esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Uncertainty-Based Optimization Design for the Multilayer TPS of Hypersonic Wing
### Experimental Validation for the Uncertainty-Based Optimization Design Method  
To validate the proposed uncertainty-based optimization design method, the thermal insulation performance experimental tests were conducted. As shown in Figure 10a, the typical thermal protection tile used in the experiment was made of five layers, including the C/C panel, the flexible insulation material (FIM), MTTI, and the strain isolation pad (SIP). The panel size was 200 mm \(\times\) 200 mm, and the thicknesses of each layer from outer to inner were 4 mm, 3 mm, 20 mm, 31 mm, 3 mm, respectively.  
Figure 10b described the schematic diagram of the thermal insulation performance test. The test piece was vertically fixed inside the square frame made of high-temperature ceramics. The outer surface of the test piece faced the quartz heat lamp, which can simulate the aerodynamic heating environment after power up. Meanwhile, to obtain the temperature responses, the high temperature thermocouples were carefully placed on the outer and inner surfaces. The flexible insulation plate was placed 20 mm away from the inner surface to prevent heat loss.  
Figure 9: Illustration of the optimization processes.  
In the test, the temperature control method was utilized to determine the heating intensity. Considering the power limit of the quartz heat lamp, the controlled temperature was set to 1200 K. The control process could be implemented by following steps. Firstly, the instantaneous temperature on the outer surface was measured and fed back to the control system. Then, the control system compared the measurable temperature and the controlled temperature to acquire the control error. After that, the power of the quartz heat lamp could be timely adjusted based on the control error to obtain the controlled temperature.
## 3 Results and Discussion  
The MRA-based MPPT technique is cross examined in this section. The use of various case situations, including start-up tests, MPPT rating, widely fluctuating temperatures, statistical analysis, and an industrial environment with 24 h stochastic temperature profiling is followed by a hardware emulator experiment. Using a low-cost TEG emulation experimental setup, the performance of the suggested approach is validated in the actual world. A comparison is provided between the MRA-based MPPT control and other popular MPPT methods including the PSO, GWO, and WOA. The maximum number of iterations for meta-heuristic approaches is four, and their population size is four.
## 4 Inversion and Verification of Mechanical Parameters
### Verification of Parameter Inversion  
To further access the reliability of PSO-XGBoost inversion model, actual monitoring data from the downstream portion segment of the No.1 auxiliary tunnel at Liujia Village, Chuxiong Section, was employed as the focal point of investigation. The monitoring points, namely CX22 + 630, CX22 + 670, CX22 + 710, CX22 + 750, and CX22 + 790, were selected for inversion. The depth of these five monitoring points is 273 m, 271 m, 270 m, 272 m, and 273 m, respectively. The results of the inversed parameters are listed in Table 5.  
Subsequently, we brought the inversed parameters into the FEM calculation to obtain the simulated values when tunnel operation became stable. A comparative analysis was performed between computed results and actual monitoring data. Figure 11 illustrates the error range of the monitoring data variation (15%). Figure 11a indicates an average error of 9.52% between the monitoring data and the computed results in crown displacement.  
Figure 10: Predicted performance of inversed parameters in the testing set. (**a**) elasticity modulus (E); (**b**) Poisson ratio (\(\mu\)); (**c**) cohesion (**c**); (**d**) internal friction angle (\(\varphi\)).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Monitoring Point**} & \multicolumn{3}{c}{**Input**} & \multicolumn{3}{c}{**Output**} \\ \cline{2-9}  & **Crown Displacement** & **B–C** & **D–E** & **Elasticity** & **Poisson** & **Cohesion** & **Internal Friction** \\  & **(mm)** & **Convergence** & **Convergence** & **Modulus** & **Ratio** & **(MPa)** & **Angle (\({}^{\circ}\))** \\  & **(mm)** & **(mm)** & **(mm)** & **(GPa)** & **(GPa)** & **(MPa)** & **(MPa)** & **(MPa)** \\ \hline CX22 + 630 & 65.5 & 70.5 & 62 & 2.506 & 0.351 & 0.664 & 26.767 \\ CX22 + 670 & 62 & 70.2 & 58.4 & 2.524 & 0.374 & 0.579 & 30.652 \\ CX22 + 710 & 49.9 & 58.1 & 55.8 & 1.887 & 0.316 & 0.608 & 25.894 \\ CX22 + 750 & 81.2 & 62.8 & 61.4 & 2.990 & 0.341 & 0.317 & 28.226 \\ CX22 + 790 & 55.6 & 56.3 & 55.8 & 1.668 & 0.347 & 0.449 & 26.752 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Inversion results of inversed parameters.  
