esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
## 2 Spatial Deformation and Soil Pressure Distribution of Retaining Structure
### Spatial Deformation of Retaining Structure
#### 2.1.1 Crown Beam Deformation  
Multiple sets of measured data and numerical simulation results have consistently shown that the deformation of the crown beam exhibits substantial spatial effects [13; 14; 15]. At the specific corner of a rectangular foundation pit, it is notable that the crown beam exhibits minimal to no horizontal displacement. Conversely, the maximum displacement is observed precisely at the middle of the foundation pit. This simplified deformation mode can be visually understood by referring to Figure 2.  
Figure 1: Pile–anchor retaining structure.  
Figure 2: Simplified model of horizontal deformation of crown beam.  
After excavation of the foundation pit, the crown beam has a large rotation at the corner of the foundation pit, resulting in bending and torsion. The corner can be assumed to be a fixed support, which is a constrained torsion problem. The crown beam deformation curve can be expressed as Equation (1):  
\[\delta=\delta_{0}\sin(\frac{\pi}{l}\pi) \tag{1}\]  
In this formula, \(\delta\) is the displacement of the crown beam at a certain distance \(x\) from the corner of the foundation pit, \(\delta_{0}\) is the displacement of the crown beam in the middle of the foundation pit towards the foundation pit, which is also the displacement of the pile top in the middle of the foundation pit, and \(l\) is the side length of the foundation pit.
###### Abstract  
In this paper, in view of the low efficiency of the traditional finite element method (FEM), which has been widely used in the insulation design of power transformers, the response surface methodology (RSM) is proposed to optimize the insulation structure of a power transformer electrostatic ring. Firstly, the power transformer model was built using the ANSYS parametric design language (APDL) to realize the automatic pre-processing of numerical calculation. Then with the objective of reducing the maximum electric field intensity, the Taguchi method was used to select the parameters that have a greater impact on the maximum electric field intensity, by which the subsequent optimization process could be effectively simplified. The test points were constructed by the central composite design (CCD) and a response surface model was established by the mutual calls of MATLAB and ANSYS. Finally, the variance analysis, diagnostic analysis, and significance test of regression were carried out to obtain the final response surface model. By comparing the result of RSM with that of FEM, we can find that the results obtained by the two methods are consistent and the maximum electric field strength is obviously reduced. The RSM is more systematic and convincing, which improves the optimization efficiency and provides a reliable and fast way for the optimization of power transformers.  
power transformer; response surface methodology (RSM); ANSYS parametric design language (APDL); electric field analysis; electrostatic ring optimization Article
# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 3 Security and Safety Issues  
UAVs are foreseen to play a major role in various domains of 5G and beyond networks, supporting not only improved communication but also safety applications and critical infrastructures. Moreover, the tremendous growth of the UAV market will result in numerous heterogeneous air-to-ground deployments with increased densities of UAV nodes, further necessitating their protection against cyber-attacks across different network layers.
# Enhanced Photocatalytic Performance of Bi2O2 CO3 Loaded Activated Carbon for Toluene Removal in Air
## 3 Results and Discussion  
### Characterization and Properties of Bi\({}_{2}\)O\({}_{2}\)Co\({}_{3}\)  
The crystal structure of the BOC nanomaterials prepared by chemical method was analyzed by XRD. As displayed in Figure 2, all diffraction peaks of the sample can be well indexed to the single phase of tetragonal Bi\({}_{2}\)O\({}_{2}\)CO\({}_{3}\) (JCPDS 41-1488) with good crystallinity, without any other impurity peaks. Moreover, the average crystal size of the BOC nanosheets was estimated to be ca. 80 nm using the XRD patterns.  
Figure 1: Schematic illustration of gas-phase photocatalysis testing system. (**a**) Thermostatic test chamber, (**b**) PVF pocket, (**c**) light source, (**d**) gas delivery pump, (**e**) gas chromatograph (GC), (**f**) vacuum pump, (**g**) gas mass flow meter, (**h**) toluene gas, and (i1–i2) three-port value.  
