esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Segmentation Framework for Detecting Adversarial Attacks for Autonomous Driving Cars
## 3 Methodology
### Image Segmentation and KAS Algorithm  
Images, being unstructured and multi-dimensional data, present vulnerabilities that attackers exploit to generate adversarial examples [63]. We propose image segmentation as a mitigation strategy to reduce this attack surface. Image segmentation, widely used in applications such as image compression [64] and object detection [65], divides an image into regions. One popular method for image segmentation is k-means clustering [66], which segments images by grouping pixels into clusters based on proximity to specified centers. This simple and effective technique only requires setting the number of clusters and helps to overwrite adversarial perturbations by focusing on dominant features in the image.  
To that end, we introduce the k-means Adaptive Segmentation Algorithm (KAS), as shown in Algorithm 1. KAS seamlessly segments the input image into \(k\) regions in an adaptive routine, depending on the number of histogram levels (color shades) of the image. To boost the segmentation process and reduce the feature space of the image, KAS limits the number of segmented regions to 35% of the color shades. The segmentation process is enhanced by a non-local-mean filter [40] that normalizes similar regions of the input image before the k-means process. This filtering process makes the k-means algorithm converge faster.  
```
1:Masked Gray-scale Image \(\mathbf{x}\)
2:Segmented and Inverted images (\(\textit{x}_{\text{seg}}\), \(\textit{x}_{\text{inv}}\))
3:\(x\gets x/255\)
4:\(\sigma\leftarrow\textit{estimate\_sigma\_noise}(x)\)
5:\(\text{mask}_{\text{NLM}}\leftarrow\textit{create\_Non\_Local\_Mean\_Filter}( \text{filter size}=(3\times 3)\), \(\text{searching area}=(13\times 13)\), \(\text{filter strength}=1)\)
6:\(x_{\text{NLM}}\leftarrow\textit{Non\_Local\_Mean\_filtering}(x,\sigma,\text{ mask}_{\text{NLM}})\)
7:\(x_{\text{NLM}}\leftarrow\textit{x}_{\text{NLM}}\times 255\)
8:\(\text{num\_shades}_{\text{NLM}}\leftarrow\textit{num\_unique\_shades}(x_{ \text{NLM}})\)
9:\(\text{seg\_percentage}\prec 35\%\)
10:\(k\leftarrow[\text{num\_shades}_{\text{NLM}}\times\text{seg\_percentage}]\)
11:if\(k<2\)then
12:\(k\gets 2\)
13:endif
14:\(x_{\text{seg}}\leftarrow\textit{k\_means}(x_{\text{NLM}},k)\)
15:\(\text{mask}_{\text{med}}\leftarrow\textit{create\_Median\_Filter}(\text{filter size}=(3\times 3))\)
16:\(x_{\text{seg}}\leftarrow\textit{Median\_filtering}(x_{\text{seg}},\text{mask}_{ \text{med}})\)
17:\(x_{\text{inv}}\leftarrow\textit{Bit\_wise\_not}(x_{\text{seg}})\)
18:return\((x_{\text{seg}},x_{\text{inv}})\)
19:end
```  
**Algorithm 1** k-means Adaptive Segmentation (KAS)  
To ensure that adversarial noise in the background is excluded from the segmentation process to further mitigate the threat of adversarial attacks, the KAS algorithm is applied only to the object of interest of the image. This is achieved by masking the image background with the generated masks using the U-net model.
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 6 Discussion of Simulation Results
### Impact of Traffic Load and Placement of Nodes
#### 6.1.1 Average PDR and Number of Packets Dropped from the Queue  
To analyze how reliable the schedulers are, we consider the average PDR, and the average number of packets dropped from the queues. Figure 9 illustrates the average PDR in the function of the traffic load. One can observe that both schedulers obtain similar performance for traffic loads lower than 10 packets per minute. For the new scheduler, the average PDR remains above 95%, regardless of the traffic load. This is a consequence of adding more cells for the most constrained nodes, namely the root's children. Another important observation is that for placement 2, the PDR is slightly lower, especially for RBS Orchestra. This phenomena is mostly visible when the traffic is above 5 packets per minute. In placement 2, the nodes are situated in an almost circular way around the sink node. The nodes have more neighbors in their vicinity, hence there is more interference.  
Figure 10 shows the average number of packets dropped from the queue in function of traffic load.  
Figure 9: We observe that when it comes to average packet delivery ratio (PDR), the new scheduler performs better than Orchestra, in high traffic situations.  
