esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN
## 5 Results and Validation
### Performance Comparison
#### 5.3.3 Convergence Comparison  
Figure 4 shows the convergence performance comparison in the NSFNet-14 and Germany-50 topologies after 3000-episode iterations of simulation, as represented by the deep reinforcement learning metric of reward convergence. It is worth noting that because PPO and DRSIR do not incorporate our security mechanism, their rewards will lack the corresponding negative values of \(r_{safety}(\tau)\) and \(r_{t}(\tau)\), which leads to a certain numerical difference in the performance of TBPPO versus PPO and TBDRSIR versus DRSIR. Figure 4a shows the rewards collected per cycle in the NSFNet-14 topology. After 3000-episode iterations, TBPPO converged to \(-20\) after 800 episodes, while PPO initially reached \(-20\) after 500-episode iterations and later re-converged to \(-12\) after 2700 episodes, both exhibiting good convergence trends. DRSIR showed poorer convergence, quickly rising to \(-60\) in the first 100-episode iterations and fluctuating widely until the end of 3000 episodes. In contrast, TBDRSIR displayed greater volatility and ultimately failed to converge. These findings indicate that PPO is more suited to mechanisms that combine the trust value and the nodes' diversity than DQN is.  
In Figure 4b, rewards collected per cycle in the Germany-50 network topology are presented, which offers more potential routes than NSFNet-14. According to the reward comparison in the figure, the convergence patterns of various algorithms can be observed. TBPPO starts to approach \(-85\) after roughly 1400-episode iterations, while PPO achieves convergence at \(-75\) after just 1200-episode iterations. DRSIR rapidly climbs to around \(-150\) within the first 300-episode iterations, but exhibits higher volatility compared to PPO-based algorithms. In contrast, TBDRSIR does not reach convergence. Clearly, TBPPO consistently performs well on larger networks, while TBDRSIR seems to show an inferior performance in our analysis, possibly due to the inclusion of the trust mechanism, which appears to negatively impact DQN's performance. Compared to NSFNet-14, both PPO and TBPPO continue to show superior convergence and optimization capabilities.
# Educational Organization's Security Level Estimation Model
## 4 Validation of Proposed Model
### Model Result Comparison to Experts Opinion  
Security management experts were asked to analyze data (values of all criteria) of all five alternatives and provide security level scores on a 100-point scale. The comparison of the model calculated value and experts provided score is presented in Figure 5.  
The results demonstrate there exists a linear relationship between the calculated model score and the experts provided score. It is noticeable that expert E1 uses a very low level of security rating as the values vary from 2.5 to 10.8 percent. This is an averagely 10 times lower score in comparison to the model. However, this tendency is noticeable for this expert only. The scores provided by the other three experts are higher in comparison to the model calculated scores (on average the score of experts E2, E3, and E4 are 40% higher than the model calculated score). This demonstrates the model is not accurate in security level score accuracy. However, the precision tendency demonstrates the high-level correlation between the modeled score and experts' provided scores (see Table 7).  
In modeling, the security level score is not as important as ranking between analyzed alternatives. Therefore, based on the security level score, all alternatives were ranked from the one with the highest security score (rank 1) to the one with the lowest security score (rank 5) too. The results of [PERSON]'s rank correlation in the previous table demonstrate that all experts (except E1) provided ranks are highly correlated to scores, obtained from proposed model security level scores (the values are 0.5 and above). Meanwhile, if a plain sum of normalized criteria values would be added, the score and ranking of alternatives would reveal lower correlations to experts' ranking. This demonstrates that the criteria importance coefficients allow a more accurate security level estimation and alternative ranking. The ranking results are visualized in Figure 6 as well.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Situation** & **Expert** & **[PERSON]’s Correlation** & **[PERSON]’s Rank Correlation** \\ \hline \multirow{4}{*}{Model results} & E1 & 0.218 & 0.500 \\  & E2 & 0.890 & 1.000 \\  & E3 & 0.915 & 0.800 \\  & E4 & 0.974 & 0.950 \\  & E1 & \(-\)0.224 & 0.000 \\  & E2 & 0.555 & 0.500 \\ \multirow{2}{*}{Plain sum of normalized criteria values} & E3 & 0.839 & 0.900 \\  & E4 & 0.765 & 0.707 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Correlation between model results and experts’ opinions.  
