esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 5 Intrusion Detection in Fog, Edge, and Cloud Computing
### Applications of Fog Computing in IoT
#### 5.3.1 Smart Home  
The innovation of Smart Homes utilizes an array of devices with censored connections. However, they use an array of devices which all have different platforms, making integration complex. The technology can provide an interface that is unified, integrating many devices (that are independent) and benefiting applications for the Smart Home with resources (that are flexible), enabling storage, real-time processing, and low latency. In the work of [PERSON] and [PERSON] [40], the authors present a framework for hierarchical IoT. The three tiers of the framework are 'Smart Home', 'Nanogrid', 'Microgrid'. The work aims to prove that fog computing is a suitable solution for reducing traffic on the network. The research addresses the use in the Smart Home environment--the framework proposes to expand the Smart Home to a microgrid level by integrating many renewable energy sources from the microgrid and achieving a greater energy optimization. In the research, the authors used a simulation of a real dataset and highlighted that Fog Computing based on predictive filters could mitigate the quantity of transmissions and lower Smart Home network energy consumption.
# Numerical Study of Internal Flow Field in a Disc Stack Centrifuge Based on Mixture-PBM Model
## 3 Results and Discussion
### Effect of Ribs on the Distribution of Flow Field Parameters  
Cross-sections made at positions 1-1, 2-2, and 3-3 are shown in Figure 8, where ribs are at positions PS and SS, with PS representing the pressure surface and SS representing the suction surface. u represents the rotational velocity of the liquid in m/s; c represents the absolute velocity of the liquid, i.e., the velocity in the stationary coordinate system, in m/s; and w represents the relative velocity of the liquid, i.e., the velocity in the rotational coordinate system, in m/s.  
To verify the reliability of the numerical simulation, the separation channel of the middle layer is selected, and the pressure distribution at the cross-section position of the separation channel 2-2 is compared. The numerical simulation results and theoretical calculations at the 50% height position of the separation channel are shown in Figure 9.  
The curve is approximately parabolic; as the radius increases, the pressure also increases, and the pressure gradient gets bigger and bigger, which satisfies the pressure distribution law of the centrifugal hydraulic pressure formula (Equation (4)). The absolute pressure at the smallest radius is 0.53 MPa, and the absolute pressure at the largest radius is 5.65 MPa.  
From Equation (4), it can be seen that the circumferential velocity of the fluid has a great influence on the pressure field. Due to the presence of ribs in the disc (PS and SS in Figure 8), the flow hysteresis is reduced. So, the difference between the theoretical results and the numerical simulation results is not big, and the numerical simulation results can  
Figure 8: Schematic diagram of the cross-section location of the separation channel.  
reach 98% of the theoretical results. The numerical simulation results of [PERSON] et al. [30] are only 73% of the theoretical results due to neglecting the role of ribs.  
The circumferential velocity is a measure of the speed of rotation, and the theoretical circumferential velocity can be calculated by the formula. The definition of the formula for circumferential velocity is as follows:  
\[V_{c}=\omega\cdot r \tag{7}\]  
In the equation, \(\omega\) is the rotational angular velocity, the unit is \(\mathrm{rad}/\mathrm{s}\); and \(r\) is the radius at any position, the unit is \(\mathrm{m}\).  
Selecting the separation channel of the middle layer and comparing the circumferential velocities at the cross-section position of separation channel 2-2, the numerical simulation results and theoretical calculations at the 50% height position of the separation channel are shown in Figure 10. The curve is linearly distributed; with the radius increasing, the circumferential velocity increases, the circumferential velocity gradient is unchanged, and the numerical simulation results are consistent with the distribution law of the theoretical results.  
Figure 10: Circumferential velocity at any radius position.  
Figure 9: Centrifugal hydraulic pressure distribution at any radius position.
