esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Analysis of PODESCA  
When developing a new evolutionary algorithm, it is essential to analyze its performance by solving some of the most important benchmark test functions (BTFs). Therefore, ten BTFs are utilized in this paper in order to study PODESCA's performance. Those BTFs are: \(f_{1}\): [PERSON], \(f_{2}\): [PERSON], \(f_{3}\): [PERSON], \(f_{4}\): [PERSON], \(f_{5}\): [PERSON] \(0,d,\beta\), \(f_{6}\): Sum squares, \(f_{7}\): Rotated hyper-ellipsoid, \(f_{8}\): Power sum, \(f_{9}\): Rosenbrock, and \(f_{10}\): Dixon-Price, where their detailed formulas and all needed data can be found in [42; 44]. The proposed algorithm's results are also compared with those from six other novel algorithms; PSO, GA, ABC, firefly algorithm (FA), DE, and SCA. The number of evaluations for all algorithms was set to 40,000. All the BTFs were minimized so that their optimal values were zero. The obtained results are illustrated in Table 2.  
Table 2 includes the minimum (Min.), maximum (Max.), mean, and standard deviation (SD) for each function. As is remarked from Table 2 for most of the BTFs, PODESCA's results are much better than those from the other algorithms. This validates the effectiveness of the proposed algorithm.  
**Remark 1**.: _The QOT has been employed to enhance the exploration and search ability of some algorithms by generating their initial populations as in [40; 41; 42]. However, this concept is applied at the initialization stage as well as inside the main loop of the proposed PODESCA in this paper._  
**Remark 2**.: _The proposed PODESCA applies two main global optimization frameworks, namely the DE and an enhanced version of SCA algorithm. Compared to the original DE algorithm previously applied for balanced systems [30], the implementation of PODESCA insures the convergence towards the optimum rapidly and reliably. The combined techniques also keep the elite solutions in each generation which guarantees their flexible flow to the optimal region inside the search space. The developed paradigm combining the above superior features is also suitable to be utilized in various engineering applications._  
**Remark 3**.: _The proposed SBDMT exploits four well-known SBIs' abilities to identify the weak buses by merging their values using TOPSIS. This technique helps extensively and effectively reducing the OPSC problem's search space while keeping the most potential buses suitable for SC placement, unlike other methods which utilize only one or two SBIs to reduce the search space and thus some critical buses are excluded from the search space, as in [26; 28; 30; 31; 32; 33; 34; 35]._
## 1 Introduction
Due to the simplicity and economic advantages of the test, it is increasingly being used to estimate the cutter consumption in many mining and tunneling machines [12; 13; 14; 15; 16; 25]. Previous research has attempted to determine the relationships between the parameters (i.e., cutter force, specific energy, and wear index/abrasiveness of rock) and the brittleness of rock. It has been found that the brittleness indices (e.g., B1, B2, and B3) have fine relationships with specific energy; however, in some cases, no significant relationship has been found between the brittleness of rock and the rock-cutting parameters. Overall, it may be accepted that the rock-cutting parameters are correlated with the brittleness of rock; however, conflicting results have been found when the different brittleness indices have been used [1; 2; 8; 26; 27; 28]. While it is believed that the relationships between the parameters and the rock brittleness depend on the rock types, cutting tools, and machine type, the research database needs to be extended for better understanding of these relationships to be obtained. \begin{table}
\begin{tabular}{c c} \hline \hline
**Definition of Rock Brittleness** & **References** \\ \hline \(\text{B}_{1}=\sigma_{c}/\sigma_{t}\) & [7] \\ \(\text{B}_{2}=(\sigma_{c}-\sigma_{t})/(\sigma_{t}+\sigma_{c})\) & [7] \\ \(\text{B}_{3}=(\sigma_{t}+\sigma_{c})/2\) & [9] \\ \(\text{B}_{4}=(\sigma_{c}<\sigma_{t})/2\) & [1] \\ \(\text{B}_{5}=\sqrt{(\sigma_{c}\times\sigma_{t})/2}\) & [8] \\ \hline \hline \end{tabular} \(\sigma_{c}\) and \(\sigma_{t}\) were uniaxial compressive strength and Brazilian tensile strength of rock, respectively.  
