esa-sceva/satcom-chunk-collection huggingface dataset:
# Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis
## 4 Experimental Results
### Testing Classification under Data Imbalanced Conditions
Figure 8d-f show the result of the second experiment, in which we used 10,000 normal data images. Compared to the first experimental result, it indicates that the numbers of misfirings and confusion were higher for the same imbalance ratio in Figure 8a-c. The accuracy also decreased in general owing to the smaller dataset but still achieved around 80% even for the most severe imbalance ratio of 400:1. As shown in Figure 9, the proposed method (CBFD with BF-NSP) gave higher classification accuracy than CNN with the continuous wavelet transform scalogram (CWTS) [17; 18] and DNN with HHT [11], in which features were extracted using the HHT and the DNN was used as a classifier  
Figure 8: Test results for fixed normal data at 20,000 and changing ratio of data imbalance: (**a**) accuracy, (**b**) misfiring, and (**c**) confusion. Test results with fixed normal data to 10,000: (**d**) accuracy, (**e**) misfiring, and (**f**) confusion. To ensure a fair comparison, both models were trained using 5000 random sample data points and tested using 1200 random samples. The comparison results represent the mean values of 10 trials for every data imbalance rate. We found that CBFD with BF-NSP had 95% accuracy, even when the imbalance ratio was 20:1. Its accuracy fell below 90% when the imbalance ratio reached 50:1. On the other hand, the DNN with HHT [11] was more sensitive to the imbalance ratio. Only for an imbalance ratio of 7:1 was the accuracy over 95%. When the imbalance ratio was 9:1, the accuracy was below 80%. In the case of the CNN with CWTS [17; 18], the accuracies in the imbalanced conditions were at least 28.1% points lower than CBFD with BF-NSP. When the image transformation using CWTS was applied to the data, the training accuracy of the CNN with CWTS was lower than 90%, so that the results of the CNN with CWTS declined in the data imbalanced conditions. Even though we tried to classify the CWTS images using our CBFD, the differences between the results of the two methods were minor. As shown in Table 3, the CNN with CWTS provided the fastest training and testing among the three methods because the size of the CWTS image (80 \(\times\) 80) was smaller than the size of the NSP image (128 \(\times\) 128). In summary, our CBFD with BF-NSP outperformed the DNN with HHT and the CNN with CWTS, and was capable of detecting bearing faults even when the data imbalance was high.
## 1 Introduction
### Comparison Statistics  
Several statistical metrics were utilized to facilitate the analysis of the results, including the Mean Error (ME) and Mean Absolute Error (MAE). We compared the transport model against in situ observations and conducted percentage difference comparisons when assessing differences in the modules of the model.  
The Mean Error serves as an assessment of the bias between the model and observations, and can be defined as follows:  
\[\text{ME}=\frac{1}{n}\sum_{i=1}^{n}(\hat{y}_{i}-y_{i}) \tag{3}\]  
where \(i\) iterates through individual observations, \(n\) indicates the total number of observations being compared, \(\hat{y}\) denotes the modeled values, and \(y_{i}\) denotes the corresponding observed values.  
The Mean Absolute Error serves as an assessment of the corresponding uncertainty, and can be defined similarly:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\lvert\hat{y}_{i}-y_{i}\rvert \tag{4}\]  
where the same variable definitions are used as in the ME equation.  
Later, we assess the percent difference between the output from different model modules, for which the equation is  
\[\text{Percent Difference}=\frac{\overline{y}-\overline{y^{\prime}}}{\overline{y }}\times 100, \tag{5}\]  
where \(\overline{y}\) is the mean output from the original model and \(\overline{y^{\prime}}\) is the mean output from the adjusted model.
# The Effects of High-Performance Cloud System for Network Function Virtualization
## 4 Experiments and Evaluations
### Results of Performance Verifications
#### 4.3.1 Different Network Techniques  
The experiment uses a VM with two cores and different improvement technologies. The test scenario is PVP. As a baseline, the VM in the OVS scenario does not apply enhancement techniques, while the others enable CPU pinning or hugepages. Experimental results in different packet sizes are shown in Figures 11-13. The reason behind each experiment is discussed as follows.  
Because OVS receives packets in kernel space, this method still needs a context switch if a packet does not match any rules. The kernel-user space interrupt degrades the network performance. As shown in these three figures, the throughputs with OVS are the lowest. When the packet size is 64, the throughput is only 0.16 Gbps. The throughput varies with the increment in packet size. The maximum throughput is 2.8 Gbps when the packet size is 1518. These results show a low performance without any enhancement.  