## 3 SRB Activities in Acidic Environments
### Response of SRB to Acidic Environments
#### 3.1.3 Change in Metabolic Pathway  
SRB was found to have flexible metabolic pathway to survive and grow in extreme environments. In acidic environments, gene expression that related to sulphate reduction of _D. vulgaris_, e.g., _DVU0499_ and _sat_ was downregulated as decreasing pH [35]. _DVU0499_ is a hypothetical protein that is predicted to carry out sulphate reduction of _D. vulgaris_[35]. ATP sulfurylase which is encoded by _sat_ gene plays an essential role in converting free sulphate into APS in dissimilatory sulphate reduction [56]. The downregulation of these gene expressions indicated that the sulphate reduction of _D. vulgaris_ was limited. Hence, bacteria need to adjust their metabolic strategy to reduce energy consumption and increase metabolic efficiency to enhance their survival and growth [35]. Indeed, sulphate reduction is not the only pathway to obtain energy of SRB as in sulphate-free environments, SRB can develop functions to perform sulphate-free metabolism which is described in previous section. Noticeably, _D. vulgaris_ showed gene expression of _DVU1556_, which is related to methyltransferase, was upregulated. Methyltransferases are enzymes which transfer methyl groups and result in methylation. Methylation was reported to initially evolved in protein activities in microbes to adapt to metabolic milieu [57; 58]. Hence, methylation could possibly play an essential role in adapting to extreme environments of SRB. Additionally, the gene expression which is related to energy metabolism, e.g., _atpA_, \(B\), \(H\) was upregulated which suggests that the increasing in metabolic efficiency to enhance survival and growth at low pH conditions [35]. These expressions of genes in acidic condition were similar to electron donor response of _D. vulgaris_.
## 5 Experimental Results and Analysis
### Experiment 3: POM-PSASIDEA Algorithm Performance Analysis  
In this section, we present how the proposed POM-PSASIDEA algorithm was judged against the AGDE-MPP algorithm [44] (Adaptive Guided Differential Evolution Algorithm on Mutation, Parameter, and Population), A-MPMO algorithm [45] (Adaptive Strategy with A Multi-population Multi-objective Algorithm), APSDE [46] (Adaptive Parameter and Strategy with Differential Evolution Algorithm), ADECSA [47] (Adaptive Clonal Selection Algorithm with Multiple Differential Evolution Strategies), and SLPS-ADE [48] (Sawtooth Linear Population Size-based Adaptive Differential Evolution) to compare the performance levels of these five algorithms. A detailed description of the algorithms is given in Appendix B, which includes the main characteristics and applicable scenarios of each algorithm. The POM-PSASIDEA, AGDE-MPP, A-MPMO, ADECSA, and SLPS-ADE algorithms were applied for the MSIMPLTS problem in the local and global regions, with the number of target tasks set as 100, 150, 200, 250, 300, and 350, and the results of the experiments are shown in Table 5 and Figure 11.
# Q8S: Emulation of Heterogeneous Kubernetes Clusters Using QEMU
## 2 Materials and Methods
### Design Validation  
In order to evaluate Q8S, the functional requirements need to be checked. Furthermore, to understand the overheads introduced by the emulation layer, we employ k8s-bench-suite [21] version 1.5.0 to measure network communication, CPU usage, and RAM usage.  
Specifically we measured pod-to-pod and pod-to-service communication throughput and latency as well as CPU and memory usage while the benchmark ran. All measurements were taken using k8s-bench-suite except for latency, which was measured with the ping command.  
To establish a baseline, the experiment was run with emulated worker nodes and worker nodes running directly on the OpenStack hosts. To ensure that the results would be comparable, we configured QEMU to emulate the same CPU as was installed in the OpenStack nodes. In our OpenStack environment, this was the AMD EPYC Processor with BPB. Moreover, to ensure that both the emulated node and the OpenStack node had the same quantities of resources available, we set the QEMU VM to only use 2 CPUs and 2 GB RAM on host nodes with 8 CPUs and 8 GB RAM, while setting the OpenStack nodes to a flavor with 2 CPUs and 2 GB RAM.
# The Importance of Modeling Path Choice Behavior in the Vehicle Routing Problem
## 3 Path Choice Problem and Assignment (Behavioral Constraint)
### Definitions and Notations
#### 3.1.1 Path Choice  
In a transport system, given a user n (n is not shown below for simplicity) between two nodes u and v of the path, for the user and for the analyst, the following assumptions are considered in this manuscript.  
