esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Software-Defined Networks to Mitigate Private VLAN Attacks
## 4 Comparative Performance Analysis
### Testbed  
The comparative analysis carried out in this work was performed in a testbed that emulated a small corporate network with a collapsed core network design. Two network scenarios were evaluated, as shown in Figure 5. Both scenarios are examples of oversubscribed network designs since the switches have more downlink bandwidth available than uplink bandwidth [39]. The first scenario comprises three different LAN segments with each switch (_S1_, S2, and _S3_) connected to the collapsed core level. The second scenario represents a single LAN segment composed of two switches at the access level (_S1_ and _S3_) and another switch (_S2_) acting as an aggregation switch connected to the collapsed core level. The second scenario was proposed due to the large difference in computational resources between the available router and switches. Thus, this will ensure that the behaviour observed during the experimentation does not depend on the network devices used.  
During the tests, a Cisco 1841 router with two 100 Mbps interfaces and an HWIC-4 ESW module with 4 Ethernet ports was used as a gateway in the collapsed core level. In  
Figure 4: PVLAN attack mitigation strategies.  
addition, three Netgear ProSAFE M4300-28G switches with twenty-four 1 Gbps interfaces and four 10 Gbps interfaces were deployed at the access/aggregation level.  
The corporate network emulated in the two scenarios also included PVLAN segmentation, as illustrated in Figure 5. Specifically, all the switches located at the access level were configured with two communities (blue and yellow) with six ports reserved for each community and six isolated ports (red). In the first scenario, a promiscuous port was configured on the switches in the switch-to-gateway links. In addition, in the second scenario, where it was necessary to extend the PVLAN segmentation, trunk links were configured between switches. Note that in this scenario the ACLs were configured in the aggregation switch and applied inbound in its trunk links.
## 5 Experiment Results
### Results of DRL-HA  
To validate the generalization capabilities of the DRL-HA, we trained 25-, 50-, 75-, 100-125-, and 150-size HCNN models for DRL-HA. These sizes of model are representative, and the tests with these sizes can comprehensively reflect the generalization capabilities. For the test data, we generate 100 instances with 25, 50, 75, 100, 125, 150, 175, and 200 tasks, respectively, according to the O-1 dataset, which is the same as the training data.  
The average task total profits and total running time of DRL-HA and its competitors are shown in Tables 6 and 7. In these two result tables, H-25 is used to represent the HCNN model trained under the scale of 25, and the HCNN models trained by other distinct scales are expressed in the same way.  
From the results, it can be seen that the DRL-HA algorithm can obtain the best value in eight scenarios with different scales, and the best HCNN model is 9.15% higher than the best competitor. The average results of 100 scenarios also fully reflect the stability and superiority of the DRL-HA to agile satellite scheduling scenarios. Figure 8 displays the average profits and total time for the HCNN at different size. The HCNN models trained by different scale datasets have similar performance when meeting the same scale test scenarios, indicating that there is no essential difference in the performances of HCNN models trained on different scales. Additionally, the Figure 9a shows the average profits of each algorithm in different scale scenarios. Obviously, the trained HCNN model can directly solve scheduling scenarios with different task scales and with great performance.  
In terms of the running time, the experimental results of each algorithm in different scale scenarios are shown in Figure 9b. The data for \"HCNN\" are the average values from all HCNN models. Obviously, the running time of the DRL-HA algorithm matches the level of the magnitude and the calculation trend compared to other heuristic algorithms. Therefore, the DRL-HA algorithm does not increase the time complexity.  
In summary, DRL-HA not only accomplishes better performance in terms of task profits but also has the advantage of running time, which demonstrates the high efficiency of the DRL-HA as a solving algorithm.
## 3 Results  
### Parameterization and Model Sensitivity  
The optimized parameters for each biome type are presented in Table 1. The slope coefficient in the Ball-Berry model, \(a_{1}\) (Equation (2)), was estimated as 7.5 to 10.3. The maximum light-use efficiency, \(\varepsilon_{\text{max}}\), was estimated as 0.0011 mg-CO\({}_{2}\cdot\upmu\)mol\({}^{-1}\) PPFD for forest and grassland and 0.0022 mg-CO\({}_{2}\cdot\upmu\)mol\({}^{-1}\) PPFD for cropland, indicating that croplands have the maximum light-use efficiency, possibly twice as high as that of forests and grasslands. However, a large site-to-site variability of the parameters was detected within each biome type. For example, the magnitude of variability (standard deviations) of \(g_{0}\) is comparable to the mean values. In comparison, the within-biome variability was least for \(\varepsilon_{\text{max}}\), which was relatively conserved across sites.  
