esa-sceva/satcom-chunk-collection huggingface dataset:
# Axial Compression Behavior of Steel Angle-Corrugated Steel Plate-Confined Concrete Columns
## 4 Finite Element Analysis
### Model Validation  
The experimental and numerical results regarding the failure modes, load-displacement curves, ultimate bearing capacity, and corresponding displacement were compared to validate the accuracy of the FE model. The SA-CS-280 column was taken as an example, whose failure modes are shown in Figure 12. The local buckling of the steel angle and the conquassation of the core concrete were highly consistent. Figure 13a-c illustrates the comparison of the load-displacement curves between the test and FE simulation, which were in good agreement. The deviation of the ultimate bearing capacity and the corresponding displacement was within 10%. Overall, the FE model established in this paper could simulate the mechanical properties of the SA-CS column with reasonable precision. In addition, using the FE model to analyze different parameters was reasonable and reliable.  
Figure 13d plots the \(N/N_{\text{u}}\)-displacement curves of each component of the SA-CS-280 column. During the loading process, the core concrete bore most of the load--more than 90% of the total. In addition, the corrugated steel plate did not bear the load, which proved the opinion presented in Section 3.4.
# Effects of Turbulence Modeling on the Simulation of Wind Flow over Typical Complex Terrains
## 2 Numerical Methodologies
(4) Modified RSM model  
In most cases, the default closure constants of the RSM model can give good results [33], while modifications to the above closure constants are also allowed in specific cases. Firstly, the wind flow simulation in complex terrain requires the accurate prediction of the ABL, and then the prediction of the wind flow on the leeward side of the hill, which is a significant challenge. In the modified linear pressure-strain RSM model (Mod RSM), \(C_{\mu}\) is modified from 0.09 to 0.05; \(\sigma_{k}\) is modified in the \(k\)-\(\epsilon\) model based on the transport equations of \(k\) and \(\epsilon\), but the Reynolds stress transport equation in the RSM is different from the \(k\)-\(\epsilon\) model, so we cannot modify \(\sigma_{k}\) as in the \(k\)-\(\epsilon\) model. In the Mod RSM model, we modify \(\sigma_{k}\) from 1 to 0.82 and \(\sigma_{\epsilon}\) from 1.3 to 1 in order to better simulate the flow in the separation zone, similar to the RNG \(k\)-\(\epsilon\) model [36]. So, in the modified linear pressure-strain RSM model (Mod RSM), the closure constants are \(C\mu\) = 0.05, \(C_{\mathit{1e}}\) = 1.44, \(C_{\mathit{2e}}\) = 1.92, \(\sigma_{k}\) = 0.82, \(\sigma_{\epsilon}\) = 1.0, \(C_{1}\) = 1.8, \(C_{2}\) = 0.6, \(C_{1}\) = 0.5, and \(C_{2^{\prime}}\) = 0.3.
# Designed on 0.18 um CMOS Process Small Size Broadband Millimeter Wave Chip Antenna
## 4 Discussion  
From the above parameter analysis, it can be seen that designing the feed line length of the patch antenna and the triangular patch antenna can help achieve a better reflection coefficient and frequency bandwidth percentage. Additionally, better impedance matching can be achieved by projecting a frog circular slot in both directions. Table 2 provides various parameters related to the chip antenna literature in recent years. The process, frequency, bandwidth, gain, efficiency, and size used in each document are listed in the table. Compared with the chip antenna design proposed in this article, it can be seen that the bandwidth is better than that of the references [18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. In terms of gain, it is better than references [18; 20; 22; 23]. The efficiency is better than the literature [18; 20; 22; 25]. The dimensions have advantages over references [19; 20; 21; 26]. Based on the above, the chip  
Figure 17: 2D Radiation Pattern (**a**) E Plane; (**b**) H Plane.  
antenna proposed in this paper is competitive in terms of bandwidth, gain, efficiency, and size. The antenna proposed in this paper has good bandwidth and radiation performance and is suitable for application in millimeter wave systems.