Users:  
* Perceive a set (\(\mathbf{I}_{\text{uv}}\)) of alternatives (k); \(\mathbf{I}_{\text{uv}}\) = {...,..., k,... } is the set of paths perceived by the decision-maker (system manager or driver) connecting nodes u and v; the entry k of the set \(\mathbf{I}_{\text{uv}}\) is a perceived path;Associate a perceived utility (u\({}_{\text{k}}\)) to each perceived alternative k; the perceived function (\(v_{\text{k}}\)) depends on its functional specification, on a vector of known or estimated quantitative attributes of the alternative (x\({}_{\text{k}}\)) and on a vector of unknown parameters (\(\theta_{\text{k}}\)) to be calibrated u\({}_{\text{k}}\) = \(v_{\text{k}}\)(x\({}_{\text{k}}\), \(\theta_{\text{k}}\));  
Analysts:  
Hypothesize that users' choice among perceived paths is modelled by discrete choice and utility theories, and they evaluate the perceived choice rate vector p\({}_{\text{uv}}\) = [...,..., p\({}_{\text{k}}\),... ] connecting all pairs of nodes u and v as a function of the perceived utility u\({}_{\text{k}}\) = \(v_{\text{k}}\)(x\({}_{\text{k}}\), \(\theta_{\text{k}}\)) associated to any path;  
The perceived choice rate depends on the vector of unknown parameters (\(\theta_{\text{k}}\)) to be estimated starting from the data observed with a disaggregated approach (interviews with a sample of users [27], in the state and in the revealed scenarios) or aggregated (traffic flow [27] and web sentiment [28] in the state scenario) by adopting a classic (i.e., maximum likelihood [27]) or Bayesian [28; 29] estimator.
# Random forest Algorithm for the Classification of Spectral Data of Astronomical Objects
## 3 Results and Discussion
### Results and Comparative Analysis for the SDSS DR16 Dataset  
As previously specified in Section 2.1, the Sloan digital sky survey DR 16 dataset contains 51,323 observations of the galaxy class (GALAXY), 10,581 of the quasar class (QSO), and 38,096 of the star class (STAR). The Imbalance ratio is \(IR=4.85\). Figure 6 shows the corresponding confusion matrix:  
If we consider the case GALAXY as positive, the values of the four performance measures: accuracy, sensitivity, specificity, and balanced accuracy are calculated based on the values in the confusion matrix. For comparative purposes, the values of these four performance measures are included in Table 3.  
In this case, when applying the proposed random forest algorithm on the SDSS DR16 dataset, the results in three of the four performance measures surpass the other classifiers. The exception is the performance of the MLP algorithm in sensitivity, which outperforms the proposed random forest algorithm. This means that the MLP algorithm is better than the proposed random forest algorithm in detecting the other two classes that are not GALAXY, which are QSO and STAR.  
Despite the above, it still holds that despite the high-class imbalance in the SDSS DR16 dataset, the proposed random forest algorithm outperforms all others in both performance measures: accuracy and balanced accuracy.  
These results are shown graphically in the bar chart of Figure 7 for balanced accuracy.  
Figure 6: Confusion matrix resulting from applying the proposed random forest algorithm on the SDSS DR16 dataset.  
Figure 7: Comparative bar chart of balanced accuracy values resulting from applying the six classification algorithms on the SDSS DR16 dataset. The proposed random forest algorithm is represented in pink.
## 5 Packet Length Control for Sparse Signal Transmission  
Considering the unit of wireless communication is packet and packet length control in a traditional communication system has brought remarkable performance promotion, we analyze the packet length control for sparse signal transmission in this section. Supposing the sampling precision is 8 bits, therefore the number of samples in one packet is the payload length.
# Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm
## 1 Introduction
### Paper Contributions  
In order to solve the above repeated search problems, we introduce TS to enhance the RA.  