One can see a significant increase in dropped packets for RBS Orchestra when the traffic is above 5 packets per minute. The nodes that often drop packets are the root's children. These nodes must pass the traffic from their entire subtree to the root, using only one Tx cell. Placement 1 has the root node situated at the top of the grid. The sender nodes are placed below and form denser subtrees. This explains why for placement 1, the number of packets dropped is higher. There are few to no dropped packets for the new scheduler.
## 4 Car-Sharing Application
### Estimation of City Sustainability  
As mentioned before, a Transport Sustainability Index (TSI) can be used to measure city sustainability at any given time with global utilities. The Transport Sustainability Indexes of car-sharing at pre-test and post-test stages are denoted by TSI(\(t_{before}\)) and TSI(\(t_{after}\)), respectively. Assuming an equal weight of 0.111 for all criteria, using global utilities (Table 7) and criteria weight, we have:  
\[TSI(t_{before})=0.1432*0.111+0.4630*0.111+0.9834*0.111\] \[+0.5722*0.111+0.9691*0.111+0.9398*0.111\] \[+0.9864*0.111+0.7449*0.111+0.8539*0.111\] \[=0.7388\] \[TSI(t_{after})=0.3565*0.111+0.8387*0.111+0.9800*0.111\] \[+0.9909*0.111+0.9863*0.111+0.9908*0.111\] \[+0.9894*0.111+0.3763*0.111+0.9615*0.111\] \[=0.8292\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluation Criteria** & \multicolumn{4}{c}{**Pre-Test Stage**} & \multicolumn{4}{c}{**Post-Test Stage**} \\ \cline{2-6}  & **1** & **D** & **\(\Theta\)** & **1** & **D** & **\(\Theta\)** \\ \hline _cost_ (C\({}_{1}\)) & 0.8413 & 0.1365 & 0.0222 & 0.5645 & 0.3226 & 0.1129 \\ _fuel consumption_ (C\({}_{2}\)) & 0.5039 & 0.4488 & 0.0472 & 0.1234 & 0.8225 & 0.0541 \\ _air quality_ (C\({}_{3}\)) & 0.9831 & 0.0161 & 0.0008 & 0.9796 & 0.0189 & 0.0015 \\ _noise perception_ (C\({}_{4}\)) & 0.4260 & 0.5714 & 0.0026 & 0.0088 & 0.9908 & 0.0004 \\ _users numbers_ (C\({}_{5}\)) & 0.9680 & 0.0285 & 0.0035 & 0.9861 & 0.0132 & 0.0008 \\ _spatial accessibility_ (C\({}_{6}\)) & 0.9375 & 0.0550 & 0.0075 & 0.9907 & 0.0890 & 0.0004 \\ _satisfaction_ (C\({}_{7}\)) & 0.9855 & 0.0117 & 0.0027 & 0.9891 & 0.0098 & 0.0010 \\ _security_ (C\({}_{8}\)) & 0.7379 & 0.2388 & 0.0233 & 0.3566 & 0.5778 & 0.0657 \\ _ congestion level_ (C\({}_{9}\)) & 0.1377 & 0.8503 & 0.0120 & 0.0343 & 0.9598 & 0.0060 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The aggregated BPA results for nine criteria.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Criteria** & **At the Pre-Test Stage** & **At the Post-Test Stage** \\ \hline _cost_ (C\({}_{1}\)) & 0.1432 & 0.3565 \\ _fuel consumption_ (C\({}_{2}\)) & 0.4630 & 0.8387 \\ _air quality_ (C\({}_{3}\)) & 0.9834 & 0.9800 \\ _noise perception_ (C\({}_{4}\)) & 0.5722 & 0.9909 \\ _users numbers_ (C\({}_{5}\)) & 0.9691 & 0.9863 \\ _spatial accessibility_ (C\({}_{6}\)) & 0.9398 & 0.9908 \\ _satisfaction_ (C\({}_{7}\)) & 0.9864 & 0.9894 \\ _security_ (C\({}_{8}\)) & 0.7449 & 0.3763 \\ _congestion level_ (C\({}_{9}\)) & 0.8539 & 0.9615 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Global utilities for nine criteria.