# Enhanced Teaching-Learning-Based Optimization Algorithm for the Mobile Robot Path Planning Problem
## 2 Problem Formulation
### Mathematical Model  
The investigated MRPP aims to find a set of optimal grid connections between the origin and destination so that some numerical metrics can be optimized or improved. Two constraints should be obeyed during the whole decision process: namely, the continuity constraint and the obstacle-avoidance constraint. The previous constraint requires that the robot must start from the origin, move to adjacent grids step by step, and finally reach its destination. The latter states that obstacle grids are not allowed to be included in the robot path.  
This research formulated the considered problem with the objective of minimizing the total route length. Given a robot route \(S=\{g_{i}|i=1,2,\ldots\ldots,n\}\), the objective definition is as follows:  
\[\min L=\sum_{i=1}^{n}d(g_{i},g_{i+1}) \tag{2}\]  
where \(n\) is the total number of grids in the robot route.
## 4 UAV Power Sources
### Hydrogen Fuel Cell and Super Capacitor Combination  
Previous studies and experimental combinations that employed supercapacitors (SC) and hydrogen fuel cells (HFC) had several limitations. These constraints will be crucial in this experiment. [PERSON] and colleagues create an HFC car by combining an SC bank and a 35 kW HFC. The HFC employs a boost DC-DC converter to achieve the required voltage for the motor (250-400 V). As a result, a smaller HFC can be employed, resulting in less vehicle mass and lower total expenses. The SC bank contains 27, [PERSON] et al. [45], where two SC-packs with a capacity of 405 V, 2 F are made up of six 2.5 V, 350 F cells connected in series.  
Because it makes the system lighter, cheaper, and more efficient, the SCs do not utilize a DC-DC converter. The SCs store the energy produced by regenerative braking and supply additional power only when necessary (during accelerations). The best fuel economy is achieved when the kinetic energy of the vehicle and the potential energy of the SCs remains constant. This study reveals that the HFC and SC combination cannot compete with the HFC and battery combination because the SC bank lacks adequate energy storage to meet peak power demand. Combining a few SC banks in parallel is recommended to boost energy storage capacity; however, this greatly increases system weight and reduces fuel consumption to an infeasible level below that of the HFC and battery combination [52].  
[PERSON] and colleagues hypothesized that an SC bank of 292 F, 500 A, 30 V, and a proton exchange membrane fuel cell of 500 W, 40 A, 13 V might power an HFC car. The HFC will use a single-direction boost converter to meet power requirements and store regenerative braking energy, while the SCs will use a two-direction boost converter. The HFC will provide most of the power for this configuration, with the SC bank being used when peak power needs are noted, or regeneration breaking energy exceeds zero. The HFC will also be utilized to recharge the SCs throughout the operation.  
This configuration allows for a reduction in the amount of HFC utilized, increased efficiency over only using HFC, and energy recovery via regenerative braking. Because the HFC requires 5-10 min of steady power, the system may fail at startup if just SCs are used as an auxiliary power source. Future improvements to either of these case studies would be to improve the energy characteristics of the SCs or to investigate the possibility of a hybrid system that uses a battery to absorb the majority of these constant power requirements [53, 54].
# Comparison of Manual and Automated Capillary Morphometry Measurements in Oral Mucosa: A Pilot Study
## 4 Discussion
### Capillary Density  
In contrast to the findings for caliber and length, the automated and manual methods showed good agreement in the assessment of capillary density. This suggests that the neural network performs well in quantifying the number of capillaries per unit area, a parameter that is less sensitive to the precise definition of capillary boundaries. The agreement in capillary density measurements is particularly encouraging, as this parameter is often used as a key indicator of microvascular health in both research and clinical settings. The ability of the automated method to accurately measure capillary density could make it a valuable tool for large-scale studies or clinical applications where efficiency and reproducibility are critical.
# Speech Act Theory as an Evaluation Tool for Human-Agent Communication
## 6 Results
### Evaluating Agent Communication
#### 6.2.1 How Can an Agent's Locutionary Act be Evaluated?  
To evaluate the locutionary act of the agent's speech, a subjective analysis of the data was undertaken (refer to Table 1). We rely on surveying participants' perceptions of their interlocutors' locutionary act to determine the plausibility of the speech structure. The survey asked the participants about their perception of the structure of the messages (rather than the messages content or meaning) used by the agent. Participants needed to select one or more keywords from the following: clear, ambiguous, natural, awkward, nice, ugly, too short, and too long. Ideally, the agent's messages should be clear, natural, nice, and expressed in as few words as possible. The results demonstrate that the \"clear\" property was selected by 42.42% of the participants (see Figure 2). Nevertheless, three times as many participants found the messages \"awkward\" rather than \"natural\" and twice as many found the messages to be \"too short\" rather than \"too long\".  
