esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Geopolymer Composites Based on Fly Ash Reinforced with Glass and Flax Fibers
## 2 Materials and Methods
### Methods  
#### 2.2.1 Raw Material Analysis  
Particle size analysis of fly ash, river sand, and glass spheres was carried out on the Anton Paar PSA 1190 LD Graz particle analyzer (Anton Paar GmbH, Graz, Austria). The study was performed using the laser diffraction method.  
\begin{table}
\begin{tabular}{c c c c} \hline
**D-Values [um]** & **Fly Ash** & **River Sand** & **Glass Sphere** \\ \hline D\({}_{10}\) & 2.210 & 173.235 & 41.944 \\ D\({}_{50}\) & 11.892 & 342.393 & 108.735 \\ D\({}_{90}\) & 34.414 & 474.750 & 169.308 \\ Mean size & 16.273 & 376.308 & 110.640 \\ SPAN * & 2.708 & 0.881 & 1.079 \\ D, [5.3] ** & 21.183 & 396.066 & 119.170 \\ \hline \end{tabular}  
* Span = \((\text{D}_{90}-\text{D}_{10})/\text{D}_{50}\), ** The volume-weighted mean area size.  
\end{table}
Table 1: Distribution of particle size of applied raw materials.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Designation** & \multicolumn{4}{c}{**Mixture Proportion [\% by Weight]**} \\  & **Fly Ash** & **Sand** & **Glass Sphere** & **Glass Fibers** & **Flux Fibers** \\ \hline S\_REF & 50 & 50 & - & - \\ GS\_REF & 50 & 50 & & \\ S\_GF & 49.5 & 49.5 & 1.0 & - \\ GS\_GF & 49.5 & 49.5 & 1.0 & \\ S\_FF & 49.5 & 49.5 & - & 1.0 \\ GS\_FF & 49.5 & 49.5 & & 1.0 \\ S\_MIX & 49.5 & 49.5 & & 0.5 & 0.5 \\ GS\_MIX & 49.5 & & 49.5 & 0.5 & 0.5 \\ \hline \end{tabular}
\end{table}
Table 2: Designation of manufactured composites.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & \multicolumn{2}{c}{**Identified Phase**} & \multicolumn{2}{c}{**Percentage Range, \%**} \\ \cline{2-4}  & **Phase** & **Chemical Formula** & \\ \hline \multirow{4}{*}{Fly ash} & Quartz & SiO\({}_{2}\) & 43.3 \\  & Mullite & Al\({}_{5}\)Si\({}_{2}\)O\({}_{13}\) & 53.8 \\ \cline{1-1}  & Hematite & Fe\({}_{2}\)O\({}_{3}\) & 0.7 \\ \cline{1-1}  & Magnetite & Fe\({}_{3}\)O\({}_{4}\) & 0.3 \\ \cline{1-1}  & Anhydrite & CaSO\({}_{4}\) & 1.5 \\ \cline{1-1}  & Rutile & TiO\({}_{2}\) & 0.4 \\ \hline \end{tabular}
\end{table}
Table 3: X-ray analysis of fly ash.  
X-ray diffraction (XRD) was performed by using a PANalytical laboratory device (PANalytical, Almelo, The Netherlands). The main aim was to analyze the mineral composition. The analysis took place on powdered samples. The phase composition was determined by using the Debye-Scherrer method. Phase analysis was performed using Cu-K\(\alpha\) radiation. Phase identifications were based on the evaluation of interplanar distances using the HighScore Plus software (V5.2) with the PDF4+ database provided by the International Center for Diffraction Data (ICDD). It is a technique that allows us to determine the phase composition and crystal structure of the tested material. The analysis consists of X-ray diffraction as it passes through the test sample. Diffractions reveal information about the distance of the atomic layers of the sample--based on which the structure is determined.
