esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobile Data-Mule Optimal Path Planning for Wireless Sensor Networks
## 3 Optimization Problem Formulation
A sub-tour is defined as a subset of locations that are connected within a route, which excludes the depot node. \[\sum_{m=1}^{M}\sum_{ \begin{subarray}{c}i=1\\ i\
eq r\end{subarray} }^{N}x_{irm}\leq 1,\quad\forall\text{ nodes }r=2,\ldots,N, \tag{8}\]  
\[\sum_{m=1}^{M}\sum_{ \begin{subarray}{c}j=1\\ j\
eq r\end{subarray} }^{N}x_{rjm}\leq 1,\quad\forall\text{ nodes }r=2,\ldots,N. \tag{9}\]  
(8) and (9) account for the requirement for connectivity within a route; these ensure that each route that starts from the depot node ends at the same node as well via a set of transitions between the vertices/nodes. Furthermore, each static node is visited at most once and only by one MoEl during the cycle of the concurrent \(M\) routes that are followed by the equal number MoEl's. \[\sum_{j=2}^{N}x_{1 jm}\leq 1,\quad\forall\text{ MoEl's }m=1,2,\ldots,M. \tag{10}\](10) ensures that each MoEl visits a different static node after leaving from the depot node. \[\sum_{ \begin{subarray}{c}i=1\\ i\
eq j\end{subarray} }^{N}\sum_{j=1}^{N}(c_{ij}+v_{t_{j}})x_{ijm}\leq B,\quad \forall\text{ MoEls }m=1,2,\ldots,M. \tag{11}\]  
(11) accounts for the requirement that each one of the \(M\) routes meets the defined time budget \(B\). This limit in time budget is imposed by the energy consumption of the MoEls and the requirement for their periodical recharging that takes place every time an MoEl returns to the depot node. \[\sum_{i\in\mathcal{F}}\sum_{ \begin{subarray}{c}j\in\mathcal{F}\\ j\
eq i\end{subarray} }(c_{ij}+v_{t_{j}})x_{ijm}\leq e_{j},\quad\forall\text{ subset }\mathcal{T}\subseteq N,\;\;\forall j\text{ nodes }\in P_{r}\subseteq N,\;\;\forall\text{ MoEls }m=1,2,\ldots,M, \tag{12}\]  
where \(\mathcal{T}\) refers to the subsets of nodes that start at node \(1\) and end at node \(j\in P_{r}\subseteq N\). (12) ensures that the time intervals \(e_{j}\) between consecutive visits to each static node/vertex \(j\) meet \(\forall j\in P_{r}\). This constraint handles the described scenario and is differentiated by Time-Dependent and Time Windows TSP versions that are reviewed in [37]. \[x_{ijm}\in\{0,1\}\quad\forall\text{ nodes }i,j=1,\ldots,N,\;\;i\
eq j,\;\; \forall\text{ MoEls }m=1,2,\ldots,M. \tag{13}\]  
(13) describes the binary nature of the decision variables. The extracted route contains the prioritized nodes, whose profits are assigned accordingly and taken into account in the MILP objective function, whereas the equalities (3) and (4) and inequalities set (12) account for the prioritization constraints.
## 4 Application to Real Data
### Geological Background  
The Pelotas Basin is situated to the North, bounded by the Florianopolis High and the Santos Basin, and to the South, by the Punta Del Este Basin and the Polonio High in Urugu. With a sedimentary section that is approximately 12,000 m thick and covering an area of more than 210,000 km\({}^{2}\), the structural framework of the Pelotas Basin is linked to extensional tectonism during the Late Jurassic to Early Cretaceous period, coinciding with the opening of the South Atlantic Ocean. The rift sequences in the Pelotas Basin started in the Mid-Cretaceous due to thermal subsidence and seafloor spreading, involving multiple stages of deposition and extensional faulting. As discussed by [17], significant volcanism is evident in certain sections of the early rift, with the basin displaying seaward dipping reflectors. Moving up in the section, a second late Miocene flooding surface signifies a stratigraphic transition from aggradational deposition to progradational depositional cycles that persist to the present day. Stratigraphic interpretations indicate that thickness  
Figure 19: Depth slice on \(z=2500\) m from the CMP inversion.  
