esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Two-Level-Partitioning-Based Double Array and Its Parallelization
## 4 Experiments
### Serial Algorithms  
#### 4.2.1 Construction Performances  
Effects of Different Number of Partitions  
In order to evaluate the construction efficiency of different partitioning algorithms under different number of partitions, construction algorithms of BP_PLA-based DA (BP_PLA-DA) (We do not compare BP here, for it has a much lower retrieval performance.), UP-DA and LP-DA are compared under different number of partitions. The results of DBLP and EN-WIKI are shown in Figure 8a-c (We assume the datasets are already sorted, because the sorting times are the same for all these algorithms.). It can be seen from the figures that the curves of both BP_PLA-DA and UP-DA decrease sharply with the increase of the number of partitions at first, and then tend to be flat. This is due to the fact that as the number of partitions increases, more small DAs are created, which reduces collisions and the collision-handling costs (e.g., the total probe lengths of BASE for the three datasets are shown in Figure 9a-c). LP-DA has the highest performance in the three methods, especially when partition number is small. For example, when partition number is 4, the construction times of LP-DA for the three datasets are 2.58 s, 4.21 s, and 108.43 s, respectively, 4x faster than BP_PLA-DA. The curve  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **EN\_WIKI** & **DBLP** & **SynthDS** \\ \hline Size (MB) & 3.25 & 2.13 & 26 \\ String number & 347,961 & 211,517 & 2,000,000 \\ Minimum length & 1 & 1 & 3 \\ Maximum length & 62 & 46 & 20 \\ Average length & 8 & 8 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical information of the three datasets.  
of LP-DA is almost flat because it always creates the same number of DAs under different number of partitions.  
Comparisons with the Other Algorithms  
To investigate the efficiency of different algorithms under different number of strings, the above three partitioned methods and the other two non-partitioned methods (DA and FST) are compared. For partitioned methods, we hereafter fix the number of partitions to 8 unless otherwise stated. The experimental results of the three datasets are shown in Figure 10a-c, respectively. It can be seen from the figures that FST has a higher construction efficient than DA-based algorithms because there are no collisions introduced in FST. Compared to DA, LP-DA is 7x-15x faster which shows that our partition can effectively boost the construction speed of DA.
## 1 Introduction
Based on these results jointly, QAM selection and spectrum allocation are suggested. Finally, numerical results under a 5G New Radio (NR) setting are presented and discussed. Simulation results are also obtained and compared with the theoretical results, which validate the analysis and its efficiency. The rest is organized as follows. Section 2 describes the considered system model. Section 3 introduces the analysis from different aspects. Numerical and simulation results, which demonstrate and verify the analysis, are presented in Section 4. Finally, our concluding remarks and discussion are made in Section 5.
## 6 Discussion
### The Computational Complexity Analysis  
Figure 5 compares these six optimization methods in terms of the required computation time to find the optimum value, using various benchmark functions with different numbers of design variables. The computation time needed to converge is the most significant factor for any method  
Figure 4: Convergence speed for Cigar (\(f_{14}\)) function (25D).  
Figure 3: Convergence speed for Griewank (\(f_{7}\)) function (30D).  
when examining its efficiency. The number of function evaluations (NFE) required by each algorithm on a set of benchmark test functions places the computation time of the six optimization methods under investigation. Figure 5 and Table 3 indicate that GWO and FFA need the lowest and highest average CPU time respectively. As an example, in Figure 5 for functions \(f_{7}\) and \(f_{13}\), the FFA method demands a 6 and 7 times higher computation time in comparison to the ABC and GWO approaches. The computational complexity results shown in Figure 5 depict that ABC is more efficient than GWO as its CPU time is almost 50% of GWO for function \(f_{3}\). ABC shows good efficiency most of the time; however, it can sometimes get stuck into local minima. BA, CSA, FFA and FPA show better performances on low-dimensional problems compared to when it is dealing with high-dimensional GO problems. The GWO algorithm requires the least computation time, suggesting that GWO is the most promising global optimization method for computationally-expensive black-box problems.  
