esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Fault-Tolerance Control Strategy
### Simulation Verification  
The simulated automobile model is built up in CarSim 2019 for various failure mode simulation experiments, and the fault-tolerant control tactics model is developed in MATLAB/Simulink. The simulation verification is shown as follows.
## Appendix A  
The graphene-based circular patch-shaped Yagi-like MIMO antenna has been numerically investigated in the frequency range of 1-30 THz. The different antenna modes were initially investigated to identify the optimized results of the MIMO structure. Table 1 shows the different structures and types of the antenna simulated to check optimized results. Figures 11-14 show the variation in the return loss, two-dimensional directivity, and three-dimensional directivity response.  
Figure 11: (**a**) Calculated S parameters for the type-2 antenna. Calculated directivity in (**b**) 2D (with co- and cross-polarization) and (**c**) 3D, by considering single-port (Port 1) excitation.
# A New Method to Determine the Steel Fibre Content of Existing Structures--Evaluation and Validation
## 1 Introduction  
Based on a recently developed easy-to-use test setup [1], a new application to assess the steel fibre content of existing structures based on the electrical resistivity analysis of drilling cores is described in this two-part study. Therefore, in the first part (see [2]), the test setup was adapted. A new numerical model was also generated to simulate the flow of the alternating current, which enables the calculation of the electrical resistivity in different specimen directions. An in-depth literature review on the methods concerning the determination of the steel fibre content is presented in [1, 2].  
Both parameters, fibre content and fibre orientation, are tremendously important for influencing the mechanical parameters of steel fibre reinforced concrete (SFRC) (see [3, 4, 5, 6]). In general, a higher fibre content and fibres, oriented in direction of the tensile forces, lead to very ductile material behaviour and an increase in the tensile strength of the composite material (see [7, 8, 9, 10, 11, 12, 13]). In the case of new structures, the global fibre content can easily be determined and monitored during the mixing process by the mass of the fibres that are added to the concrete or alternatively by washing the fibres out of a fresh concrete sample. After the casting process, the determination of the fibre content and the detection of areas with locally lower fibre contents or an unfavourable fibre orientation is no longer possible via an easy method. Hence high safety factors are required for the structural design of SFRC (see [14, 15, 6, 16]). Additionally, the repair and restoration of SFRC buildings only can only be planned and realised in a safe way when the fibre content of the existing structureis known (see [10, 17]). In case it is not possible to determine the actual fibre content of concrete structures, a repair process is only possible with very pessimistic assumptions for the flexural strength of the SFRC elements. Based on this, an economical method of construction is not possible, and additionally, areas with a very low percentage of reinforcement cannot be detected and could lead to a local collapse of the whole structure. Electrical resistivity measurements are one possible non-destructive test method for this analysis. This approach is beneficial since it is easier to apply and less costly compared to other methods such as CT-scanning, cross-sectional analysis via microscope or inductive techniques (see [12, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]). With the help of an optimised experimental setup, this study focuses on the validation of electrical resistivity as a novel approach to assess the fibre content and orientation of drilling cores extracted from concrete plates with fibre contents up to 80 kg/m\({}^{3}\). Correlations between the electrical resistivity and the fibre content and fibre orientation are derived, which enable a later non-destructive use of adjusted electrode configurations on structural elements and buildings.
## 2 Materials and Experimental Procedure
### Slurry Pot Tester  
There are several types of E-C test rigs, including a slurry pot tester, rotating disc rig, slurry jet erosion wear test rig, and flow-through slurry wear tester. The slurry pot and jet impingement testers are the most commonly used rigs to test erosion and corrosion. A slurry pot tester is inexpensive and easy to operate [36; 37; 38]. A slurry pot tester was designed and fabricated to study the E-C of a variety of materials [9]. The setup for E-C experiments consists of a stainless steel cylindrical vessel to handle the slurry. A cylindrical disc with a diameter of 200 mm and thickness of 25 mm is fastened to a shaft and the entire assembly is attached to the mandrel of a drilling machine through a coupler. Both the disc and coupler were made of aluminum alloy. A 1.5 kW motor with varying speeds from 340 rpm to 1670 rpm is used for rotating the shaft. In order to have various linear velocities, the samples were mounted at different radial distances. The sample holder can be revolved to enable the slurry impacting the surface of the specimens at different angles. No interaction effects from multiple samples mounted on the disc at different radii. Figure 3 shows the details of the radial positioning of the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Channel Diameter,** \\ **mm** \\ \end{tabular} & \begin{tabular}{c} **Lubricant** \\ **Temperature** \({}^{\circ}\)**C** \\ \end{tabular} & \begin{tabular}{c} **Channel** \\ **Angle** \\ \end{tabular} & \begin{tabular}{c} **Outer Corner** \\ **Angle** \\ \end{tabular} & \
\begin{tabular}{c} **Ram Speed,** \\ **mm/s** \\ \end{tabular} &
\begin{tabular}{c} **Route** \\ \end{tabular} \\ \hline
20 & MoS\({}_{2}\) & 28 \(\pm\) 3 & 90\({}^{\circ}\) & 17\({}^{\circ}\) & 2 & B\({}_{\text{C}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: ECAP conditions.  