Figure 18: \(YZ\) slice plane on \(X=0\) m from the CMP inversion. The real body has 100 m of thickness, starting on 2400 m of depth.  
variations are influenced by the Lagoa dos Patos, which contributes substantial sediment volumes to the Atlantic Ocean system.  
The MCSEM survey was designed to cover part of the Rio Grande Cone. It is believed that organic material transported during this phase of deposition was well preserved in the intervals and likely matured to generate gas and gas-hydrates. Features related to the hydrates and free gas accumulations are identified in 2D seismic data as well as in previous studies in the Pelotas Basin [18].  
On the Pelotas middle-lower continental slope, two high-amplitude reflectors extend almost parallel to the sea bottom surface and cut through the stratified Eocene-Moocene succession. These features triggered unstable conditions of the lower slope and deep-marine sediment immobilization, followed by debris deposition in the oceanic basin during the Neogene. According to [19], the layer with gas hydrate is under a layer of gaseous mud and has an average thickness of 400 to 500 m. Ideal conditions of temperature and pressure allow for the freezing of the water, trapping the gas.
## 3 Results and Discussion
### Verification of Sowing
#### 3.2.1 Device of Verification  
The seeder used in the actual test was a spoon-type seeder with a self-designed spoon plate. The seeder primarily comprises a seed-filling shell, a seed-sowing spoon plate, a seed isolation board, a seed warehouse, an outer shell, and a seed-clearing brush. The seed-filling shell was constructed from PMMA, the spoon plate was fabricated using PLA, the seed isolation board was made of SAE 304 stainless steel, and the seed warehouse was composed of 6061 aluminum alloy. To enhance the simulation efficiency, the seeder structure was simplified by excluding components that would not come into contact with the seeds. The simplified model is presented in Figure 9.  
Figure 8: Calibration simulation results: (**a**) simulated stacking state; (**b**) vectorization of seed model; and (**c**) simulated stacking angle.  
Figure 9: Spoon-type seed metering device structure: (1) outer shell; (2) seed warehouse; (3) seed isolation board; (4) seed sowing spoon plate; (5) seed-clearing brush; and (6) seed-filling shell.  
During the seeder's operation, seeds are transferred from the seed-filling shell into the seeder. As the seed-sowing spoon plate rotates, the spoons' mouths carry the seeds to the top of the seeder. Impacted by the dual factors of gravity and the seed-clearing brush, the seeds in each spoon's mouth pass through the seed isolation board and fall into their corresponding seed warehouses. The seed warehouses rotate synchronously, and ultimately, the seeds are discharged through the opening of the outer shell, thereby achieving mechanical seeding.
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 4 Results and Discussion
### Interference Mitigation Methods in Prohibited Zone  
To optimize the deployment of the base station and reduce interference with the RA, two methods were analyzed, namely Tx power control and angle shutoff. These methods were studied in the scenario of the largest prohibited zone, with RMa BSs and \(AGL=2000\) ft for the omnidirectional antenna and the beamforming antenna.
# A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit
## 3 Association Rule Mining Algorithm
### Eclat Algorithm  
Eclat algorithm [37] works in a vertical manner as shown in Figure 3.  
In the first step, the database was scanned and all single items were listed, as well as their belonging item sets. The support value of each item equaled to the number of their belonging item sets. Next, the support value of each item was compared with the threshold, and those items meeting requirement constituted frequent 1-item sets. Then, every two items paired together, the intersection of their belonging item sets was listed. Afterwards, 2-item sets sharing one item combined with each other to form 3-item sets. This recursive process was continued until no item sets could be combined. The Eclat algorithm only scanned the whole database once, in the first step.
