esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 ICS Security Assessment
### The Probability of Attacking Behavior  
AGs are effective modeling tools for multi-step attacks and are widely used in network security risk assessment, but the attack graph itself cannot quantitatively represent the security and risk level of the network [25]. Therefore, we need to obtain the success rate of the corresponding vulnerability utilization. The vulnerability availability refers to the probability that the attacker will succeed in the atomic attack by exploiting the vulnerability. The vulnerability availability is related to the difficulty of the vulnerability.  
This paper uses the combination of CVSS [26] and expert experience to evaluate the vulnerability availability. CVSS provides three vulnerability assessment indicators, namely base, temporal, and environment. Each indicator is quantified with a numerical score from 0 to 10. The sub-items of the base attribute include Access Vector (AV), Access Complexity (AC), and Authentication (AU), and the sub-items of the temporal attribute include the Exploit Code Maturity (E), the Remediation Level (RL), and the Report Confidence (RC). The base score describes the intrinsic characteristics of the vulnerability, and the temporal score describes the characteristics of the vulnerability as a function of temporal stages. This paper uses base and temporal in CVSS and combines relevant expert experience to comprehensively evaluate the exploitability of vulnerabilities. Therefore, the probability obtained in this way is more in line with the actual situation at the time of evaluation.  
The exploitability of a vulnerability can be expressed as Equation (1).  
\[Exploitability=2\times AV\times AC\times AU \tag{1}\]  
where \(AV\) is the access vector, \(AC\) is the access complexity, and \(AU\) is the authentication, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Rank** & **Score** \\ \hline \multirow{3}{*}{Access Vector (AV)} & local access & 0.55 \\  & adjacent network access & 0.62 \\  & network accessible & 0.85 \\ \hline \multirow{3}{*}{Access Complexity (AC)} & high & 0.44 \\  & low & 0.77 \\ \hline \multirow{3}{*}{Authentication (AU)} & multiple instances of authentication & 0.50 \\  & single instance of authentication & 0.62 \\ \cline{1-1}  & no authentication & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CVSS Score (base metric).  
Since vulnerabilities in different time stages have different properties, we use the temporal metric of CVSS. These metrics adjust the value of exploitability (Equation (1)). The final exploitability can be expressed as Equation (2).  
\[PE=(E\times RL\times RC)\times Exploitability \tag{2}\]  
where \(PE\) represents the probability of exploiting a vulnerability during risk assessment. The specific content is shown in Table 2.
# Cross-Layer MAC/Routing Protocol for Reliability Improvement of the Internet of Things
###### Abstract  
The IEEE 802.15.4 standard is recognized as one of the most successful for short-range low-rate wireless communications and is used in Internet of Things (IoT) applications. To improve the performance of wireless networks, interest in protocols that rely on interaction between different layers has increased. Cross-layer design has become an issue in wireless communication systems as it can improve the capacity of wireless networks by optimizing cooperation between multiple layers that constitute network systems. Power efficiency and network scalability must be addressed to spread IoT. In multi-hop networks, many devices share wireless media and are geographically distributed; consequently, efficient medium access control (MAC) and routing protocols are required to mitigate interference and improve reliability. Cross-layer design is a novel network design approach to support flexible layer techniques in IoT. We propose a cross-layer protocol for the MAC layer and routing layer to satisfy the requirements of various networks. The proposed scheme enables scalable and reliable mesh networking using the IEEE 802.15.4 standard and provides robust connectivity and efficient path discovery procedures. It also proposes a novel address-allocation technique to improve address-allocation methods that cannot support large sensor networks. Simulation results indicate that the proposed scheme could improve reliability and reduce end-to-end delay.  
