esa-sceva/satcom-chunk-collection huggingface dataset:
# Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas
## 5 Simulation Results
### Performance Evaluation of CJ with Multiple Antennas  
Next, we analyzed the performance of the CJ with multiple antennas when a QPSK-modulated signal was used at the transmitter. Note that the channel between the CJ and the receiver and the channel between the CJ and the eavesdropper had Rayleigh fading. Note that \(h_{i,j}\sim\text{Rayleigh}(0,1)\) if specified otherwise. Figure 9 presents the success of covertness when the CJ transmits an adversarial perturbation with \(q=2\) antennas using the different attack methods introduced in Section 4. We observe that all different methods using multiple antennas outperform the attack generated by the CJ with one antenna. Furthermore, randomly selecting one antenna at the CJ performs worst among attacks using multiple antennas and the performance of the IPCG attack is similar to the performance of the PCG attack. Moreover, the EMCG attack outperforms other attacks by fully utilizing the channel diversity.  
Figure 9 presents the BER performance of different attack methods. We observe that the CJ using one antenna gives the largest BER whereas the PCG and IPCG attacks give the smallest BER. Furthermore, the EMCG attack gives a moderate BER increase while successfully making communications covert.  
The performance of the CJ with different number of antennas is presented in Figure 10. As the number of the antennas at the CJ increases, the success of covertness also increases suggesting that using more antenna at the CJ helps the covertness of communications. Furthermore, the BER decreases when more antennas are used at the CJ as we can see from  
Figure 8: 5G communications BER performance at the receiver.  
Figure 7: 5G communications covertness performance at the eavesdropper.  
Figure 10b. Therefore, using more antennas at the CJ is always beneficial for communications in terms of covertness and BER when the EMCG attack is used at the CJ.  
Next, we varied the SNR levels to analyze how the SNR affected the covertness and the BER in Figure 11a,b. As expected, the CJ needs a higher PNR to fool the eavesdropper when the SNR is high. Furthermore, we observe that the BER slightly increases when the PNR increases and the BER is higher for a lower SNR.  
Finally, we increased the variance of the Rayleigh fading between the CJ and the eavesdropper to analyze the effect of the channel on the covertness of communications. In Figure 12a, we observe that a lower PNR is needed to fool the eavesdropper when the variance of the Rayleigh fading is high. Furthermore, as a consequence of using a lower PNR at the CJ, a higher variance of Rayleigh fading results in a lower BER at the receiver.  
Figure 11: Performance with respect to different SNR levels: (**a**) success of covertness and (**b**) BER at the receiver.  
Figure 10: Performance with different number of antennas at the CJ: (**a**) success of covertness and (**b**) BER at the receiver.  
Figure 9: Performance when CJ has \(q=2\) antennas: (**a**) success of covertness and (**b**) BER at the receiver.
# Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods
## 2 Materials and Methods
### Combination of Multi-Weight Methods
#### 2.3.4 Evaluation Methodology  
The flood risk of Liaocheng City, as studied here, is a comprehensive function of three aspects, namely the severity of disaster-causing factors (\(H\)), the exposure of the disaster-prone environment (\(E\)), and the vulnerability of the disaster-bearing body (\(V\)). The \"H-E-V\" framework [10; 54; 55; 56] is comprehensive and clear, yet operational, and is widely used by scholars and research organizations [57].  
The comprehensive flood risk assessment model for Liaocheng City can be calculated using the following equation:  
\[FDRI=X_{H}W_{H}+X_{E}W_{E}+X_{V}W_{V} \tag{14}\]  
\[X=\sum_{j=1}^{n}w_{j}r_{ij} \tag{15}\]  
where \(FDRI\) (flood disaster risk index) is a composite flood risk index; \(X_{H}\), \(X_{E}\), and \(X_{V}\) denote the values of the evaluation indexes for severity, exposure, and vulnerability, respectively; \(W_{E}\), \(W_{H}\), and \(W_{V}\) denote the respective weights; \(r_{ij}\) and \(w_{j}\) are the standardized value and corresponding weight of the \(i\)th indicator in the evaluation index, respectively; and \(j\) is the number of indicators for each individual evaluation.
# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
## 8 Simulation
### Simulation Results
#### 8.3.3 Dead Nodes Investigation in Matlab  
Figure 12a shows the comparison of dead nodes vs. UAV rounds in different routing schemes. The number of rounds is shown on the X-axis and number of dead nodes is shown on the Y-axis. Different colored curves represent different data gathering schemes. The lower the curve, the higher the performance. Red and purple lines show the performance of proposed UAV Routing Protocol (URP). We tested the developed system at constant and fixed height (the UAV is not going down to take data) as in case of other networks (LEACH and HEED) and the results are shown in the red curve. When the UAV is flying at a fixed constant height even then our proposed system is performing quite well. The purple curve URP-adopted height is the best case scenario when the UAV negotiates the best height with the CH in advance and respect it while collecting the data. Thanks to the fact that there are no periodic updates, no flooding of information, better CH selection and duel frequency use, that helps us to optimize the node energy up to a maximum extent. The UAV and CH negotiate a suitable height for data collection. As long-range communication is the main source of energy depletion, adjusting has in a good impact on overall system lifetime.
## 1 Introduction  
Red mud is a highly alkaline waste of the industrial production of alumina. It is composed of different metallic oxides and hydroxides (Si, Ti, Al, Fe, Na), but it also contains mineral phases of numerous other chemical elements. These crystalline materials are either of natural origin developed due to different geological processes or represent phases that formed during the industrial treatment (the Bayer process). Some minerals in a common red mud, like goethite, reflect the source alumina ore, bauxite, but minerals may also form following the deposition of the red mud (bauxite processing residue) in pitfalls.  
Because of its complex chemical and mineral phase composition, a wide spectrum of application of the red mud as a raw material has been in practice for decades. The cement industry and agriculture (e.g., soil amelioration) among many others are the main users ([1] and references therein). On the other hand, raw material recovery of major (e.g., iron, [2]) and specific components (Ti, rare earth elements, Sc, etc.) from the red muds is also a real possibility (e.g., [3; 4]) and is the focus of numerous research studies nowadays. Despite the extensive research on potential end use, only about 3% of global red mud is currently re-used [5].  
In NW Hungary, around the small town of Almasfuzito (Figure 1) an alumina plant operated between 1950 and 1977. At that time, it was the largest one in Central Europe. As a by-product some 15 millions of tons of red mud were produced, which is deposited in eight pitfalls with a total area of ~200 ha. Although the entire deposition history cannot be reconstructed after several decades, the factory predominantly digested Hungarian karst bauxites. These ores are of different ages (from Cretaceous up to Oligocene) and represent diverse geological circumstances; they nevertheless are of similar mineralogical and chemical compositions. The characteristic Al-phases are boehmite and gibbsite, while the major Fe-minerals are hematite and goethite. These ores are typically high in silica; the main Si-phase is kaolinite. Titanium is represented by anatase and rutile. The maximal depth of these pitfalls is around 8 m. Although a thick layer of soil-like material already covers a significant part of them, the red mud is still wet with a gelatinous state. Although the dump area is safe environmentally, usage of the huge amount of the mud as a secondary ore of rare elements is in focus of interest ([6] and references therein). Previous measurements [6] pointed to a significant amount of rare earth elements (lanthanides and scandium, from here on REE) in the Almasfuzito red mud deposit, but the real concentrations seemed varying in a rather wide interval from one sampling point to another (unpublished industrial reports). The major aim of the present study is to understand the processes that are responsible for element accumulation (first of all Sc and the REEs) in the Almasfuzito red mud. In the frame of this project, mineralogical and geochemical data are evaluated in order to make reliable plans for its future usage as a secondary REE and Sc ore.