## 4 Discussion
### Comparison with Other State-of-the-Art Works  
So far, few researchers have produced considerable work regarding NIDS using the BoT-IoT dataset. Most relevant work has been put forth by [27], who chooses a single machine-learning model using his proposed bijective method. He uses a large number of features of data. This can lead to underfitting and large training time. In [11], the authors evaluated the performance of several supervised machine-learning algorithms in order to discover the most effective algorithm. Although they used the BoT-IoT dataset, their feature-selection method was inappropriate. They selected features by examining and observing data. Only a tried and tested feature-selection method can ensure the extraction of the most significant features. The work of [28] offers a novel NIDS pertaining to IoT networks using the BoT-IoT dataset. They used a deep-learning approach and generic features from field information in the packet. Their developed model can detect only DoS, DDoS, Reconnaissance, and Information Theft attacks. In [29], the authors used a Graph Neural Network (GNN)-based framework for IoT and trained their data using the BoT-IoT dataset. As we know, GNN has more time and space complexity, and hence, the model requires more time for training as well as space. Moreover, the work of [30] explores the capacity of various machine-learning algorithms in detecting the threats of IoT networks, but some of these models are not well trained and hence give low accuracy. The model presented by [31] [PERSON] et al. (2022) is based on a modern IoT dataset, but they implemented only one classifier with fewer attack categories. Hence, their model cannot be regarded as robust. The Random Forest model employed by [18] is an SDN-based detector, but its accuracy is not remarkable, and it is trained using old datasets not representing present-day IoT scenarios. In a recent work [32], the authors trained a Deep Neural Network (DNN) to detect threats in an IoT environment, but its accuracy was low because the dataset used was not well balanced.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Specificity** & **AUC** \\ \hline LR & 0.926 & 1.00 & 0.931 & 0.964 & 1.00 & 0.77 \\ SVM & 0.963 & 1.00 & 0.964 & 0.981 & 1.00 & 0.78 \\ KNN & 0.971 & 1.00 & 0.972 & 0.986 & 1.00 & 0.85 \\ NB & 0.988 & 1.00 & 0.989 & 0.994 & 1.00 & 0.92 \\ DT & 0.961 & 0.955 & 0.971 & 0.963 & 1.00 & 0.79 \\ RF & 0.992 & 1.00 & 0.993 & 0.997 & 1.00 & 0.93 \\ ANN & 0.931 & 0.9763 & 0.935 & 0.955 & 1.00 & 0.80 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance evaluation of classifiers.
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 6 Testbed and Performance Evaluation
### Performance Evaluation
#### 6.2.1 Scalability Analysis
Formally, this is defined as  
\[c=\sum_{i=1}^{N}\rho_{ij}. \tag{2}\]  
Figure 17 confirms that the most significant difference in terms of the rule installation time between the SRv6 and non-SRv6 approaches is given by the request interval. This is due to the fact that in the solution without SRv6, it is necessary to configure a much larger number of devices. The other components, transmission time, and processing time of a request are similar in both cases, as expected. In the previous experiments, we analyzed the scalability in terms of the rule installation time for a single client-server pair. We now consider the scalability in terms of the number of write operations to be performed, within a scenario with multiple client-server pairs. In our SRv6-based solution, the ONOS must configure only the ingress and egress edge routers, maintaining the number of write operations constant despite the depth of the core network. We define \(C\) as the number of clients, \(ES\) as the number of edge nodes receiving SRv6 rules to be installed, and \(EI\) as the number of edge nodes receiving IPv6 rules to be written. The number of operations to be performed in the SRv6-based case, \(SR\), for both the initial configuration and the migration update are the following:  
\[SR=C\times(ES+EI). \tag{3}\]  
In the case of the initial path allocation, both \(ES\) and \(EI\) are equal to 1. In the case of path migration, we have \(ES\) equal to 2 and \(EI\) equal to 1. As a result, we have a complexity in both cases of \(\mathcal{O}(C)\). Considering the non-SRv6 approach instead, all the core nodes must be configured in addition to the edge ones. The number of core layers in the latter case significantly impact the result. Exploiting the above variables and defining \(K\) as the number of core layers, the number of operations for the allocation procedure, \(noSR_{A}\), are the following:  
\[noSR_{A}=C\times(2\times K+2\times EI). \tag{4}\]  
The complexity of the path allocation procedure, using the non-SRv6 approach, is \(\mathcal{O}(K\times C)\). For what concerns the path migration procedure, identifying the number of core devices that must receive a rule update as \(\overline{K}\), the number of required write operations, \(noSR_{M}\), are defined as  
\[noSR_{M}=CU\times\big{(}\overline{K}+EI\big{)}. \tag{5}\]  
Figure 17: Worst-case network configuration overhead. Therefore, the complexity of the path migration procedure in the non-SRv6 scenario is \(\mathcal{O}\big{(}\overline{K}\times C\big{)}\).