To test the significance of the difference in participants' perception of the agent's messages, a Chi square test was utilized. The result of Chi square test showed that the percentage of participants that perceived the agent's verbal communication as \"nice\" was significantly different from the percentage of participants that perceived the agent's verbal communication as \"ugly\", \(\chi^{2}(2,\text{N}=66)=3.636\), \(p<0.01\). Additionally, the percentage of participants that perceived the agent's verbal communication as \"too short\" was significantly different from the percentage of participants that perceived the agent's verbal communication as \"too long\", \(\chi^{2}(2,\text{N}=66)=66.091\), \(p<0.01\). Moreover, the percentage of participants that perceived the agent's verbal communication as \"awkward\" was significantly different from the percentage of participants that perceived agent's verbal communication as \"natural\", \(\chi^{2}\) (\(2,\text{N}=66)=10.182\), \(p<0.01\). Although the percentage of participants that deemed the agent's messages to be \"clear\" was higher than that of participants who found them to be \"ambiguous\", there was no significant difference between these perceptions (\(\chi^{2}\) (\(2,\text{N}=66)=2.545\), \(p>0.01\)).
## 4 Simulation Tests and Results
### Case Study 4  
The aim in the fourth case study is to maximize the coverage of a two-dimensional, squared area of \(20\times 20\) provided that k-coverage of six pre-defined spots has been satisfied. More specifically, the points (5, 5), (10, 5), (15, 5), (5, 15), (10, 15), and (15, 15) are considered to have three-coverage each one. The number of the available sensors was equal to 45 while their sensing range varied between 1 and 2. More specifically, 18 sensors had a sensing range equal to 1, 20 sensors had a sensing range equal to 1.5, and the last seven had sensing ranges equal to 2. The size of the population in the GA algorithm was selected to be equal to 600 while the swarm size in the PSO algorithm was adjusted to 600.  
The results of the simulations performed are synoptically presented in Table 6.  
Figure 14: Best fitness value for each iteration of the PSO algorithm in case study 3.  
Figure 13: Optimal node positions with the use of the PSO algorithm in case study 3.  
Similarly, the optimal positions of nodes calculated by using PSO algorithm are illustrated in Figure 17 while the corresponding best fitness values for each iteration are graphically presented in Figure 18.
## 3 Results and Discussion  
### Characterization of Copolymers  
M-DCPD resin were obtained through reaction of MAH and DCPD resin, which were named as M0, M2, M4, M6, and M8 on the basis of MAH addition. The FTIR spectra of MAH and M-DCPD resin were shown in Figure 2. The sharp peaks around 1780 cm\({}^{-1}\) and 1860 cm\({}^{-1}\) observed in spectrum of M-DCPD resin are assigned to the typical stretching vibration of anhydride groups. Considering that the unreacted MAH has been removed by purification after graft polymerization of MAH and DCPD  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Control Group (phr)** & **Test Group (phr)** \\ \hline
6 PPD & 1.5 & 1.5 \\ RW287 & 1.5 & 1.5 \\ RD & 1.5 & 1.5 \\ NS & 1.2 & 1.2 \\ Sulfur & 1.8 & 1.8 \\ D & 1.8 & 1.8 \\ M0 & 10 & 0 \\ M-DCPD resin & 0 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._resin as is shown in Figure 2, it could conclude that anhydride groups have been successfully grafted to the polymer chain. Moreover, an increasing tendency is observed in spectrum with the increasing dosage of MAH, as well as the increasing weight ratio of anhydride from titration method shown in Table 1, indicating that anhydride content in M-DCPD resin increases within more MAH addition.  