The morphology of the BOC sample was investigated by SEM. As shown in Figure 3, the BOC has a flower-like shape assembled by nanosheets during the crystallization process, with lateral sizes of several micrometers and a thickness of a few nanometers. Thus, the flower-like shape can provide more reactive sites owing to the unique layered structure of Bi\({}_{2}\)O\({}_{2}\)CO\({}_{3}\)[29, 30].  
The photocatalytic activity of the BOC sample for removing gaseous toluene in air was investigated at a concentration of 50.0 ppm (450 mL) under irradiation by a Xe lamp as the light source. As displayed in Figure 4, the pristine BOC sample demonstrated an excellent photocatalytic efficiency for toluene removal with a degradation rate as high as 96.8% over 3 h.  
Figure 3: SEM images of Bi\({}_{2}\)O\({}_{2}\)CO\({}_{3}\).  
Figure 2: XRD patterns of the as-synthesized Bi\({}_{2}\)O\({}_{2}\)CO\({}_{3}\).
## 3 Cooperation Architecture and Models  
### Virtualized-Service and Event Based Cooperation Architecture  
As aforementioned, deep cooperation permeates the whole system, from tasks at the top level to heterogeneous resources at the bottom level. Obviously, although mapping one task to certain resources is possible, it is hard to carry out every task by scheduling corresponding resources directly. The main reason is the properties of each resource are too simple and crude to manage and schedule; furthermore, these distributed heterogeneous resources are always used in different manners. Thus, one main step for this work is to establish a general schedulable model that encapsulates diverse distributed resources with the idea of Infrastructure as a Service (IaaS) [50]. On this basis, it is necessary to establish relevant task planning and execution mechanisms. Considering these features and requirements, we proposed a cooperation architecture with the essential concepts of resource encapsulation and virtualized service. The whole structure and information/control flows are shown in Figure 1. At the bottom, all physical resources are basically separated into four types, namely the platform resource (e.g., robots and UAVs, also called the carrier resource), processing resource (e.g., diverse processors and storage), payload resource (e.g., camera and LiDAR.), and data resources that are valuable information shared among members. All these resources are encapsulated with a unified service model that will be detailed in the following sections. Furthermore, all such encapsulated resources will be exposed to applications as schedulable services living within a virtualized service pool. The management agent of service pool is event-based, and handles any cooperation events. Its functionalities involve the automatic management of all services, assessment of the capabilities of services, and scheduling of special services.  
Task parsing component is at the top layer, and includes a three-level task model described in next subsection. According to parsing rules, this component resolves one mission into a set of atomic tasks and a set of cyber-physical constraints. The core part of this architecture is the intelligent planner. This component will, firstly, evaluate whether all requested services can provide required capabilities. If satisfied, it will then try to find out one feasible optimal map from the task set to the service pool under multiple cyber-physical constraints. If there exists such a solution, it will further generate the event set with respect to these constraints. Event scheduler on each member of one swarm system finally schedules certain services in accordance with the events being triggered. The information and control flows for this architecture are also implied in Figure 1.
# Boosting Safety Protocol Compliance with Eye-Tracking in Chemical Plants
## 2 Materials and Methods
### Experiment Environment
#### 2.3.1 Chemical Safety Training Simulation Platform  
The chemical safety training simulation platform was composed of four interconnected subsystems: (1) the eye-tracking data acquisition module, (2) the data processing center, (3) the human-computer interaction (HCI) interface, and (4) the VR simulation system (Figure 3). These components worked in tandem to deliver real-time feedback to participants, monitor their visual attention, and analyze performance metrics related to safety protocol adherence. The wireless communication infrastructure ensured seamless data exchange between the modules, creating a cohesive and dynamic training system.