The influence of increasing the number of variables on the CPU time is presented in Figures 6 and 7. As shown, when the number of variables increases, FFA and CSA yield a higher computational complexity compared to the other methods. Furthermore, BA is less sensitive to increasing the number of variables than the other methods while its accuracy is less. Comparing GWO and ABC, to ascertain the best algorithms, shows that GWO needs a relatively lower CPU time, making this algorithm an efficient method for solving many practical GO problems with expensive objective function and constraints.
## 3 Proposed Methodology
### Description of the Flow Chart  
Figure 3 shows the flowchart of the proposed architecture system. During the proposed technique, the IDS starts broadcasting the status packet periodically to all nodes within its set range. All of the nodes in the IDS nodes' set range receive a status packet from the IDS node. When a legitimate node receives this status packet, it proceeds genuinely. Meanwhile, when a malicious node receives this packet, it reacts with abnormal behavior or provides false information to IDS nodes. According to the status packet, the first question is about whether any node is sending a high sequence number. Reaming nodes that send an average sequence number and drop all data packets are declared as black hole nodes. If any node is providing an average sequence number, not considered high, alongside the dropping of selective data packets, it is declared as a gray hole node. The second and third questions enquire about the data packets the malicious node has received and the data packets it has forwarded during communication. It is obvious that a malicious node provides bogus information to the IDS node to attract traffic and drop the data packets instead of forwarding them to the next node. The last question relates to the reason for dropping the data packets and, if any malicious node claims that it dropped data packets because of the queue size or traffic congestion, the proposed technique has a solution for this problem. Generally, in the network, each node has an equal traffic load when compared to other legitimate nodes. There is no reason why that particular node (malicious node) should be dropping the data packets if the traffic load is the same for every node, so it establishes that the node in question is a liar and its aim was to drop the data packets. Once it is confirmed that the node is a malicious node, the IDS node broadcasts a block message consisting of the malicious node's ID and rejects all types of information sent by that particular malicious node, adding it to the blacklist. If any node found to be malicious in the set range moves to another set, then the IDS node already has information about that malicious node (calculated in the previous IDS set). Therefore, the IDS node also broadcasts a block message to their own range to block that malicious node.
# Ionospheric Electron Density Model by Electron Density Grid Deep Neural Network (EDG-DNN)
## 3 Results
### Global Distribution Characteristic of Ionospheric Electron Density  
Figure 8 compares the ionospheric electron density tomography of the EDG-DNN model and the IRI model on 20 March 2012 and 20 September 2012, respectively, at an altitude of 300 km. The equatorial bimodal structure may be detected in the EDG-DNN tomography at all times. Areas with high electron density shift as the sun's angle of incidence changes throughout the day. This modification is well represented by EDG-DNN, which is congruent with the IRI-2016 model. The EDG-DNN model and the IRI model are very comparable in most places; however, the difference graph demonstrates that the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Latitude**} & \multicolumn{2}{c}{**EDG-DNN Model**} & \multicolumn{2}{c}{**IRI-2016 Model**} \\ \cline{2-5}  & _MAD_ & _RMSE_ & _MAD_ & _RMSE_ \\ \hline \(70^{\circ}\) S–\(90^{\circ}\) S & 0.877 & 0.992 & 0.979 & 1.158 \\ \(50^{\circ}\) S–\(70^{\circ}\) S & 0.339 & 0.424 & 0.495 & 0.633 \\ \(30^{\circ}\) S–\(50^{\circ}\) S & 0.434 & 0.503 & 0.368 & 0.414 \\ \(10^{\circ}\) S–\(30^{\circ}\) S & 0.462 & 0.579 & 0.559 & 0.629 \\ \(10^{\circ}\) S–\(10^{\circ}\) N & 0.415 & 0.575 & 0.746 & 0.893 \\ \(10^{\circ}\) N–\(30^{\circ}\) N & 0.424 & 0.523 & 0.628 & 0.751 \\ \(30^{\circ}\) N–\(50^{\circ}\) N & 0.311 & 0.407 & 0.511 & 0.609 \\ \(50^{\circ}\) N–\(70^{\circ}\) N & 0.285 & 0.353 & 0.399 & 0.457 \\ \(70^{\circ}\) N–\(90^{\circ}\) N & 0.726 & 0.819 & 1.002 & 1.201 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The averaged performance of models on the test set.  
