esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Frequency Characteristics for Boosting the Invisibility of Adversarial Attacks
## 4 Experimental Results and Analysis
### Visualization Analysis
#### 4.3.1 Detail Comparison  
We visually compare the image details of the adversarial examples generated by attacking methods. In the same experimental setup as before, we compare examples generated in the same alternative model under the same attack scenario.  
In Figure 6, we show the adversarial examples generated by all methods on the substitute model Res50. The adversarial perturbation generated by the comparison method has a very obvious impact on the visual quality of the examples and leaves visual traces in the smooth region of the image. In contrast, the adversarial examples generated by our method have little impact on the smooth region of the image and have a significantly better visual quality than those of the other methods, which are closer to the original image for the HVS. The results in the targeted attack and untargeted attack scenarios reflect the same phenomenon: the perturbation generated by the comparison method significantly affects the visual quality of the examples on all three different datasets. In comparison, our perturbation significantly reduces the impact of relatively smooth regions in the images.  
Figure 6: Comparison of the image details of the adversarial examples of the attacking methods. These adversarial examples come from the Tiny-ImageNet, CIFAR100, and CIFAR10 datasets from top to bottom by two rows. For each dataset, the upper row shows the targeted attack scenario, and the lower row shows the untargeted attack scenario. The leftmost column shows the original clean images. The adversarial examples generated by different methods are listed from left to right.  
The comparative methods measure the overall change in pixel values of images using \(L_{p}\) norms, which ignores image content and leads to visible traces in smooth regions. We design the perturbation from the frequency domain and restrict the frequency domain distribution of the perturbation to the high-frequency region, which has less effect on the visibility, significantly improving the invisibility of the adversarial examples.
# A Two-Archive Many-Objective Optimization Algorithm Based on D-Domination and Decomposition
## 2 Materials and Methods
### Comparison and Analysis of Results  
Tables 1 and 2 show the average and standard deviation of IGD obtained by Two Arch-D and other seven comparison algorithms running independently 20 times on 45 test problems (including 5, 10 and 15 objectives). In the table, the best performance for each test question is highlighted in bold. \"+\" indicates that the comparison algorithm performs better than Two Arch-D, \"-\" indicates that the comparison algorithm performs worse than Two Arch-D and \"=\" indicates that the two algorithms are similar. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Problems** & **Two Arch-D** & **NSGAIII** & **MOEA/DD** & **KaEA** & **RVEA** & **hpqEA** & **VaEa** & **NSAGAISDR** \\ \hline MaF1-5 & \(\mathbf{1.6199\times 10^{-1}}\) & \(2.0558\times 10^{-1}\) & \(2.9579\times 10^{-1}\) & \(1.7136\times 10^{-1}\) & \(2.8832\times 10^{-1}\) & \(1.6475\times 10^{-1}\) & \(1.6959\times 10^{-1}\) & \(1.6728\times 10^{-1}\) \\  & \(\mathbf{8.91\times 10^{-2}}\) & \((6.77\times 10^{-3})=(4.68\times 10^{-5})-(1.91\times 10^{-3})=(4.10\times 10^{-2})-(7. 