esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Problem Description and Results  
In the \(\gamma-R_{\theta}\) transition model, two transport equations with an extra nine model coefficients should be solved additionally based on the SST k-\(\omega\) turbulent model. If a total of nine model coefficients are considered for multiple random inputs, tremendous computational time and cost are required. For example, when the order of the polynomial chaos \(p\) is 2 or 3, the required number of random inputs is 19,683 or 262,144, respectively in the Non-Intrusive Spectral Projection (NISP) method. First, the sensitivity analysis of each model coefficient was conducted with \(\pm 10\%\) deviation for the flat plate and the three most effective model coefficients (\(C_{\mathrm{a2}}\), \(C_{\mathrm{e1}}\), and \(C_{\mathrm{e2}}\)) were obtained for the multiple random inputs. The deviation and distribution of the model coefficients were set based on References [16; 17; 18]. Table 1 presents the results of the sensitivity analysis which show the difference with the reference value of the deterministic solution. To carry this out, each model coefficient was assumed to have a uniform distribution with the fixed constant from the original model [21] and \(\pm 10\%\) deviation. Then, we applied the UQ technique with the single random input to the target problem. We calculated the variance depending on each random model coefficient and determined the model coefficients that show the largest variance of the quantities of interest. Considering the simulation time and cost, the three top model coefficients \(\mathrm{c_{a2}}\), \(\mathrm{c_{e1}}\), and \(\mathrm{c_{e2}}\) were selected and applied to the NIPC and NISP methods with multiple random inputs. The following Table 2 shows the probability distributions of the three coefficients.  
From the probability distributions of the three coefficients shown in Table 3, in our calculations, we used multi-dimensional Legendre polynomials, which are orthogonal in the interval for each random dimension. Stochastic solutions to the target problems were obtained using three approaches: Monte Carlo with 500 samples, NIPC, and NISP methods. For NIPC, the oversampling rate \(n_{p}\) was set to 2, as recommended by [PERSON] et al. [16]. The Gaussian quadrature rule was adopted to select the sample points by the specified order, \(p+1\) for NISP.  
The \(\gamma-R_{\theta}\) transition model introduced by [PERSON] [21] was applied to two target problems: transitional flow over a flat plate and flow around Aerospatiale A-airfoil [32; 33; 34]. These are well-known validation problems for validation of CFD algorithms and transition/turbulence models. Experimental data are presented on the site of _turbulence modeling resources_ by NASA [34]. The deterministic solutions of two transitional flows were validated by comparison with reference data.
# Automatic Generation of Guidance for Indoor Navigation at Metro Stations
## 3 Materials and Methods  
### The Key Assumptions of the System  
Current indoor positioning algorithms that can be implemented in public buildings or metro stations have poor accuracy. The most popular systems based on Bluetooth or Wi-Fi positioning have accuracies of a few meters in real-world conditions [39, 40]. The accuracy of 1 m, as declared by some researchers, has not been confirmed by the observations of real users testing applications on their own mobile phones. Given that, firstly, it is difficult to obtain an accurate position indoors, especially in complex transportation hubs, and secondly, that the system is built for people with special needs, the authors decided to focus on the research on algorithms that generate navigation guidance. By design, the system can be used without a positioning system. The user finds a route, as in a typical urban commuter planner, and receives directions to follow. The developed algorithms can be used alone or integrated into a system with a positioning module, providing additional improvement.  
The analysis confirmed by tests with people with disabilities showed that without a positioning system, a kind of spatial reference is necessary anyway, without which the user may accidentally leave the route and not be able to return to it. This need is addressed in the LIFT system by appropriately designed navigation guidance. In addition to a schematic presentation of the route on a building plan, each direction displays a photo taken at the decision point. Such pictures facilitate the identification of route points and reassure the user that they are following the route.