## 1 Introduction
According to the evaluation, these methods possess effective numerical dissipation, and the \(\beta_{1}/\beta_{2}\)-Bathe method is the special case of the \(\rho_{\theta}\)-Bathe method [26]. Compared with the generalized-\(\alpha\) method, this series of Bathe methods show much better performance [24]. Some multi-step algorithms are further proposed based on the methods mentioned above [27; 28; 29; 30]. They are usually composed of a non-dissipative algorithm, e.g., trapezoidal rule, and a strongly dissipative one [29]. Focusing on the nonlinear problems, a time integration method based on Galerkin weak form, i.e., the Galerkin weak form (GGW) method is proposed [31], considering weighted residuals of equations [32]. The Newton-Raphson iteration method is used first so that the iterative linear procedure can be constructed. To solve the resulting linear equations, a time-domain finite element method (FEM) based on Galerkin weak form is established [33; 34; 35], in which the Gaussian numerical integration is used to solve the integral term. For different degrees \(m\) (\(m\) = 1, 2, 3) of the time elements, the implicit time integration schemes are derived. At the same time, the numerical damping is controlled by modifying the numerical integration form. According to the analysis, the GGW method is an unconditionally stable, self-starting and one-step method. Compared with the Newmark method, Wilson-\(\theta\) method, and Bathe method, the GGW method has better stability and efficiency. However, for some representative nonlinear problems, it is found that in the GGW method, the numerical dissipation may decay too fast and the speed of the dissipation cannot be controlled in some cases. Hence, the GGW method is expected to be improved to possess a more effective numerical dissipation performance. Based on the deep study of the GGW method and inspired by the idea of the Bathe method, an improved two-step time integration method is proposed hereinafter. The time step of this method is divided into two sub-steps. In the first sub-step, the GGW method is used, and in the second sub-step, another parameter is introduced by the trapezoidal integral, i.e., a non-dissipative algorithm is combined with the GGW method so that the improved algorithm can solve the defects of the GGW method but retaining its excellent characteristics. After that, the corresponding iterative procedure is established and presented, and then the accuracy and numerical stability of the algorithm are discussed. Typical numerical examples including an undamped single-degree-of-freedom (single-DOF) system, the nonlinear pendulum, and a large-span plane skeletal system are solved, through which it can be seen that the numerical dissipation is controllable in the proposed two-step time integration method and sensitivity of parameters is reduced. It dramatically improves the performance of the GGW method. Compared with other algorithms, such as the trapezoid rule, the Wilson-\(\theta\) method, and the Bathe method, the results sufficiently show the high accuracy and computational efficiency of the proposed method.
# Deep Feature Retention Module Network for Texture Classification
## 3 Proposed Method  
### Model Architecture  
The structure of DenseNet is shown in Figure 2a. DenseNet has a DenseBlock comprising several layers. The input value of the previous layer was closely linked to that of the next layer using DenseBlock, allowing the feature information of the previous layer to be reused. DenseNet is also a model developed to preserve different levels of information compared to ResNet, which uses a skip connection technique to solve the gradient loss problem as the network deepens. Because of these structural characteristics, DenseNet is believed to be more helpful for extracting and utilizing detailed feature information about textures than general models such as VGG. The structure shown in Figure 2b is proposed based on the characteristics of DenseNet. The proposed model has a multiple-feature structure that can consider all features, from low to high levels, in the learning process. The output information generated for each DenseBlock is extracted using various features through each feature retention module (FRM). The generated output features are concatenated and used for the final classification. The values extracted and combined through the structure of this model contain features identified at each level, and preserved information. In addition, because these values are used through the structure proposed during learning, they can help one classify patterns of texture datasets with regularity and irregularity.  
The expected effects of the proposed model structure based on DenseNet are as follows:  
* DenseNet has a structural characteristic that uses information from the input layer connected to the output layer in the feed-forward process. Therefore, it can consider the advantages of features and continually learn all feature information from low to high levels.
* FRM was used for each DenseBlock to minimize the loss of information as the layers of the backbone model deepened. This is expected to have an important influence on the use of the detailed features of textures for learning.
* We aimed to use various detailed features of the texture dataset for learning. Therefore, the structural characteristics of the model proposed in [18] were referred to. By combining the FRM with the backbone, it is expected that the outputs through the  
Figure 2: (**a**) DenseNet structure. (**b**) shows the proposed model structure based on DenseNet as the backbone.  