Similarly, discrepancies of 6.28% and 8.68% in B-C convergence and D-E convergence exist, as shown in Figure 11b,c are within the project's allowable error range [46]. The full results revealed an acceptable error interval, as was specified in the engineering scale. The findings further indicated that the parameter inversion model based on the PSO-XGBoost algorithm demonstrates a commendable level of reliability, and can also serve as a valuable reference for the recognition of surrounding rock mass properties under tunnel operation.
# Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon
###### Abstract  
In recent years, the incidence of localized heavy rainfall has increased as abnormal weather events occur more frequently. In densely populated urban areas, this type of heavy rain can cause extreme landslide damage, so that it is necessary to estimate and analyze the susceptibility of future landslides. In this regard, deep learning (DL) methodologies have been used to identify areas prone to landslides recently. Therefore, in this study, DL methodologies, including a deep neural network (DNN), kernel-based DNN, and convolutional neural network (CNN) were used to identify areas where landslides could occur. As a detailed step for this purpose, landslide occurrence was first determined as landslide inventory through aerial photographs with comparative analysis using field survey data; a training set was built for model training through oversampling based on the landslide inventory. A total of 17 landslide influencing variables that influence the frequency of landslides by topography and geomorphology, as well as soil and forest variables, were selected to establish a landslide inventory. Then models were built using DNN, kernel-based DNN, and CNN models, and the susceptibility of landslides in the study area was determined. Model performance was evaluated through the average precision (AP) score and root mean square error (RMSE) for each of the three models. Finally, DNN, kernel-based DNN, and CNN models showed performances of 99.45%, 99.44%, and 99.41%, and RMSE values of 0.1694, 0.1806, and 0.1747, respectively. As a result, all three models showed similar performance, indicating excellent predictive ability of the models developed in this study. The information of landslides occurring in urban areas, which cause a great damage even with a small number of occurrences, can provide a basis for reference to the government and local authorities for urban landslide management.  
s 2020 10 8189 10.3390/app10228189 19 November 2020
# On the Role of Clustering and Visualization Techniques in Gene Microarray Data
## 2 Clustering for Microarray Gene Expression Data
### Other Approaches
Biclustering [56; 57], also named subspace clustering, aims at finding a subgroup of genes with similar expressions belonging to a subset of samples. Rows, or genes, and columns, or samples, of a gene expression matrix are clustered simultaneously. The rationale in using biclustering is that, among the large number of genes, only a subset contributes to the target in which a researcher is interested in, while the remaining ones might mask the role of relevant genes in pursuing that target. Furthermore, it has been argued that coexpressed genes might behave independently. * **Multi-objective evolutionary clustering algorithms**. A very recent trend in clustering gene expression data tries to overcome two main deficiencies in clustering techniques when facing with different molecular data sets, namely, (i) the impossibility to discriminate among the importance of features during the cluster formation; indeed, different features could have, and frequently do actually have, different effects on clustering, and (ii) the lack of multiple internal evaluation functions. To this end, in [19], a multiobjective framework has been proposed in order to gain robustness when facing several and different molecular data; to this end, the authors select five diverse group validity indices as multiobjective functions simultaneously optimized, in order to properly seize multiple characteristics of the evolving agglomerations.