# Waveform Design for 5G and beyond Systems
## 2 Review of the Contributions in This Issue
As part of this category of research, the article \"Improved Pair-Wise Detections of Differential Quasi-Orthogonal Space-Time Modulation with Four Transmit Antennas\" [4] proposes a new complex and real pair-wise detection for conventional differential space-time modulations, based on a quasi-orthogonal design with four transmit antennas for general QAM. Owing to the independent joint ML detection of two complex and real symbol pairs, respectively, the decoding complexity is the same as, or lower than, the conventional differential detections. The proposed detections exhibit almost identical performance to an optimum maximum-likelihood receiver, as well as an improved performance when compared with conventional pair-wise detections, especially for higher modulation order. Last but not least, the topic of this Special Issue is multiple access (MA) capability, which is one of the most crucial measures in designing waveforms. The last article \"Performance Analysis of LDS Multi Access Technique and New 5G Waveforms for V2X Communication\" [5] contributes to the waveform design for 5G and beyond, concerning multiple access capability. The authors of this article have proposed new efficient MA schemes, where the low-density signature (LDS) structure is combined with two waveforms for 5G systems and beyond, i.e., universal filtered multi-carrier (UFMC) and filtered-OFDM waveforms. They also have simulated their proposed schemes under a vehicular channel of a vehicle to everything (V2X) communication. From this simulation, it was shown that the LDS-F-OFDM significantly achieves higher performance improvements when compared with the LDS-OFDM and LDS-UFMC in all scenarios, while maintaining an affordable complexity at the transmitter and receiver sides. These improvements are attributed to the advantages that the filtered-OFDM waveform offers through addressing the adequate filter. Funding: This work was partly supported by the Technology development Program of MSS [S2967489], the NRF grant funded by the Korea government (MSIT) (No. 2021R1A2C1010370), and the 2021 Yeungnam University Research Grant. As the Guest Editor, I convey gratitude to all the authors of this Special Issue for their great contributions. All the peer reviewers who helped to evaluate the submitted manuscripts and made beneficial suggestions are also gratefully acknowledged. I also would like to thank the editorial board of _Electronics_, who graciously invited me to guest edit this Special Issue. Finally, I owe my special thanks to [PERSON] at the _Electronics_ Editorial Office. The timely completion of the Special Issue would not have been possible without her diligence. The author declares no conflict of interest.
# The Role of Microorganisms and Their Antibacterial Compounds in Food Biopreservation
## 5 Role of Bacteriophages as Antimicrobial Agents  
Bacteriophages (phages), as natural predators of bacteria ubiquitous in the environment, are harmless to humans and animals (because they can multiply only in prokaryotic organisms). They have been identified as future-proof antimicrobials that can allow specific bacterial pathogens to be controlled at every stage of food production [101; 102]. Lytic bacteriophages isolated from the environment are used to \"attack\" pathogenic bacteria and eliminate them from food (or, at least, a significant reduction in their level). The use of bacteriophages in food biopreservation represents a new trend in food technology [103]. There is an increase in the number of bacteriophage preparations approved for use in food [104].  
A very big advantage of using bacteriophages as a technological solution is the fact that they do not affect the non-target microflora, which is particularly important in the production of fermented food. Nevertheless, phages still offer excellent food biocontrol options as they do not contain any additives or preservatives in their formulation and several are certified Kosher, Halal, and organic, and have no impact on the sensory, nutritional, and rheological properties of the food [105]. However, the challenges associated with the choice of such a method of ensuring the microbiological safety of food are as the risk of transferring antibiotic resistance between bacteria with the participation of bacteriophages or the possibility of the inclusion of bacterial phage resistance mechanisms [106].  
There are many companies registered on the global market that develop and distribute bacteriophage preparations for the food industry. These measures are aimed at combating pathogenic bacteria in the food environment, such as _Salmonella_, _Listeria monocytogenes_, _Escherichia coli_ O157:H7, or _Shigella_[107].
## 4 Results and Discussion
### Variation of Membership of Different Stage
The same phenomenon is  
Figure 6: Variation of \(x\) values and integrated memberships of different sludge-processing stages: (**a**) Relative abundance of different genera of methanogens (letters a to f represent the six paralleling sludge samples from one stage, roman numerals I, II, and III represent Stage I, Stage II and Stage III of the three-stage sludge-treatment process); (**b**) Integrated memberships of different processing stages. (Stage I stands for the state of sludge samples after treated by centrifugal thickening and pre-dewatering, Stage II stands for the state of sludge samples after treated by thermal hydrolysis, Stage III stands for the state of sludge samples after experiencing anaerobic digestion, and stage uncertain stands for the uncertainty of the processing stage affiliation of a sludge sample.) also illustrated in Figure 6a. The integrated memberships generated from paralleling samples of Stage III are 0.00, 0.00, 0.99 and 0.01 for \(S_{\text{L}}\), \(S_{\text{II}}\), \(S_{\text{III}}\), and \(S_{\text{un}}\), whose total value is 1.00 and \(C(S_{\text{III}})\) is the biggest. It could be made a conclusion that the model could make a correct judgment on the processing stage affiliation of this group of paralleling samples being Stage III, and there is a little fluctuation of the wastewater quality in the aerobic digestor of different sampling time. In general, the four integrated memberships generated from any group of paralleling samples aggregate to 1, paralleling samples from one stage could generate the corresponding largest \(C(S)\) representing the same stage. Therefore, the membership functions could make an accurate judgment on the processing stage affiliation of the examined paralleling sludge samples, and the vagueness and uniqueness of the methanogen community structure of one processing stage could be characterized properly by the model. By analyzing both the \(x\) values and the integrated memberships of the paralleling samples belonging to the three stages, the rationality of the membership function format, the statistical method to determine parameters, and the methodology to process paralleling data are all proved, which in turn verifies the accuracy of the membership-fusing model.