86\times 10^{-4})=(6.82\times 10^{-4})=(1.48\times 10^{-3})-(4.86\times 10^{-3})- \\ \hline MaF2-5 & \(1.0182\times 10^{-1}\) & \(1.3232\times 10^{-1}\) & \(1.3670\times 10^{-1}\) & \(1.3505\times 10^{-1}\) & \(1.3208\times 10^{-1}\) & \(9.4521\times 10^{-2}\) & \(9.4724\times 10^{-2}\) & \(9.5646\times 10^{-2}\) \\  & \(2.78\times 10^{-3}\) & \((2.26\times 10^{-3})=(3.22\times 10^{-3})-(5.05\times 10^{-3})=(1.24\times 10^{-2})=(1. 63\times 10^{-3})+(1.51\times 10^{-9})=(1.72\times 10^{-3})+(7.27\times 10^{-3})+ \\ \hline MaF3-5 & \(\mathbf{8.2699\times 10^{-2}}\) & \(9.0837\times 10^{-2}\) & \(1.1896\times 10^{-1}\) & \(1.6911\times 10^{-1}\) & \(8.4313\times 10^{-2}\) & \(2.3479\times 10^{-1}\) & \(2.0613\times 10^{-1}\) & \(1.4569\times 10^{-1}\) \\  & \(\mathbf{4.18\times 10^{-3}}\) & \((3.56\times 10^{-3})-(3.27\times 10^{-3})-(7.46\times 10^{-2})-(7. 70\times 10^{-3})=(4.96\times 10^{-3})-(1.99\times 10^{-1})-(1.06\times 10^{-2})- (1.06\times 10^{-2})-(1.06\times 10^{-2})-(1.06\times 10^{-2})- \\ \hline MaF4-5 & \(\mathbf{3.058\times 10^{0}}\) & \(3.4086\times 10^{-6}\) & \(7.8385\times 10^{-3}\) & \(3.8998\times 10^{6}\) & \(4.9479\times 10^{-1}\) & \(3.1327\times 10^{-6}\) & \(3.1139\times 10^{-6}\) & \(3.2691\times 10^{-6}\) \\  & \(\mathbf{5.97\times 10^{2}}\) & \((2.28\times 10^{-1})-(2.34\times 10^{-3})-(1.85\times 10^{-3})-(1. 133\times 10^{-3})-(4.82\times 10^{-2})-(5.58\times 10^{-3})-(7.07\times 10^{-3})- (4.86\times 10^{-3})-(7.07\times 10^{-3})- \\ \hline MaF5-5 & \(2.4984\times 10^{0}\) & \(2.5687\times 10^{-6}\) & \(6.8677\times 10^{-6}\) & \(2.7249\times 10^{0}\) & \(2.6461\times 10^{0}\) & \(2.6487\times 10^{0}\) & \(\mathbf{1.7817\times 10^{0}}\) & \(1.3930\times 10^{-1}\) \\  & \(7.43\times 10^{-1}\) & \((8.57\times 10^{-2})-(1.82\times 10^{-1})-(4.53\times 10^{-2})-(8. 82\times 10^{-3})-(1.25\times 10^{-3})-(1.25\times 10^{-3})-(1.25\times 10^{-3})-(1.
# BDS-3 Integrity Risk Modeling and Probability Evaluation
## 3 BDS-3 Integrity Failure Model and Effect Analysis Algorithm
### BDS-3 Integrity Risk Tree Modeling  
#### 3.4.1 Integrity Risk Tree  
The risk tree method is a graphical model that expresses the logical relationship between a specific abnormal condition and the cause or exception that causes the condition.  
According to the integrity risk factors analysis results shown in Table 3, BDS-3 integrity risk factors include factors in the space segment and ground control segment.  
Based on the above analysis, a BDS-3 integrity risk tree model is constructed, as shown in Figure 3.
## 4 Results
### Comparison of the Proposed Feature Extraction Method with Different Classification Models  
The proposed BSL algorithm's efficiency was scrutinized by applying various classifiers documented in the literature, including SVM, CNNs, k-NN, and LDA. SVM, k-NN, and LDA are developed using Scikit-Learn in Python version 3.6. The architecture of the CNN was developed using TensorFlow (version 2.2), as proposed in [41]. Table 3 details the performance outcomes, with the GCN model achieving the highest classification accuracy of 98% for the beta band and maintaining an average accuracy of 91% across subjects, thus surpassing the efficiencies of the other classifiers examined. SVM attains the second-best accuracy levels for both the highest-scoring subject and, on average, across all subjects, followed by the CNN. However, k-NN and LDA had an accuracy below 60%.