## 3 Results
### Temperature Analysis  
To analyze the temperature effects in the fiber laser, the MFI was set over a hotplate, and the temperature was varied from 90 \({}^{\circ}\)C to 210 \({}^{\circ}\)C. This range responded to the cross-sensitivities presented by the MFI [41], which requires temperatures over 100 \({}^{\circ}\)C to recognize thermal effects. The laser spectrum at 90 \({}^{\circ}\)C was comprised of triple-laser emissions. For P2 and P3, the spacing mode between them was around 1.6 nm and separated 13.23 nm from a new laser emission, P6, which was centered at 1543.33 nm. The laser emissions had the following SMSR: 37 dB (P2), 41 dB (P3), and 43 dB (P6). Their peak wavelengths were \(-\)23.40 dBm (P2), \(-\)19.19 dBm (P3), and \(-\)17.04 dBm (P6). The fiber laser's response to temperature can be seen in Figure 8. The increase in temperature produces a shifting response in the multi-wavelength. However, at 145 \({}^{\circ}\)C, the laser spectrum becomes a quadruple-laser emission; this new spectrum is comprised of P2, P3, P5, and P6. As we increase the temperature, some peaks, such as P2, P5, and P6 continue shifting to longer wavelengths. At the same time, other laser emissions start to appear, such as P1 and P0 (wavelength centered at 1528.86 nm (P0) and 1530.33 nm (P1), peak wavelength \(-\)23.08 dBm (P0) and \(-\)20.02 dBm (P1), and SMSR of 38 dB (P0) and 41 dB (P1) at 210 \({}^{\circ}\)C).  
Figure 8: Laser emission response for temperature variation from 90 \({}^{\circ}\)C to 210 \({}^{\circ}\)C.  
Figure 7: Loop hysteresis analysis for refractive index variation using the P2 peak.  
In analyzing peaks P2, P3, and P6, the following thermal-wavelength relations can be expected: 21.01 pm/\({}^{\circ}\)C (P2), 16.99 pm/\({}^{\circ}\)C (P3), and 16.97 pm/\({}^{\circ}\)C (P6), as well as adequate linear responses of 0.96306 (P2), 0.99507 (P3), and 0.9890 (P6). As can be observed in Figure 9 the dynamic range is different for each peak; however, the temperature variations generated different triple or quadruple spectra. The tunable multi-wavelength effect is only presented in the range from 90 \({}^{\circ}\)C to 125 \({}^{\circ}\)C. According to the MFI temperature's analysis, the visibility remained almost constant and only presented wavelength shifting. As a result, the multi-wavelength shifting range was shorter than the one presented in the refractive index analysis. The appearance of other lasing modes was evident as the temperature increased because of the modal competitions was similar for all the lasing modes involved. It is well recognized that fiber-optic interferometers exhibit a suitable sensitivity for refractive index and temperature variations [(44)]; however, the ring laser cavity represents an alternative method to extract phase information [(1; 2; 3; 4; 5; 6; 7; 8; 9)].
## 3 Congestion Prediction Model  
### Preliminaries  
#### 3.1.1 Cobb-Douglas Production  
When evaluating the mutual influence of all variables of economics, relative change (_i.e._, resilience) is typically more useful than absolute value. It has been noted that the value indicating resilience is similar to the derivative of a natural logarithm. Therefore, if an expression can be expressed as a natural logarithm, then resilience analysis will be convenient. The formula that is most convenient for analysis is a linear function of the primary addition, _i.e._, the equation of the straight line. To satisfy both of these requirements, the Cobb-Douglas production function was defined as follows:  
\[Q=AL^{a}K^{\beta} \tag{1}\]  
where \(Q\) is product, \(K\) is capital, and \(L\) is labor. \(A\) is an arbitrary constant with the value of the amount, \(\alpha\) and \(\beta\) are constants greater than zero and less than 1, respectively. The Cobb-Douglas production function is a production function of linear homogeneity. If each factor increases its ratio at the same time, the product also grows at the same rate and attribution principles are established.  
