esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Deep Learning-Based Intrusion Detection and Prevention System (IDPS)  
Researchers have used broad concepts in their proposed intrusion detection and prevention system (IDPS) based on deep learning (DL) and machine learning (ML) approaches. Based on this, the following review is worth discussing to ensure that deep learning-based intrusion detection is worth investigating in intrusion detection and prevention.  
The author in [44] proposed an innovative intrusion detection system with superior network performance and detected unknown attack packages using a deep learning neural network. They used binary and multiclass classification to detect the intrusion attacks. The authors proposed that the method could be used in large-scale IoT security datasets and could use IDPST. The proposed research had a high accuracy and could also utilize the DL intrusion detection method, but it lacked any prevention method.  
Consequently, the authors in [45] explained how neural networks prevent and detect intrusion attacks for data security. The proposed method studied various IDS and IPS (intrusion prevention) methods. The study was concerned with using neural networks to solve related problems. The study also did not demonstrate intrusion detection or the prevention of IoT networks with IDPST, nor did it show any large-scale IoT dataset used in investigating intrusion attacks.  
The authors of [46] just discussed the differences between IDS and IPS without providing the details of how to achieve them. The proposed method was only based on examining the advantages and disadvantages of using IDS and IPS. The study also explained current developments in machine learning and how they improve the IDPS. The study did not depend on a large-scale IoT dataset, including IDPS topology, with the intrusion detection and prevention systems deployed together.  
Therefore, the authors of [47] used the hybrid IDS method, which detailed intrusion detection and prevention. The proposed hybrid method combined J48 DT (J48 decision tree) and SVM (support vector machine learning). However, the proposed method did not depend on large-scale IoT dataset deployment and utilized an IoT network with IDPST.  
From the literature discussion, the design of a deep learning-based multilayer perceptron intrusion detection and prevention model, DLMIDPSM, is important. Our proposed research method includes urgently needed designs of the DLMIDPSM to investigate intrusion detection and prevention in this research. We also investigate a large-scale IoT dataset in our proposed IoT intrusion detection and prevention IoT device topology (IDPST) for assessing and capturing intrusion attacks. Our new proposed DLMIDPSM can re-evaluate performance metrics, such as precision, accuracy, F1-score, and confusion matrix, for the new datasets proposed in this research, such as the CICIoT2023 dataset with IDPS capability.
# The Performance Study of a Novel Vertical Variable Stiffness Disc Spring Seismic Isolation Bearing
## 4 Test Results and Discussion
### Results and Analysis of the Cyclic Load Test
During the initial stage, its lower stiffness manifests as a gentle ascending curve, enabling a flexible response under minor displacements to effectively absorb seismic energy. As displacements increase, the bearing transitions into the second phase where stiffness is significantly enhanced. This design provides stronger supporting forces to meet the isolation requirements under larger displacements. Overall, the VVSDSI exhibits smoother and more efficient isolation characteristics during seismic events, thereby enhancing the seismic performance of structures. Figure 12: Equivalent damping ratios of the VVSDSI bearing under different preloading displacements and loading amplitudes. (**a**) Different preloading displacements: (**b**) different loading amplitudes. Figure 13: Normalized load–displacement curves of VVSDSI and CSDVI bearings.
## 6 Conclusions  
The AHP and entropy methods are utilized to determine subjective and objective weights, respectively, and a combination of these two methods is employed to derive comprehensive weights for evaluating indicators of existing asphalt pavement in highway reconstruction and expansion projects. Building upon the derived indicator weights, a performance assessment model for existing asphalt pavement in highway renovation and expansion projects is established, integrating combination weighting and the normal cloud model. This model is applied and validated in the renovation and expansion project of the Jingjintang Expressway (Tianjin section) highway reconstruction and expansion project. The research findings can be summarized as follows:  
1. The normal cloud framework addressed limitations of conventional methods by addressing input data characteristics and reconstructing the fuzziness and randomness inherent to pavement evaluation.
2. Application to a reconstruction project case study yielded evaluation results demonstrating good consistency with objective 3D GPR detection. The cloud model exhibited an enhanced ability to discern marginal variations in condition and generate continuous membership outputs versus absolute ratings. This improves its utility for reconstruction and extension planning demand of graded condition assessments.  
In conclusion, the proposed model establishes an innovative and practical means of appraising existing pavement informed by engineering fundamentals. It provides reconstruction and extension project managements with an effective decision support tool capturing input ambiguities. The cloud theoretic methodology also displays potential for continued refinement and wider pavement management applications  
Conceptualization, Y.X. and X.S.; methodology, Y.X.; software, X.S.; validation, Y.Y.; formal analysis, Y.Y.; investigation, Y.X. and X.S.; resources, Y.X.; data curation, Y.X.; writing--original draft preparation, Y.X. and X.S.; writing--review and editing, X.S. and Y.Y.; visualization, X.S.; supervision, Y.Y.; project administration, Y.X.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Chongqing Transportation Bureau Science and Technology Project, grant number 2021-04.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare that this study received funding from Chongqing Transportation Bureau Science and Technology. The funder had the following involvement with the study: Acquisition and analysis of pavement detection data.