###### Abstract  
In this research, the design of a robust curved-line path-following control system for fixed-wing unmanned aerial vehicles (FWUAVs) affected by uncertainties on the latitude plane is studied. This is undertaken to enhance closed-loop system robustness under unknown uncertainties and derive the control surface deflection angle directly used to control FWUAVs, which has rarely been studied in previous works. The system is formed through the mass center position control (MCPC) and yaw angle control (YAC) subsystems. In the MCPC, the desired yaw angle, which is treated as the reference signal for the YAC subsystem, is calculated analytically using path-following errors, current flow angles, and the yaw angle. In the YAC, a disturbance estimator is designed to estimate uncertainties such as nonlinearities, couplings, time variations, model parameter perturbations, and unmodeled dynamics. Predictive functional controllers are designed to target nominal systems in the absence of uncertainties, such that the estimations of the uncertainties can be incorporated through feedback for closed-loop system robustness enhancement. The simulation results show that higher path-following precision and stronger robustness for the FWUAVs based on the proposed approach can be achieved using only rough model parameters compared with the conventional nonlinear dynamic inversion, which requires detailed model information.  
Fixed-wing unmanned aerial vehicles; path following disturbance estimator; predictive functional control; nonlinear dynamic inversion +
Footnote †: journal: Applied sciences
## 3 Results
### Analysis of CTCs from Metastatic Breast Cancer Patients to Identify Targeted Therapies  
After validation with cell line cells we analyzed CTCs from 13 metastatic breast cancer patients. 11 of those patients had a primary tumor of luminal subtype and two patients were diagnosed with triple negative breast cancer. CellSearch analyses determined 6 to 15,340 CTCs per 7.5 mL blood. The median CTC count was 94 per 7.5 mL blood. For our study, 1 to 15 CTCs per patient were analyzed.  
In seven patients, mutations in the _PIK3 CA_ gene were identified. Five of those mutations are well-described mutations located in the nucleotide triplets coding for the amino acid N345, E545, and H1047, respectively. _ESR1_ mutations were detected in CTCs from two patients. One of those in the coding triplet for L536 is located in a well-known hotspot region. In CTCs from three patients, the _AKT1_ mutation E17K was found, and in CTCs from five patients we discovered mutations in the _ERBB2_ gene, including hotspot mutations in the nucleotide triplets coding for S310, L755, and V777 (Figure 3).  
Mutations were detected in 16.7% (_ERBB2_ A775T, patient 4) to 100% (e.g., _PIK3 CA_ H1047R, patient 8) of the CTCs analyzed per patient. In two cases, mutations were detected in two genes (_AKT1_ and _ERBB2_, patient 6; _PIK3 CA_ and _ERBB2_, patient 8) per patient, in 1 patient we found mutations in three genes (_PIK3 CA_, _ESR1_, and _AKT1_, patient 3). Those mutations either occurred in different CTCs (e.g., _AKT1_ and _PIK3 CA_ mutations in CTCs from patient 3) or together in one cell (e.g., _PIK3 CA_ and _ERBB2_ mutations in patient 8). One CTC from patient 3 harbored two activating mutations within the same gene (_PIK3 CA_ N345K and H1047R). Most mutations were detected with frequencies of 100% (e.g., _AKT1_ E17K, patient 2) or about 50% (e.g., _PIK3 CA_ H1047R, patient 8).  
Figure 2: Sequencing data of cells from reference cell lines. Displayed values show the variant allele frequency. Empty boxes indicate that no mutation was detected. LTC, long term cultured.  
Figure 3: Sequencing data from CTCs. Displayed values show the variant allele frequency. Empty boxes indicate that no mutation was detected.
## 6 Discussion
### Battery Capacity  
The output actuator torque was estimated based on the induced current in the actuator. Figure 8b shows a time trajectory of the electrical power loss based on the relationship in Equation (15):  
\[\text{Electrical Power loss}=(\tau_{m}/Nk_{t})^{2}R_{m} \tag{15}\]  
where a torque constant \(k_{t}\) is 0.0407, a gear ratio \(N\) is 100, and electric resistance \(R_{m}\) is 0.814.  
Based on the electrical power consumption of the actuator during individual tasks, the battery capacity requirement is calculated for scenario \(A\) of working conditions in Section 2 that includes 40% of walking phase (24 s) and 60% sitting phase (36 s). More specifically, the sitting phase included stand-to-sit for 3 s, sitting for 30 s, and sit-to-stand for 3 s. The energy consumption during the scenario  
Figure 10: Normalized muscle activations of major knee extensors compared: walking with and without the KARE-1 (**a**), stand-sit-stand motions with different body assistance ratio (**b**).  
was estimated from the experimental data and was calculated to be approximately 0.5 Wh. Energy consumed by the control board and sensors was not included in the calculation. Based on the energy density of recent batteries (100 Wh/kg), the proposed KARE-1 could operate for over eight hours with a battery mass of only 100 g.