\begin{table}
\begin{tabular}{c c c} \hline
**Types of Network** & **Traffic Prediction Method** \\ \hline Traditional & Markov, Poisson, Linear Regression, Time Series Forecasting \\ \hline \multirow{5}{*}{WSN} & \multirow{2}{*}{Defend Attack} & Intrusion Detection: [23], AR [24], ARMA [25], S-ARMA [21], \\  & & Anomaly Detection: [26] \\  & \multirow{2}{*}{Poison} & [PERSON] [27] \\  & [PERSON] & [PERSON] [28] \\ \cline{1-1}  & \multirow{2}{*}{Markov} & [PERSON] [29] \\ \cline{3-3}  & & Traffic Arrival Process \\ \cline{1-1}  & & \multicolumn{1}{c}{} \\ \multirow{2}{*}{Traffic Analysis [26]} & Sequence Relations \\ \cline{1-1}  & & Data Traffic Load Distribution \\ \cline{1-1}  & \multirow{2}{*}{Others} & Mobility-Aware [22] \\ \hline \end{tabular}
\end{table}
Table 2: Traffic Analysis and Modeling method. AR: Auto-Regressive model; ARMA: Auto-Regressive Moving Average model; S-ARMA: Swam intelligence Auto Regressive Moving Average model.
## 4 Results and Discussion
### Problem Structuring
#### 4.1.1 Technological Condition  
Obsolescence  
Rapid innovation and technology development has led to a significant factor to shorten the life cycles of products and thus obsolescence. A product has technical or functional  
Figure 5: Hierarchy of decision levels for the incoming core quality sorting problem.  
obsolescence when customers are more interested in new products with better quality performance as a result of the introduction of new technology. To assess grades for technical obsolescence, [PERSON] et al. [35] proposed five criteria to guide the qualitative evaluation of used products.  
Upgradability  
An upgrade is technical mitigation to handle the uncertain quality of used products. Meanwhile, the term \"upgradeability\" here refers to the potential level of used products to be upgraded efficiently and effectively to maintain an admissible presence on the market. Upgradeability represents the relative technological ease or viability of fostering continuous system renewal and enhancement at the level of engineering characteristics, parts, and the overall system. Remanufacturing with component upgrades may be an efficient alternative to used product obsolescence. In addition, an upgrade action in remanufacturing will improve the reliability of the used product. To evaluate the feasibility of upgrading, [PERSON] et al. [36] proposed an index that could be assessed by an accuracy improvement.  
The principle of multiple life cycles for products is a key technique in product development for remanufacturing. Since it is one of the strategies for prolonging a product life cycle after the end of life. Durable products are more effective for multiple life cycles. To assess the average life cycle number for a component, [PERSON] et al. [37] proposed a quantitative approach by dividing the average component life by the average product use.  
Disassemblability  
Disassembly is characterized as a complete assembly being dismantled down to its parts. Meanwhile, disassemblability can be loosely described as a level of ease with which a used product could be disassembled. The prerequisite of disassembly for remanufacturing is that it is non-destructive. Difficulty in obtaining parts may also contribute to a long time and high cost of disassembly. The principle of ease of disassemblability represents disassembly without force and via simple mechanisms [38]. For this reason, [PERSON] et al. [35] and [PERSON] et al. [39] used five criteria to provide a qualitative assessment of disassemblability. Meanwhile, [PERSON] et al. [40] recommended a guideline for easy disassembly. On the contrary, [PERSON] et al. [36] quantified the index of ease of disassembly. Similarly, [PERSON] [41] has suggested comprehensive methods to quantitatively evaluate disassemblability based on product design, process technology, and incoming quality assessment.