Furthermore, the discrepancy in anhydride content probably enhance the resin polarity in varying degrees. DSC was conducted accordingly to characterize the resins. Figure 3a shows that the more MAH was added, the higher the glass transition temperature that was observed from M0-M8. Polymers before and after modification show similar elution time in gel permeation chromatography (GPC) as is shown in Figure 3b. Therefore, general increasing glass transition temperature demonstrated increasing polarity of resins. Since M-DCPD resin can totally dissolve in tetrahydrofuran, it is reasonable that most MAH added only reacted with the side-chain groups without cross-linking. The slight increasing of molecular weight from M0 to M8 was due to the increasing usage of MAH.  
Figure 3: Curves obtained by DSC (**a**) and GPC (**b**)for M-DCPD resin.  
Figure 2: FTIR spectra of maleic anhydride (MAH), DCPD resin and M-DCPD resin.
## 2 Materials and Methods
### Statistical Analysis  
A software package called EddyPro Version 5.1.1 (LI-COR, Lincoln, NE, USA) was inhabited for acquisition and processing of eddy covariance data. In order to verify the reliability of the model results, measured data and simulated data were linear fitted, and r values were obtained as an index. Spatial variability of NPP/NEP from 1979-2011 in Central Asia was calculated in ArcGIS version 9.3 (ESRI, Redlands, CA, USA). The averaged NPP/NEP was calculated in ArcGIS using the \"Raster calculator\" tool from NPP/NEP data from 1979-2011. NPP/NEP values and standard deviation for different vegetation types was extracted by the \"Classification statistics\" tool in ArcGIS. The temporal variation and trend of NPP/NEP values was analyzed in the Origin software version 8 (OriginLab, Northampton, MA, USA) using \"linear fitting\".  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Longitude (\({}^{\circ}\))** & **Latitude (\({}^{\circ}\))** & **Value** & **References** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: Validation points across Central Asia’s grassland biomes.  
Figure 2: Research flowchart. VPD—vapor pressure deficit; NPP—net primary productivity; NEP—net ecosystem productivity; Biome-BGC—BioGeochemical Cycles.
## 2 Materials and Methods
### Methods  
The CBH retrieval algorithm proposed in this study is a two-stage hybrid approach that integrates DL with a physical model. First, we employ a CNN model, trained with MODIS cloud products as labels, to retrieve CTH and CWP from the AHI full-disk infrared observations. Second, we establish a physical ECWC LUT from CloudSat-CALIPSO-detected cloud profiles, which maps the relationship between the CNN-derived AHI (CTH, CWP) pairs and CGT. By synergizing the CNN and the LUT, the algorithm ultimately estimates the target CBH in diurnal cycles from AHI infrared observations.
## 2 Results and Discussion
### Self-Aggregation of FYNF-NH\({}_{2}\) Investigations
#### 2.4.3 Transmission Electron Microscopy (TEM)  
In both solid and solution phases, under specific conditions, peptide subunits engage in intricate noncovalent interactions, leading to the formation of self-assembled architectures. These supramolecular constructs subsequently undergo further self-association, culminating in diverse nano- and micro-architectures pivotal for the advancement of functional materials [45]. Consequently, to evaluate the morphological features of the newly synthesized FEYNF-NH\({}_{2}\) peptide, transmission electron microscopy (TEM) was employed (Figure S5a,b, Supplementary Material). In addition, the elemental composition and the purity of the synthetized peptide were confirmed by the presence of characteristic C, N, and O elements (see Figure S5c,d in Supplementary Materials), using energy-dispersive X-ray spectrometry (EDX) coupled with TEM. It should be noted that the differences in C intensities are mainly due to the use of TEM carbon grids.  
The TEM images of the pentapeptide (previously dispersed in phosphate buffer solution and dimethyl sulfoxide (DMSO)) are depicted in Figure S5a (in Supplementary Material). One may see the self-assembled peptide as a highly extended nanopage with overlapping areas. This could be attributed to an increase in the density of transitory noncovalent contacts between self-assembled nanostructures [46]. In addition, this arrangement (flat, extended shape) is characteristic of \(\beta\)-sheets. The corresponding TEM image for the dehydrated peptide after incubation is delineated in Figure S5b (Supplementary Materials). In contrast to Figure S5a, the incubation of the FEYNF peptide appears to instigate the development of structures resembling fibrils, along with some regions exhibiting tape-like characteristics.