The main contributions of this paper can be summarized into three aspects: (1) The appropriate definitions of neighborhood structure, tabu table, aspiration criterion and stopping criterion involved in the TS-based RA are provided; (2) Based on the inherent clustered structure of beam space, design schemes of direction boundary and index boundary are developed. The former guarantees the continuity of the index at the direction boundary, and the latter guarantees the continuity of the search direction at the index boundary; and (3) Inspired by the TS algorithm, an enhanced RA based on the tabu table is developed to solve the problem of repeated search, which is caused by rounding to the integer to get the beam index. The enhanced algorithm is proved by both theoretical analysis and experimental simulations. The results show that compared with the existing popular technologies, the proposed technology can considerably improve search success percentage and reduce the energy consumption during the beam search. The simulation results also show that this technology can achieve near optimal performance.  
The paper is organized as follows. Section 2 will give a complete BF system model. Subsequently, the beam training based on RA is developed in Section 3. Then, the RA based on SA is introduced in Section 4. Section 5 is devoted to the enhanced RA based on tabu table. Experimental simulation and performance evaluation are given in Section 6. Finally, we conclude this article.
## 4 Experimental Results and Discussion
### Bubble Dynamics  
Bubble rise velocity and the bubble size depend upon the bubble dynamics within the fluidized bed for each distributor plate. The larger openings of distributor plates tend to produce larger bubbles than smaller holes of distributor plates--three types of distributor plates used in this study have different holes and slot sizes. The perforated distributor plate has numerous holes through which smaller bubbles are formed. As they travel up the column, they tend to coalesce and collapse after interacting with the emulsion phase, forming relatively larger and intermediate bubbles. In a 45\({}^{\circ}\) slotted plate, as the opening of slots is larger compared to holes, larger bubbles are produced, and after amalgamating, they form even larger bubbles. Due to the larger surface area, the drag force they experience during their upward travel is also higher, so they move slower than smaller bubbles. The absolute bubble rise velocity for each distributor plate differs from the ratio of minimum  
Figure 8: Effect of particle size on bed pressure drop with different distributor plates.  
bubble rise velocity (U\({}_{mft,b}\)) and minimum fluidization velocity (U\({}_{mft,l}\)) due to different volumetric flow rates of fluidizing air to achieve an incipient fluidized state. The ratio of U\({}_{mft,b}\) to U\({}_{mft,f}\) indicates the bubble dynamics, which depicts bubbles' early or delayed approach to the bed's surface.
# VSFCM: A Novel Viewpoint-Driven Subspace Fuzzy C-Means Algorithm
###### Abstract  
Nowadays, most fuzzy clustering algorithms are sensitive to the initialization results of clustering algorithms and have a weak ability to handle high-dimensional data. To solve these problems, we developed the viewpoint-driven subspace fuzzy c-means (VSFCM) algorithm. Firstly, we propose a new cut-off distance. Based on this, we establish the cut-off distance-induced clustering initialization (CDCI) method and use it as a new strategy for cluster center initialization and viewpoint selection. Secondly, by taking the viewpoint obtained by CDCI as the entry point of knowledge, a new fuzzy clustering strategy driven by knowledge and data is formed. Based upon these, we put forward the VSFCM algorithm combined with viewpoints, separation terms, and subspace fuzzy feature weights. Moreover, compared with the symmetric weights obtained by other subspace clustering algorithms, the weights of the VSFCM algorithm exhibit significant asymmetry. That is, they assign greater weights to features that contribute more, which is validated on the artificial dataset DATA2 in the experimental section. The experimental results compared with multiple advanced clustering algorithms on the three types of datasets validate that the proposed VSFCM algorithm has the best performance in five indicators. It is demonstrated that the initialization method CDCI is more effective, the feature weight allocation of VSFCM is more consistent with the asymmetry of experimental data, and it can achieve better convergence speed while displaying better clustering efficiency.  
fuzzy clustering; fuzzy c-means; cluster center initialization; machine learning; image processing; fuzzy sets +
Footnote †: journal: Applied Sciences  
0
Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 2 Methodology
### Extracting \(t_{A}\)  
According to the working mechanism of spaceborne SAR, while the satellite is in work, the chirp signal is transmitted at a fixed pulse repetition frequency (PRF) while the ground receiver receives the SAR chirp signal. Since the distance between the satellite and the ground receiver is constantly changing, there exists range migration effect. Pulse signals that are transmitted at equal intervals will not be equally spaced when received at the ground receiver. The changes in the interval of the received pulse signal reflect the changes in the spatial relationship of the satellite and the ground receiver. Figure 4 shows this process. It can be seen that at the time \(t_{A}\) the satellite is right over-the-top  
Figure 4: The situation where the squint angle of the antenna beam is not 0. R denotes the distance between the satellite and the ground receiver.