Figure 2: Setup of equal channel angular pressing (ECAP). (**a**) Schematic of ECAP process; and (**b**) two halves of die and sample after ECAP.  
samples on the disc. The turbulence, vortex, and leakages of liquid were prevented by fixing the cover of the pot tightly with a rubber \"O\" ring. Additionally, the pot was fully filled with the slurry erodent medium during the experiments.
## 3 Results
### Metabolite Identification Based on UPLC-Orbitrap-MS
Compounds **6**, **8**, and **9**, showed a molecular ion peak at \(m/z\) 431.0984, 447.0934, and 463.1410, respectively, and showed a similar fragmentation pattern. Compounds **6** and **8** showed a similar fragment ion peak at \(m/z\) 285, 255, and 227, corresponding to kaempfer aglycone. Compound **6** showed fragment ion at \(m/z\) 285 [M-H-146]\({}^{-}\) due to the loss of deoxyhexose [33], and compounds **8** showed fragment ion at \(m/z\) 285 [M-H-162]\({}^{-}\) due to the loss of hexose [32]. Both compounds **6** and **8** further exhibited fragment ions at \(m/z\) 255 and 227 by the loss of CH\({}_{2}\)O (30 Da) and the further loss of CO (18 Da), respectively [32]. As a result, compounds **6** and **8** were tentatively identified as afzelin and kaempfer-3-O-glucose, respectively. Fragment ions of the aglycone ion at \(m/z\) 301 and the aglycone radical ion at \(m/z\) 300 were observed from the loss of hexose unit (-162 Da) for compound **9**. Along with other products, ions at \(m/z\) 271 and 255 showed an identical resemblance to the previously identified hyperoside [34]  
Figure 2: Structures of flavonoids and phenolic compounds that were tentatively identified from _Raphanus sativus_ samples. In the case of compounds **11-15**, their pseudomolecular ion peaks [M+H]\({}^{*}\) and [M-H]\({}^{-}\) were represented as \(m/z\) 563.1407, 579.1710, 595.1663, 727.2083, and 741.2238, respectively. The MS2 spectrum of compound **11** showed a fragment ion peak \(m/z\) 431 or 433 in the positive ion mode together with the formation of a radical anion at \(m/z\) 430, which corresponds to the loss of pentose [M-H-132]\({}^{-}\). This was followed by the fragment ion peak at \(m/z\) 285 indicating the loss of deoxychexose -146 Da. Thus, compound **11** was tentatively identified as kaempfer 3-O-xyloside-7-O-rhammoside. Compound **12** fragment ions were observed at  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**No.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 3 Research Gaps Addressed by This Study  
While the body of research on Quiet Eye (QE) in sports performance has expanded, certain areas remain underexplored, particularly in the context of badminton and the integration of QE metrics with machine learning models for predicting shot accuracy:
## 1 Introduction
But the spatial resolution of the available instruments is relatively low. In the exploration of helium resources, a low spatial resolution may lead to misjudgments regarding the extent of helium-rich layers, which is unfavorable for helium resource exploration. In order to achieve a high spatial resolution, we designed a helium detector based on a quadrupole mass spectrometer. Through the combination of different inlet valves, high-spatial-resolution helium concentration detection was achieved, which can not only avoid the omission of helium-rich layers, but also provide important information for the study of helium migration. In this paper, the helium detector was applied to the Dongsheng gas field in the northern Ordos Basin. Combining the instrument test results and previous research data, we discuss the spatial distribution characteristics of helium and provide a model of its migration and accumulation in this area.