Sensitivity analysis results indicated that the modeled ET was most sensitive to NDVI; 10% changes in NDVI resulted in approximately 4% changes in ET (Table 2). NDVI was followed by \(a_{1}\) (or \(\varepsilon_{\text{max}}\), ca. 3%), \(g_{0}\) (1.0% to 1.8%), \(b_{2}\) (0.5% to 2.5%) and \(b_{3}\) (0.5% to 1.3%). The sensitivity to soil water content and soil depth was minor. Ten percent changes in each of these two parameters introduced changes of less than 1% in ET (except a 1.7% change for SWC in grassland).  
Comparing the parameter sensitivities among the vegetation types revealed that the model was significantly more sensitive to the parameters when estimating \(r_{88}\) in grassland than in cropland and forest (Table 2). For example, the sensitivity of \(b_{2}\) in grassland (2.5%) was three-fold greater than that in forest (0.8%) and five-fold greater than that in cropland (0.5%). In addition, the model sensitivity of SWC in grassland (1.7%) was obviously much larger than in forest (0.5%) and cropland (0.4%). These results imply that the accurate estimation of soil surface resistance is more important for grassland than for the other two biome types.
## 3 Related Works
Handling missing data is a key challenge, particularly in sensor-limited environments. [PERSON] et al. (2024) [38] reviewed methods for restoring missing sensor signals, categorising approaches into finite element, sparse representation, statistical inference, and machine learning methods. Among these, hybrid deep learning models, such as CNN-BiGRU with Squeeze-and-Excitation [39], have shown significant promise. This approach combines CNNs and BiGRU to capture spatial and temporal correlations, achieving a 33.07% improvement in \(R^{2}\) and a 93.02% reduction in NRMSE under environmental disturbances. These insights are relevant to the mini case study in this paper, where sensor dropouts in the CartPole environment mimic real-world incomplete observations [40]. Techniques like data imputation and hybrid deep learning could enhance reinforcement learning algorithms under partial observability, bridging the gap between theory and practical deployment.
# A Practical Positioning Method in End-Plate Surface Distance Measurement with Nano-Meter Precision
## 1 Introduction  
End-plate surface distance is one of the important geometric parameters that are widely used in industry and science. The end-plate surface distance objective standards such as gauge block, step master or step height gauge, and step gauge are extensively applied to the calibration of length instruments. Besides, the end-plate surface distance such as the length of glass cavity is one of important parameters for the frequency stabilization of the laser and the performance of the F-P interferometer, so accurately measuring the end-plate surface distance measurement is needed. The end-plate surface distance of objective length standards is in the range from a few tens of nanometers to one meter. Different sizes of objective length standards have respective calibration applications. For example, the step gauge with nanometer size is generally used for calibration of relevant measurement instruments such as AFM(Atomic Force Microscope) and SPM(Scanning Probe Microscope) [1; 2; 3]. The step master with micrometer size is generally used for the z-axis (vertical direction) calibration of optical instruments. No matter what size of gauge block it is, the positioning of surface is the key to realizing high precision. Generally, interference methods are used for the measurement of end-plate surface distance with high precision. The traditional interference method is the excess fraction method [4],known from the CMCs(Calibration and Measurement Capabilities) published at the website of BIPM (International Bureau of Weights and Measures Metre Convention signatories); this method has about 20 nm positioning expansion uncertainty for end surface distance measurement of the gauge block. Although the positioning precision is very high for the excess fraction method, it needs at least two kinds of frequency stabilized lasers with different wavelength. Nowadays new methods using a femtosecond mode-locked pulse laser have appeared with the development of laser technology; researchers in [5] gave a description of this method and showed a measurement repeatability of 19 nm. Due to use of the femtosecond mode-locked pulse laser, the system is very complex. The common shortfall of both methods is that they are not easy to popularize because of their high cost and the complexity. For these reasons, a practical method realized by the combination of white light interference and laser interference is presented in this paper. The feature of this method is that it takes advantage of the positioning function of white light interference and good coherence of the laser. An experimental system was created to verify this method, by optimizing the different parts of the system such as the signal processing part, and by using dynamic measurement, 10 nm measurement repeatability was obtained in ordinary laboratory conditions. The measurement repeatability should be better if using this method to the standard device.