###### Abstract  
This study explores the impact of incorporating conductive materials and biogeochemical systems (BES) on the efficiency of anaerobic digestion (AD) of sewage sludge. The research consists of two phases: biodegradability tests using 3D-printed polylactic acid-based conductive fillers (PLA/Carbon Black and PLA/Graphene) and semi-continuous assays integrating an external BES into the AD process. Results from biodegradability tests indicate that conductive fillers enhance around 50% methane production during the start-up phase, with microbial communities adapting over time to reduce variability in methane yields. Moreover, as the experiment progressed, the methane yields of the digesters with and without fillers became equal. Semi-continuous experiments demonstrate that BES integration improves process stability and methane production by achieving a 5-10% improvement in the amount of methane in the biogas throughout the entire operation, even under high organic loads, by facilitating diverse electron transfer pathways. The challenges of BES operation highlight the need for optimized designs to ensure scalability. Microbial analyses reveal that BES application shifts methanogenic pathways, favouring acetoclastic methanogenesis. Overall, the findings underscore the potential of conductive materials and BES to improve biogas quality and production, contributing to sustainable wastewater management and renewable energy generation.  
a pacs: 15, 143 +
Footnote †: journal: Applied sciences  
_Article_
## 3 Results
### Dynamic Analysis
#### 3.3.4 Stresses  
The values of the von Mises stresses in the whole model were also determined. Only the maximum values in each time step over a considered time period were collected. The relationship between the corrugation amplitude and the maximum von Mises stress of the element over a given period of time for the considered thicknesses of a steel sheet is shown in Figure 18.  
The analysis of the maximum stress over time for plates of various thicknesses and corrugations reveals several key insights. Firstly, it is evident, as in the static analysis, that thicker elements experience lower absolute stresses. This finding aligns with the fundamental understanding that an increased material thickness enhances the ability to distribute and withstand applied loads, thereby reducing stress concentrations.  
Additionally, the analysis indicates that larger corrugation amplitudes also contribute to stress reduction, similar to the effects observed in static conditions. Larger waves in the corrugated structure enhance the stiffness and load-bearing capacity, leading to a decrease in the maximum stress values over time. However, this stress reduction effect has a limit. Beyond a certain corrugation amplitude, further increases do not result in noticeable decreases in stress, indicating a threshold that, beyond which, additional corrugation would provide minimal, if any, further benefit.  
Figure 17: The relationship between the corrugation amplitude and the maximum acceleration of the element over a given period of time for the chosen thickness of a steel sheet: (a) \(t=3.0\) mm; (b) \(t=4.0\) mm; (c) \(t=5.0\) mm; and (d) \(t=6.0\) mm.
## 2 Content Sharing Graphs  
This section introduces content sharing graphs and defines key concepts associated with these structures with the aid of an example wherever necessary. In particular, we propose two graph models to represent the content sharing for the same set of storage objects in a deduplicated storage system.
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 3 Proposed Routing Optimization Algorithm
### Performance Analysis
#### 3.3.2 Simple Estimate of the Computational Time Complexity  
The MPFDR algorithm primarily selects the next hop from nodes in the forward neighbor area, and the nodes in the area are fixed after the WSN is deployed. Each forward neighbor node calculates the five routing evaluation parameters defined by this algorithm, respectively. Hence, the calculation of these evaluation parameters is distributed parallel. When routing-decisions are made, the node calculates the parameter contribution degree and fuzzy contribution degree of each forward neighbor node. The two values are linearly related to the number of nodes \(N\); hence, their time complexities both are \(O(N)\). Then the fusion contribution degree is also a linear correlation with \(N\); its time complexity is \(O(N)\). Finally, all forward neighbor node's fusion contribution degrees are sorted by the merge sort algorithm to attain the maximum. The algorithm's time complexity is \(O(NlogN)\). To summarize, the time complexity of the MPFDR algorithm is \(O(NlogN)\).
# An IoT-Based System for Efficient Detection of Cotton Pest
## 1 Introduction
### Gap Analysis and Comparison  
Implementing sensing technology in the agriculture domain is a revelation for everyone. This study chose six parameters (main contribution of work, level of cost, methodology communication technology used, type of sensors used, and targeted plant/species) to compare this work with related works. In this research, we are monitoring insects through optical technology along with wireless communication.  
In a study [7], an old acoustic technique was used that was limited to specific sound-generating pests and thus the technique was not universally able to detect all pests. In another study [8], traditional voice recognition techniques and hearing devices were used; nevertheless, the study did not involve the networking aspects. In a comparison, three proposals [16; 17; 18] from recent years were found to be using optical technologies (quite similar to this study). Nevertheless, among them, two [16; 17] proposed the design of a trap system while the third [18] proposed the use of sensors along with video cameras. Therefore, it would not be wrong to state that the detection mechanism proposed in this study, to monitor and detect cotton bugs using a custom-based cost-effective IoT prototype, is a unique one.  