cross-layer protocol; reliability; end-to-end delay; address allocation; IoT; wireless sensor network +
Footnote †: journal: Sensors  
1
Footnote 1: [[https://doi.org/10.3390/s22239429](https://doi.org/10.3390/s22239429)]([https://doi.org/10.3390/s22239429](https://doi.org/10.3390/s22239429))
## 3 Results
### An Increased Manganese Resistance of CRN/PPN1 Is Realized through PHO-Pathway Genes  
To understand the enhanced manganese resistance of the CRN/PPN1 strain, we paid special attention to particular groups of genes (Table 1). First of all, with manganese excess, the genes encoding manganese transport of the plasma membrane, ER and Golgi (_SMF1_, _SMF2_, _PMR1_), are up-regulated, while the genes of vacuolar transporters are either stable (_CCC1_) or down-regulated (_GDT1P_). In the control medium, there are no major changes in expression of the above genes in CRN/PPN1 versus CRN. The expression of the putative manganese chelator encoded by _MNC1_[47] is specific to manganese excess. Figure 4: The micrographs of the cells of CRN and CRN/PPN1 strains of _S. cerevisiae_ stained with 1,2,3-dihydro rhodamine. The strains were cultivated in control YPD medium with 14 mM KH\({}_{2}\)PO\({}_{4}\), and in the same medium supplemented with 5 mM MnSO\({}_{4}\) to the logarithmic stage (control—12 h growth, both strains; 5 mM manganese—40 h growth, CRN; 16 h growth, CRN/PPN1). (**A**) CRN, control YPD; (**B**) CRN/PPN1, control YPD; (**C**) CRN, manganese excess; (**D**) CRN/PPN1, manganese excess. Figure 3: The effect of H\({}_{2}\)O\({}_{2}\) on the growth of the CRN, CRN/PPN1, and CRN/PPX1 strains of _S. cerevisiae_ in YPD medium. CRN is shown in gray, CRN/PPN1 in green, and CRN/PPX1 in blue. Three replicates were performed for each experiment; mean \(\pm\) s.d. is given. The plasma membrane ATPase Pma2 is essential for manganese adaptation [47]. It is up-regulated in manganese-grown cells of both strains and in CRN/PPN1 grown in the control medium, although with a weak significance (FDR \(\sim\)0.08).
## 6 System Verification and Experiment Results
### System Verification  
We load the testing data to the buses to verify the convolution engine and extract the output from the adder module. Due to the limitation of the pages, we only show a few testing data, which may not comprehensively represent the whole convolution engine, but it does not lose the generality. Figure 21 shows how the data is imported to Bus1 and streamed to other PEs. First, the weights are preloaded to the PE array, and in this case, the weights \([w_{0,0},\,w_{0,1},\,w_{0,2},\,w_{0,3}]\) are {weight0, weight01, weight02, weight03} in Figure 21, which equal to {1, 0, \(-1\), 1}. We extract four sets of data as an example to show the waveform. The data that comes into Bus1 are Bus1_in0, Bus1_in1, Bus1_in2, and Bus1_in3, which multiply the weights and generate the products. The products are product00, product01, product02, and product03. As shown in Section 4.3.1, the PE(Col0, Row0), accepting the data from BUS1_in0, streams the data to PE(Col1, Row0) and PE10_in in Figure 20, in the next clock cycle.  
For verifying the adders, the filter cases \(k=7\) are used as an example, which contain the utilization of both column adder and row adder. Figures 22 and 23 show the column adder Col0 and Col9's operations for summing up the products from the PE array. All the data signals in Figures 22 and 23 are illustrated in the schematic view in Figure 24.  
Figure 9 shows that rows 7-10 in the PE array are also used to map the input features and weights. To present the signals in the row adder module, we use PE array row 7 as an example. The example uses the row adder, R7, to summarize the products. In Figure 25, the signals, product47, product57, product67, product77, product87, product97, and product107, are the products generated by the PE(Col4, Row7), PE(Col5, Row7), PE(Col6, Row7), PE(Col7, Row7), PE(Col8, Row7), PE(Col9, Row7), and PE(Col10, Row7), respectively. The signal product97 locates at Col9 wire (b) in Figure 24 and is redirected to wire AR7C9 in Figure 19. Similarly, all the other products, such as product47, product57, product67, product77, product87, and product107, are also redirected to R7 for generating the partial sum.  
It is worth mentioning that the test data are created on our own to verify the structure. The testing data are 16 binary bits for input features and weights. In future work,  
Figure 21: Example of the input feature data loading into Bus1 and streaming to Col1.  
the testing will use actual picture pixel data and pre-trained weights for performance specification collection.
# Phase-Matching Quantum Key Distribution with Discrete Phase Randomization
## 2 The Protocol of PM-QKD
### Protocol Description  
The implementation process of the PM-QKD is similar to Reference [39].  