FRM from low to high levels are aggregated and can help distinguish classes with high probability.
## 5 Conclusions  
This paper introduces the principle of empirical mode decomposition and the deep convolutional neural network model. The PD original signal of the T-type cable terminal is obtained by the experiment, and the IMF signal is obtained by EMD decomposition. The signal with characteristic information is used as input to identify the types of three kinds of T-type cable terminal defects obtained by the experiment. The recognition results of the proposed method and the traditional method are compared and analyzed. The conclusions are as follows:  
1. As an improved CNN network model, the EMD-DCNN network used in this paper can extract image features at a deeper level. Compared with the traditional model, it has a better recognition effect and faster training speed in partial discharge defect type recognition.
2. In the process of sample training, the learning rate and the number of iterations have an impact on the recognition results of the improved CNN network. It has been verified that when the learning rate is 0.001 and the number of iterations is 320, the training recognition rate of the network can be stabilized at about 95%, and the network training loss value is also stabilized at a low level.
3. The average recognition rate of the EMD and DCNN method proposed in this paper is 95.3%, while the average recognition rate of the EMD-SVD-RF method is only 89.7%, which is 5.6% lower than that of EMD-DCNN method, and the recognition rate of each defect is lower than that of EMD and DCNN method. In addition, the EMD-DCNN recognition method is also superior to the traditional mechanical learning algorithm, RF, in the recognition speed, which makes EMD-DCNN deal with real-time signals well.  
Due to the above advantages, the EMD-DCNN method is more suitable for online monitoring platforms and applied to engineering practice. It reduces operation and maintenance costs at the economic level, extends equipment life, and promotes the development of intelligence and standardization at the technical level.  
Conceptualization, S.C.; methodology, S.C.; software, Y.G.; validation, C.F. and G.Z.; formal analysis, J.L.; investigation, S.C.; resources, C.F.; data curation, S.C.; writing--original draft preparation, S.C.; writing--review and editing, C.F.; visualization, J.L.; supervision, Y.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# Multi-Controller Deployment in SDN-Enabled 6G Space-Air-Ground Integrated Network
## 2 Problem Formulation and Solution
### Algorithm Complexity Analysis  
Firstly, considering the time complexity analysis of Algorithm 1 (multi-controller deployment strategy based on the simulated annealing algorithm), the algorithm is the outermost cycle of the temperature drop T, each temperature K iteration, each iteration process \(N\) switch, and \(M\) controllers; the time complexity is therefore \(O(TKMN)\). For Algorithm 2, each time slice handles \(N\) switches and \(M\) controllers at a time, so the algorithm complexity is \(O(MN)\).  
Secondly, for the space complexity, Algorithm 1 needs to update the control deployment information \(N\), the delay matrix \(M*N\), the relation matrix \(M*N\) and the loss function value, so the space complexity is \(O(N+2*M*N)\). For Algorithm 2, the relational matrix and the controller load need to be stored so the controller complexity is also \(O(N+2*M*N)\).  
Figure 4: Flow chart of switch migration policy.
## 3 Results and Discussion  
### Effects of Pressure, pH, and Temperature under AP and HHP on Cytolase PCL5 Activity  
To determine the appropriate pressure for the reaction, the hydrolytic activity of cytolase PCL5 was evaluated at pressures ranging from 0 to 400 MPa (Figure 2). The relative activity increased to 423% as the pressure increased to 150 MPa, and then decreased to almost 38% at 400 MPa. Therefore, all subsequent experiments were performed under HHP at 150 MPa. Even when HHP was applied to isoquercetin production with \(\alpha\)-l-rhamosidase in the previous study, it showed the highest productivity at 150 MPa [25]. However, it showed approximately 2.6-fold higher productivity than that under AP, which was lower than the increase in the hydrolytic activity of cytolase PCL5.  
