference attacks. Furthermore, default configurations often expose Generic Attribute Profile (GATT) services without requiring secure pairing or session integrity, which leads to privacy risks in both clinical and non-clinical settings [12,13].  
Mobile operating systems compound these risks by enabling overprivileged applications and maintaining persistent background services. Android is particularly vulnerable to permission abuse, third-party software development kit (SDK) injection, and firmware modification because it is an open and widely deployed platform in mHealth. Recent analyses propose multi-authority, ABE mechanisms to strengthen data confidentiality, especially in decentralized mHealth contexts. However, practical implementations remain rare or difficult to verify in production environments [14].  
At the backend, the shift towards containerized deployments, such as Docker or Kubernetes-based services, has enabled scalable mHealth infrastructures. However, these platforms often rely on insecure defaults, exposed management interfaces, or outdated libraries. This introduces new vectors for privilege escalation and lateral movement within healthcare data networks. Several authors emphasize the need for zero-trust container orchestration and hybrid cloud-edge deployments to mitigate latency and improve the real-time response to anomalies [15].  
Despite the variety of proposed solutions, most studies focus on one layer, such as wearable BLE encryption, Android permission models, server-side hardening, or simulating threats without validating them against a live system. In contrast, our study aims to bridge this gap by conducting a multi-layered audit covering server, mobile, and wearable components on real-world mHealth deployment. This practical assessment provides insights into the cumulative risk posed by misconfigurations, permission abuse, insecure Bluetooth usage, and fragmented system design across layers.
## 3 Dynamical Results  
As a foundation for the design of a multi-body constellation, dynamical models with varying levels of fidelity have been employed. For the preliminary analysis, the Circular Restricted Three-Body Problem (CR3 BP) has been implemented in MATLAB (R2023a) [22] (Figure 2a), providing a reliable approximation of the Earth-Moon system dynamics and enabling a deeper understanding of the behaviour of the selected orbital mechanics. This low-fidelity analysis yields the initial state vectors of the candidate orbits, which subsequently serve as the input for high-fidelity simulations. The need to introduce high fidelity is related to the fact that in reality corrective manoeuvres are required to ensure the periodicity of orbits. These simulations, conducted using GMAT and FreeFlyer [23, 24], incorporate a full ephemeris model, accounting for the Moon's gravitational field up to the 50 th harmonic, as well as the perturbative influences of the Earth, the Sun, Jupiter, and solar radiation pressure (SRP). The high-fidelity phase ultimately enables the construction of the final constellation and a comprehensive global coverage analysis.
## 3 Experimental Results and Discussion
### Effect of Water Content on Energy Conversion  
For the convenience of analysis, the average elastic energy, elastic energy ratio, and stress conversion coefficient measured using five groups of soil bodies with different water contents are sorted, and the data obtained are shown in Figure 12. As observed in Figure 12, with an increase in soil moisture content, the proportion of elastic properties decreases, and the proportion of dissipated energy increases in the soil compression failure process. This indicates that the soil with higher water content experiences more plastic deformations during the failure process, and the mechanical properties of the soil obviously change from being brittle to plastic. Therefore, the water content has a significant effect on the mechanical properties of soil. The higher the water content, the more obvious the plasticity of the soil.  
At the same time, it can be observed in Figure 12 that water content also has a significant impact on the soil stress conversion coefficient, which is manifested in that the stress conversion coefficient increases with an increase in water content. When the water content is 10%, the stress transfer coefficient of the soil is only 1.06%, but when the water content increases to 24%, the stress transfer coefficient increases by six times to 6.87%. In terms of the failure mode, the brittleness of the soil is more obvious when the water content is 10%, and the soil mainly exhibits shear failure. The water content is 24%, the soil is obviously plastic, and the failure mode comprises tensile failure. The soil with moisture content between 10% and 24% exhibits the tensile-shear coupling failure mode.  
Based on the data in Figure 12 and their conclusions, it can be concluded that the stress transfer coefficient of the soil under compression increases with an increase in water content, and the more obvious the plasticity of the sample, the greater the stress transfer coefficient; moreover, the failure mode tends towards tensile failure.
## 7 Results and Discussions
### Effect of the Frequency Content of the Excitation  
To evaluate the influence of the characteristics of the input excitation, the PI of the seismic responses of both squat and slender tanks are depicted in Figure 12 against the predominant frequency of the considered earthquake. The predominant frequency is determined through the signal processing of considered earthquake ground motions and by selecting the maximum spectral acceleration occurring in their acceleration response spectra.  
Besides, a linear regression line is plotted to observe the overall trend of the responses over the frequencies of the considered earthquake ground motions. Generally, it is seen that the PI of the displacement of the impulsive mass, overturning moment, and base shear of the squat tank is less than one for all of the ground motions with the predominant frequency higher than the frequency corresponding to impulsive mass. Conversely, both a PI of less and more than one were observed for the aforementioned responses when the system was subjected to ground motions with the predominant frequency lower than the frequency of the impulsive mass. In the case of the slender tank, the PI of more than one was observed for the impulsive displacement, overturning moment, and base shear when the system was subjected to excitations with the predominate frequency lower than the impulsive frequency. Besides, for those earthquakes with a predominant frequency higher than the impulsive mass frequency, a PI of less and more than one can be observed.