* State preparation. In each round, the coherent state \(\left|\sqrt{\alpha_{\rm A}}e^{i\left(\pi\kappa_{\rm A}+\frac{2\pi}{D}d_{\rm A }\right)}\right\rangle\) is prepared by [PERSON], the intensity \(\alpha_{\rm A}\in\{\mu_{\rm A},\
u_{\rm A},\omega_{\rm A}\}\), where \(\mu_{\rm A}\) is she signal state, \(\
u_{\rm A}\) is the decoy state, \(\omega_{\rm A}\) is the vacuum state, the random key bit \(\kappa_{\rm A}\in\{0,1\}\), the discrete phase randomization number \(d_{\rm A}\) is randomly chosen from \(\{0,1,\cdots,D-1\}\), \(D\) is the number of maximum discrete phase that is modulated by [PERSON], for simplicity, assume \(D\) is an even number. Similarly, [PERSON] prepares the coherent state \(\left|\sqrt{\alpha_{\rm B}}e^{i(\pi\kappa_{\rm B}+\frac{2\pi}{D}d_{\rm B})}\right\rangle\), therein, \(\alpha_{\rm A}=\alpha_{\rm B}=\frac{\alpha}{2}\in\left\{\frac{\mu}{2},\frac{\
u }{2},\frac{\omega}{2}\right\}\).
* Measurement. [PERSON] and [PERSON] send their quantum states to [PERSON] with transmittances \(\eta_{\rm A}\) and \(\eta_{\rm B}\), [PERSON] performs an interference measurement with a beam splitter and records which detector (L or R) clicks.
* Announcement. The detection result is announced by [PERSON] for each round; the intensity settings \(\alpha_{\rm A}\), \(\alpha_{\rm B}\) and phase numbers \(d_{\rm A}\), \(d_{\rm B}\) are also announced by [PERSON] and [PERSON].
* Sifting. After that, the phase post-compensation method is used by [PERSON] to calculate and then [PERSON] announces the phase match pairs. Assume the phase compensation \(d_{\delta}\in\{0,1,\cdots D/2-1\}\), only one of the two detectors clicks is the successful detection. If the left detector clicks and \(|d_{\rm A}-d_{\rm B}-d_{\delta}|\) mod \(D=0\), [PERSON] and [PERSON] keep \(\kappa_{\rm A}\) and \(\kappa_{\rm B}\) as the raw key. If the right detector clicks and \(|d_{\rm A}-d_{\rm B}-d_{\delta}|\) mod \(D=D/2\), [PERSON] flips his key bit \(\kappa_{\rm B}\). If \(|d_{\rm A}-d_{\rm B}-d_{\delta}|\) mod \(D\
eq 0,D/2\), for simplicity, we discard the phase mismatch pairs.
* Parameter estimation. [PERSON] and [PERSON] estimate the information leakage from the raw data that they have kept.
* Key generation. After reconciling the corresponding key string to perform error correction, [PERSON] and [PERSON] use privacy amplification to produce the final keys.
# mmS-TCP: Scalable TCP for Improving Throughput and Fairness in 5G mmWave Networks
## 4 Evaluations
### Experimental Scenarios  
In this study, various scenarios for TCP connection through 5G mmWave link were constructed to evaluate the performance of the redesigned mmS-TCP. Scenarios for evaluating the performance of single-path TCP over mmWave link are largely divided into three categories. Through these three scenarios, we evaluated the number of duplicated ACKs and the throughput of a single TCP CCA, and the results are covered in Sections 4.3 and 4.4.  
For this, an experiment was performed based on the CCAs implemented in ns-3. S-TCP [29], CUBIC [19], HS-TCP [31], Hamilton TCP (H-TCP) [38], and Yeah [39] algorithms were used as CCAs for performance comparison with mmS-TCP. CUBIC uses a cubic function for cwnd size control. CUBIC is an algorithm that overcomes the problems of fairness and complexity compared to the BIC algorithm and is currently used as a default congestion control in most operating systems. HS-TCP is an algorithm that provides fast cwnd size growth rates with a modified TCP response function when the cwnd size is greater than a specified parameter. H-TCP is an algorithm that increases aggression over time after a previous loss event. Yeah, the only hybrid-based algorithm among CCAs used for evaluation, is a modification of the Scalable TCP, and has two operating modes: high-speed and low-speed.  
Figure 8 shows a graph of the SINR measured from the UE in each scenario. Scenario 1 assumes a situation in which the SINR decreases by 20 dB due to a single instantaneous outage event. In Scenario 2, it is assumed that the UE experiences a short but deep NLoS of 0.5 s four times. In Scenario 3, it is assumed that a long and large NLoS of about 1.5 s is experienced once, and a short and shallow NLoS four times. Table 1 shows the set values of the network parameters used in each simulation scenario.