The referred research work suggested a standalone device, while this study proposed a network-based approach. Some of the specific features of the proposed mechanism are expected to be as follows:  
* Locally made low-cost IoT device;
* Easy to use by farmers without any complicated training;
* Data can be monitored remotely using an IoT;
* The device can be used in any season with certain modifications;
* With a change in sensors, we can detect other crop pests.  
Our research and invention can be compared with any current research in terms of its price, working methodology, and effectiveness in the context of implementation. After discussing the shortcomings of the existing system and a comparative analysis of the proposed system with the related work, we present the details of the proposed system in the next section.
## 3 Noise in PLC Networks
### Noise Library for NBPLC-PRIME
#### 3.2.2 Normative Noise Signals  
All the aforementioned voltage/current equipment and components have some potential for causing EMI, for which also a combination of the different sorts has to be considered in a double way: (1) cumulative effect of voltage components from all emitting equipment connected to a supply network; and (2) proliferation of different sorts of electric equipment and its different durations of operation.  
The main standards related to EMC regulation and guidelines are:  
* EN 50065 [55];
* CISPR15 [56];
* and IEC TS 62578 [57].  
EN 50065 is a standard used for the certification of equipment transmitting in the PLC communications band. It is, therefore, used for all PRIME equipment. Its limits are mandatory for all the equipment transmitting in the band, but it is possible to find equipment that transmits up to the limit of the band in multiple frequencies. As standard, EN 50065 only applies to PLC transmitters, so there may be equipment (e.g., inverters, luminaires) that transmit above these limits.  
The standard CISPR15 is used for the certification of certain equipment that is not for communications, but that produces a considerable energy in frequencies close to those of PLC communications. Its limits are mandatory for all equipment that meets this standard, but it is possible to find equipment that transmits up to the band limit in multiple frequencies. As the limits of CISPR15 are not sufficient to allow the work of PLC communications, a more constrained limitation has been analyzed between 30 and 150 kHz.  
The standards used for the certification of certain non-communications equipment do not always adequately limit the transmission of signals in the frequency bands used by PLC communications. This is why the EU seeks to create a regulatory framework that, without hindering the development of equipment such as inverters or luminaires, allows adequate coexistence between PLC communications equipment and other equipment. One of the proposals analyzed in the TC22 appears in TS 62578 and covers the range of the PRIME communications. Figure 4 summarizes the main standard emissions limits between 2 and 150 kHz.  
The red dash line in Figure 5 shows the agreement that has been recently reached in the IEC/SC 77A/WGS on the compatibility level for the frequency band ranging from 30 to 150 kHz as a trade-offbetween the two main opposing options (namely, option A in blue continuous line and option B in green continuous line) [58].  
Figure 4: Standardized emission limits, values, and Power Quality levels for the frequency range 2–150 kHz [54].  
Figure 5: Compromise compatibility level between 30 and 150 kHz agreed in the IEC /SC 77A/WGS [58].
## 5 Conclusions  
In this article, we proposed a method for estimating the ITI or ISI using an RNN. With a pretrained RNN, the received signal from the output of the HDS channel was passed through the RNN to predict the ITI or ISI values. Simultaneously, we implemented experiments to find out the best structure for the RNN in an ITI/ISI estimator. The ITI and ISI were supplied and removed from the received signal, which are the outputs of the equalizer, with the higher accuracy. Finally, the original signal was detected using 1D SOVA. In the simulations, the results showed that our proposed model can achieve better performance than the previous studies. Compared to the VA estimator, at a BER of \(10^{-4}\), our model can obtain a gain of ~1 dB. Simultaneously, under misalignment and blur conditions, the proposed model still achieves the best performance. However, because of using two RNNs for the parallel structure, the complexity must also be high to achieve this performance. Therefore, future work must develop a model to reduce the complexity by grouping two RNNs into one RNN. In addition, it is necessary to modify the structure of the RNN to achieve a simpler structure to minimize the processing time.  