The difference between OVS and OVS-DPDK is to exploit vhost-net or vhost-user for NIC emulation. OVS uses vhost-net to enable the virtual switch to process the packets in kernel space. This method introduces the overhead of context switches and deteriorates the performance. On the contrary, OVS-DPDK uses vhost-user to enable the guest to communicate with the virtual switch. Therefore, OVS-DPDK mitigates the performance degradation. On the other hand, the VM with OVS is not improved with CPU pinning and hugepages. CPU pinning helps the VM possess dedicated cores. These cores avoid the VM sharing CPU resources with other processes in the host. Therefore, the overhead of context switches is relieved. Hugepages are used to speed up memory access.  
Considering the enhancement of the virtual switch, the throughputs of using OVS-DPDK are better than that of OVS. The throughput of OVS-DPDK is 1.4 Gbps when the packet size is 64. This throughput of OVS-DPDK is 8.2 times as high as the throughput of OVS. The maximum throughput with OVS-DPDK is 9.9 Gbps when the packet size is 1518. This value is 3.5 times as high as the throughput with OVS. Therefore, the throughput of OVS-DPDK ranges from 3.5 to 8.2 times as high as the OVS. However, the throughputs with OVS-DPDK are lower than a hardware-based solution.  
Figure 12: Throughputs of different technologies in PVP scenario when packet size is 512.  
Figure 13: Throughputs of different technologies in PVP scenario when packet size is 1518.  
SR-IOV is the hardware-assisted solution. This technique improves network performance significantly and allows a VM to access the physical NIC directly via the virtual function. The throughput of SR-IOV approaches 9.5 Gbps when the packet size is 64. When the packet size is higher than 64, the throughputs are 9.9 Gbps. As shown in the results, the performance of SR-IOV is near the line rate. With SR-IOV, the system assigns dedicated virtual functions to the VMs. These virtual functions help the VM access the physical NIC directly. Therefore, the impact of VMM is relieved.
# Line-of-Sight Initial Pointing Model of Space Dynamic Optical Network and Its Verification
## 3 Multi-Link LOS Pointing Model
### The Principle of LOS Pointing
#### 3.1.2 Coordinate Conversion and Transfer Principle
If four coordinate systems of LOS are established in the same plane at the same time, \(\theta_{x1}=\theta_{y1}=0^{\circ}\); \(\theta_{x2}=\theta_{y2}=90^{\circ}\); \(\theta_{x3}=\theta_{y3}=180^{\circ}\); \(\theta_{x4}=\theta_{y4}=270^{\circ}\); \(\theta_{z1}=\theta_{z2}=\theta_{z3}=\theta_{z4}=0^{\circ}\).
## 3 Results of the Direct Scanning and Step Scanning Calorimeter Methods  
### Results for Paraffin RT58  
The results of DSC characterization are presented in Figure 4. The thermograms show a very good reproducibility, even if the first heating was always slightly different because of the poor contact between the sample (introduced in solid form) and the measuring cell. The melting was progressive with a starting temperature located at around 51.2 \({}^{\circ}\)C. The temperature at the end of the melting process could not be determined with precision because of the sample's internal heat transfers. An empirical method enabled the estimation of the temperature between the top of the thermogram and the inflection point during the heat flux decrease. For RT58, the end of the melting process was evaluated at around 59.5 \({}^{\circ}\)C. On cooling, an abrupt variation was observed at 58.7 \({}^{\circ}\)C. This is characteristic of the break of supercooling. The supercooling is thus weak and of the order of the degree Celsius.  
In Figure 5, the enthalpy was reconstructed in an approximate way by integrating the thermogram of DSC and fixing an arbitrary reference at the temperature of 51.2 \({}^{\circ}\)C identified on the thermogram. Curves with a similar appearance were obtained, with stabilization occurring from 60 \({}^{\circ}\)C which corresponded to the temperature above, where the material was completely in a liquid state. The latent heat of melting, which was approximated by the variation of the enthalpy between the temperature at the beginning of melting (impossible to determine with precision, but estimated at 51.2 \({}^{\circ}\)C) and the end of melting (approximately 60 \({}^{\circ}\)C) gave a probable value ranging between 132 and 145 kJ/kg. The STEP analysis gave similar results, with an estimation of the latent heat in the low range. The cooling curves show a supercooling of about 1.5 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\rho_{S}\) [kg.m\({}^{-3}\)] & \(\rho_{L}\) [kg.m\({}^{-3}\)] \\ \hline RT58 & 880 & 808 \\ PEG6000 & 1200 & 1070 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of solid and liquid densities identified experimentally.
# Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic
## 2 The f-MC-SC Model
### TC Probabilities via the Proposed BF Method  
Let us define the set of steady-states, \(\mathbf{\Omega}_{w,s}^{C,<T}\), in which there are no more RRUs in the \(w\)-th RRH for service class \(s\) calls, excluding those states in which there are also no CRUs left in V-BBU for the same service class calls. This set can be written as \(\mathbf{\Omega}_{w,s}^{C,<T}=\{\mathbf{\Omega}_{w,s}^{C}\cap\mathbf{\Omega}_{w,s}^{C,T}\}\) with \(\mathbf{\Omega}_{w,s}^{C}=\{\mathbf{n}:C-b_{w,s}^{\text{tr}}<\sum_{y=1}^{S_{w}}n_{w,y} b_{w,y}^{\text{tr}}\leq C\}\) and \(\mathbf{\Omega}_{w,s}^{C}=\{\mathbf{n}:\sum_{x=1}^{W}\sum_{y=1}^{S_{x}}n_{x,y}b_{x,y}^{ \text{c}}\leq T-b_{w,s}^{\text{c}}\}\). Then, the values of \(B_{\text{r},w,s}^{\text{TC}}\) can be computed (using PFS (4)) as follows:  
\[B_{\text{r},w,s}^{\text{TC}}=\sum_{\mathbf{n}\in\mathbf{\Omega}_{w,s}^{C,<T}}P_{\text{ fin}}(\mathbf{n}). \tag{6}\]  
Similarly, by considering \(\mathbf{\Omega}_{w,s}^{T}=\{\mathbf{n}:T-b_{w,s}^{\text{c}}<\sum_{x=1}^{W}\sum_{y=1}^ {S_{x}}n_{x,y}b_{x,y}^{\text{c}}\leq T\}\) as the set of steady-states in which there are no CRUs left in the V-BBU for the service class \(w,s\) calls, the values of \(B_{\text{c},w,s}^{\text{TC}}\), can be computed via:  
\[B_{\text{c},w,s}^{\text{TC}}=\sum_{\mathbf{n}\in\mathbf{\Omega}_{w,s}^{T}}P_{\text{ fin}}(\mathbf{n}). \tag{7}\]  
Note that the set \(\mathbf{\Omega}_{w,s}^{T}\) includes states that lead to TC due to the unavailability of CRUs, as well as due to both insufficient CRUs and RRUs.  
Computing \(B^{\text{TC}}_{\text{r},\text{w},s}\) and \(B^{\text{TC}}_{\text{c},\text{w},s}\) is accurate (if one compares the analytical results with the corresponding simulation results) but complex especially for a system of many RRHs that have large capacities and serve many service classes. This is because it is required to enumerate/process \(\mathbf{\Omega}\) so as to compute the normalization constant \(G\) of (4) and determine all blocking states required in (6) and (7). For this reason, the BF method can be adopted only for small C-RAN examples (of tutorial nature). To circumvent this problem, in the next subsection we propose a convolution algorithm that leads to an efficient determination of the TC probabilities.
## 3 Hypothesis and Functional Specification
### VERTEX AI in the Proposed Solution Architecture  
The proposed solution architecture uniquely applies VERTEX AI in three distinct ways, leveraging its language model capabilities for handling various tasks. The paper explicitly utilizes the Bison (text-bsion) model within the PaLM-2 family of models on the VERTEX AI platform. This model, designed for text-based tasks, is employed in the paper for predictions and response generation, playing a crucial role in the architecture.  
* Prediction without context: In this method, the model is asked to provide answers without any supporting context. The user poses a question, and the model responds  
Figure 4: Data Preparation and Vectorization Workflow for Semantic Similarity Retrieval.  
based on its internal knowledge and instructions on the role it should assume. However, this method often leads to hallucinations--instances where the model generates information not present in its training data. These issues are discussed in the testing results section, where it was observed that the model frequently provided inaccurate responses.
* Contextual prediction (using the RAG method): The second approach integrates the RAG method, wherein the model is supplied with relevant context before generating responses. This context typically comes from a set of documents or literature provided to the model. In this method, the model uses the supplied information to refine its answers, significantly improving accuracy. The RAG method plays a crucial role in solving queries that rely on external data and is central to the hypotheses tested in the paper.
* The third approach, learning from one or more patterns, involves providing the model with examples of correct responses (one-shot or few-shot learning) to guide its predictions. This method, akin to human knowledge, is particularly beneficial when limited contextual information is available, as it helps the model understand how to answer future questions, thereby enhancing its predictive capabilities.  
The usage of VERTEX AI in these three ways demonstrates how different generative techniques can be applied to improve language model performance in specific scenarios, balancing between hallucination risks and improving accuracy with additional context.