## 3 Results and Discussion
### Flood Simulations
Therefore, simulations using such data require adjustments of parameter values associated with precipitation quantities, and the IMERG product exhibited relatively larger differences in this aspect. Figure 10 shows the simulated hydrographs, which provide an overall flood analysis simulation result for this study. With a few exceptions, the majority of the simulated hydrographs closely resemble the observed hydrographs. In terms of the statistical comparison of the simulation results (Table 9), the model simulations using gauged data exhibit an average Nash-Sutcliffe efficiency (E\({}_{\rm NS}\)) of 0.895, a coefficient of determination (R\({}^{2}\)) of 0.906, and a percent bias (PBIAS) of 7.42%. For the ground-borne radar precipitation used model, the E\({}_{\rm NS}\) is 0.863, R\({}^{2}\) is 0.873, and PBIAS is 7.49%. Although there are some differences, as mentioned earlier, the simulations of RAR precipitation data perform well without the need for parameter adjustment, reaffirming the utility of detailed grid-based precipitation data for flood discharge simulations. Additionally, the space-borne radar data simulations also demonstrate relatively satisfactory results with an E\({}_{\rm NS}\) of 0.776, an R\({}^{2}\) of 0.787, and a PBIAS of 7.15%, despite the lower correlation with the observed values. Figure 9: Calibrated parameters from the initial values of each model: black checkmarks represent parameters that require model calibration in all three data simulations, while red checkmarks do not; No changes for CN & number of subreaches; Baseflow parameter values are the same for both model simulations. In addition, Figure 11 and Table 10 compare the hydrographs and evaluation statistics of the simulated results for each water level/flow gauge location. In the comparison with individual flow discharge observations, the models using RAR ground-borne radar-based gridded precipitation exhibit good agreement with the observed values, with an average \(\mathrm{E_{NS}}\) ranging from 0.53 to 0.92, without the need for adjustment of parameter values related to travel times. However, for the models using areal mean of the gauged data and IMERG space-borne gridded radar precipitation, the average \(\mathrm{E_{NS}}\) ranges from 0.66 to 0.87 and 0.43 to 0.84, respectively. In the case of IMERG, the precipitation grid is relatively larger compared to RAR, which indicates that the spatial information of the applied precipitation in each watershed is not sufficiently reflected, leading to unsatisfactory results. For reference, Figure 11 illustrates only the hydrographs for storm event #1 adopted in this study, while the hydrograph results for the remaining seven events (Figures A1-A7) have been included in Appendix A.
# Wideband Ring-Monopole Flexible Antenna with Stub for WLAN/C-Band/X-Band Applications
## 2 Antenna Design and Parametric Study
### Parametric Study  
Figure 5 shows the S-parameter results of a parametric study when the sizes of r1 are different. r1 is the size of the diameter of the ring and has a great influence on the antenna characteristics. Depending on the value of r1, the S-parameter value of the C-band changes. When r1 is 24 mm, the antenna does not operate in the 4.68-5.82 GHz. Meanwhile, the 5.1\(\sim\)5.8 GHz band is an upper band of the WLAN band. Since it does not operate in this range, there is an advantage that it can be used as an UWB antenna that can avoid signal interference with the WLAN band. The diameter of the ring of the proposed antenna is designed to have margins for S-parameters so that it can operate in a wideband without a notch.  
Figure 6 shows the S-parameter results of a parametric study when the sizes of Wsh1 are different. Wsh1 is the length of the stub and has a great influence on the impedance matching of the antenna. The stub is designed to operate as a \(\lambda/4\) monopole. However, the stub is close to the feeding part, which greatly affects the overall antenna characteristics. The stub length of the proposed antenna is designed to operate over a wider bandwidth by operating between the C-band and X-band.  
Figure 3: S-parameter simulation for each step of the design.  
Figure 2: Step-by-step antenna design (**a**) antenna 1; (**b**) antenna 2; (**c**) antenna 3; (**d**) antenna 4; (**e**) antenna 5.  
Figure 4: Current distribution of the proposed antenna; (**a**) current distribution at 2.4 GHz; (**b**) current distribution at 3.5 GHz; (**c**) current distribution at 7.5 GHz.  
Figure 5: S-parameter according to the size of r1.