# RoBERTa-GRU: A Hybrid Deep Learning Model for Enhanced Sentiment Analysis
## 3 Sentiment Analysis with RoBERTa-GRU
### Data Preprocessing  
Data preprocessing is a crucial step in sentiment analysis as it ensures that the input text is in a standardized format that can be effectively processed by the machine learning algorithms. The preprocessing steps applied in the proposed system are aimed at cleaning the raw text and removing any elements that may negatively impact the sentiment analysis results.  
Case folding is an important preprocessing step that ensures that the text is standardized into a consistent case. This helps to prevent case-sensitive issues that may arise due to the inconsistent use of upper- and lower-case letters in the text. As the texts used in the analysis are mostly tweets, the removal of numbers, punctuation, and special symbols is carried out to eliminate any irrelevant information from the text.  
Stop-words removal is another important preprocessing step as stop words bring less meaning in sentiment analysis. Stop words, such as \"a\", \"an\", \"the\", etc., act as functional words but do not contribute much to the sentiment of the text. By removing these words, the focus is shifted towards the content words that carry more meaning and relevance to the sentiment analysis task.  
Overall, the data preprocessing steps applied in the proposed system help to improve the efficiency and accuracy of the sentiment analysis by removing any irrelevant information from the raw text and standardizing the text into a format that can be effectively processed by the machine learning algorithms.
## 2 Regulation of CTPS Activity by GTP
### Structure-Activity Studies
#### 2.3.3 The Lid L11 Loop  
Based on the structures of _Ec_CTPS (vide infra) [32] and _Tr_CTPS [45], [PERSON] and co-workers identified a loop region denoted as lid L11 (residues 354-362 of _Ec_CTPS, shaded dark green in Figure 2) [32], which immediately preceded the amino acid residues comprising the oxyanion hole as a potential structural motif involved in the GTP-dependent activation of CTPS [84]. Indeed, some of the residues of lid L11 were not detected in electron density maps [45], indicating that this loop is highly flexible. The R359M, R359P, and G360P _L_UCTPS variants (R356 and G357 in _Ec_CTPS) exhibited 10- to 50-fold reductions in GTP-dependent activation of Gln-dependent CTP formation accompanied by 4- to 10-fold increases in the \(K_{\text{A}}\) for GTP (Table 1). While the uncoupled glutaminase activities for these variants were not activated by GTP, the G360A variant was about 2-fold more active than wild-type _L_UCTPS [84]. Overall, these observations suggested that interaction of GTP with the lid L11 is required for the activation of Gln-dependent CTP synthesis. Indeed, deletion of the charge of the side chain in the R359M _L_UCTPS variant supported the notion put forward by [PERSON] and co-workers [32] that Arg 359 interacts with GTP and may act as a lever to alter the conformation of the lid L11, leading to a conformation favoring Gln hydrolysis [84].  
Interestingly, one of the first site-directed mutagenesis studies to probe the role of residues in the GAT domain in the region of the lid L11 was conducted by [PERSON] and [PERSON] [31]. These authors altered Gly 352 in the _Ec_CTPS GAT domain, showing that the G352P variant (Table 1) exhibited no detectable Gln-dependent CTP formation but was almost fully active when NH\({}_{4}\)Cl was the ammonia source. Furthermore, the catalytic Cys 379 at the active site could not be labeled by DON. Although these authors suggested that the mutation either prevented formation of the covalent glutamyl-enzyme intermediate or disrupted NH\({}_{3}\) transfer, their results did not preclude the possibility that GTP-dependent activation was disrupted. Only later did the structure of _Ec_CTPS reveal that Gly 352 resides at the junction between the rigid portion of the oxyanion hole and the lid L11 loop. Substitution of Gly by Pro at position 352 likely abrogated the activation of the glutaminase activity by hindering the flexibility of the lid L11 region [84].