# A Novel Malware Detection Model in the Software Supply Chain Based on LSTM and SVMs
## 2 Related Work
The model first converts malware samples to color images using migration learning, then uses VGG19 for feature extraction; after that, PCA is used for feature selection to reduce the dimensionality of the features, and finally the One-Class SVM (OCSVM) is used for the final detection. The OCSVM is an SVM model that is trained using only negative class samples. [PERSON] et al. [20] proposed a static detection method for Android malware based on the LSTM-SVM model. The outcomes of the experiment demonstrate that the LSTM-SVM model detection method based on multiple features and models has a higher detection accuracy compared to the single-feature model, multi-feature single model, and multi-feature cascade model. [PERSON] et al. [21] proposed a malware detection method based on integrated classification. The method consists of a stack of five fully connected layers and one layer of convolutional neural network which performs the first stage of classification, after which the final classification is performed using extreme random trees. [PERSON] et al. [22] proposed a hybrid detection method. The method uses AdaBoost, RF, a hybrid approach (Artificial Neural Network (ANN) with SVM), and a customized DL method together to classify malware. However, some issues still exist in the above studies. For some complex malware behaviors, simple ML models may have difficulty in capturing their patterns, leading to their weak generalization ability. DL models may be overfitted when the amount of data are limited, leading to subpar performance on fresh data. Hybrid models, which combine the features of ML and DL, can improve the accuracy and robustness of detection in some cases. However, effectively integrating these two approaches is a research question.
# Efficient Traffic Flow Guidance Feedback Strategy Considering Drivers' Disobedience in ITS
## 4 Simulation Results and Discussion
### Simulation Results and Analysis
The mean velocity could be maintained at a high level; the peak value of the mean velocity not using SVM was nearly 14 m/s, while it is a little bit lower when SVM is employed. We can also observe that for a short period of time, the mean velocity dropped to zero, which was not observed in the simulation with SVM. As illustrated in Figure 4b, the number of stopped vehicles on the original route increased several times due to the traffic light but decreased right after; this is because of the instructions given by the system. However, the peak number of stopped vehicles reached 18, which is a little bit higher compared to the result of the simulation with SVM. Figure 4c shows that the vehicle flux went up and down multiple times during the time steps, just like the cycle displayed in the figure of mean velocity. However, the values of flux are lower than those of the simulation with SVM. Figure 4: _Cont._  
Analysis of Different Scenes  
In this sub-section, 100 durations of simulations are taken for each type of instruction (the three types discussed above) in order to compare the mean duration of congestions in different scenes. To ensure the accuracy and credibility of the comparison, the 300 durations of simulations all use the same set of driver data. The three types of instructions are: (a) the instruction with pre-filtering using SVM; (b) the instruction without pre-filtering using \(K\)-means; and (c) the instruction without pre-filtering using Fuzzy Logic. We can observe in Figure 5 that the mean duration for instruction without pre-filtering using Fuzzy Logic varies considerably, although it has a better performance than SVM several times. The mean duration is too unstable, and the mean value for the 100 durations is much greater than that of SVM. The mean duration for instructions without pre-filtering using \(k\)-means is more stable than that of Fuzzy Logic, but the mean duration value is still higher than with SVM. This result shows that although instructions without SVM can help reduce congestion, they are not as efficient as the instructions with SVM and \(k\)-means or Fuzzy Logic. Figure 4: Results of instruction without SVM. (**a**) Mean velocity without instruction; (**b**) Number of stopping vehicles; (**c**) Vehicle flux.