# The Impact Assessment of Water Supply DMA Formation on the Monitoring System Sensitivity
## 3 Results and Discussion
### Model Network
#### 3.1.2 Second Phase Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Network** & \(A\) & \(B\) & \(C\) & \(D\) & \(E\) & \(F\) & \(G\) & \(H\) & \(I\) & \(J\) & \(K\) & \(L\) \\ \hline \multicolumn{10}{c}{q0, p0} \\ \hline H1 & 900 & 1100 & 900 & 500 & 100 & 200 & 200 & 600 & 0 & 300 & 200 & 500 \\ H2 & 900 & 100 & 300 & 300 & 300 & 400 & 200 & 400 & 0 & 500 & 400 & 500 \\ H3 & 900 & 900 & 500 & 500 & 800 & 200 & 400 & 400 & 800 & 300 & 500 & 300 \\ \hline average & 900 & 700 & 567 & 433 & 400 & 267 & 267 & 467 & 267 & 367 & 367 & 433 \\ \hline \multicolumn{10}{c}{q0, p0, p1} \\ \hline H1 & 600 & 100 & 500 & 0 & 0 & 200 & 0 & 0 & 0 & 0 & 0 \\ H2 & 0 & 100 & 0 & 0 & 0 & 100 & 0 & 0 & 0 & 0 & 0 \\ H3 & 0 & 100 & 100 & 100 & 300 & 200 & 400 & 300 & 300 & 300 & 100 & 300 \\ \hline \multicolumn{10}{c}{average} & 200 & 100 & 200 & 33 & 100 & 167 & 133 & 100 & 100 & 100 & 33 & 100 \\ \hline \multicolumn{10}{c}{Open hydrant} \\ \hline H1 & 100 & 100 & 0 & 0 & 100 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ H2 & 200 & 0 & 0 & 0 & 300 & 200 & 200 & 0 & 0 & 0 & 0 \\ H3 & 0 & 100 & 0 & 0 & 0 & 100 & 0 & 0 & 100 & 0 & 100 & 300 \\ \hline \multicolumn{10}{c}{average} & 100 & 67 & 0 & 0 & 133 & 100 & 67 & 0 & 33 & 0 & 33 & 100 \\ \hline \multicolumn{10}{c}{Open hydrant} \\ \hline H1 & 0 & 200 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ H2 & 0 & 100 & 100 & 0 & 200 & 200 & 200 & 0 & 0 & 0 & 0 \\ H3 & 0 & 100 & 0 & 0 & 0 & 100 & 0 & 0 & 100 & 0 & 300 \\ \hline \multicolumn{10}{c}{average} & 0 & 133 & 33 & 0 & 67 & 100 & 67 & 0 & 33 & 0 & 33 & 100 \\ \hline \multicolumn{10}{c}{Total length (m)} & 2400 & 2400 & 2400 & 2600 & 2600 & 2600 & 2700 & 2800 & 2900 & 3200 & 3200 & 4000 \\ \hline \multicolumn{10}{c}{Loops number (-)} & 0 & 0 & 0 & 1 & 1 & 1 & 4 & 4 & 4 & 8 & 8 & 16 \\ \hline \multicolumn{10}{c}{Fractal dimension} & 1.010 & 1.013 & 1.039 & 1.055 & 1.056 & 1.044 & 1.07 & 1.075 & 1.069 & 1.092 & 1.092 & 1.109 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calculated length of the error (m) and total pipe length, number of loops, and fractal dimensions for all \(A\)-\(L\) networks. Figure 10: Length of the error depending on the network variant and the number of monitoring sensors applied.
## 2 Materials and Methods  
### Experimental Area and Use of Plant Protection Products  
In 2015, we carried out the experiment on a hop plantation in the village of Kaplan (46\({}^{\circ}\)14\({}^{\prime}\)49.76\({}^{\prime\prime}\) N; 15\({}^{\circ}\)1\({}^{\prime}\)11.91\({}^{\prime\prime}\) E; 303 m) on the outskirts of the Lower Savinja Valley. The experiment included 0.54 ha (plot with a length of 100 m and a width of 54 m) of a 7.33 ha hop plantation. The hop cultivar \"Savinjski golding\", which is placed among tall cultivars, was sown in 2002 at an inter-row distance of 2.4 m. The distance between plants in a row was 1.2 m. The experimental plot was divided into three blocks; within each block we randomly arranged four treatments, namely, the control treatment (spraying with fungicides and insecticides, without acaricides) (marked as Control in the figures), biological control (a single inundative release of the predatory mite and spraying with fungicides) (marked as Biological in the figures), Acaricides 1 (spraying with two acaricides (twice with one product and once with the other), fungicides and insecticides; indicated as Acaricides 1 in the figures) and Acaricides 2 (spraying with two acaricides (once with both preparations), fungicides and insecticides; indicated as Acaricides 2 in the figures). There were 160 hop plants in each treatment (plot with a length of 33.3 m and a width of 13.5 m). More detailed descriptions of the treatments, with the relevant spraying dates, are presented in Table 1. Table 2 presents a detailed list of the plant protection products used for protection against diseases and harmful insect pests in 2015. Decisions regarding which synthetic products would be used in our experiment were made according to the IOBC database [13], as we did not want to use products that would have an effect on the predatory mite in the biological control treatment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Treatment** & **Description** \\ \hline Control treatment & Spraying with fungicides and insecticides, without acaricides \\ Biological control & 4 July—inundative release of _N. californicus_ (Californicus Breeding system 500) \\ Acaricides 1 & Spraying with fungicides, insecticides and acaricides: Nissorun 10 WP—27 June, Vertimec Pro—3 July, Nissorun 10 WP—17 July \\ Acaricides 2 & Spraying with fungicides, insecticides and acaricides: Vertimec Pro—3 July, Nissorun 10 WP—17 July \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed description of treatments used in our survey.