## 3 Numerical Solution and Code Validation  
The numerical calculation method adopted in this study is based on the Galerkin method, which is a kind of finite element method, solved by the Comsol software (version 5.5). In the solution process, the parallel direct solver (Pardiso) is adopted, and the convergence accuracy of relative tolerance is set as \(10^{-6}\). The built-in grid module is used for mesh plotting, and a typical grid structure is shown in Figure 2; the level of the grids is finer and the total number of grids is 3024. The grids are dense close to the sides, but sparse away from the sides due to the existence of the boundary layer, which is able to achieve both the high precision requirement and calculation efficiency. In order to verify the numerical calculation method adopted, the natural convection of the air in a cavity has been calculated and the average Nusselt number obtained at the high-temperature wall, both of which are comparable with those in the literature [32]. Table 1 gives a comparison between the average Nusselt numbers. It can be seen that the maximum error is only 0.41% for \(Ra=10^{6}\) and the minimum error is 0.13% for \(Ra=10^{4}\), which indicates that the method adopted in this paper is correct. From Table 2, it can be seen that the average Nusselt numbers change little for different grids for \(Ra=10^{5}\); that is, the average Nusselt numbers are largely independent of a grid's level. By comprehensive consideration of the calculation accuracy and cost, the extra fine level of the grids was chosen in this paper.  
Figure 2: Computational grid structure of the model.
# Layered-Cost-Map-Based Traffic Management for Multiple AMRs via a DDS
###### Abstract  
A traffic management system can be used to control multiple automated mobile robots (AMRs) effectively. This paper proposes traffic management for multiple AMRs based on a layered cost map in ROS 2 for multiple purposes. Using the layered cost map, the new concepts of a prohibition filter, lane filter, fleet layer, and region filter are proposed and implemented. The prohibition filter can help a user set an area that would prohibit an AMR from trespassing. The lane filter can help set one-way directions based on an angle image. The fleet layer can help AMRs share their locations via the traffic management server. The region filter requests for or receives an exclusive area, which can be occupied by one AMR from the traffic management server. Multiple AMRs communicate via a data distribution service (DDS), which is shared by topics in the same DDS domain. The traffic management server in the domain sends or receives topics to each of the AMRs. The experiments of AMRs under the proposed traffic management show the effectiveness of our approach.  
traffic management; AMR; layered cost map; DDS; ROS 2 202220220220220220220220220220220220220220220220220220220220220220220220222022022022202202220220222022202220222022220222202222022222022222222  
only one AMR via cost maps for path planning have been studied [1; 2; 3; 4; 5]. However, traffic management for multiple AMRs has not been extensively studied yet. This is the first approach for the traffic management of the multiple-robot system via cost maps in ROS. Our novel concepts of prohibition and lane filters allow the user to set a prohibited area and a one-way driving rule, respectively. The fleet layer is used to effectively avoid collision among AMRs sharing information with the DDS. To effectively operate multiple AMRs, the region filter is used to reserve an area for only one AMR, whereas the remaining AMRs can wait outside of the area. Experiments with real AMRs have been performed to validate the proposed algorithms of the prohibition filter, lane filter, fleet layer, and region filter.
# A Metamaterial Inspired AMC Backed Dual Band Antenna for ISM and RFID Applications
## 2 Materials and Methods
### Iterations of the Designed Antenna  
The proposed antenna in the design stage underwent different iterations to operate at dual band characteristics, as illustrated in Figure 2. The fundamental iteration of the microstrip patch antenna started with the design of the L-shaped stub attached to the microstrip feed line, as depicted in Figure 2a. Subsequently, the iteration was modified with the addition of T-shaped stub to obtain narrow BW, as shown in Figure 2b. The antenna was altered by placing another two stubs with the T-shaped stub, as presented in Figure 2c. The final antenna was restructured by positioning the metamaterials on both sides of the feed line with a partial ground on the other side of the substrate as illustrated in Figure 2d. The metamaterial arrangement consisted of double split ring resonators with a gap in between the resonators.  
Figure 1: Overview of Design, Modelling analysis.  
Figure 3 highlights the results of iterations of the antenna. The black line depicts the effect of iteration 1, in which the antenna resonates at a single frequency at 1.1 GHz with S\({}_{11}\) value of \(-43\) dB. The red line shows the result of iteration 2, which specifies that the antenna radiates at a dual band with less reflection parameter while, iteration 3 of the antenna is indicated by the blue line, which operates in a dual band but with less S\({}_{11}\) values at 0.85 GHz and 1.6 GHz frequencies. Iteration 4 is indicated in the green line, wherein a successful dual band is observed with the characteristics of the S\({}_{11}\) value of \(-35\) dB at 0.915 GHz and \(-44\) dB at 2.45 GHz frequencies, respectively.