Figure 7: Change of electron density profile from 15 to 24 September 2011: (**a**) shows the change of electron density after the superposition of vertical electron density; (**b**) shows the vertical heat map of electron density at the ionosphere from 100 km to 800 km (\(28^{\circ}\) N, \(115^{\circ}\) E).  
prediction effect of EDG-DNN in these regions deviates significantly from the IRI model. These variations are scattered throughout different regions, which does not indicate that the prediction findings are biased. Because the IRI-2016 model is empirical, it tends to represent the statistical features of the electron density; hence, it loses the local properties of the electron density. The COSMIC data utilized by EDG-DNN can represent more detailed features than the IRI-2016 model. The results reveal that the EDG-DNN model predicts the electron density distribution well and can capture the general structure of the ionospheric electron density distribution.  
Figure 8: Comparison of global electron density tomography by EDG-DNN (first row) and IRI-2016 (second row), and the third line is the difference between the two: (**a**) shows the electron density tomogram on 20 March 2012; (**b**) shows the electron density tomogram on 20 September 2012.
## 3 Controlled Energy Transfer
### Optically Controlled EET
Hence, mechanism (**b**) will always be effective in, to some degree, modifying EET efficiency when D and A are polar, whereas mechanism (**a**) provides a basis to switch on transfer processes between states that would otherwise not be allowed. Examining the latter in more detail, we now analyse the case of coupling between a non-polar donor and acceptor whose transitions are electric dipole (E1) forbidden but two-quantum (E1\({}^{2}\)) allowed. This signifies that the initial excitation of the donor cannot occur via direct one-photon absorption but, for example, may involve initial excitation to a higher energy electronic state followed by relaxation. In such a case, only terms involving the transition polarisability arise, since they represent two-quantum allowed transitions at D and A, i.e., the absorption (or emission) of the input beam and the energy transfer interaction. Here, since this system is electric dipole (E1) forbidden, EET could not, in the absence of a beam, occur--except via extremely weak higher multipole interactions (associated with exceptionally sharp \(R^{\,\pi}\) dependence of the rate, where \(n\gg 6\)). Therefore, energy transfer can only reasonably occur when the input beam is applied; this is the origin of the switching action. For this scheme, since the input-field-independent contribution does not arise, Equation (11) is null and the leading term of the rate, which is dependent on \(I^{2}\), then becomes;  
\[ \begin{array}{ll}\Gamma_{\text{SWITCH}}&=V_{jk}(0,\mathbf{R})V_{mn}(0,\mathbf{R}) \bigg{\{}\bigg{(}\frac{I^{2}}{4c^{2}c^{2}_{0}}\bigg{)}e_{i}\bar{p}_{e}q_{p} \Big{(}S^{\prime\,\perp}_{ij}(\text{D})S^{\prime\,\perp}_{lk}(\text{A})\\ &+S^{\prime\,\uparrow}_{ij}(\text{A})S^{\prime\,\perp}_{ik}(\text{D})\Big{)} \left(S^{\prime}_{pm}(\text{D})S^{\prime}_{q\pi}(\text{A})+S^{\prime}_{pm} \text{(A)}S^{\prime}_{q\pi}(\text{D})\right)\bigg{\}}\end{array}  \tag{12}\]  
which is the term that represents the possibility of all-optical switching [56; 57; 58]. The effect of an off-resonant throughput beam on simple optical processes is covered in our recent review article [59]. Another feasible system, based on this form of all-optical switching, is known as an optical transistor. Here, it is estimated that stimulated emission might be increased by up to 16 orders of magnitude, on input of an off-resonant input beam with sufficient intensity, for a three-level population-inverted material pumped just below its lasing threshold [60].