\end{table} 
Table 1: Different definitions of rock brittleness based on rock strengths in previous studies (modified from [6]). To contribute to the literature, this study summarized the linear cutting machine tests and Cerchar abrasiveness tests that have been conducted for the Korean rock type. Using the research database, the study attempted to find empirical correlations between the rock brittleness and cutting performance (i.e., cutter force and specific energy) and the abrasivity of rock. In particular, the study investigated the relationships between the different rock brittleness concepts, cutter force, specific energy, s/p ratio, and CAI, which are importation design parameters in TBM tunneling. Finally, some prediction models for cutter forces, specific energy, and CAI were proposed as a function of the brittleness of rock.
## 4 Head Node Checkpointing
### Energy Consumption Model
#### 4.3.3 The Optimal Checkpointing Interval  
Figure 8 shows the time line of the clustering protocols that use TDMA-based scheduling. Each round is composed of a setup stage and a steady stage. All clusters are formed during the setup stage, and data transmission occurs during the steady stage. A steady stage comprises several frames, and each frame is divided into time slots that are assigned to each member node so as to send data to a head node without communication interference. There is some guard time (_GT:_\(\delta\)) in order to avoid acoustic collisions at the head node when two member nodes using adjacent time slots send their data message [22]. In this scheme, the head node can calculate the delay and distance to each member node through the time-of-arrival approach that measures the round-trip time of an acoustic signal between the head node and a member node [19].  
If the failure rate of each node is \(\lambda\), we define \(\mathrm{e}^{-\lambda\,T}\), as there is no failure of each node during the total data collection time from all member nodes (\(T\)). Under this condition, the probability of failure is \(P_{k}=\left(\mathrm{e}^{-\lambda\,T}\right)^{k-1}(1-\mathrm{e}^{-\lambda\,T})\) when the head node gathers data from the \(k_{\mathrm{th}}\) node. In underwater environments the energy consumption of data transmission between two nodes is defined by Equation (7):  
\[E_{\mathrm{tx}}=S\ \times T_{\mathrm{tx}}\times K,S=2\pi\times H\times I \tag{7}\]  
To compare the energy consumption of our checkpointing scheme with that of an existing scheme, we define \(E_{\mathrm{pre}}\) and \(E_{\mathrm{ckpt}}\) as in Equations (8) and (9), respectively:  
\[E_{\mathrm{pre}}=\ \sum_{k=0}^{N-1}\{(1-P_{k})\times E_{\mathrm{tx}}+P_{k} \times E_{\mathrm{elec}}\},\,E_{\mathrm{elec}}=\{\ (N-1)^{2}+2(N-1)\}\times E_{\mathrm{tx}} \tag{8}\]\[\mathrm{E_{ckpt}}=\sum_{k=0}^{N-1}\{(1-P_{k})\times E_{tx}+P_{k}\times(N-1)\ \times E_{tx}\}\ +\ n\times\left[\frac{k}{1_{ckpt}}\right]\times E_{tx} \tag{9}\]  
In the previous clustering routing protocol, which does not support the checkpointing mechanism, when a head node fails, this causes the election of a new head node which broadcasts the remaining energy notification messages, finds the member nodes and constructs a routing table [4].  
If a head node fails at the \(k_{th}\) step, the difference between the energy consumption of the previous scheme and that of the proposed scheme is defined by Equation (10), as we assume that a head node failure occurs only once per round (\(T\)):  
\[P_{k}\times E_{\mathrm{elec}}\geq\ P_{k}\times(N-1)+\ n\times\left[\frac{k}{1_ {ckpt}}\right],I_{\mathrm{ckpt}}>0 \tag{10}\]  
The optimal checkpointing interval is derived through Equation (11). The minimum value of I\({}_{\mathrm{ckpt}}\) that satisfies this condition is optimal.  