## 3 Results and Discussion
### Variation of Dataset in PCA  
As described above, PCA is an effective method to easily check trends in a dataset as the scores can be graphically illustrated. Although a PCA cannot be performed to predict answers in classification and regression, the variance and reproducibility of the data can be easily checked from the graphs. Figure 5 shows the PCA scores and eigenvectors from the sensor responses for a single gas on sensor arrays C and L. The cumulative variance of the first and second principal components (PC1 and PC2) of all panels in Figure 5 was greater than 80%. The first two PCs are normally sufficient to retain the originality of the data [28]. Focusing on the eigenvectors, although the eigenvectors of TGS2600, TGS2610, and TGS2620, and CeZr10 + M and ZnO + M tend to overlap, the eigenvectors of the other sensors did not overlap in their respective directions. Therefore, each sensor generally had unique response characteristics for each target gas.  
The data in Figure 5a,b were from the 1 min data. The PCA scores for each target gas tended to converge in the direction of (0, -2) coordinates as the concentration decreased. Figure 5b, which shows the PCA score of sensor array L, indicates that the scores are generally independent at any concentration level for any target gas. However, in Figure 5a, which presents the PCA score of sensor array C, there are variations in the plot in the PC2 axis direction. For example, numbers 4, 10, 11, 12, and 13, which have alcohol, ester, and aldehyde functional groups, have overlapping plot areas and are difficult to discriminate. This is because the sensor responses of TGS2612 and TGS2444, whose eigenvectors strongly influenced the PC2 axis direction, were small and affected by variations in the noise level.  
The data in Figure 5c,d were obtained from the 12 min data. In Figure 5c, which shows the PCA score of sensor array C, the scatter in the plots is even greater than that in Figure 5a, and the areas and overlap of the plots of the different gas species have expanded. Figure 5d, which shows the PCA score of sensor array L, displays that some plots varied in a direction parallel to the ZnO eigenvector. As shown in Figure 4b,d, particularly for ZnO, the resistance drifted during the introduction of the target gas. Thus, the influence of drift appeared when the interval was expanded to obtain the sensor response values.
# Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering
## 4 Simulation Results and Analysis
### Throughput  
In this part, thoughput performance of IOTA and RRC is compared under different \(\alpha\). The throughput is calculated as  
\[\text{Troughput}=(1-\text{BLER})*\text{TBS}/(T1\times\alpha+T2), \tag{16}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**MCS** & **Mod** & **R** & **Min \(\alpha\)** & **Min \(\alpha\)** & **Throughput** \\
**Index** & **Order** & **[/1024]** & **SE** & **-IOTA** & **-RRC** & **Gain** \\ \hline
0 & 2 & 120 & 0.2344 & 0.25 & 0.38 & 0.52 \\ \hline
1 & 2 & 379 & 0.7402 & 0.38 & 0.5 & 0.32 \\ \hline
2 & 2 & 679 & 1.3262 & 0.5 & 0.64 & 0.28 \\ \hline
3 & 4 & 340 & 1.3281 & 0.5 & 0.64 & 0.28 \\ \hline
4 & 4 & 490 & 1.9141 & 0.64 & 0.77 & 0.20 \\ \hline
5 & 4 & 658 & 2.5703 & 0.64 & 0.83 & 0.30 \\ \hline
6 & 6 & 438 & 2.5664 & 0.64 & 0.83 & 0.30 \\ \hline
7 & 6 & 719 & 4.2129 & 0.77 & 0.9 & 0.17 \\ \hline
8 & 6 & 948 & 5.5547 & 0.77 & 0.9 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Throughput Gains under different MCS Cases.  
Figure 12: Performance of BER based on 16 QAM with \(R=658/1024\).  