# Repetition with Learning Approaches in Massive Machine Type Communications
## 1 Introduction
For the selection of the CE level and repetition times, we adopted KNN and SVM to propose a learning-based repetition approach, which effectively saves energy efficiency and improves overall network transmission performance. In the present study, a repetition with the learning approach (RLA) was proposed and developed with the aim of substantially increasing the network transmission efficacy. The main contributions of this paper are summarized as follows: (1) we study the problem of repetition number selection for the eMTC system in the mMTC scenario, where the main goal is to minimize the average energy consumption and block error rate (BLER) and maximize the successful transmission probabilities of UE pieces and resource utilization. (2) Due to the limited UE energy efficiency and slave resources of eMTC, we provided the easy-to-implement and learning-based policy for repetition number selection to ensure that eMTC saves energy efficiency  
Figure 1: CE level and repetitions for eMTC. and improves transmission efficiency. (3) We adopted the KNN approach and selected the appropriate training parameters meaningfully so that the KNN approach could be easily implemented and could have high accuracy. (4) We adopted the SVM approach to avoid the neural network structure selection and local minima problem and chose the kernel function significantly. (5) Finally, we conducted an extensive numerical analysis to evaluate the performance of the proposed repetition number selection approaches, and the simulation results show that our proposed approaches (RLA-KNN and RLA-SVM) are more efficient than the common lookup table (LUT) method. The proposed repetition with the learning approach can effectively improve the probability of successful transmission, resource utilization, average number of repetitions, and average energy consumption. The remainder of this paper is organized as follows. Section 2 describes the eMTC system. The proposed approaches are presented in Section 3. In Section 4, we evaluate the performance of our proposed approaches through simulation. Finally, Section 5 provides the conclusions.
## 3 Background
### Other Key Components Selection
#### 3.5.2 Primary Structure  
CubeSat structures are a rapidly expanding technology; recent developments include composite, 3D printed and custom machined structures. As CubeSats are of fixed U dimensions [1] a large variety of off-the-shelf CubeSat structures are readily available from a range of suppliers. A key purpose of the CubeSat structure is to provide attachment points for the payload and sub-systems, as well as transmitting loads from the spacecraft. This is the primary structure. The secondary system encompasses the solar panels and thermal blankets, components which typically support themselves [23]. A primary structure failure would be fatal to the mission therefore selecting a suitable component is vital to mission success. The structure also acts as a thermal manager, radiation shield, and pressure containment vessel and strain actuation device [23].  
A large share of the off-the-shelf structure market consists of machined 6061-T6 or 7071 aluminum configurations. The Monocoque design developed by Pumpkin, Inc. (San Francisco, CA, USA) [48] has been selected as the CubeSat's primary structure (Figure 32). This design carries load via an external skin, maximizing internal volume. Thus, structure is machined from an Aluminum 6061-T6 block and is assumed to be isotropic. This structure is priced at $8750.00 [48] which as of March 2016 is equal to $7057.02 [49].  
Many other designs are available on the market, each with their respective advantages and disadvantages. Other remarkable designs include Modular Frame and Card Slot systems.
## 3 Proof of Security
### External Eavesdropping  
Firstly, [PERSON] cannot obtain full key information by beam-splitting attacks and intercept-resend attacks, which will result in bit errors in the secret key [22]. As for a general individual attack, based on the assumption that [PERSON] will conduct the same attack in differential phase shift QSS [20], we can derive that information leakage to [PERSON] is given by a fraction \(\mu_{a}(1-\eta_{a})+\mu_{b}(1-\eta_{b})\) of the sifted key [25].
## 2 Algorithm Architecture
### Localization Algorithm Architecture  
Without Differential Global Positioning System(DGPS), vehicle localization is essentially required to drive properly on the designated driving route. Therefore, a precise level of vehicle position estimation is conducted with environment sensors, GPS, and proprioceptive sensors in order to carry out vehicle control. The output of the localization part, vehicle global position, and yaw angle is delivered to the mission planner part and the vehicle control part.  
The vehicle positioning process can be performed in several steps. Figure 2 depicts the scheme of the proposed localization algorithm. The chassis sensor and commercial GPS are used for the dead reckoning(DR) process of vehicle position filters. Lane marks are detected by processing monocular Mobileye vision. A guardrail is perceived by the front radar by distinguishing low-speed object measurements and by accumulating several steps to extract wall features. The high definition map that we utilized for map-matching is constructed and contributed by the National Geographic Information Institute (NGII) of Korea. Then, the amount of position correction is calculated between current measurement data and the loaded map based on the iterative closest point (ICP) algorithm. The correction result of lanes and guardrails is checked by a passing validation gate in order to fuse them properly. Time delay due to the scanning rate of the sensor and system is compensated to enhance correction accuracy. Finally, the proposed algorithm provides the vehicle's global position, yaw angle, and covariance of position estimation.  