\[I_{\mathrm{ckpt}}\geq\frac{nk}{(N-1)^{2}+\ (N-1)} \tag{11}\]
# Application of Thin-Film Microextraction to Analyze Volatile Metabolites in A549 Cancer Cells
###### Abstract  
Volatile organic compounds (VOCs) have been proposed in the last two decades as biomarkers for disease detection and therapeutic monitoring. Model in vitro experiments with established cell lines are fundamental to clarify whether given VOCs originate from normal human cells or pathogens, including transformed cancer cells. Due to the trace concentrations of target metabolites, adsorptive enrichment is needed before gas chromatography-mass spectrometry (GC-MS) analysis, with solid-phase microextraction (SPME) being perfectly suited for this purpose. Here, a modification of SPME, the thin-film microextraction (TFME) technique, is proposed for analysis of cellular VOCs, which utilizes a planar mesh coated with stationary phase to increase the extraction phase volume and active surface area. In this study, four different adsorbents were compared: carboxen, divinylbenzene, hydrophobic- lipophilic balanced and polydimethylsiloxane. Amongst them, HLB sheets using poly(divinylbenzene-co-N-vinyl-pyrrolidone) skeleton structure proved to be the most versatile, enabling the most sensitive analysis of VOCs with a broad polarity and volatility. For HLB, sampling type (internal static headspace, external bi-directional headspace), extraction temperature and extraction time were also examined. An established method was successfully applied to analyze metabolites produced by A549 cells revealing five volatiles at significantly higher (additionally benzaldehyde at lower) levels in cell culture medium compared to the cell-free reference medium headspace.  
TFME; SPME; volatile metabolites; VOCs; A549; cancer cells; in vitro models; cellular metabolites; extraction techniques; GC-MS +
Footnote †: journal: Metabolites
## 3 Results  
### Classification of LULC and Assessing Its Accuracy  
Using the subset of the Landsat-5 (TM), Landsat-7 (ETM), and Landsat-8 (OLI) images, the LULC maps were independently generated by applying the maximum likelihood classification approach. The classified maps are displayed in Figure 3a-d. Throughout the entire study period (1990-2020), the transformation from vegetation cover and bare land to urban was dominant.  
The overall accuracy of the final LULC maps extended from 90.2% to 95.9%, and the user's and producer's accuracy ranged from 68% to 100% (Table 2). The values of the omission and commission errors ranged from a min. value of 0 for both to max. values of 32 and 15, respectively (Table 2). Generally, these accuracies indicated a significant correlation amongst the LULC maps and the reference points, indicating the reliability of the classification method used and the obtained results.
## 3 Research Methodology
### Research Questions  
In this subsection, research questions (RQs) are defined. Recalling that the aim of our work is to understand the main components used in typical ACN systems and to assess the security of these architectures, three questions have been formulated.  
* **RQ1**: Which are the components used for creating a typical ACN system?
* **RQ2**: Which are the typical communication means used in the ACN system for connecting components?
* **RQ3**: Which are the external services used in the ACN system?  
On the one hand, it seems that ACN systems involve only a few components; on the other hand, the possible combination makes room for a large number of threats. The authors of this paper strongly believe that the automotive industry is an incremental field where more systems are included in new vehicles.
# Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks
###### Abstract  
The recent development and adoption of unmanned aerial vehicles (UAVs) is due to its wide variety of applications in public and private sector from parcel delivery to wildlife conservation. The integration of UAVs, 5G, and satellite technologies has prompted telecommunication networks to evolve to provide higher-quality and more stable service to remote areas. However, security concerns with UAVs are growing as UAV nodes are becoming attractive targets for cyberattacks due to enormously growing volumes and poor and weak inbuilt security. In this paper, we propose a UAV- and satellite-based 5G-network security model that can harness machine learning to effectively detect of vulnerabilities and cyberattacks. The solution is divided into two main parts: the model creation for intrusion detection using various machine learning (ML) algorithms and the implementation of ML-based model into terrestrial or satellite gateways. The system identifies various attack types using realistic CSE-CIC IDS-2018 network datasets published by Canadian Establishment for Cybersecurity (CIC). It consists of seven different types of new and contemporary attack types. This paper demonstrates that ML algorithms can be used to classify benign or malicious packets in UAV networks to enhance security. Finally, the tested ML algorithms are compared for effectiveness in terms of accuracy rate, precision, recall, F1-score, and false-negative rate. The decision tree algorithm performed well by obtaining a maximum accuracy rate of 99.99% and a minimum false negative rate of 0% in detecting various attacks as compared to all other types of ML classifiers.  