By adjusting the MCSs in Table 4, we derive the red line in Figure 13 which depicts the envelope of the achievable throughput of RRC-based waveform under different SNRs. For the RRC-based waveform, due to the ISI brought by FTN, the signal to interference plus noise ratio (SINR) required for successful decoding of the MCS with a larger index can not be fulfilled even with increased SNR up to 20 dB. This makes the achievable throughput maintain a constant value in a large SNR region. However, for the IOTA-based waveform, reduced ISI enhances the SINR and throughput can be enhanced by changing the MCSs. We observe that significant throughput improvement can be seen with SNR between 15-18 dB, which corresponds to the relative SE of MCS 1 and 5 in Table 4. Similar observations can be drawn in Figures 14 and 15. The throughput performance gain of IOTA is mainly due to its effective ISI reduction.
### Water Sources Analysis and Model of High Water Production  
#### 4.4.1 Water Sources  
The Shouyang Block is generally a monoclinic structure, with unclear regional runoff and drainage zoning. The groundwater flow is not active, and the coal seam exhibits low permeability. Additionally, the coal seam itself has a low water content. Therefore, external water is the main factor affecting the difference in water production between wells [30]. The groundwater sealing coefficient, groundwater stability index, and total coal seam water content related to hydrodynamic factors and coal reservoir factors have a lower impact on water production compared to other factors.  
The water discharged from coalbed methane wells in the Shouyang Block exhibits similarities with that from the Gujiao Block in the northwest of the Qinshui Basin, as well as the Shizhuang and Pan-zhuang Blocks in the southern Qinshui Basin. Due to the control of the water-rock interaction, the hydrogen and oxygen isotopes of the water discharged from coalbed methane wells are distributed near or to the right of the atmospheric precipitation line [45; 46]. The isotopic composition of hydrogen and oxygen in the water sample remains consistent with the pattern of atmospheric precipitation, suggesting that the primary source of the water sample was indeed atmospheric precipitation (Figure 12) [47].  
The Na\({}^{+}\) content in the water sample is higher than the Cl\({}^{-}\) content, and Na\({}^{+}\) shows a positive correlation with Cl\({}^{-}\), indicating that the Na\({}^{+}\) and Cl\({}^{-}\) in the water mainly come from the leaching of salt rocks, as well as other sources [48], such as the dissolution of silicate minerals (Figure 13a) [49]. When (0.5 HCO\({}_{3}\)\({}^{-1}\) + SO\({}_{4}\)\({}^{2-}\))/(Ca\({}^{2+}\) + Mg\({}^{2+}\)) = 1, this indicates that the ions in groundwater mainly come from the dissolution of carbonate and sulfate rocks [50]. In the Shouyang Block, (0.5-HCO\({}_{3}\)\({}^{-1}\) + SO\({}_{4}\)\({}^{2-}\))/(Ca\({}^{2+}\) + Mg\({}^{2+}\)) is much greater than 1, indicating that the dissolution amounts of carbonate and sulfate are both small (Figure 13b), and the contribution of limestone aquifers to the water production of coalbed methane wells is relatively small. Multiple sets of limestone and sandstone aquifers are developed near coal seam No. 15 in the Shouyang Block. Therefore, according to hydrochemical analysis, the water source may mainly come from the sandstone aquifer.  
Figure 12: Hydrogen and oxygen isotope characteristics of water discharged from coalbed methane wells in Shouyang Block.
## 4 Experimental Results and Analysis  
In this section, we compare the performance of our proposed measurement matrix with several existing ones: the Gaussian random measurement matrix [7], the random symmetric measurement matrix [9], the random binary measurement matrix [9], and the bipartite graph measurement matrix [17]. The phase transitions and practical characteristics of all of these measurement matrices were analyzed in Section 3. To ensure greater versatility, we employed two types of signals as test signals: randomly synthesized signals and images. The OMP algorithm [22] was used for signal recovery.  