## 4 Dataset, Experimental Settings, and Evaluation
### Experimental Settings  
Experiments were conducted using Python 3.7 and PyTorch 1.7.1 on a desktop computer with an Intel i9-13900 KF 3.0 GHz CPU (Intel, Santa Clara, CA, USA), which has a performance of 153,377 MOps/s and 64 GB RAM. It used 80% of the data in the dataset for training and another 20% for test data. LSTM and GRU have a two-layer network with 128 neurons each. The deep encoder network was set up to 12 heads. The learning rate was set to 0.001, the training iteration was set to 100 times, and the batch size was set to 64.  
The typical approaches used for time series data prediction were adopted as the baseline methods, including LSTM, GRU, and the transformer framework.
## 5 Experiment and Performance
### Laboratory Experiments
After the establishment of the WSN, the serial debug terminal received information on network address, physical address, coordinate location, remaining power, and their parent's network address from all WSN nodes. According to the information, the network tree topology, the rough location and the remaining power of each WSN node could be obtained. Therefore, the functions of topology management and energy management are working stably. In order to test the fault management, a WSN node's electricity power was shut off manually, then the serial debug terminal received a message that a WSN node failed after several periods. By parsing the network address, the failed node was identified. Based on the failed node's coordinates and ID posted on it, it could be quickly located in the corridor. Thereby, the functions of location management and fault management are both working well. Based on the above experiments, we conclude that the designed mesh WSN with multi-parameter monitoring for underground coal mine is easy to establish and works well. In both periodic inspection mode and interrupt service mode, the WSN can correctly collect environmental information, aggregate it, and provide early warning of overrun parameters. The proposed network management scheme is easy to implement, it can correctly reflect the network topology, diagnose the network faults in a timely way and repair them, thus ensuring the stable operation of the WSN.
# Performance of Dynamic Time Division Broadcast Protocol with Rateless Coding
## 3 Performance Analysis
### Outage Probability and Expected Rate
 \tag{11}\]  
where \(P_{direct}\) is the outage probability of the direct link transmission in TDBC-IR and \(P_{direct}=P_{r}\left(\alpha\log\left(1+\rho\left|h_{S_{1}S_{2}}\right|^{2} \right)<\left(1-\alpha\right)C\right)=1-e^{-z_{0}/\Omega_{0}}\); \(P_{r}^{0}=1\), \(P_{r}^{i}\) is the outage probability for \(S_{1}\) or \(S_{2}\) in sub-DTDBC-RC that the direct link fails after the \(i\)-th (\(i=1,2,...,N-1\)) time slot transmission (i.e., \(P_{r}^{i}=P_{r}\left(\frac{\alpha i}{N}\log\left(1+\rho\left|h_{S_{1}S_{2}} \right|^{2}\right)<\left(1-\alpha\right)C\right)=1-e^{-\left(2^{N\left(1-\alpha \right)C/\left(\alpha\right)}-1\right)/\left(\Omega_{0}\rho\right)}\)), and \(P_{r}^{i}-P_{r}^{i+1}\) is the probability that \(S_{1}\) or \(S_{2}\) exactly fails after the \(i\)-th time slot but succeeds after the \(i+1\)-th time slot transmission.
## Materials and Methods  
### Biomass Sources and Sampling  
The agro-industrial waste of palm empty fruit bunches (EFB) and mesocarp fiber (PMF) of _Elaeis guinensis_ (oil palm residues) was obtained from Tacotalpa, Tabasco, Mexico, and the _Pinus_ sp. sawdust (pine sawdust) was obtained from a sawmill from Durango, Durango, Mexico. The biomasses were conditioned up to moisture content of 15%. Residues of both materials were chipped to reduce sizes, milled, and sieved up to 3 mm (to uniformize particle size). Oil palm residues were mixed at the ratios of 100:0, 80:20, 60:40, 40:60, 20:80, and 0:100 with pine sawdust.