Figure 5: Experts score relation to model calculated security score.  
Figure 6: Security level ranking for each alternative, evaluate by the proposed model, experts, and plain sum of criteria.
## 3 Case Study
### Comparison of Effects Before and After Improvement
#### 3.5.2 Prediction Accuracy Across Different Timescales  
The prediction results over 5-, 10-, and 30-ring intervals were examined to compare the settlement prediction capability of the algorithm before and after the improvement across  
Figure 10: Comparison between prediction data and monitoring data: (**a**) settlement; (**b**) residual.  
different timescales, as shown in Figure 11. As the timescale increased, the prediction errors for both the original and improved Informer algorithms increased. For the 5-ring prediction, the advantage of the improved Informer algorithm was not obvious. However, for the 10- and 30-ring predictions, the R\({}^{2}\) value for the original Informer algorithm decreased by 7.32% and 5.99%, respectively, whereas the improved Informer algorithm exhibited reductions of only 5.99% and 3.32%. Thus, the accuracy gap between the two algorithms widened as the prediction timescale increased. By replacing standard convolution with dilated causal convolution, the perception range of the improved Informer algorithm was optimized, and its prediction accuracy was significantly increased.
## 3 Fouling Mitigation Strategies
### Physio-Chemical and Chemical Cleaning
In addition, NaOH reacts with fats and oils to form water-soluble soap micelles (saponification process) [139] and can be successfully used to remove silicates and inorganic colloids [146]. In [147], it was indicated that the combination of oxidants and alkaline agents removes organic foulants with greater efficiency than oxidant agents alone. However, it should be pointed out that chemical cleaning with the use of NaOH and NaClO solutions may lead to a loss of the polymeric membranes' integrity the shortening of their lifespan [110; 148; 149; 150]. Hence, in order to prevent membrane damage, chemical cleaning should be carried out at a suitable frequency [151] with the use of suitable agents of an acceptable concentration. It is well known that improving the membrane cleaning efficiency may be achieved by sequentially combining alkaline or oxidant reagents with acids, such as citric and nitric acids. This can be explained by the fact that acids are effective agents for the dissolution of scale compounds and metal oxides via solubilization and chelating processes [138]. Moreover, HNO\({}_{3}\) may also be used to clean organic and biological foulants by nitration [137]. This strategy of chemical cleaning has been well adopted in studies focused on the applications of AnMBRs for biogas production [79; 80; 90; 91; 92; 96]. For instance, in [91], in order to restore the MF performance, a chlorinated polyethylene (PE) membrane was first cleaned with tap water and then was soaked in solutions of citric acid and NaClO solutions. Importantly, the above-mentioned agents are commonly used to neutralize residual alkalinity after alkaline cleaning and to remove mineral deposits that have been formed during the cleaning procedure [152].
## 8 Discussion
### Power Consumption  
In IIOT systems, many devices such as sensors are powered from batteries. From power consumption point of view, it is important to save energy while devices communicate with others. Due to wireless radio interferences from other systems, PER may increase in IWSNs. Though standard IWSN protocols such as ISA100.11a and WirelessHART increase the reliability of wireless networks by TSCH mechanism, our proposal scheme increases retry queries in harmful network conditions in order to collect data at high success ratio when PER increases. As we described in Section 7, we assume it is stable and constant in this paper. Detailed evaluation is left as future work.
###### Abstract  
The presented design for an additive manufacturing (DfAM) concept features an internal metastructure that combines different density parts to enhance the energy absorption capacity and mechanical resistance of a drone fuselage segment, meeting the targeted weight requirement.  