## 6 Simulation Evaluation
### Simulation Results
#### 6.2.1 Success Rate  
In our first experiment, the success rate and failure rate of the entire service sessions were evaluated and compared with the abovementioned existing methods, i.e., the Greedy, Always Migrate, and Random methods, which are summarized in Table 1. The results in Table 1 are generated by a large dataset which is offloaded by the SN and intelligently divided and processed in parallel between two BSs. As shown in Table 1, our proposed method outperforms the existing methods.  
We compare our experiment results with those of the other three algorithms, namely the Greedy algorithm, Always-Migrate, and Random Process. In the Greedy method, the BS\(i\) sends the whole task to BS\(j\) with a higher SNR in the Greedy method, and the division and cooperation of the tasks between \(i\) and \(j\) are not considered. In addition, in the Always-Migrate algorithm, the entire computing task offloaded by the SN is fully migrated to a BS which the SN is approaching. Therefore, in this algorithm, when another BS is closer to this SN, the whole task is migrated to the BS closer to this SN. In the Random Process, the BS\(i\) divides the task into two random components. One component is processed by BS\(i\), and another component is transmitted to a nearby BS, BS\(j\), for cooperation. However, the choice mechanism of BS\(j\) is not considered in this method, thus resulting in some of the components being lost. For example, when one BS moves out of the SN's transmission range, the result of the task will not be delivered to the SN, and the service session can be terminated in failure.
# Chronic Obstructive Pulmonary Disease Warning in the Approximate Ward Environment
## 4 Monitoring Breathing and Its Analysis
### Monitoring Coughing Pattern  
Monitoring coughing in COPD is very important. We used both C- Band sensing and the digital respiratory sensor method. As shown in Figure 2, we obtained the wireless channel information for coughing using C-Band.  
Figure 2 shows the amplitude information of all 30 subcarriers. To examine the coughing activity of a COPD patient, we obtained the CFR form C-Band sensing as shown in Figure 3.  
Figure 3 shows the fluctuation in the amplitude for all the subcarriers against the number of packets. After obtaining this information we performed the filtration process and filter CFRed the raw CFR data by using median filters. The obvious choice for the median and wavelet filter is due to its maximum reduction of noise and smoothing the edges for the available signal.  
The above Figure 4 shows the filtered data for the coughing after applying the median filter. The rising and falling edges are much smoother than before with minimum noise effect. This experiment was performed on multiple persons imitating COPD patients, as mentioned in Table 2. We took multiple readings for approximately 1 min each.  
Figure 3: Raw CFR (channel frequency response) for coughing using C-Band.  
Figure 2: Raw WCI (wireless channel information) for human coughing.  
Now Figure 5 shows the HKH-11C sensor data for coughing; it shows both the raw and filtered data. The person stays in the straight lying position for 60 s wearing the sensor on the chest and imitates the coughing of a COPD patient. Figure 5a shows the raw CFR data for the coughing and Figure 5b shows the filtered data for coughing.  
The RF signal is always prone to external noise, so frequency selective fading shows greater impact on all the subcarriers. All the 30 subcarriers show different behavior for every power level w.r.t time. As shown in Figure 6, we have selected the sub-carrier # 7 for extracting the finest information for coughing pattern. After obtaining the data from subcarrier # 7, it then passed to the median filter to remove the noise and sharpen the edges.  
From Figure 6, we can examine the breathing cycles; the person almost completed nine breathing cycles, as shown in Figure 5. This shows that the result from C-Band and HKH-11C is somehow similar.  
Figure 4: Filtered CFR for coughing.  
Figure 5: Coughing pattern obtained using HKH-11C sensor. (**a**). Raw coughing pattern. (**b**). Filtered coughing pattern.