# A Multi-Frame PCA-Based Stereo Audio Coding Method
## 4 PCA-Based Parametric Stereo
### PCA-Based Parametric Stereo  
Given that the original matrix is \(X=\left[\begin{array}{ccc}a_{1}&\ldots&a_{225}\\ \vdots&\ddots&\vdots\\ a_{16}&\ldots&a_{240}\end{array}\right]\), and the rotation matrix is \(P=\left[\begin{array}{ccc}p_{1}&\ldots&p_{76}\\ \vdots&\ddots&\vdots\\ p_{15}&\ldots&p_{90}\end{array}\right]\), the reduced-dimensional matrix is \(Y=XP=\left[\begin{array}{ccc}b_{1}&\ldots&b_{49}\\ \vdots&\ddots&\vdots\\ b_{16}&\ldots&b_{64}\end{array}\right]\). For the coefficients in the reduced-dimensional matrix \(Y\),  
\[b_{1} =a_{1}p_{1}+a_{17}p_{2}+\ldots a_{225}p_{15} \tag{13}\] \[b_{2} =a_{2}p_{1}+a_{18}p_{2}+\ldots a_{226}p_{15}\] (14) \[b_{16} =a_{16}p_{1}+a_{33}p_{2}+\ldots a_{240}p_{15} \tag{15}\]  
The first column is only related to the first column of \(P\) (the first base). As Figure 9 shows, main energy of the first base in the rotation matrix is entirely concentrated on the data in the first column of the first row. Therefore, the matrix \(Y\) can be approximated as  
\[b_{1} =a_{1}p_{1} \tag{16}\] \[b_{2} =a_{2}p_{1}\] (17) \[b_{16} =a_{16}p_{1} \tag{18}\]  
While \(p_{1}\) in the matrix \(P\) is approximately equal to 1. Therefore the first column in the matrix \(Y\) is equal to the first column originally in matrix \(X\). When the sampling rate is 48 kHz, the first column in \(X\) indicates the coefficients from 0 to 1.6 kHz, which means that when calculating the restored signal, the points below 1.6 kHz in the frequency domain happen to be the first principal component. So, the first principal component can be used to restore signals below 1.6 kHz in frequency domain instead of introducing MDST and estimating binaural cues. In decoder, the spectrum of the left and right channels above 1.6 kHz can be restored according to the downlink reduced-dimensional matrix, rotation matrix, and spatial parameters. The spectrum of the left and right channels below 1.6 kHz can be restored according to the first principal component and the downlink reduced-dimensional matrix.
# Evaluation of the Long-Term Performances of SMA-13 Containing Different Fibers
## 4 Results and Discussion
### Water Stability  
The results of immersion Marshall test is presented in Table 7.  
It can be seen from Table 7 that the residual Marshall stability of the BFSMA-13 is slightly higher. The residual Marshall stability of the short-term aged and the long-term aged BFSMA-13 decrease by 0.74% and 2.76% respectively compared with that of the unaged BFSMA-13. The corresponding decreasing percentages of LFSMA-13 are 1.42% and 3.18%. Therefore, the effect of the aging degree on the water stability of LFSMA-13 is more obvious. When the aging degree increase, the Marshall stability and immersed Marshall stability of BFSMA-13 increase, and the two indexes of LFSMA-13 decrease. This phenomenon is consistent with the test results of the high-temperature performance.  
In the field asphalt pavement, the water damage of asphalt mixtures is not only related to water but also related to the driving speed or other kinds of loads applied on it. The asphalt mixture will generate some micro-cracks when the loading is applied, and then, when the water goes into the mixture, it will further result in water damage. The above test results show that the Marshall stability, immersed Marshall stability, and the residual Marshall stability of BFSMA-13 are all better than those of the LFSMA-13, which means that basalt fiber can better enhance the strength and the water stability of SMA-13 mixture.