Conceptualization, T.A.N. and J.L.; methodology, T.A.N. and J.L.; software, T.A.N.; validation, T.A.N. and J.L.; formal analysis, T.A.N.; investigation, T.A.N. and J.L.; writing--original draft preparation, T.A.N.; writing--review and editing, T.A.N. and J.L.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2023-2020-0-01602), supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Mul/Div** & **Add/Sub** & **Log/Exp** \\ \hline Our model & 3053 & 3122 & 102 \\ Interference estimator in [17] & 508 & 1007 & 0 \\ Serial detection in [16] & 252 & 700 & 0 \\
1D GPR [15] & 33 & 48 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Complexity of our proposed model and previous studies.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Time (Millisecond)** \\ \hline Our model & 2.572 \\ Interference estimator in [17] & 0.261 \\ Serial detection in [16] & 0.1386 \\
1D GPR [15] & 0.006 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time for detecting one bit.
## 5 Results and Discussion  
In the previous works, floods and traffic models were mostly integrated statically on the day of flood occurrence. In this paper, both dynamic and static integration of flood  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**O/D** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline
**1** & 0 (0) & 0 (0) & 0 (0) & 0 (5) & 49 & 60 & 0 (0) & 24 & 0 (0) & 0 (0) \\
**2** & 0 (0) & 0 (12) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) \\
**3** & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) \\
**4** & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) \\
**5** & 49 (67) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) \\
**6** & 60 (69) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) \\
**7** & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) \\
**8** & 24 (20) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) \\
**9** & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) \\
**10** & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Estimated average travel times for the first ten origin/destination (OD) pairs in the network. (Values in parentheses show the current research predictions).  
and traffic models for assessing the indirect impacts of flood, respectively during the flood and after passing it, are investigated. In the next two subsections, the same data are used, taking into account that a higher uncertainty is found for the situation where the disruption took place. First, a dynamic approach in Section 5.1 is used for estimating the effects of the flood on the transport system during the flood day. This is the valid alternative modelling approach where dynamic integration is demonstrated. Then, a more standard static approach in Section 5.2 is applied for estimating the impacts of the flood on the transport system in the following days of the flood. However, this static integration presents weaknesses that are pointed out at the end. The selection of the spatial scale was taken based on the nature of each phase of the flooding event. The time resolution for the dynamic component was assumed as 24 h (1 day) since this is the time in which the water level is increasing. It is worth mentioning that only one day is considered for the static simulation as the equal duration of the dynamic simulation. Since this work is an exploratory analysis of different models in the context of flood consequences analysis, such assumptions are considered reasonable. In future advancements in this work, the correlation between the choice of these parameters and the underlying type of action will be considered.
# Effect of LNR-g-MMA on the Mechanical Properties and Lifetime Estimation of PLA/PP Blends
## 3 Results and Discussion
### Mechanical Properties
#### 3.3.3 Water Absorption  
The water absorption of pure PLA and PP and PLA/PP (70:30 wt%) blend without and with LNR\(-\)g\(-\)MMA compatibilizer is shown in Figure 5e. It was found that pure PLA has a much higher absorption percentage than pure PP. Pure PLA is a polar molecule and a hydrophilic material consisting of hydroxyl groups that form intermolecular hydrogen bonds with the water molecules, resulting in absorbed high water in the pure PLA. Figure 5e shows that the PLA/PP (70:30 wt%) blend without LNR\(-\)g\(-\)MMA compatibilizer absorbed water more than that of pure PLA. In general, the incorporation of PP into the PLA matrix exhibits interfacial regions which occur in many micropores in the polymer blend due to an immiscible system with a two\(-\)phase morphology. The micropores between the two phases allow for the absorption of water. When the compatibilizer was added, the blend became a more uniform dispersion of PP throughout the PLA matrix, reducing the number and size of the micropores, and preventing water from reaching the polymer chains. Consequently, the water absorption percentage decreased as the LNR\(-\)g\(-\)MMA content increased when it was added to a PLA/PP blend. The LNR\(-\)g\(-\)MMA made the PLA/PP composite more miscible, reduced the number and size of micropores, and homogenized and refined the matrix [37; 38].