This study investigates the potential of low-density polymeric materials to enhance the deformation energy absorption of drone fuselage components manufactured using fused filament fabrication (FFF). Two materials--PLA (polylactic acid) and LW-PLA (lightweight polylactic acid)--were selected based on their accessibility, printability, and prior mechanical characterizations. While PLA is widely used in additive manufacturing, its brittleness limits its suitability for components subjected to accidental or impact loads. In contrast, LW-PLA exhibits greater ductility and energy absorption, making it a promising alternative where weight reduction is critical and structural redundancy is available. To evaluate the structural efficiency, a simplified analysis scenario was developed using a theoretical 300 J collision energy, not as a design condition, but as a comparative benchmark for assessing the performance of various metastructural configurations. The experimental results demonstrate that a stiffening core of the LW-PLA metastructure can reduce the component weight by over 60% while maintaining or improving the deformation energy absorption. Modified prototypes with hybrid internal structures demonstrated stable performances under repeated loading; however, the tests also revealed a buckling-like failure of the internal core in specific configurations, highlighting the need for core stabilization within metastructures to ensure reliable energy dissipation.  
design for additive manufacturing (DfAM); polylactic acid (PLA); laboratory tests; prototyping; deformation energy; mechanical resistance +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Research Methodology
#### 2.2.6 Maize Growth Information Extraction  
Following the selection of the classification method that yielded higher accuracy, the maize sown area inside the study area was determined. Subsequently, a mask representing the maize sown area in the study region was generated, and the mask area was estimated by integrating the MODIS image data. In order to maintain the integrity of data and mitigate the impact of cloud cover. This work focused on the processing of MODIS data. The re-projection of the image depicting the maize growing season in Huinong District from 2017 to 2021 was conducted based on the Sentinel2 image projection. Subsequently, the image data were amalgamated using the maximum Normalized Difference Vegetation Index (NDVI), followed by the use of the NDVI difference model [49] to assess the growth patterns of maize throughout the 2021 growing season in comparison to the same time frame in prior years (2017-2020). Equation (5) presents the NDVI difference model.  
\[NDVI_{n}=NDVI_{2021}-NDVI_{N-1} \tag{5}\]  
where \(NDVI_{n}\) is the \(NDVI\) difference between 2021 and the same period of previous years, \(NDVI_{2021}\), and \(NDVI_{N-1}\) is the maximum \(NDVI\) synthetic image value of 2021 and previous years.
# Adversarial Training Methods for Deep Learning: A Systematic Review
## 4 Findings
### Projected Gradient Descent and Basic Iterative Method
#### 4.3.4 Adversarial Regularization  
Adversarial regularization is another category of training schema that modifies the cost function or regularization terms to cooperate with adversarial training. The general idea is to construct a standalone cost formulation for the purpose of adversarial training to achieve better overall performance of the model. [PERSON] et al. [47], [PERSON] et al. [48], [PERSON] et al. [49], and [PERSON] et al. [50] proposed the solutions to corporate PGD adversarial training with a modified regularizer for better performance. [PERSON] et al. [47] used a method called TRADES to improve the robustness of the model. They presented a concept of a trade-off between standard generalization and adversarial generation. A regularizer with a combination of natural and boundary errors was proposed to minimize the loss of the model to balance the standard accuracy and robustness. The formulation can be written as follows:  
\[minE\big{\{}\phi(f(X)Y)\big{\}}+max\phi\big{(}f(X)f\big{(}X^{\prime}\big{)}/ \lambda\big{)}\big{\}}, \tag{4}\]  
where \(\phi\) is any loss function defined, \(f\) is the model, \(\lambda\) is a control variable, \(Y\) is the output label, and \(X\) and \(X^{\prime}\) are the clean input and perturbed input, respectively. However, [PERSON] et al. [71] suggested that the model generalization and robustness could coexist. [PERSON] et al. [48] considered the logit results of the model produced by both clean and adversarial data. They created a regularization term by reducing the distance between the logit pairs. [PERSON] et al. [49] considered the situation when the train data were correctly or incorrectly classified initially and designed two regularizers for each case. [PERSON] et al. [50] adapted a triplet loss to push the samples in different classes further while pulling the sample with the same class closer. [PERSON] et al. [51] used a similar method, but they only considered the margin between the clean data and the adversarial data.