## 1 Introduction
EO data provide an alternative approach to monitor and assess water storage variation in the Lancang-Mekong River basin. For example, [PERSON] et al. [12] provided an overview of the total water storage dynamics in the Lancang-Mekong River basin from 2003 to 2016 and compared GRACE and the Global Land Data Assimilation System (GLDAS) data. [PERSON] et al. [12] tried to analyze the impact of reservoirs on TWS but did not quantitatively provide the extent of the impact. Estimating surface water storage (SWS) is difficult due to the sparse monitoring network and limited access to in-situ data. However, advancements in technology have enabled the estimation of SWS through satellite-based methods, including surface water extent (SWE) and water surface elevation (WSE) [41; 42; 43]. For example, [PERSON] et al. [14] used GRACE-derived TWS, Sentinel-3-derived WSE, and Sentinel-2-derived SWE to limit hydrological model parameters and to simulate the TWS, SWS, and streamflow variation in the Lancang River basin. Additionally, the impacts of Xiaowan and Nuozhadu reservoirs on TWS and streamflow were quantitatively analyzed. Overall, the existing studies provide a solid foundation for further research on water storage in the Lancang-Mekong River basin. However, these studies have limitations. Firstly, they lack comprehensive and quantitative analysis of individual changes in water balance components and their interrelationships in the context of climate change. Secondly, the impact of Xiaowan and Nuozhadu, the two major reservoirs, on each water balance component has yet to be adequately quantified. Therefore, future research should focus on a more comprehensive analysis of water balance components and their interactions, as well as a more detailed assessment of the impact of reservoir construction on water storage in the region. In this study, we demonstrate the utilization of several types of satellite Earth observation (EO) datasets that monitor different water balance components for quantitatively evaluating water resources in the lower Lancang River basin before and after the construction of Xiaowan and Nuozhadu reservoirs. Specifically, gravimetry, multiple satellite altimetry, spectral/SAR imagery, and outputs of land surface models (i.e., soil moisture wa ter storage) are used to assess the spatial-temporal dynamics of both surface and subsurface freshwater. Overall, this study intends to address the following objectives: (1) to explore the spatial and temporal variations of TWS as derived from different GRACE solutions; (2) to estimate variations in SWS using gauged monthly discharge, Sentinel-1/2 imagery, and Jason-2, Cyosat-2, Sentinel-3 A/B altimetry; (3) to evaluate groundwater storage (GWS) dynamics.
# Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing
## Materials and Methods
### Methods  
Mapping Fire Damage  
The burn severity, which is defined as the degree of environmental change that is directly caused by fires, can be interpreted as the degree of degradation of the pre-fire vegetation community. The burn severity directly determines the rate of forest-regeneration [44]. Some studies show that the normalized burn ratio (NBR) is particularly useful for monitoring the burn severity. To ascertain the burn severity in conifer forests, the pre-NBR values are necessary as a control scenario. Here, the differenced normalized burn ratio (DNBR) was the spectral index that was applied to evaluate the burn severity [45, 46]. The DNBR was calculated using Equation (4), and the NBR was calculated using Equation (3), where NIR and SWIR2 refer to the near infrared and shortwave infrared 2 bands 4 and 7, respectively, in the Landsat TM and ETM+ data:  
\[\mathrm{NBR}=\frac{\mathrm{NIR-SWIR2}}{\mathrm{NIR+SWIR2}} \tag{3}\]  
\[\mathrm{DNBR}=\mathrm{NBR}_{\mathrm{pre-fire}}-\mathrm{NBR}_{\mathrm{post- fire}}, \tag{4}\]  
where \(\mathrm{NBR}_{\mathrm{pre-fire}}\) is the 1999 NBR value and \(\mathrm{NBR}_{\mathrm{post-fire}}\) is the NBR value in the fire year. The calculated DNBR was assigned to one of four levels based on the DNBR thresholds: high severity (DNBR \(\geq\) 0.6), moderate severity (0.3 \(\leq\) DNBR < 0.6), low severity (0.1 \(\leq\) DNBR < 0.3) and unburned (DNBR < 0.1).
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 2 Background Concepts  
In this section, we explain two of the background concepts that are expected to become the pillars of future networks, namely utilizing mmWave networks to boost capacity and ANM as a conceptual framework to make use of the potential gains of AI/ML towards higher resource efficiency. The optimization of the converged fiber-wireless mmWave network resources is the focus of this study, and this brief introduction aims to highlight the motivation, the main concepts, and the technological enablers behind the solutions provided in the presented resource management papers from Sections 4-7.
## 3 Results
### Factors Influencing Vegetation Growth Changes in the Mining Area
#### 3.3.2 Factors Influencing Spatial Patterns in Vegetation Growth  
The larger the q value, the stronger the explanatory power of a single factor. Factor detection results indicated that X11 (land use) had the highest explanatory power for the spatial differentiation of vegetation growth, with a q value of 0.43 (Figure 15). Combined with Figures 6b and 8, it is evident that areas with fewer human activities tend to have better vegetation growth. Factors X9 (TPI), X8 (slope), and X7 (DEM) also exhibited significant explanatory power, with q values of 0.35, 0.33, and 0.33, respectively. These results, in conjunction with Figures 4 and 8, show that vegetation growth is more favorable in areas  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Trend Grade** & **Basis of Division** & **Area (km2)** & **Area Percentage (\%)** \\ \hline Significant improvement & \(\theta_{\text{slope}}\) \textgreater{} 0.05 & 64.23 & 57.35 \\ Slight improvement & \(0.01\) \textless{} \(\theta_{\text{slope}}\) \textless{} 0.05 & 31.12 & 27.79 \\ Significant degradation & \(\theta_{\text{slope}}\) \textless{} \textless{} \textless{} 0.05 & 0.98 & 0.87 \\ Slight degradation & \(-0.05\) \textless{} \(\theta_{\text{slope}}\) \textless{} \textless{} \textless{} 0.01 & 5.47 & 4.88 \\ Stable area & \(-0.01\) \textless{} \(\theta_{\text{slope}}\) \textless{} 0.01 & 10.20 & 9.11 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Inter annual variation trend of fractional vegetation growth age in the Huoijitu mining area.  