## 7 Experimental Results and Discussion  
In this section, the results are discussed for the experimental setup discussed in the earlier section. The Mean Square Error (MSE) is one of the ways to estimate the accuracy of the estimation of coefficients for different architectures utilized. The MSE when No DPD is applied is measured to be \(-27\) dB, while for GMP it is \(-30\) dB. The value reaches as low as \(-35\) dB for CPWL and MSA while NN has a MSE of \(-39\) dB.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Architecture** & **Parameters** \\ \hline
**Component** & **Value/Specification** \\ Optimizer & ADAM \\ Activation function & ReLu \\ O/P layer activation function & Softmax \\ Loss- function & Mean Square Error (MSE) \\ No. of Hidden-Layers \(N\) & 10 \\ No. of Neurons per layer \(K\) & 30 \\ \hline
**Component** & **Value/Specification** \\ Regularization method & Dropout \\ Regularization factor & 0.001 \\ Learning rate & 0.01 \\ Batch size & 16, 32, 64, 128, 256, 512, 1024 \\ Number of epochs & 100 \\ Validation split & 0.4 \\ \hline
**Training** & **Value/Specification** \\ Number of training samples & 1,000,000 \\ Number of testing samples & 1,000,000 \\ \hline
**Complexity** & **Value** \\
**Equation** & **Value** \\ \((N-1)\)\(K^{2}\) + \((4+N)\)\(K\) + 6 & 8526 \\ \hline \hline \end{tabular}
\end{table}
Table 6: ONN parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**GMP** & **Parameters** \\ \hline
**Component** & **Value** \\ Nonlinearity Order (\(K\)) & 3 \\ Memory depth (\(Q\)) & 3 \\ Delay taps length (\(R\)) & 3 \\ \hline
**DVR** & **Value** \\
**Component** & **Value** \\ Elements in partition (\(K\)) & 3 \\ Memory depth (\(Q\)) & 3 \\ Breakpoint (\(\beta\)) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: GMP and DVR parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**CPWL** & **Value** \\ \hline Memory Depth (\(M\)) & 3 \\ Number of partitions (\(L\)) & 4 \\ Length of the FIR filter (\(K\)) & 4 \\ \hline
**MSA** & **Value** \\ Memory Depth (\(M\)) & 3 \\ Number of partitions (\(L\)) & 4 \\ Length of the FIR filter (\(K\)) & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 8: CPWL and MSA parameters.  
In addition to MSE, the wellness of the proposed methods is compared and are presented in form of Adjacent Channel Leakage or Power Ratio (ACLR/ACPR) and Error Vector Magnitude (EVM).
# Variance of the Infection Number of Heterogeneous Malware Spread in Network
## 1 Introduction  
The research of modeling and analysis of infectious diffusion processes in networks, e.g., epidemics, rumors and computer viruses, have attracted ample attention over the past decades [1; 2]. Compartmental models are used to describe this sort of diffusion process. The commonly used basic compartment models are the Susceptible-Infectious-Susceptible (SIS) model and the Susceptible-Infected-Recovery (SIR) model. Several theoretical frameworks such as Markov theory, message passing and mean-field approximation are employed to model the dynamics of these diffusion processes. For the sake of convenience, most conventional methods to model the epidemic dynamics implicitly hold the assumptions that, for example, the underlying network size is infinite, all individuals are fully mixed and the infection and recovery rates are homogeneous. The usage of these assumptions helps to reduce the complexity of modeling and analysis. However, real networks are usually limited in size and heterogeneous nodal infection rate. It is intriguing to investigate the impact of a given finite-size network topology over the SIS dynamics.  
In this work, we focus on the heterogeneous SIS epidemic dynamics in networks with a given finite-size topology characterized by the adjacency matrix. Specifically, we investigate the SIS epidemic dynamics with each individual having a non-homogeneous infection rate or recovery rate. We derive the variance of the number of infected nodes for the heterogeneous SIS epidemic dynamics in a bipartite network and a start graph with a finite size. We experimentally verify the proposed theories.  
The paper is organized as follows. Section 2 introduces several related works. In Section 3, we elaborate on the theoretical framework, followed by the approximation solutions to the variance of infection fractions for bipartite graphs and star graphs. Theproposed theoretical formulas are derived. Also, we describe the simulation method employed in this paper. In Section 4, we first demonstrate the comparison between the theory and the simulation for the infection change versus time. Then, we verify the accuracy of the proposed theoretical formulas of the variance for the heterogeneous SIS process in bipartite graphs and star graphs. In Section 5, we analyze the accuracy of the proposed theoretical approximation formulas and provide several explanations. Finally, we conclude the paper in Section 6.