## 5 Experimental Results
For joined migration, the propagation velocities of 20 cm/ns and 13 cm/ns have been exploited, namely, those providing the best focusing of targets B and A, respectively. Target A is focused correctly but appears to be surrounded by further two targets on the left-hand and right-hand sides:  
Figure 14: Preprocessed data with background removal. Figure 13: Preprocessed data with subtracted average on 51 traces. this is an effect of the subtracted average, which mitigates (namely, reduces its extension) but does not completely erase the production of the horizontal belt. In particular, a similar effect is also visible with regard to target B. Instead, targets C and D are \"lost\" because they are very close to the interface between the layer and the bottom of the tank, respectively. In Figure 16, the result of a combined time-depth conversion applied to the data of Figure 15 is applied. For the time-depth conversion, we instead exploited the values 20 cm/ns (provided by the diffraction hyperbolas) and 10.6 cm/ns, somehow retrieved in another way. As can be seen, the depths of both targets A and B are now correctly retrieved. The seam line visible in Figures 15 and 16 is an artifact due to the separation line between the zone migrated with the \"shallower\" propagation velocity and the \"deeper\"  
Figure 16: Result of the combined time–depth conversion. Figure 15: Result of joined migration. propagation velocity. It is well-recognized because it is a discontinuity that is too spatially sharp to be ascribable to the real scenario. However, due to the improvement in the focusing and the displacement of the targets of interest achieved in both layers, this is a tolerable artifact.
## 3 Optimization Framework  
This paper formulates the optimization problem of optimal allocation of the APF as a nonlinear optimization problem, which is treated as a _black-box model_ within a co-simulation process.  
The co-simulation process involves the interconnection of a metaheuristic optimization method with a simulation tool for modeling and analyzing power networks. The _black-box_ optimization problem in this co-simulation setup is an LV radial distribution network model developed in the software DIgSILENT PowerFactory 2022, SP4, which is a simulation tool specialized for power system modeling and analysis. This simulation tool has developed models of typical power system components, electrical constraints, and the implementation of load flow and harmonic flow analysis.  
Figure 1 shows the co-simulation optimization framework used, which illustrates the exchange of information between the optimization method and the simulation tool for modeling and analyzing networks. The optimization framework uses the PSO optimization method realized in the _Python 3.10_ programming language.  
The co-simulation process was performed on a computer with an i5-11400 processor, 16 GB RAM, and Win 10 and begins with the exchange of information, where the DIgSILENT Power Factory 2022 SP4 sends load flow and harmonic load flow calculation results as input data to the optimization method. Using the input data, the optimization method determines the objective function values and generates decision variables, considering the set constraints. These generated decisions are then returned as input data to the DIgSILENT Power Factory 2022, SP4 to perform load flow and harmonic flow calculations, closing the co-simulation process.  
Harmonic compensation, as presented in this paper, is necessary when the amount of harmonic distortion exceeds the standard limits across all buses of the test network. This analysis includes each bus's phases and their corresponding \(\mathit{THD_{V}}\) values. Considering the future transformation of electricity networks in the context of smart grids, the inevitable increase in the integration of nonlinear loads, and the variability in DGs, this analysis determines the relative amplitudes of current harmonic components for all defined models.  
Figure 1: Block diagram of the co-simulation optimization framework for determining optimal APF.  
Throughout this process, it is ensured that the \(THD_{V}\) values in all phases and the overall \(THD_{V}\) never exceed the limit specified by the EN 50160 standard.