# Optimizing Sustainable Energy Transitions in Small Isolated Grids Using Multi-Criteria Approaches
## 4 Results and Discussion  
As outlined in previous sections, the analysis conducted includes an initial simulation of the scenario prior to the installation of the Gorona del Viento renewable power station. In the second step, the current scenario is simulated, where for average resource years, approximately 50% of the generation comes from diesel groups and 50% from the renewable plant. In the final step, analyses are carried out that include a multivariate optimization process aimed at achieving the optimal combination of renewable generation. The main criteria considered in this optimization process are economic factors and the reduction in GHG emissions during the life cycle of the employed technologies, although other criteria such as land occupation, landscape pollution, restricted aerial or maritime use, and other existing restrictions have also been considered.  
Regarding economic criteria, a cost evaluation and financial feasibility analysis of the proposed generation mix will be conducted. In terms of GHG emission reduction, the decrease in CO\({}_{2}\) equivalent emissions achieved with the optimal generation mix will be measured. Finally, both criteria will be reduced to economic terms by estimating the costs per ton of GHG emitted during the life cycle of the installations, resulting in the \"optimal scenario\". In this way, the main stages between fully fossil fuel-based and fully renewable generation can be compared. Thus, the findings are applicable to various locations around the world, as most isolated systems are still largely or entirely fossil fuel-based, and the inevitable transition to renewable generation must be addressed or, in many cases, is already being addressed. For these purposes, the Results section is organized into several subsections that will be developed in the following pages.
## 3 Data-Driven Dynamic Design Model for Product Structure
### Dynamic Design Model Based on Bayesian Network  
#### 3.2.1 Structure of the Network Model  
In this study, in order to represent the dynamic evolution rules of product structure design, product requirements and design elements are first abstracted into nodes of the Bayesian network. Moreover, the structure of the Bayesian network model is determined by means of data training and domain expert modification. Firstly, the network structure is trained based on the historical data and network structure learning, which has been proved to be a NP-hard problem to date. The commonly used learning algorithms include \(K2\) (used in this paper), TANC, GS, HC, and so on [32]. Usually, the network measures the dependency relationships between nodes via mutual information. If the value of the mutual information is larger than a certain threshold, then there should be an edge between the nodes in the network. The calculation of the mutual information \(I(X_{1},\,X_{2})\) between two random nodes \(X_{1}\), \(X_{2}\) can be represented as follows.  
\[I(X_{1},X_{2})=\sum_{X_{1}X_{2}}P(X_{1},X_{2})\log\frac{P(X_{1})P(X_{2})}{P(X_ {1},X_{2})} \tag{2}\]  
The structure learning of a Bayesian network is to find a Directed Acyclic Graph (DAG) to explain the data set in the best way under the circumstance of data completeness. The principle is shown below.  
The training dataset is denoted as \(D\) and the hypothesis of the network structure is denoted as \(S^{h}\). Thus, the process of finding out the best structure for data set \(D\) is essentially locating the maximum value of \(P(S^{h}\,|\,D)\). According to Bayesian theory, the calculation can be denoted as follows:\[P(S^{h}|D)=\frac{P(S^{h},D)}{P(D)}=\frac{P(D\Big{|}S^{h})P(S^{h})}{P(D)} \tag{3}\]  
where \(P(D)\) is a constant that has nothing to do with the network. \(P(D\,|\,S^{h})\) represents the boundary likelihood of the network. \(P(S^{h})\) is also a constant that represents the prior probability of the network. Consequently, determining the posterior distribution of the network structure is equivalent to the calculation of the data process hypothesis for every potential structure.  
In order to ensure the established network structure can not only be objectivity learned but also reflect the special regularities of the dynamic evolution of product structure, we need to consider factors such as improvement of market demand, the progress of technology as well as the external environment, which all have an impact on the product structure design.  