# Integrating Blockchains with the IoT: A Review of Architectures and Marine Use Cases
## 1 Introduction
Collectively, these approaches demonstrate a wide array of strategies to address specific needs within MIoT and IoUT applications, spanning energy optimization, security, and scalability. Building on the aforementioned, this study examines the intersection of blockchain technology and the IoT, putting a particular focus on their application in marine environments. The aim is to highlight both the benefits that BIoT systems can offer as well as the challenges involved in implementing them. This study is guided by the following research questions:  
1. How can blockchain technology support the performance and reliability of IoT systems in challenging marine and underwater environments? 2. What kinds of blockchain platforms and types of solutions are utilized in MIoT and IoUT systems? 3. What are the optimal architectural choices and consensus mechanisms for designing effective BIoT systems that address the challenges of marine and underwater environments? In selecting the related works for this study, a comprehensive approach was employed, primarily utilizing Google Scholar as the key search tool. Specific keywords such as \"blockchain\", \"MIoT\", \"IoUT\", and \"BIoT\" were used to locate relevant articles, papers, and journals that provide insights into the integration of blockchains with the IoT in marine and underwater environments. This search strategy led to the identification of foundational and recent research within the field. Given the specificity of the topic, which intersects both IoT and blockchain technologies in marine settings, some sources were also uncovered through the examination of references in related papers and reviews of the literature. This targeted search helped include only the most relevant and recent studies, reflecting the ongoing developments in this specialized research area. The paper is organized as follows: Section 2 provides an overview of the core architectures of IoT and blockchain technologies. It also introduces MIoT and IoUT systems, highlighting their complexities, detailing their architectures, and discussing how these technologies can be integrated into BioT systems. Section 3 delves into the architecture and communication models of the BIoT, emphasizing the benefits of integrating blockchains with IoT technologies. Section 4 focuses on the implementation of blockchains in MIoT and IoUT systems, discussing the specific challenges these systems encounter in marine environments, such as a limited communication bandwidth, energy constraints, and the complexities of blockchain integration. Additionally, a range of potential solutions to these challenges is presented. Section 5 provides a discussion of these findings and solutions, considering their limitations, while also emphasizing the importance of strategic design decisions in order to realize the full potential of BIoT technology in marine ecosystems. Finally, the paper concludes by summarizing all of the key insights.
## 4 Simulation and Analysis of Results
### Result of Simulation  
We selected four datasets of TSP in TSPLIB; ulysses16, city31, eil51, gr96, and bier127. Of these, city31 is the data set of longitude and latitude coordinates of the locations of 31 cities in China. Multiple sets of data are selected to ensure the reliability of the conclusions. At the same time, the number of the four groups of datasets is 16, 31, 51, 96, and 127, separately and increasing in order. It increases the difficulty of optimization and fully verifies the performance of the algorithm. Figures 5-9 are the simulation results obtained about five groups of TSP data by SMA and TPSMA. It can be seen from the figures that the results under the five groups of data are obviously improved by TPSMA.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**A Two-Way Parallel Slime Mold Algorithm by Distance and Flow**} \\ \hline input: & TSP path points \\ output: & TSP shortest path \\ (a) & Initialization process \\ Step 1 & Initialize variables and parameters \\ (b) & Calculate the distance and flow \\ Step 2 & Get the distance \(L_{ij}\) according to Formula (1) \\ Step 3 & Get the flow \(Q_{ij}\) by the Formulas (2) and (3) of Kirchhoff’s Laws \\ Step 4 & Update the conductivity by Formula (5) \\ Step 5 & Return to Step 3 to cycle until iterative condition is terminated and \\  & obtain the stable flow value \(Q_{ij}\) \\ (c) & The point selection by a two-way parallel method \\ Step 6 & Select the points \(P_{Libest}\), \(P_{Libster}\), \(P_{\textit{Object}}\) and \(L_{\textit{Q}ighter}\), and gain \\  & two sets of points according to the Formula (9) and (10) \\ Step 7 & Obtain the value \(e_{LO}\) by subtracting \(L_{\textit{Q}liest}\) from \(L_{\textit{Q}ighter}\). \\  & Then, complete the selection of point in turn by using the two-way \\  & parallel Formula (11) and (12) \\ Step 8 & Return to Step 6 to finish the iterations until the iterative condition \\  & is satisfied, then get all the paths \(L_{ength}\) \\ (d) & Obtain the result \\ Step 9 & Obtain and output the optimal path \(L_{best}\) by the Formula (13) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The steps of TPSMA.  