Optimal pH and temperature for cytosase PCL5 activity on platycoside E have been reported as pH 5.0 and 50 \({}^{\circ}\)C [23]. In this study, the maximal activity of cytosase PCL5 was observed at pH 5.0, under both AP and HHP. However, maximum hydrolytic activities were observed at 50 \({}^{\circ}\)C and 55 \({}^{\circ}\)C under AP and HHP, respectively (Figure 3). At 55 \({}^{\circ}\)C, the enzyme activity under HHP was 5-fold higher than that under AP. This result indicates that changes in pressure can affect enzyme activity at a given temperature.
###### Abstract  
Accurately monitoring heavy metal stress in crops is vital for food security and agricultural production. The assimilation of remote sensing images into the World Food Studies (WOFOST) model provides an efficient way to solve this problem. In this study, we aimed at investigating the key periods of the assimilation framework for continuous monitoring of heavy metal stress in rice. The Harris algorithm was used for the leaf area index (LAI) curves to select the key period for an optimized assimilation. To obtain accurate LAI values, the measured dry weight of rice roots (WRT), which have been proven to be the most stress-sensitive indicator of heavy metal stress, were incorporated into the improved WOFOST model. Finally, the key periods, which contain four dominant time points, were used to select remote sensing images for the RS-WOFOST model for continuous monitoring of heavy metal stress. Compared with the key period which contains all the available remote sensing images, the results showed that the optimal key period can significantly improve the time efficiency of the assimilation framework by shortening the model operation time by more than 50%, while maintaining its accuracy. This result is highly significant when monitoring heavy metals in rice on a large-scale. Furthermore, it can also offer a reference for the timing of field measurements in monitoring heavy metal stress in rice.  
remote sensing; key period; heavy metal stress; WOFOST model; Harris algorithm; data assimilation +
Footnote †: journal: Sensors
## 3 Experimental Results
### Analysis of Network Structure  
In this subsection, the impact of network structure is examined. The CNN and fully connected networks were treated as the basic network structure. The attention network (AN) and the LSTM network in the proposed network structure were sequentially removed and the experimental results are shown in Table 4. Comparing the basic network structure, the prediction accuracy obtained by using the network structure with AN or the LSTM network can be improved. The proposed network structure, which uses both AN and LSTM networks, can achieve the highest accuracy.  
The results of CNN + LSTM are slightly better than that of CNN + AN, but the layers for CNN + LSTM and CNN + AN are 2 and 1, respectively. CNN + AN and CNN + LSTM focus on identifying meaningful features and modeling the characteristics of time series, respectively. These two approaches focus on different characteristics of input sequences; thus, integrating these two networks can greatly improve the accuracy of BP estimation.
## 6 Conclusions  
This paper explored a variety of effects on DRFM system performance against a modern coherent radar, including architecture, ADC resolution, received SNR, and the signal time-bandwidth product. These factors are of particular importance to the science of designing an effective Electronic Attack (EA), and how an EA must be adjusted to compensate for these distortions.  
The key factor in effective EAs is the Jammer-to-Signal Ratio (JSR) as--while it often does not need to exceed 3 to 6 dB--falling short of the required number can be fatal. While each scenario is unique, the factors discussed in detail here all weigh on the processing loss of the DRFM system and will affect the required jammer transmit power to achieve the requisite JSR.  
Similarly, the jammer power must be placed in the appropriate tracking gate or it is wasted, having no effect on the JSR. The guidance developed here on how the effective range delay changes with an envelope DRFM with the signal bandwidth and other factors, is a critical adjustment when conducting Range Gate Pull-Off (RGPO) attacks or similar techniques.  
Finally, the paper proposed and implemented a novel DRFM architecture capable of generating effective EA against radars with sophisticated forms of EP.  
[PERSON] contributed through conceptualization, formal analysis, investigation, methodology, software, writing the original draft, and validation. [PERSON] contributed through supervision, reviewing and editing, and methodology. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by resources from the Royal Canadian Air Force, through the Directorate of Testing and Aerospace Engineering Support-8.  
The authors declare no conflict of interest.  
Figure 21: The component signals that, when superimposed, create the signal from the DRFM shown in Figure 20.