## 4 Discussion
### Evaluation  
Through Environment 2 in Figures 8 and 9, we can see that installing flow rules with only idle timeout keeps the most flow rules and attack flow rules as well. Because Environment 2 does not consider which flow rule is the most useless or suspicious, it  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Domain** & **Solution/Techniques** & **Timeout Usage** \\ \hline
[4] & SDN-aimed DoS & Source address validation/stateful & - \\
[5] & Efficient Timeout for Data Centers & Caching flow rules, dynamic timeout & Idle timeout only \\
[6] & Flow Table Overflow Attack & Categorizing flows and applying hard or idle & Hard timeout or Idle \\
[7] & Efficient Flow Table Utilization & timeout based on the categories & timeout \\
[8] & Flow Table Overflow Attack & Caching flow rules, proactive eviction & Idle timeout only \\
[9] & SDN-aimed DoS & Dynamic monitoring/detouring & - \\
[16] & Flow Table Overflow Attack & Identifying possible victim switch and applying & - \\
[17] & Efficient Flow Table Utilization & Optimized timeout based on the & - \\
[18] & Flow Table Overflow Attack & mathematical modelling & Mainly hard timeout \\
[19] & DDoS in SDN & Prediction/eviction/scoring flow rules with & - \\
[20] & Flow Table Overflow Attack & Deep Learning with LSTM + Autoencoder model & - \\
[20] & Flow Table Overflow Attack & Moving monitoring/mitigation logic to data & - \\ Proposed & Flow Table Overflow Attack & Dynamic timeout based on the flow history & Hard timeout and Idle timeout \\ Scheme & & & timeout \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison Table of related work.  
allows even the attack flow rules to reside in the flow table. This characteristic may easily cause flow table overflow. Meanwhile, Environment 3 assigns a hard timeout and idle timeout on a flow rule to tackle the problem of Environment 2 according to our intuition. By setting both kinds of timeout simultaneously, we receive a more improved result than Environment 2. However, setting timeouts with a static value does not bring a satisfactory result because it still does not consider whether each flow rule is reliable or not. However, our proposed scheme assumes that a flow rule is dependable or not based on historical information. By doing so, Environment 1 could get rid of most of the attack flow rules from the flow table while sustaining the normal flow rules.
## 3 Results
### Load-Displacement Curve  
Figure 16 presents the load-displacement curve of the gridded hard crust structure. Considering that the destruction mode of the gridded hard crust structure under the limit state was investigated in this study, the ultimate load of the structure, namely, the peak load, was emphasized in the load-displacement diagram. The relationship between the ultimate load and cement content and model construction is demonstrated in Figure 17. As observed in the figure, the cement content exerted a great influence on the law of destruction load change. Under the same thickness of hard crust, the ultimate load increased with the increase in s when the cement content was low. At t/h = 0.5, the ultimate loads of MN-8 and LN-8 increased by 24.1% and 55.7% compared to SN-8, and at t/h = 1, the ultimate loads of MP-8 and LP-8 increased by 12.5% and 61.8% compared to SP-8, respectively. However, the opposite situation appeared when  
Different cement contents obtained different ultimate load variation laws corresponding to destruction modes. Less cement content led to less structural stiffness. Therefore, when the load was applied, the structure and soil bore the load together, and the destruction of the structure appeared at the hard crust. As s increased, the contact area between the hard crust and the lower soil enlarged, and the soil bore more load. Therefore, the destruction load increased with the increase in s/d in the load-displacement diagram of the 8% cement content. The destruction mode with large cement content was majorly the destruction of the grid structure. At this time, the ultimate load mainly depended on the strength of the grid structure. As s increased, the load area of the hard crust enlarged, and the load transferred to a single bottom grid structure also grew. Therefore, the structure with the larger s was more likely to be destroyed compared with the structure with the smaller s. Hence, the ultimate load in the displacement-load diagram with 15% cement content decreased with the increase in s/d. The increase in t has a positive effect on the increase in ultimate load, especially at high cement admixtures. When the cement content is 8% and t/h is increased to 1, the maximum increase in the ultimate load of the model is only 39.1%, whereas when the cement content is 15% and increased to the same thickness, the maximum increase in the ultimate load of the model reaches 67.2%. On the one hand, the increase in t changes the distribution of stresses and increases the flexural stiffness of the hard crust, thus increasing the load-carrying capacity of the low cementitious structure; on the other hand, as the flexural stiffness of the hard crust increases, more loads are assumed and the loads distributed in the lower grid structure will be reduced; thus, the ultimate load-carrying capacity of the high cementitious structure controlled by the strength of the grid increases with the increase in t.  
Figure 16: Load-displacement curves of grided hard crust of (**a**) 8% cement content and (**b**) 15% cement content.  
Figure 17: Relationship between ultimate load and structure of the gridded hard crust.