All experiments were conducted using Matlab R2014a on a computer equipped with an Intel Core i7-6700 CPU (3.4 GHz) and 16 GB of RAM, running Windows 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(\Phi\)} & **Randomness or** & \multirow{2}{*}{**Measurement-Flexible**} & **Memory Cost (Bits)** & **Multiplierless** \\  & **Deterministic** & & & \\ \hline Gaussian random & Randomness & Yes & _BMN_ & No \\ Random symmetric & Randomness & Yes & _MN_ & No \\ Random binary & Randomness & Yes & _MN_ & No \\ Bipartite graph & Deterministic & Yes & \(N\times d\times log_{2}M\) & Yes \\ This paper & Deterministic & Yes & \(N\) & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational complexities of various measurement matrices.
## 3 Comparison Traditional Access Control vs. Security Access Control Based on Three Layers
### Security Access Control Based on Three Layers (Ideal Security Policy)
#### 3.2.1 Operational Layer  
This layer encompasses onboarding access control, which facilitates the identification of authorized users who can access the organization's resources and network through their BYOD devices [1]. Within this layer, two key security functions are performed. Firstly, device account registration control allows users to create and register their accounts, requiring verifiable information such as employee ID, job role, and assigned services. Secondly, a service level agreement (SLA) is established, requiring BYOD users to agree to the terms of use and assume responsibility for the information and services provided by the corporate system.
## 3 Wireless Sensor Networks
### ZigBee  
The ZigBee standard is built on top of the IEEE 802.15.4 standard. The IEEE 802.15.4 standard defines the physical and MAC (Medium Access Control) layers for low-rate wireless personal area networks [33]. The physical layer supports three frequency bands with different gross data rates: 2,450 MHz (250 kbs\({}^{\text{-1}}\)), 915 MHz (40 kbs\({}^{\text{-1}}\)) and 868 MHz (20 kbs\({}^{\text{-1}}\)). It also supports functionalities for channel selection, link quality estimation, energy measurement and clear channel assessment. ZigBee standardizes both the network and the application layer. The network layer is in charge of organizing and providing routing over a multi-hop network, specifying different network topologies: star, tree, peer-to-peer and mesh. The application layer provides a framework for distributed application development and communication.  
Aside from the agriculture and food industry, it is widely used in home building control, automation, security, consumer electronics, personal computer peripherals, medical monitoring and toys. These applications require a technology that offers long battery life, reliability, automatic or semiautomatic installation, the ability to easily add or remove network nodes, signals that can pass through walls and ceilings and a low system cost [2].
## 4 Field Evaluation of Risk Model
A series of nondestructive surveys were then conducted using a 500 MHz antenna equipped with ground penetrating radar (GPR) and electrical resistance (ER) survey installing twelve electrodes with one-meter spacing to investigate the following: the presence of buried utilities, abnormality of ground condition, and the presence of underground cavities. Once the nondestructive test was completed, PCP tests were conducted at the locations where abnormalities of ground were detected and normal locations for comparison. Figure 8 shows examples of nondestructive test results. As shown in Figure 8, as risk indices increase, the subsurface profile obtained from GPR becomes irregular, and the average ER values become relatively lower. To determine the average ER values per test section, image analysis was employed to quantify the area of individual ER zones. Once the area was quantified for individual ER zones, the weighted average considering the quantified area was then computed to represent the test section. In this manner, the ER survey was found to be effective  
Figure 8: Examples of nondestructive test results. in the assessment of risk index along with GPR survey, which was useful to detect the presence of buried utilities as long as the depth of cover was within 3 m. Pneumatic cone penetration testing was conducted in accordance with DIN ISO 22476-2 up to 2 m below the surface. The PCP tests had an advantage of penetrating deeper depths with constant impact energy, controlled by pneumatic force, compared to a typical dynamic cone penetrometer test. From this test, two indices were assigned to evaluate the bearing capacity of subsurface. For instance, \(N_{50}\) indicates the number of blows to penetrate the pneumatic cone up to 0.5 m. Higher numbers of blows indicates larger load bearing capacity of the subsurface. Generally, the number of blows tends to decrease at the locations where abnormality of ground was detected compared to normal conditions. The relationships between average electrical resistivity, the number of blows from PCP tests, and the risk index were established based on the risk model, as shown in Figure 9. The \(N\)-value of PCP test was found to have a promising relationship with risk index and average electrical resistivity even though the R-square value was not sufficiently high to ensure statistical accuracy. Consequently, it was deemed that further research needed to be conducted to validate this relationship.