What is more, the prior knowledge of the experts in the product design domain should be fully considered in learning the Bayesian Network structure, including dependency and probability of the nodes, leading to a network structure suitable for network reasoning and the interrogation of the learned model. A typical network analysis model is revealed in Figure 3, which can be used to calculate the joint probabilities of a variety of events given the parametric network model.
## 4 Performance Analysis in the Absence of Non-Idealities
### Numerical Results
Indeed, the SCCC is shown to achieve a feasible SNR region larger than those of the LDPC codes and, therefore, it can exploit better the potential benefits brought by the use of the unbalanced SNR power control strategy, which is designed for an ideal scenario. In Figure 7, the outage probability is shown, as a function of the average SNR at the sources, considering (a) regular LDPC-coded schemes, (b) DD LDPC-coded schemes, and (c) SCCCed schemes. The performance in the presence of the balanced SNR power control is compared with that associated to the absence of power control. Considerations similar to those carried out for the average BER still hold. In this case, however, a significant difference can be highlighted with respect to the performance in terms of average BER. In fact, the outage probability increases when the number of sources \(n\) increases (e.g., from 2 to 4). This is due to the fact that when the number of sources and, consequently, of transmitted packets increases, it is more likely that at least a bit is in error. On the other hand, if the outage probability is the performance metric of interest, the beneficial presence of a-priori information from the other (less faded) links is less noticeable than in the BER-based analysis, and the worst link dominates. Figure 5: Average BER, as a function of the average SNR at the sources, considering: (a) regular LDPC-coded schemes, (b) DD LDPC-coded schemes, and (c) SCCCed schemes. The performance in the presence of the balanced SNR power control strategy is compared with that associated to the absence of power control. In Figure 8, the outage probability is shown, as a function of the average SNR at the sources, considering (a) regular LDPC-coded schemes, (b) DD LDPC-coded schemes, and (c) SCCCed schemes. Both balanced SNR and unbalanced SNR power control strategies are considered. In this case as well, SCCC-based schemes exploit better the gain brought by the use of unbalanced SNR power control with respect to the schemes with balanced SNR power control. However, when an LDPC-based scheme is considered, this gain drastically reduces and it may happen that the unbalanced SNR power control strategy leads to a slightly worse performance.
# Numerical Investigation on NOx Emission of a Hydrogen-Fuelled Dual-Cylinder Free-Piston Engine
## 3 Simulation Results
### Regulation between NOx Emission and Ignition Advance Timing  
Figure 17 shows the relationship between NOx production and ignition advance timing (which is converted from the corresponding ignition advance angle). From Figure 17: when \(\varphi<0.5\), there's no apparent effect brought by the ignition advance time on NOx production, when \(\varphi>0.5\), NOx production rises with the enlargement of the ignition advance time, and the range sharply increases with the increase of \(\varphi\).  
Figure 16: Highest mean in-cylinder temperature with equivalent ratio.  
Figure 17: NOx production changes with ignition advance timing.  
Figure 18 shows relationships between the combustion duration and CA50 and the ignition advance angle, where CA50 represents the specific time of 50% heat release. As shown in Figure 18, for different equivalent ratios, the combustion duration shortens with an increase in ignition advance timing. In addition, because of the increased combustion speed of hydrogen, the CA50 point becomes closer to TDC. When \(\varphi=0.43\), the effect of ignition advance timing on the combustion duration is greater than that of \(\varphi=0.59\), which means that the leaner the mixture is, the greater the contribution of ignition advance timing to shortening the combustion duration would be.  
Figure 18: CA50 changes with the combustion duration.  
Under a fixed engine frequency and condition of \(\varphi<1\), temperature is the major factor that affects NOx generation. According to Figure 19, the highest in-cylinder temperature increases with the increase in ignition advance timing, and when \(\varphi=0.43\), with the increase in ignition advance timing, the average highest in-cylinder temperature increases from the stagnation temperature of NOx production to a higher level, and in the meantime, the highest in-cylinder temperature reaches 1986 K; thus, the ignition advance timing contributes relatively little to NOx production for lean mixture. When \(\varphi=0.59\), the average highest in-cylinder temperature increases from 2119 K to 2349 K with the increase in ignition advance timing, a slight increase in temperature would cause an exponential increase in NOx production within this temperature range.