Figure 1: Overall architecture.  
The key issues of research are considered in localization: environment detection with installed environment sensors and map-matching-based correction, fusion, and filtering.
## 3 Results
### Comparative Analysis of GC-IMS and GC-MS  
In this study, 25 substances were detected using GC-IMS, while 62 substances were identified with GC-MS. As shown in Figure 4E, both methods detected esters, alcohols, ketones, aldehydes, and furans. Additionally, GC-IMS detected ethers, whereas GC-MS identified olefins, aluminum, acids, and various other substances. The compounds detected by GC-MS were more comprehensive, while GC-IMS exhibited high sensitivity to specific substances. The differences in the VOCs characterized by the two methods can be attributed to several factors: 1. Different pre-treatment; 2. different extraction methods for aroma compounds; 3. different detection principles of the instruments; and 4. variations in the spectrum libraries of the processing software. As illustrated in Figure 4F, esters accounted for the largest proportion of detected substances in both methods. GC-IMS detection involved the initial separation of substances through the chromatographic column, followed by their introduction into the IMS tube, where the particles were distinguished based on their different drift rates in the electric field [45]. A unique characteristic of GC-IMS is its ability to differentiate between monomers and polymers. IMS operates at atmospheric pressure, eliminating the need for instrument vacuum time. However, during the analysis process, ion-ion and ion-molecule competition reactions occur, which can reduce the substance discrimination capability of IMS [46]. Although GC-IMS effectively demonstrates differences and changes between samples, accurate quantification remains challenging. When employing GC-MS, the SPME method is necessary for extraction to achieve optimal detection results, which is time-consuming and complex to perform. Nevertheless, GC-MS enables accurate characterization through ion fragment comparison and is suitable for quantitative determination. This method also exhibits strong separation and recognition capabilities [47]. Both GC-IMS and GC-MS have their advantages and limitations. They can be used in combination or selected based on specific experimental requirements. In practical applications, GC-IMS is commonly used for rapid detection scenarios, such as distinguishing the quality of edible oil, the type and quality of coffee beans, etc. By contrast, GC-MS is often used for component determination in professional fields due to its comprehensive spectral library and accurate detection capabilities, which is commonly used in fields such as medicine, biology, and chemistry [48; 49; 50]. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{3}{*}{**Category**} & \multirow{3}{*}{**NO. **} & \multirow{3}{*}{**Compound**} & \multirow{3}{*}{**CAS**} & \multicolumn{3}{c}{**Concentration (ng/g)**} & \multicolumn{3}{c}{**Std**} \\ \cline{5-10}  & & & **0** & **42.4** & & & & **Std** & & \\ \cline{5-10}  & & & & **CX** & **FT** & **IT** & **LT** & **FT** & **IT** & **LT** & **  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**NO.
## 2 Materials and Methods
### Implementing a Concept CDHM Software on WASM3 Container  
So, what are the proposed new methods of developing CubeSat software with the intent of using SCRUM-like development life-cycle and containerization? They are:  
* Each container represents either one functional block of the CubeSat (ADCS, EPS, etc.) and/or the separate OBC or payload function, see Figure 13;
* During the development and V&V process of each separate container, the separate team or the team member develops each separate container;
* Due to the fact that the containers are cross-platform, the verification and development of each separate container is carried out at the PC and not at the CubeSat hardware.  
As the basis of the solution of the container's engine implementation on the typical CubeSat hardware, the WASM3 interpreter engine was chosen. To be able to run WASM3 on the embedded target of any type, the following minimal infrastructure is to be ported:  
* The file system: this is required to store containers and be able to upload those for execution;
* CLI (command line interface): This allows manipulation with the containers (run, stop, load, etc.) and allows the user to see the system parameters in real-time;
* The HTTP server: This will allow for the simple and easy-to-implement OTA (over-the-air) transfer of the container images from a PC to the embedded target (for the communication with the ground stations, AX.25 over HTTP can be used).  
Figure 13: A CubeSat CDHM containerization concept.  