U  
1
###### Abstract
The analytical expression presented is the same as in [13] and as the one presented here later on, specifically in Equation (27). There seems to be agreement between the numerical and analytical results presented in Figure 1 of [14], but a few numbers of points were used, and no error bars were displayed. In this paper, the discrepancy pointed out here earlier was not mentioned. The analytical expressions derived here are validated in a wide range of signal and data acquisition parameters using numerically generated data and a Monte Carlo type procedure. Even with the numerical validation carried out, there are some important questions that should be posed, namely how well the assumptions introduced in the mathematical derivations cover the range of cases that one might find in practice using data from the real world. One may ask, for example, does the non-linearity of the data acquisition module affect the results obtained? Is the independence between the signal and the type of noise considered valid? Have all the relevant factors been accounted for in the numerical simulations carried out? To answer these questions, it is important to carry out, also, some validation using real data, which is also performed here. Specifically, the voltage samples acquired and digitized with a data acquisition module are going to be used. The values obtained will be used with a least squares fitting procedure to estimate the underlying sinewave amplitude. The experimental setup used in this research comprises a data acquisition module and two function generators. The primary function generator produces the main sinusoidal signal, while the secondary generator creates a rectangular clock signal that controls the sampling instants. The estimation procedure is repeated many times, and the estimates made, in this case the sinusoidal amplitude, are used to compute their standard deviation. Note that the amplitude values obtained are not equal due to the random phenomena present in the signals generated and the hardware used. The amount of jitter present will be controlled by phase-modulating the clock signal with normally distributed noise produced by a function generator. It will thus be necessary to calibrate the amount of jitter being introduced prior to carrying out the main study. In addition to validating the theoretical models, this work provides a detailed presentation of the technical aspects involved in the experimental validation. This includes the implementation of the Monte Carlo procedure, instrument interfacing, programming languages used, and the development of automated measurement systems. These insights are intended to assist other engineers and researchers in replicating and extending the findings presented here. One should note that the bias of this estimator in the presence of jitter has been addressed in [15; 16] and that the work presented here complements the work already published by the _Journal of the Franklin Institute_ on the uncertainty of the initial phase estimation of a sinewave [17].
## 5 Discussion  
Compared to the CPU-only implementation, the GPU-based solutions significantly decreased computation time and improved the performance of Algorithm 1. In particular, the CUDA solution was approximately 9-13 times faster than the hybrid MPI/OpenMP version, exploiting the GPU micro-architecture without compromising the quality of the reconstructed images. For all datasets, the normalized root-mean-square error remained within the specified tolerance of the reconstruction kernel. For many years, one of the primary bottlenecks in GPU-based computing was the limited amount of device memory, which often forced data to be shuttled back and forth between the host and the device to fit larger workloads. This frequent transfer not only added latency but also introduced complexity in managing data pipelines for HPC applications. Recently, however, a new generation of GPUs (such as A100 GPU) has emerged with significantly expanded memory capacities, enabling developers to hold entire datasets directly in GPU memory. By offering more robust storage space and reducing reliance on host-to-device transfers, these GPUs improve the overall performance of such applications [25; 26].  
The roofline analysis showed substantial performance improvements with the CUDA implementation. However, the actual performance remains noticeably below the theoretical upper bound for the corresponding operational intensity. Potential future optimizations include the following:  
* Data storage in half precision: Reducing the memory footprint by using FP16 could significantly enhance memory bandwidth utilization. An initial test was conducted with this constraint, and the reconstructed image quality was not compromised.
* Improvement of cache usage: Achieved by optimizing memory access patterns to benefit from the large L2 cache on the A100 GPU.
* The Utilization of tensor cores could be also investigated to accelerate matrix-based kernels.  
Figure 5: Comparison of GRICS reconstructions with the three implementations for dataset \(D_{4}\). (**A**) Uncorrected reconstruction. (**B**): The CPU-only solution. (**C**): The Matlab–GPU solution. (**D**): The CUDA solution. Improvements of the reconstruction quality were noticed on the frontal (yellow square) and the parietal (green square) lobes.  
The CUDA back-end is markedly cheaper to run. In our code, the A100 GPU performed each reconstruction in 5.3-18 s, whereas the 36-core EPYC 75F3 needed 53-180 s--a roughly 10-fold speedup. Even though a data-center A100 draws more instantaneous power than a single-socket EPYC (400 W versus 280 W, per vendor TDPs), the order-of-magnitude shorter runtime means the energy per study falls by approximately 80%. In a department that performs 2000 such motion-compensated scans a year, that difference translates to roughly 1 MWh saved--enough to pay the GPU's electricity bill for the entire 5-year depreciation window.