# Thermostable D-Allulose 3-Epimerase for Long-Term Food-Compatible Continuous Production Systems
## 4 Conclusions  
In this study, we systematically investigated the catalytic properties and industrial potential of a thermostable D-allulose 3-epimerase (TcDAEase) from _Thermogemmatispora carboxidiorans_. The enzyme exhibited outstanding thermostability, broad pH tolerance, and high catalytic activity toward D-allulose and D-fructose, even in the absence of metal ion supplementation. Among the tested cofactors, food-grade Mg\({}^{2+}\) significantly enhanced enzymatic activity, emphasizing the suitability of TcDAEase for food-grade rare sugar production. To improve practical applicability, TcDAEase was immobilized on a chitosan-diatomaceous earth matrix, resulting in favorable activity recovery and excellent operational stability. The immobilized biocatalyst achieved an activity recovery of 48.7% \(\pm\) 2.4% and retained 90.3% \(\pm\) 1.5% of its initial activity after seven consecutive cycles. Remarkably, the immobilized enzyme enabled continuous D-allulose production in a packed-bed reactor for 150 days with minimal decline (approximately 4%) in conversion efficiency, outperforming several previously reported systems. These results demonstrate that the integration of enzyme thermostability, food-compatible activation, and cost-effective immobilization offers a promising strategy for the scalable and sustainable production of D-allulose.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15137318/s1](https://www.mdpi.com/article/10.3390/app15137318/s1)]([https://www.mdpi.com/article/10.3390/app15137318/s1](https://www.mdpi.com/article/10.3390/app15137318/s1)), Figure S1: Original SDS-PAGE image of TcDAEase (corresponding to Figure 3 in the main text); Figure S2: Appearance of the filtered immobilized TcDAEase particles; Figure S3: Photograph of the packed-bed reactor system used for continuous D-allulose production.  
Conceptualization, M.Y. and Y.L.; methodology, J.C.; validation, J.C.; formal analysis, J.C.; investigation, J.C.; resources, M.Y. and Y.L.; data curation, J.C.; writing--original draft preparation, J.C.; writing--review and editing, J.C., M.Y. and Y.L.; visualization, J.C.; supervision, M.Y. and Y.L.; project administration, M.Y. and Y.L.; funding acquisition, M.Y. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key-Area Research and Development Program of Guangdong Province, Guangdong Provincial Department of Science and Technology, Grant No. 2022B0202120001. The article processing charge (APC) was also funded by the same grant.  
Not applicable.  
Data are contained within the article.  
The authors declare no conflicts of interest.
# A Study on Sensitive Bands of EEG Data under Different Mental Workloads
## 3 Data Analysis Method  
In this section, the proposed EEG data processing and classification analysis methods are presented. First, the EEG data is preprocessed according to the two criteria of \"high performance\" and \"fewer artifacts\" for subsequent analysis; then feature extraction is performed by calculating the power spectral density and energy. After preprocessing and feature extraction of EEG signals, the importance rating of EEG is evaluated by calculating the Gini index, and the most sensitive band under different mental loads is selected. Finally, the EEG signals are classified by the SVM classifier according to the most sensitive band and full band data. A detailed introduction is described in the following sections.
# High-Reliability Underwater Acoustic Communication Using an M-ary Cyclic Spread Spectrum
## 1 Introduction
We recommend employing a Doppler bank within the maximum range of the Doppler to compensate for detailed Doppler. An artificial Doppler shift was generated, and a simulation was implemented to compare the performance. The doppler frequency was estimated in the passband per symbol for MSS and MCSS signals. In the case of the CSK and MCSK signals, the Doppler frequency was measured at the preamble part and then the phase was compensated in the baseband using a phase-locked loop (PLL) [24]. Section 2 explains the principle of the proposed method and the Doppler compensation mechanism in the receiver. Section 3 replicates the proposed and existing approaches and compares the results using SNR. The proposed method's performance in sea trials is described in Section 4, and the results are shown. Section 5 provides the conclusion.
# Sand Production Characteristics of Hydrate Reservoirs in the South China Sea
## 7 Discussion  
This article focuses on three aspects of research on the sand production characteristics of hydrate muddy silt reservoirs.  
1. In terms of sand production simulation in hydrate reservoirs, different methods of sand production simulation using logging data have yielded results consistent with existing research, all of which suggest that hydrate reservoirs in the South China Sea are prone to sand production.
2. In terms of the sand production morphology of muddy silt, this article uses hydrate formation samples from the South China Sea and observes the \"Umbrella seed page state\" and \"Umbrella handle convergence state\" in Figure 12 through experiments. This indicates the process of changes in the pore structure around the wellbore during hydrate extraction. This is somewhat different from models such as earthworm-like wormhole-like and continuous collapse commonly mentioned in existing research.
3. In terms of quantitative exploration of water and sand production, although this article has conducted preliminary explorations through multiple experiments, only preliminary analysis has been carried out, and there is no clear understanding of the regularity. At the same time, there are almost no studies of the same type. The reason for this is that undigested hydrates, as part of the reservoir skeleton, degrade the mechanical properties of the reservoir after decomposition, along with the multi-field evolution of Temperature-Hydroley-Mechanics-Chemistry, making it difficult for existing theories to guide experiments and extraction. Further theoretical analysis and experimental research are needed to establish a quantitative model for water and sand production during hydrate extraction, providing support for the development of stable production plans.