Figure 13: Interannual variation trend and fluctuation of vegetation growth in the Huoijitu mining area from 1999 to 2023: (**a**) slope value of kNDVI change, (**b**) change trend of vegetation growth, and (**c**) Significant interannual change trend of vegetation growth.  
with minimal topographic relief, gentle slopes, and higher elevations. Factors X1 (SOM), X3 (TN), X10 (thickness), and X2 (TP) showed moderate explanatory power, with q values of 0.26, 0.22, 0.21, and 0.19, respectively. Combining Figure 3, Figure 5, and Figure 8, it is shown that vegetation growth is promoted in areas with higher soil organic matter, total nitrogen, and total phosphorus content, as well as thinner excavation thickness. Factors X5 (AN), X4 (AP), and X6 (pH) had the least impact on the spatial differentiation of vegetation growth, with q values of 0.12, 0.08, and 0.08, respectively. Among the influencing factors with notable explanatory power for the spatial differentiation of vegetation growth, human activity factors include X11 (land use); topographic factors include X9 (TPI), X8 (slope), and X7 (DEM); soil nutrient factors include X1 (SOM) and X3 (TN); and mining intensity factors include X10 (thickness). The results demonstrate that the spatial differentiation of vegetation growth in the study area is influenced by human activities, topography, soil nutrients, and mining intensity. Human activities play a dominant role, while soil nutrients and mining intensity exert regulatory effects.
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 3 Opportunities Given by System-Level Testing
### Opportunities Given by TID Tests
#### 3.1.1 Margins Given by the System Are Incorporated in Test Results  
One of interests in performing system-level testing is to evaluate margins given by the system. It is expected that the system may withstand a higher TID level than it results from component-level testing performed in the worst case conditions for specific components. This prediction is based on the fact that: (1) typically not all of the components in the system are working in their worst-case conditions, and (2) system designers apply safety margins (derating).  
TID irradiations of the PoL allowed to observe these margins: it was observed that although degradation of the oscillator frequency is beyond design limits and beyond level obtained during component-level qualification of the core part of the oscillator, the system remains functional. Furthermore, although the system was qualified up to 50 krad based on Co-60 testing following the classical standard procedures [7], it could withstand 400 krad at X-ray (high dose rate) and at least 118 krad at Co-60 (low dose rate)--although, the test was not performed in the worst case conditions. Analysis of the results have shown that the extension of the TID tolerance of the system is related to compensations between some electrical blocks in the complete system--a broad description of this example was given in [22].  
In general, it might be expected that system TID performance will be higher (or at least not lower) than the one derived from component-level testing and system-level testing is an interesting tool to estimate this overall performance. On the other hand, there is also a risk of overestimating system TID performance, if system tests are not performed in the worst-case condition--and if this condition will be met in the real use situation.
## 4 Numerical Simulation Analysis
### The Seismic Response under Different Ground Motions
#### 4.1.2 Displacement Responses  
The horizontal deformation of the side wall causes eccentric compression and a large bending moment, which can lead to damage of the side wall. Therefore, the horizontal layer drift of the underground substation is the major factor of side wall damage, and it is necessary to analyze the horizontal relative deformation of the underground substation under different ground motions.  
The relative displacement time-history curves of the top floor under El-Centro motion and Lanzhou motion are shown in Figure 16, in which the relative displacement is defined as horizontal displacement values, and layer drifts in M1 and M3 under 0.20 g are shown in Table 4.  
It can be seen from Figure 16 and Table 4 that layer drifts in the Y-direction are larger than those in the X-direction, which coincides with the feature that stiffness in X-direction is larger than that in the Y-direction. There is only a 2% difference in the value of the drifts between one-way input motions and multi-way input motions in the X-direction, which indicates that there is no obvious difference in torsion coupling in the X-direction. On the other hand, in the Y-direction, the horizontal drifts with multi-way input motions are about 1.4 times larger than those with one-way input earthquake motion. Therefore, the drifts of the corner column under multi-way input motions are larger than those under one-way input motions, which leads to severe damage of the corner columns. It is essential to improve the constraints of corner columns and increase the number of stirrups in these columns, which can strengthen the deformation ability of such components.