## 4 Results and Analysis
### Consistency Assessment with WELD Products
#### 4.2.2 Radiometric Consistency Assessment
Figure 7: Scatter plots of WELD TOA reflectance bands (X axis) and HJ TOA reflectance bands (Y axis) for the middle month of every season. The standard seasonal definition for the Northern Hemisphere adopted by the climate modeling community is used where spring is defined by the months March to May. **Columns 1–4** are the scatter plots of Blue, Green, Red and Near-infrared bands, respectively. The red solid lines show linear fitted results of the scatter points. The dotted lines are 1:1 lines for reference. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multicolumn{4}{c}{**Slope**} & \multicolumn{4}{c}{**Intercept**} & \multicolumn{4}{c}{**R\({}^{2}\)**} & \multicolumn{4}{c}{**RMSE**} \\ \cline{2-13
# Local Government Cybersecurity Landscape: A Systematic Review and Conceptual Framework
## 4 Analysis and Results
### Types and Techniques of Cyber-Attacks in Local Governments  
Local governments are frequently targeted for cyber-attacks [6; 68]. The types and techniques of attacks are diverse and continuously evolving. A comprehensive understanding of common attack types and techniques can help practitioners and researchers develop effective defensive measures and a more targeted approach to policymaking to safeguard critical data and digital infrastructure in local government. Therefore, we made an attempt to list all the types and techniques of cyber-attacks addressed by the existing literature in this field. [PERSON], [PERSON], and [PERSON] [14] initially contributed to the local government cybersecurity research field by investigating cybersecurity issues in 67 counties in Florida, USA. Their study marked viruses and worms as the most common types of attack, followed by phishing, bots, spyware, and Domain Name Systems (DNS). The first nationwide survey in the USA on local governments' cybersecurity by [PERSON], [PERSON], [PERSON], and [PERSON] [15] highlighted ransomware as the most prominent form of cyber-attack. In a different study by [PERSON], [PERSON], and [PERSON] [6] on 11 cities and three counties in the USA revealed that 85.5% of the cyber-attack had been caused by phishing or spear phishing, followed by zero-day target attacks, brute force attacks, Denial of Service (DoS), Distributed Denial of Service (DDoS), and man-in-the-middle. [PERSON], [PERSON], and [PERSON] [10] conducted a study on the cybersecurity practices and policies of local governments in the USA, and they mentioned ransomware, phishing, and DNS attacks as the most common types and techniques of cyber-attacks on local governments. An assessment of the cybersecurity posture of a cloud infrastructure in a local government in Australia by [PERSON] et al. [69] revealed IP spoofing, man-in-the-middle, malicious insiders, and DoS as common types and techniques of cyber-attacks. We compiled a few common cyber-attack events on local governments from the reviewed literature in Table 3 to further investigate the various types and techniques of cyber-attacks targeting local governments. \begin{table}
\begin{tabular}{c c c c c} \hline
**Year** & **Location** & **Attack Type** & **Description** & **Reference** \\ \hline \hline
2022 & \
\begin{tabular}{c} Somerset County, \\ NJ, USA \\ \end{tabular} & Ransomware &
\begin{tabular}{c} The county suffered a ransomware \\ attack, disrupting operations and \\ limiting access to essential data like \\ land records, vital statistics, and \\ probate records, causing email service \\ loss and Internet-reliant \\ services’ disruption. \\ \end{tabular} & [12; 70] \\ \end{tabular}
\end{table} 
Table 3: Cyber-attacks on local governments.
## 4 Results and Discussion
### Results for Model 1 on Dataset 1
Table 5 compares the performance of the models with and without augmentation strategies. The augmentation strategies were applied to enhance the training datasets by introducing variations in facial attributes, such as hairstyles, glasses, and makeup. The metrics reported include training accuracy, testing accuracy, training loss, and testing loss. It can be observed that, in most cases, the models with augmentation strategies achieved higher accuracy and lower loss compared with the models without augmentation. This indicates that augmentation strategies have a positive impact on the performance of the models by enriching the training datasets with diverse facial variations. These results highlight the effectiveness of augmentation strategies in improving the accuracy and robustness of the gender and ethnicity classification models.