# Mixed Method for Isogeometric Analysis of Coupled Flow and Deformation in Poroelastic Media
## 4 Numerical Examples
### [PERSON]'s Problem  
[PERSON]'s problem is a classical one-dimensional consolidation problem with an analytical solution, which makes it suitable for code validation. A saturated porous medium subjected to an external loading under plane-strain condition is considered where the fluid is allowed to dissipate only at the top boundary, hence resulting in a one-dimensional consolidation. A no flux boundary condition is assumed for the lateral and bottom boundaries. The displacement boundary conditions are such that the lateral sides are constrained from horizontal deformation and the bottom boundary is fixed in both the horizontal and vertical directions. The external load is applied as a Neumann traction \(p_{0}\) at the top boundary. Note that we use \(x\) and \(y\) to denote the horizontal and vertical directions, respectively, while it is also common to use \(z\) for the vertical direction in one-dimensional poroelastic problems as in [28]. The domain and boundary conditions considered are shown in Figure 3.  
The analytical solution for the pressure field as a function of time and space is given by:  
\[\frac{p^{\text{f}}(t,y)}{p_{0}}=\frac{4}{\pi}\sum_{i=1}^{\infty}\frac{\left(-1 \right)^{i-1}}{2i-1}\exp\left[-(2i-1)^{2}\frac{\pi^{2}t_{\text{s}}}{4}\right] \cos\left[(2i-1)\frac{\pi y}{2h}\right]\]  
where the dimensionless time \(t_{\text{s}}\) is given as a function of the consolidation coefficient \(c_{\text{v}}\) and drainage path \(h\) (total height for one-way drainage) by:  
\[t_{\text{s}}=\frac{c_{\text{v}}}{h^{2}}t. \tag{39}\]  
The consolidation coefficient \(c_{\text{v}}\) is given by:  
\[c_{\text{v}}=\frac{(1-\
u)Ex}{(1+\
u)(1-2\
u)} \tag{40}\]  
The material parameters used for this problem are given in Table 1, as used in [35]. The choice of the storativity value \(c=0\) effectively corresponds to assuming incompressible solid grains and an incompressible fluid.  
Figure 3: [PERSON]’s problem: Domain and boundary conditions.  
Figure 5: Numerical solution to the Terzaghi problem with \(N_{\rm c}=72\) using a sub-critical time step of \(\Delta t=0.1\Delta t_{\rm c}\) for different polynomial degrees. All plots are shown for the first time step. (a) Mixed. (b) Equal order.
## 4 Discussion
The impedance can possibly vary depending on the volume and ionic concentrations of the fluid inside the uterine cavity and the conductivity of the endometrial tissue, which might be subject to changes from participant to participant and during the menstrual cycle. This interparticipant variability is assumed to be the most likely reason for the high variability in the VJZE-RMS. The frequency-related features are more robust to electrical measurements. However, the variability is still considerable, due to interparticipant changes. It is important to note that the clear interparticipant variability trends and statistically significant differences across the menstrual cycle observed regarding the frequency and voltage amplitude of the electrical activity agree with the previously aforementioned findings [8]. In future studies, we would like to investigate how obesity and menstruation change the EUG features, as, in this study, we had only one participant with a BMI > 30 and only one recording during menstruation. Furthermore, we would like to investigate whether there is a difference in the EUG values between porous and nulliparous women. However, this study would require more strict and complicated selection criteria to minimize the risk of recruiting nulliparous women with infertility problems. Uterine peristalsis is an important function of nonpregnant women, for which deviations from normal could increase the risk of gynecological pathologies and infertility. The existing tools for recording uterine peristalsis are intricate, costly, and lack reproducibility. This study highlights that the EUG presents a standardized, minimally invasive, safe, and pain-free procedure, offering reproducible electrical measurements from the endometrial cavity [11]. The clinical assessment in this article focuses on the EUG's ability to monitor the uterine contractility across the menstrual cycle in healthy, porous, nonpregnant women. An analysis of the recordings indicates that the measured electrical activity aligns with previously observed patterns of uterine contractions throughout the menstrual cycle found in the literature [24, 25, 26]. When comparing the JZE peristalsis measurements taken using TVUS and MRI, the EUG measurements are independent of the operator and directly contact the JZE. Recognizing the significance of uterine contractility in nonpregnant women, the EUG shows promise for assessing the uterine physiology. Further prospective studies in multi-center settings will enhance our understanding of the clinical applications of the EUG.