## 3 Results and Discussion
### Analysis of the Phenolic Compounds Content in GV Extracts  
Phenolic acids comprise a carboxyl group and a hydroxyl group (-OH) on the benzene ring and are classified as hydroxybenzoic acid and hydroxycinnamic acid [81; 82]. Among them, 4-hydroxy benzoic acid (4-HBA) is a phenolic compound found in diverse seaweeds [83; 84] and has been used as an antioxidant and food preservative [85]. Naringenin and naringin are flavonoids mainly present in citrus and grapefruit; naringenin is an aglycone form of naringin [86]. Naringin is associated with the bitter taste of grapefruit and is used as a food additive [87]. Naringenin and naringin have antioxidant, anticancer, and anti-inflammatory activities [88; 89; 90]. Thus, the 4-HBA, naringenin, and naringin contents in the GV extracts were analyzed to develop functional products and increase their applicability using GV extracts (Table 5). The 4-HBA content in GVW was 1.91 \(\pm\) 0.01 \(\mu\)g/g; however,  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Inhibition Activity (\%)** & **GVE** & **GVM** & **GVW** & **Ascorbic Acid** \\ \hline Collagen & 10.1 \(\pm\) 1.3 \({}^{\text{b}}\) & 16.4 \(\pm\) 1.0 \({}^{\text{a}}\) & 0.9 \(\pm\) 0.1 \({}^{\text{c}}\) & 23.7 \(\pm\) 1.1 \\ Elastase & 10.8 \(\pm\) 0.4 \({}^{\text{c}}\) & 21.2 \(\pm\) 0.2 \({}^{\text{a}}\) & 16.8 \(\pm\) 0.1 \({}^{\text{b}}\) & 52.2 \(\pm\) 0.2 \\ Tyrosine & 6.1 \(\pm\) 0.5 \({}^{\text{c}}\) & 37.9 \(\pm\) 1.4 \({}^{\text{a}}\) & 22.4 \(\pm\) 1.5 \({}^{\text{b}}\) & 50.2 \(\pm\) 2.7 \\ \hline \end{tabular}
\end{table}
Table 4: Collagenase, elastase, and tyrosinase inhibition activities of _Gracilaria verrucosa_ extracts.  
4-HBA was not found in the GVM and GVE. Meanwhile, the GVE showed naringenin and naringin contents of 1.33 \(\pm\) 0.01 and 666.88 \(\pm\) 4.07 \(\mathrm{\SIUnitSymbolMicro g}\)/g, respectively; however, GVW and GVM did not contain these compounds.  
[PERSON] et al. [91] reported that the 4-HBA contents of _Gracilaria gracilis_ extracts were found in the methanol, ethanol, and acetone extracts (167.00, 124.93, and 59.36 mg/100 g, respectively). These results differed from the 4-HBA contents in the GVM and GVE in this study. Notably, bioactive components in seaweeds, including polyphenols, polysaccharides, and amino acids, are affected by storage and drying conditions [92]. Overall, various conditions, such as extraction methods, extraction solvents, sample storage, and drying methods, could have affected the phenolic compound content of the seaweeds.
###### Abstract  
Background: The aging population is increasing rapidly, with individuals aged 65 and older now representing more than 15% of the global population. This demographic shift is associated with a rising incidence of age-related cardiovascular diseases (CVDs). Early prediction and prevention of cardiovascular aging are essential to improve health outcomes among elderly patients. Objective: This study aimed to develop and externally validate a mathematical model for predicting cardiovascular aging in individuals aged 65 and older, based on general clinical and behavioral data. Methods: The model was built using data from 800 individuals aged 65+ from Almaty, Kazakhstan. Predictors included sex, marital status, education, smoking, alcohol use, disability, physical activity, total cholesterol, hypertension, BMI, coronary artery disease (CAD), myocardial infarction, diabetes mellitus, and chronic heart failure. A system of ordinary differential equations was used to simulate the dynamic interactions of these factors. Numerical integration was performed using the Runge-Kutta, Adams-Bashforth, and backward Euler methods. The model was verified statistically using Pearson correlation analysis and externally validated on independent age cohorts. In addition, we applied k-means clustering to identify hidden patterns and risk profiles within the dataset. A Random Forest classifier was trained to distinguish between high-risk and low-risk individuals using the same feature set. These machine learning approaches were used as complementary tools to enhance the robustness and interpretability of the modeling results. Results: The model trained on the 65-74 age group achieved an external validation accuracy of 98.8% and an AUC of 0.989 when applied to the 75-89 group. Risk modeling showed that in the 65-74 group, smoking and alcohol increased the risk of myocardial infarction, hypertension, and obesity by up to 53%. In the 75-89 group, these factors increased the likelihood of hypertension by 21%, chronic heart failure by 16%, and CAD by 14%. Among individuals aged 90+, hypercholesterolemia increased the risk of chronic heart failure by 17%, while hypertension increased myocardial infarction risk by 16%. Conclusions: The proposedmodel demonstrated high accuracy in predicting cardiovascular aging and identifying high-risk individuals across elderly subgroups. The integration of clustering and classification methods (k-means and Random Forest) provided additional insights and confirmed the consistency of the findings. This multi-method approach may serve as a valuable tool for developing personalized prevention strategies in geriatric care and improving healthy life expectancy.  
cardiovascular aging; prediction; mathematical modelling; elderly patients; physiological factors; differential equations