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 4 Outcomes and Discussion
### Machine Learning Models with TF-IDF  
Table 1 presents the outcomes of various machine learning models trained with TF-IDF, which focus on the frequency-based importance of words. Support Vector Machines (SVMs) achieved the highest F-score of 94%, indicating its strong ability to handle text classification with sparse high-dimensional data. This performance suggests that TF-IDF effectively captures important words that contribute to hate speech classification when used with SVM. Other models, such as Logistic Regression (LR) and Naive Bayes (NB), performed moderately well, but their reliance on word independence assumptions limited their overall recall and precision.  
On the other hand, Random Forest (RF) and Gradient Boosting Machines (GBMs) performed well, showcasing their effectiveness in feature selection. However, they slightly underperformed compared to SVM due to their tendency to overfit on TF-IDF features, which are sparse and high-dimensional. K-Nearest Neighbors (KNNs) performed the weakest, mainly because they struggle with large vocabulary sizes, as seen in TF-IDFembeddings. The strong performance of SVM highlights the effectiveness of traditional ML classifiers for hate speech detection, particularly when using TF-IDF representations.
## 3 Experiments and Results
### Evaluation Criteria  
Four evaluation criteria were used: the OIF, the distance-preserving property, pixel separability, and the average Euclidean distance between classes. The four criteria are described in some detail below.  
1. The optimum index factor has often been used in the literature [23] for band selection. The OIF comprehensively considers the information of single-band images and the relevance between various bands. The method of information/redundancy was used in this study to measure the usefulness of the information located in the images. The larger the OIF, the more information the image contains. It is formulated as follows: \[OIF\ =\ \sum_{i\ =\ 1}^{3}std(C_{i})/\sum_{i\ =\ 1,j\
eq i}^{3}R_{ij}\] (5) where \(std(C_{i})\) is the standard deviation of Euclidean distance in the color space of the \(i\)-th band of image data \(C\), and \(R_{ij}\) is the correlation coefficient between the \(i\)-th band and the \(j\)-th band.
2. The distance-preserving property \(\rho\) means that the differences in distance between each pixel of the generated images are as correlated as possible between spectral vectors in the HSI data. The image spectral distance-preserving property is good when \(\rho\) is closer to 1. The distance-preserving property can be represented as follows [3]: \[\rho\ =\ \frac{\left(X^{\mathrm{T}}Y\right)\left/\left|X\right|-\overline{XY} \right.}{std(X)\times std(Y)}\] (6) where vector \(X\) is the spectral angle vector of each pair of pixels in the original hyperspectral space. Vector \(Y\) is the Euclidean distance between each pair of pixels in the CIELab color space, \(\overline{X}\) is the mean of \(X\), \(\left|X\right|\) is the cardinality of \(X\), and \(std(X)\) is the standard deviation of \(X\).
3. Pixels of the output image should not only show the relationship between pixels but also make the different pixels easily distinguishable. Pixel separability is the evaluation criterion of this property.  
Figure 4: Color labels for the classes. (**a**) Indian Pines; (**b**) Pavia University; (**c**) Salinas; (**d**) SalinasA.  
Figure 3: The 80 th band of the HSIs. (**a**) Indian Pines; (**b**) Pavia University; (**c**) Salinas; (**d**) SalinasA.  
Pixel separability can be evaluated by the average value \(\delta\) of the color difference between pixels. The bigger the \(\delta\), the more obvious the difference between individual elements and better the between-class separability. The \(\delta\) can be computed as follows: \[\delta\ =\ |Y|_{1}/|Y|\] (7) where \(|Y|_{1}\) and \(|Y|\) are the L1 norm and the cardinality of vector \(Y\), respectively.
4. \(\lambda\), the average Euclidean distance between classes, is used to compare the separability between all classes. Larger \(\lambda\) indicates that the categories are more distinguishable. It can be represented as follows: \[\lambda\ =\ \sum\Delta d^{\prime}\bigg{/}C_{n}^{2}\] (8) where \(\Delta d^{\prime}\) is the Euclidean distance between two different classes and \(C_{n}^{2}\ =\ \frac{2}{n!(n-2)!}\).