## 2 Multi-Physics Coupling Theory and Finite Element Method for FP Cavity
### Finite Element Method and Model Establishment  
The finite element method (FEM), which originated in the early 1960s, is by far the most commonly used approach in the numerical analysis and engineering fields [31]. We take a multidimensional steady-state heat conduction process as a model problem, which can be described by the Poisson equation with homogeneous boundary conditions:  
\[\begin{split}-\
abla^{2}u=f&\quad\text{in } \Omega,\\ u=0&\quad\text{on }\partial\Omega,\end{split} \tag{8}\]  
with domain \(\Omega\subset\mathbb{R}^{d}\), where \(u\) is the unknown function to be solved and \(f\) is the known function (source term). The weak form of Equation (8) can be achieved by choosing a function \(v\) from a space \(U\) of smooth functions, then forming the inner product of both sides with \(v\), i.e.,  
\[-\Big{\langle}\
abla^{2}u,v\Big{\rangle}=\langle f,v\rangle. \tag{9}\]  
Assume that in addition to having the necessary smoothness, the functions which are to be the solutions satisfy the boundary conditions; the space \(U\) of the test functions is of the form \(U=\{v:v\in C^{2}(\Omega),v=0\text{ on }\partial\Omega\}\). More concretely, let \(d=2\); then, the weak form of Equation (8) is provided by  
\[\iint_{\Omega}\
abla u\cdot\
abla vdxdy=\iint_{\Omega}fvdxdy. \tag{10}\]  
To obtain a numerical method, it is required that \(U\) be finite-dimensional with basis \(\{u_{1},\ldots,u_{n}\}\). Then, the approximate solution \(u^{h}\) of Equation (8) can be represented as  
\[u^{h}=\sum_{j=1}^{n}c_{j}u_{j}. \tag{11}\]  
When a basis has been chosen for the approximation space \(U\), the next step is to determine the coefficients \(c_{j}\) in Equation (11). By inserting \(u^{h}\) into the weak form of Equation (10) and selecting the basis functions of \(U\) as the trial functions \(v\), a system of equations  
\[\iint_{\Omega}\
abla\left[\sum_{j=1}^{n}c_{j}u_{j}\right]\cdot\
abla u_{i}dxdy =\iint_{\Omega}fu_{i}dxdy,\quad i=1,\ldots,n \tag{12}\]  
is obtained, which is known as the Ritz-Galerkin method and can be written in matrix form as \(Ac=b\), where \(A\) is the stiffness matrix  
\[A_{i,j}=\iint_{\Omega}\
abla u_{j}\cdot\
abla u_{i}dxdy. \tag{13}\]
## 2 Antenna Design and Performance
### Proposed Antenna Design and Performance  
Figure 16a shows the proposed dual-band dual-polarization Tx/Rx antenna with the optimized metamaterial absorber. The 1.6 mm thick FR4 substrate containing the P-band feeding structures is shared with the reflectors of the Ka-band patch antenna array. Figure 16b shows the simulated S-parameters of the P-band dipole antennas. Since the proposed antenna structure is electrically large at 35 GHz, the simulation for the Ka-band antennas could not be carried out. The simulated \(-\)10 dB reflection coefficient band ranges from 311.26 to 838.12 MHz, which is sufficient to cover the targeted P-band. The simulated isolation characteristics are improved by inserting the metamaterial absorber. The simulated isolation characteristics are higher than 23.44 dB over the target band. Note that the simulated \(\ket{S_{31}}\) and \(\ket{S_{42}}\) improvements are approximately 28.94 dB and 40.56 dB at 609.23 MHz, respectively. As intended, remarkable improvement of the isolation characteristics is achieved around the resonance frequency of the absorber when it is tilted at 45\({}^{\circ}\). To clearly show the effect of the metamaterial absorber on S-parameters, simulated S-parameter performances of the proposed antenna with and without metamaterial absorber are compared in Table 5. As shown in Table 5, both \(-\)10 dB reflection coefficient bandwidth and isolation characteristics in the target band are improved by inserting the absorber. Figure 16c shows the simulated radiation patterns at 580 MHz when Port 1 is excited. The simulated radiation gain is 1.83 dB at \(\theta\) = 0\({}^{\circ}\), and FBR is 1.81 dB. The deterioration of the radiation performance is due to the influence of the metamaterial absorber.  
Figure 15: Proposed absorber: (**a**) geometry of an absorber panel, (**b**) dual-polarization Tx/Rx antenna with proposed absorber, and (**c**) simulated E-field distributions at 580 MHz for various absorber layers when Port 1 is excited.