## 3 Results and Discussion
### Biocompatibility of the Nanocarrier  
The cytotoxicity associated with many cancer drugs often gives rise to undesired side effects. To address this concern, nanocarriers that gradually release the drug have emerged as a promising approach for cancer treatment. The primary objective of this study was to compare the toxicity of free MTX with UiO-66-MTX and UiO-66-NH\({}_{2}\)-MTX on monolayer cultures of cancerous cells (MDA-MB-231, MDA-MB-468, and MCF7) and normal breast cell (MCF10A). A series of experiments were conducted to evaluate the impact of different concentrations of all three compounds on cell viability.
# Effects of Flexural Stiffness on Deformation Behaviour of Steel and FRP Stress-Ribbon Bridges
## 4 Numerical Modelling  
Numerical models were developed to analyse the flexural stiffness effect of the ribbons on the deformation behaviour of the suspension bridges. The analysis employs a finite element (FE) software Atena. The deformation problem is solved in the 3D formulation using the Newton-Raphson iteration procedure [28].  
The loading conditions include the dead load (\(g\)) distributed over the bridge span (\(l\)) and the live load (\(q\)) positioned either over the entire or half of the span. At the first stage, the permanent action is simulated. After that, the model is subjected to the live load. Both load components are applied incrementally (in five steps). Two analyses are carried out, investigating the deformation behaviour of the ribbons.  
At the first analysis stage, the numerical models' adequacy is verified using the test results presented in Section 3. Two FE models are considered. One model corresponds to the bridge with flexible bands (_Model-1_); another represents the bridge with flexural-stiff ribbon-profiles (_Model-2_). As shown in Figures 6 and 7, a horizontal movement is characteristic of the supports. Springs are introduced in the numerical models to represent the support movements.  
An inverse analysis was used to determine the spring stiffness corresponding to the measured horizontal displacements of the supports and the estimated thrust forces. The readings of the strain gauges \(T_{i}\) (Figure 5) determined the thrust forces under the assumption of elastic behaviour of the steel ribbons (\(E_{s}\) = 208 GPa). The horizontal spring stiffness was selected to predict the support movement similar to the observed during the symmetrical loading test (\(g\) = 2.22 kN/m). The resultant stiffness is equal to 22 kN/mm. Both models assume the same stiffness of the springs.  
At the second simulation stage, a parametric analysis is performed to identify the efficient combination of the ribbons' cross-section parameters. A balance between stresses in flexural-stiff ribbon-profiles and vertical displacement gradient was the optimisation criterion--three numerical models with different flexural stiffness of the stress-ribbons are built up.
## 1 Introduction
During contention time period, all of the nodes attempt to transmit a Data Transmission Request (DTR) frame to compete for the channel. The centralized Access Point (AP) receives the DTR frame from the node and inserts it into a potential sequence queue without any transmitting requirement of acknowledgement (ACK) frame in the meantime. In TP, the AP successively schedules all nodes in the queue to send the data consecutively by an (acknowledgement) ACK frame piggybacking approach and, thus, no collision occurs during this period. DMAC also is designed to adjust dynamically the length of CP by an adaptive algorithm in order to further improve the channel utilization. This decoupling channel access mechanism has been demonstrated that it can significantly improve the channel utilization in our previous work [13]. Subsequently, in this paper, we also inherit the merit of the decoupling channel access and simultaneously address the problem of performance anomaly in the dense multi-rate network. Furthermore, we propose two airtime fairness algorithms for different application scenarios, referred to as I-DMAC1 and I-DMAC2 algorithm, respectively. I-DMAC1 provides multiple contention opportunities for high-rate nodes during the contention period, and I-DMAC2 enables the nodes to transmit multiple packets according to their data rates during TP. The main contributions of this paper are summarized, as follows:  
* Firstly, an efficient hybrid channel access mechanism DMAC for dense multi-rate network is proposed. The proposed mechanism can achieve collision-free data transmission by decoupling channel contention and data transmission. Additionally, an adaptive algorithm is designed in DMAC to dynamically adjust the length of CP and further improve the channel utilization. Secondly, we also propose two airtime fairness algorithms I-DMAC1 and I-DMAC2 based on DMAC, and tackle the performance anomaly for different application scenarios in multi-rate network, such as delay-sensitive and high-throughput networks, respectively. * Thirdly, we establish a mathematical analysis model for DMAC. It is theoretically proven that the system throughput can be improved by decoupling the data transmission and contention process. * Lastly, we extensively evaluate the performance of the proposed algorithms in saturated and non-saturated network scenarios, and the simulation results verify that I-DMAC1 algorithm is more suitable to the delay-sensitive data transmission scenarios and I-DMAC2 algorithm can perform the better performance in some high throughput demand scenarios. The remainder of the paper is organized, as follows. Section 2 introduces some literature related to this paper. Section 3 presents the DMAC and I-DMAC algorithm. In Section 4, we validate the performance of this decoupling channel access algorithm by numerical analysis. In Section 5, we present the simulation results. Finally, Section 6 concludes this paper.