Figure 5: The simulation of ulysses16.  
Figure 6: The simulation of city31.  
Figure 7: The simulation of eil51.
## 2 Background and Related Works
### Energy Model Analysis
The energy model for this part is represented by the overall measurement as shown in Equation (4). \[E_{send}=\int_{0}^{T}P_{send}(t)dt=V\int_{0}^{T}I_{send}(t)dt, \tag{4}\]  
where \(V\) represents the voltage of the microcontroller unit (MCU) and the current, \(I_{send}(t)\), which depends on the transmission schedule. In summary, we measure the power consumption by collecting the current from each module and scenario to estimate the overall power consumption. Moreover, we assume that the voltage across the system is constant. Additionally, we evaluate the total current of the microcontroller unit (MCU) that connects to the power source and distributes power to the attached modules.
## 6 Conclusions and Future Works  
### Conclusions  
This paper proposes a PSO-based model abstraction and explanation generation method for RNNs. This work presents three primary novel contributions as follows:  
* Learning PFA to integrate the information into the abstraction model;
* Adding PSO module to optimize the state merging algorithm;
* Abstracting RNN models to simplify the research of explainability;  
Compared with the existing RNN abstraction techniques, it performs better in model accuracy and running efficiency. PSO4 RNN optimizes the parameter search instead of trial and error, providing a novel technical perspective for the application of heuristic algorithms in state merging. The abstraction provides a novel perspective for explainable artificial intelligence (XAI). Existing works learned the abstraction model a global structure explanation. Moreover, it generates high-related explanation words and their importance scores based on PFA, which can be seen as a well-adapted global explanation. This adaptability mainly comes from the good generalization performance of abstraction model PFA, not only in training set samples. It also provides visualization ways, such as word clouds and feature word highlighting, to increase explainability, greatly enhancing the user friendliness and credibility of abstraction models.  
However, this work has several limitations. (1) Restricted by hardware resources, empirical studies are merely investigated on two real-world datasets. The clustering parameter \(k\) is selected through haphazard tests. If PSO4 RNN is applied to other real-world datasets containing many more samples, the clustering process may not perform optimally. Therefore, choosing a proper \(k\) to achieve a satisfying performance becomes meaningful. (2) Computational efficiency can be improved. During experiments, an issue of inadequate computational resources appeared in the state merging process. This means that the throughput of merging processing needs enhancement.
# Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm
## 2 Preliminaries and Notation
### Kinematic Models of Space Robotic Systems
\right]+\mathbf{J}_{m}\mathbf{\dot{q}}=[\mathbf{J}_{m}+\mathbf{J}_{b} \mathbf{J}_{bm}]\mathbf{\dot{q}}=\mathbf{J}_{\dot{s}}\mathbf{\dot{q}} \tag{5}\]  
The symbols in Equations (1)-(5) are explained as follows:  
* \(\mathbf{q}\in\mathbf{R}^{n}\): joint configuration with unit of each element of \(\mathbf{q}\) deg;
* \(\mathbf{\dot{q}}\in\mathbf{R}^{n}\): angular velocity of joint configuration with unit of each element of \(\mathbf{\dot{q}}\) deg/s;
* \(\mathbf{x}_{b}\in\mathbf{R}^{6}\): pose of base with unit of [m; m; m; deg; deg; deg];
* \(\dot{\mathbf{x}}_{b}\in\mathbf{R}^{6}\): velocity of base with unit of [m/s; m/s; m/s; deg/s; deg/s];
* \(\mathbf{v}_{e}\in\mathbf{R}^{3}\): linear velocity of end effector with unit of [m/s; m/s; m/s];
* \(\mathbf{\omega}_{e}\in\mathbf{R}^{3}\): angular velocity of end effector with unit of [deg/s; deg/s; deg/s];
* \(\mathbf{J}_{b}\in\mathbf{R}^{6\times 6}\): Jacobian matrix of base;
* \(\mathbf{J}_{m}\in\mathbf{R}^{6\times n}\): Jacobian matrix of manipulator;
* \(\mathbf{J}_{bm}\in\mathbf{R}^{6\times n}\): coupling Jacobian matrix;
* \(\mathbf{H}_{b}\in\mathbf{R}^{6\times 6}\): inertia matrix of base; and
* \(\mathbf{H}_{bm}\in\mathbf{R}^{6\times n}\): coupling inertia matrix.