###### Abstract  
This paper presents an efficient computer-aided diagnosis (CAD) approach for the automatic detection of Alzheimer's disease in patients' T1 MRI scans using the voxel-based morphometry (VBM) analysis of the region of interest (ROI) in the brain. The idea is to generate a normal distribution of feature vectors from ROIs then later use for classification via Bayesian regularized neural network (BR-NN). The first dataset consists of the magnetic resonance imaging (MRI) of 74 Alzheimer's disease (AD), 42 mild cognitive impairment (MCI), and 74 control normal (CN) from the ADNI1 dataset. The other dataset consists of the MRI of 42 Alzheimer's disease dementia (ADD), 42 normal controls (NCs), and 39 MCI due to AD (mAD) from our GARD2 database. We aim to create a generalized network to distinguish normal individuals (CN/NC) from dementia patients AD/ADD and MCI/mAD. Our performance relies on our feature extraction process and data smoothing process. Here the key process is to generate a Statistical Parametric Mapping (SPM) \(t\)-map image from VBM analysis and obtain the region of interest (ROI) that shows the optimistic result after two-sample \(t\)-tests for a smaller value of \(p\) < 0.001(AD vs. CN). The result was overwhelming for the distinction between AD/ADD and CN/NC, thus validating our idea for discriminative MRI features. Further, we compared our performance with other recent state-of-the-art methods, and it is comparatively better in many cases. We have experimented with two datasets to validate the process. To validate the network generalization, BR-NN is trained from 70% of the ADNI dataset and tested on 30% of the ADNI, 100% of the GARD dataset, and vice versa. Additionally, we identified the brain anatomical ROIs that may be relatively responsible for brain atrophy during the AD diagnosis.  
Footnote 1: School of Information Communication Engineering, Chosun University, 309 Pilmun-Daero, Dong-Gu, Gwangju 61452, Korea; [EMAIL_ADDRESS] (B.K.); [EMAIL_ADDRESS] (G.-R.K.)  
Alzheimer's disease; VBM; AAL; \(t\)-test; feature extraction; BR-NN; computer-aided diagnosis (CAD)
## 3 Experimental Results
### Amount of Data for Training  
Training data were used for the neural network model to learn the properties of each class. The larger the amount of training data, the stronger the ability for the neural network to generalize the properties of each class. An inadequate amount of data can easily lead to overfitting of the neural network because it cannot summarize the rules from more data. To improve the training accuracy, the neural network continuously adapts the model parameters to the characteristics of the training data set, which eventually leads to a high training accuracy, but the test accuracy is not ideal. As the training error decreases, the test error increases instead and overfitting occurs.  
To evaluate the influence of the amount of data on the performance of the model, 5%, 10%, 30% and 60% of the total data were used as the training data set. The comparison results are shown in Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **Image Resolution** & **mIoU** \\ \hline Original Xception & 2048 \(\times\) 1024 & 56.49\% \\ Improved Xception & 2048 \(\times\) 1024 & 76.88\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of results with different hyperparameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Optimization Algorithm** & **Backbone** & **Image Resolution** & **mIoU** \\ \hline SGDM & Xception & 2048 \(\times\) 1024 & 76.88\% \\ RMSprop & Xception & 2048 \(\times\) 1024 & 17.40\% \\ Adam & Xception & 2048 \(\times\) 1024 & 38.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of training results with different optimization algorithms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Backbone** & **Image Resolution** & **mIoU** \\ \hline ResNet-18 & 2048 \(\times\) 1024 & 51.14\% \\ ResNet-50 & 2048 \(\times\) 1024 & 71.53\% \\ MobileNet-v2 & 2048 \(\times\) 1024 & 66.15\% \\ Xception & 2048 \(\times\) 1024 & 76.88\% \\ Inception-ResNet-v2 & 2048 \(\times\) 1024 & 57.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of results with different pre-trained networks with improved parameters.  
Because the amount of 5% is very small, choosing this sample set randomly may lead to different results. To investigate this, the training was conducted with 10 runs in which the sample set was randomly selected from the total data. In this regard, a standard deviation of mIoU of 0.36% has been obtained. Figure 5 shows two original images and the prediction results with different training sets. The larger the training set, the better the prediction results. An increase in the amount of training data improved the overall results. In this case, due to using a pre-trained network and because the change in performance is small in the range from 85 to 169 images, an amount of about 85 images already led to a good compromise between performance and calculation time. Whether a neural network can learn better in a particular detail cannot be answered by the amount of training data, but the more training data that are available, the more statistically significant and stable the training results are.