# Motion Target Localization Method for Step Vibration Signals Based on Deep Learning
## 2 Location Scenario Description  
In a 2D space of size \(m\times n\), the floor is made of tile material. \(M\) seismic geophone nodes \(S=\{\text{s}_{1},\ \dots,\text{s}_{i},\ \dots,\text{s}_{\text{M}}\}\) are uniformly deployed in the corners to establish a vibration sensing network, with the coordinates of node \(i\) being \(L_{i}=[x_{i},y_{i}]^{T}\). This configuration ensures that the sensing network's detection range covers the observation area and captures effective signals, as shown in Figure 1.  
To simplify the problem and validate the effectiveness of the localization algorithm, this paper focuses on positioning a single moving target (as shown in Figure 1). When the moving target generates vibrations, the working principle of the geophones involves the relative movement of the internal coil and permanent magnet due to the ground vibrations, which cuts through the magnetic field lines and induces an electromotive force in the wire. This induced voltage is proportional to the vibration acceleration. Thus, the vibration detected by the \(i\)-th node is converted into a voltage signal, which is then amplified by an operational amplifier. The amplified signal is subsequently digitized by an analog-to-digital converter to yield the vibration signal, denoted as \(e_{i}(t)\). The entire vibration sensor network can collect \(M\) channels of vibration signals \(e(t)=\{e_{1}(t),\dots,e_{i}(t),\dots,e_{M}(t)\}\).  
To train the localization model, supervised learning is employed. The actual trajectory of the moving target is recorded by a camera and used as supervised information to ensure the accuracy of the supervision data, as shown in Figure 2.
## 5 Conclusions  
With the randomness of the mechanical parameters of the surrounding rock and lining materials, the ISSA-Kriging-MCS model was constructed based on the theory of structural reliability analysis. The model can effectively deal with implicit performance functions and complex nonlinear problems, providing a scientific basis for evaluating the rationality and reliability of reinforced concrete lining structures during high-pressure tunnel construction. The main research conclusions are as follows:  
1. The ISSA-Kriging-MCS method is suitable for dealing with projects with a high computational volume and a high analysis cost. Compared with other classical examples, the proposed method effectively improves the computational efficiency and fitting accuracy, thus verifying its effectiveness and practicability in the field of reliability analysis.
2. The reliability indexes of the surrounding rock-reinforced concrete lining structure under multiple failure modes met the requirements of the corresponding engineering structure standards. This proves the safety and reliability of the adopted lining form in engineering applications.
3. The change in random variables can affect the sensitivity of different failure modes to reliability indexes, leading to corresponding changes in the main failure modes of tunnel structural systems. Additionally, the reliability of engineering structures can change over time due to the material aging and environmental conditions. Therefore, considering time-varying reliability issues will be the direction of future research.  
Y.W.Writing--Review and Editing, Resources, and Validation. C.X.: Writing--Original Draft and Data Curation. L.Y.: Supervision and Visualization. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 12: The variation curves of random variables under different failure modes.  
Data Availability Statement: The data that support the findings of this study are available upon request from the corresponding author.  
Author [PERSON] was employed by the company Beijing Engineering Corporation Limited. The remaining authors declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.