As the typical RTOS for the CubeSats is the FreeRTOS by Amazon, the overall architecture of the solution looks as follows (Figure 14):
## 4 Results
### Descriptive Statistics and Linear Regression Estimation
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Mean** & **SD** & **FL** & **AVE** \\ \hline \multicolumn{4}{c}{Output Variable (USE)} \\ USE1 = ChatGPT & 1.58 & 1.81 & — & — \\ USE2 = Geminii & 0.385 & 1.07 & — & — \\ USE3 = Microsoft Copilot & 0.571 & 1.29 & — & — \\ USE4 = Perplexity & 0.099 & 0.56 & — & — \\ USE5 = Other & 0.632 & 1.37 & — & — \\ USEINT & 1.87 & 1.87 & 1 & 100\% \\ \multicolumn{4}{c}{Input Variables} \\ \hline  & Mean & SD & FL & AVE \\ \hline \multicolumn{4}{c}{Performance expectancy (PE)} \\ PE1 & 7.88 & 1.91 & 0.79 & 55.60\% \\ PE2 & 6.28 & 2.32 & 0.69 & \\ PE3 & 7.56 & 1.93 & 0.78 & \\ PE4 & 6.41 & 2.1 & 0.72 & \\ \multicolumn{4}{c}{Innovativeness (INNOV)} \\ INNOV1 & 4.63 & 3.07 & 0.73 & 56.10\% \\ INNOV2 & 4.1 & 2.79 & 0.82 & \\ INNOV3 & 4.52 & 2.94 & 0.68 & \\ \multicolumn{4}{c}{Privacy risk (PRI)} \\ PRI1 & 3.44 & 0.654 & 0.62 & 50.02\% \\ PRI2 & 3.06 & 0.762 & 0.75 & \\ PRI3 & 2.72 & 0.851 & 0.72 & \\ PRI4 & 3.23 & 0.705 & 0.76 & \\ PRI5 & 2.74 & 0.863 & 0.67 & \\ \multicolumn{4}{c}{Knowledge (KNOW)} \\ KNOW & 4.36 & 2.23 & 1.00 & 100\% \\ Social usefulness (SU) & & & & \\ SU1 & 1.63 & 0.753 & 0.74 & 54.10\% \\ SU2 & 1.62 & 0.763 & 0.74 & \\ SU3 & 1.78 & 0.837 & 0.73 & \\ \multicolumn{4}{c}{Social risks (SR)} \\ SR1 & 4.37 & 1 & 0.71 & 50.04\% \\ SR2 & 4.1 & 1.18 & 0.75 & \\ SR3 & 3.79 & 1.33 & 0.72 & \\ SR4 & 3.57 & 1.35 & 0.64 & \\ \multicolumn{4}{c}{Regulation (REG)} \\ REG1 & 4.42 & 0.905 & 0.84 & 55.40\% \\ REG2 & 4.54 & 0.824 & 0.70 & \\ REG3 & 4.52 & 0.843 & 0.86 & \\ REG4 & 4.01 & 1.15 & 0.64 & \\ REG5 & 4.36 & 0.95 & 0.77 & \\ \multicolumn{4}{c}{Education (ED)} \\ ED & 2.53 & 0.543 & 1.00 & 100\% \\ \multicolumn{4}{c}{Income (IN)} \\ IN & 3.46 & 1.92 & 1.00 & 100\% \\ \hline \end{tabular} Note: In PE, INNOV, PRI, SU, SR, and REG, the unicity of the correlation matrix was rejected using the Bartlett test with \(p\) \(<\) 0.001. SD, standard deviation; FL, factor loading; AVE, average variance extracted.  
\end{table} 
Table 3: Descriptive statistics of variables used in this paper. ED (\(\beta\) = 0.199), and SU (\(\beta\) = 0.140), all statistically significant with moderate-to-small effect sizes. In contrast, GEN and SEX show significant negative effects (\(\beta\) = -0.730 and \(\beta\) = -0.156, respectively), suggesting that being female may act as a barrier to adoption. Additionally, REG (\(\beta\) = \(-\)0.153, \(p\) < 0.001) negatively predicts usage, indicating that regulatory concerns may discourage engagement with GAI tools. Other variables, such as PE, PR, and IN, exhibit small but statistically significant positive effects. In summary, the results from linear regression support all the proposed hypotheses except H3 and H6, which were not statistically significant.