## 3 Modeling Method Considering Uncertainty
### Model Extension Based on Interval Analytic Redundancy  
Taking the resistive element as an example, for any pressure variable \(p\):  
\[p=(R_{n}+\Delta R)(SSQ_{M}+\zeta_{SSQ})=R_{n}SSQ_{M}+[(R_{n}+\Delta R)\zeta_{SSQ }+\Delta R\cdot SSQ_{M}] \tag{6}\]  
Therefore, \(p_{u}=[(R_{n}+\Delta R)\zeta_{SSQ}+\Delta R\cdot SSQ_{M}]\) is defined as an uncertainty term and \(R_{n}SSQ_{M}\) is the residual term without considering the uncertainty.  
Traditional analytical redundancy relations (ARRs) [42] are a constraint relationship obtained from a system model based on energy conservation rules.  
\[\left\{\begin{array}{l}\text{ARR}_{i}=f_{i}(k_{i},\theta_{i})\quad i=1, \cdots,n\\ \text{ARR}_{i}=[\text{ARR}_{1},\text{ARR}_{2},\cdots,\text{ARR}_{n}]\\ r_{i}=\text{ARR}_{i}=0\end{array}\right. \tag{7}\]  
where \(i\) is the number of the ARR, \(k_{i}\) is the system variable, \(\theta_{i}\) is the system parameter, and \(f_{i}\) is a function of the \(k_{i}\) and \(\theta_{i}\). \(r_{i}\) is the residual generated by the system node.  
After introducing the theory of interval analytic redundancy, the traditional analytic redundancy is extended. \([\delta_{\theta}]=[-\Delta\theta_{l}/\theta_{n},\Delta\theta_{r}/\theta_{n}]\) is defined as the relative uncertainty of parameter interval. For parameter uncertainty:  
\[\left[\theta,\overline{\theta}\right]=[\theta_{n}-\Delta\theta_{l},\theta_{n }+\Delta\theta_{r}]=\theta_{n}(1+[\delta_{\theta}]) \tag{8}\]  
where \(\overline{\theta}\) and \(\theta\) are the upper and lower limits of parameters, respectively. \(-\Delta\theta\) and \(\Delta\theta_{r}\) are the left and right extreme values of parameter uncertainty respectively.  
For measurement uncertainty:  
\[\left[S_{T},\overline{S_{T}}\right]=[S_{M}-\Delta S_{l},S_{M}+\Delta S_{r}]=S _{M}+[\xi_{S}] \tag{9}\]  
where \(\overline{S_{T}}\) and \(S_{T}\) are the upper and lower limits of true value. Respectively. \(-\Delta S_{l}\) and \(\Delta S_{r}\) are the left and right extreme values of measurement uncertainty, respectively.  
Therefore, for pressure variable \(p\), the expanded system level interval analytical redundancy relations are:  
\[p=\left[R,\overline{R}\right]\cdot\left[SSQ_{T},\overline{SSQ_{T}}\right]=R_{ n}SSQ_{M}+(R_{n}SSQ_{M}[\delta_{R}]+[R_{n}(1+[\delta_{R}])]\cdot[-\Delta S_{l}, \Delta S_{r}]) \tag{10}\]  
At this time, the uncertainty is expanded to \((R_{n}SSQ_{M}[\delta_{R}]+[R_{n}(1+[\delta_{R}])]\cdot[-\Delta S_{l},\Delta S_{ r}])\). \(R_{n}SSQ_{M}\) is the residual term without considering the uncertainty.  
As shown in Figure 13, the diagnostic bond graph for the uncertain landing gear R/E hydraulic system is established after considering the above uncertainties and negative factors.