# Fabrication of Random Microlens Array for Laser Beam Homogenization with High Efficiency
## 4 Experiments
The power (\(P_{in}\)) of the input beam measured with the power meter was about 3.5 mW, and the output powers (\(P_{out}\)) after passing through the honeycomb rMLA and the rectangular rMLA were all about 3.2 mW. According to Equation (14), the energy utilization rate was about 90%. Therefore, it can be concluded that both the honeycomb rMLA and the rectangular rMLA have a quite high energy utilization rate. \[RMS=\sqrt{\sum_{j}^{N}\left(I_{j}-\overset{2}{I}\right)\ /N} \tag{13}\]  
\[P=\frac{P_{out}}{P_{in}} \tag{14}\]  
Many incident laser beams with arbitrary profiles and uneven intensity distribution, in addition to those mentioned above, can also be modulated by the rMLA to generate a homogenized light spot. For instance, utilizing the fabricated rectangular rMLA, the laser beam with the cross spot (Figure 12a) can be shaped into a rectangular homogenized light spot (Figure 12b). Figure 11: Homogenization spot effect collected by CCD: (**a**) the incident laser beam; (**b**) the circular homogenization spot reconstructed by the honeycomb rMLA; and (**c**) the rectangular homogenization spot reconstructed by the rectangular rMLA. Figure 12: Beam homogenization with an arbitrary shape laser beam: (**a**) free-form incident laser beam; and (**b**) the reconstructed rectangular homogenization spot.
# On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection
## 5 Experimental Setup and Initial Results
In this case, given that the detection algorithm as shown in Figure 5 always assumes that the spoofing signal is present in Unit1, the first stage of the detection would fail, but the second step will detect the spoofing signal because the signal processed by Unit1 will not change its power while the relative delay is changing. No matter in what stage of the algorithm the spoofing decision is made, once \(D_{i}(t)=2\), we can assume that the spoofing signal is the one with the higher power, because this is needed to take control of the receiver. In such a case, in order to mitigate the effects of the spoofing attack, information from the correct unit needs to be used for pseudorange computation, as shown in Figure 4, as well as in the PLL of Figure 1, where \(\hat{S}_{b,0}\) should be adjusted accordingly. For the spoofing detection, it can be seen that Unit1 starts following the signal that is being pulled-off after it has a \(\delta\tau\) of at least 300 ns, at around 150 s of the dataset. Having a small bandwidth of the front-end will cause the detection to have a delay with respect to the time when the spoofer actually starts modifying the position velocity and time (PVT)solution, due to the rounding effect in the correlation function. Nevertheless, as demonstrated in [13], this latency can be improved using a better product between front-end bandwidth and the time of chip (\(B\cdot T_{c}\)). These results show the ability of the ECADLL to detect and track the spoofing signal. It is important to notice that given the many possible configurations of spoofing attack scenarios and the many different parameters of the ECADLL architecture, making a statistical analysis in terms of detection probability is a difficult task left for further investigation. Figure 11: Amplitude estimation for Unit0 (**top**) and Unit1 (**bottom**) for TEXBAT ds3. In blue, the in-phase amplitude estimation, and in red, the quadrature estimation are shown.
# Analysis of the Effect of Temperature on the Sound Transmission Loss of a Curved Plate
## 5 Conclusions  
In this study, based on the fluctuation and heat conduction theories, in conjunction with the Donnell equation for curved plates, a model of the STL of a curved plate was established under simply supported boundary conditions. The solution was obtained using the modal superposition method, and the accuracy of the model was verified using the finite element method. The following conclusions can be drawn based on the analysis of the theoretical model.  
1. Temperature affects modal frequencies of different orders to different extents. For the curved plate, the minimum modal frequency is most affected by temperature, and the (1, 1)-order modal frequency is least affected by temperature. The modal frequency ratio varies approximately linearly with decreasing temperature.
2. Temperature changes cause a change in the order of appearance of the mode shapes, and the greater the temperature change, the more the order of the mode shapes changes. The effect of temperature on STL is due to temperature changing the modal frequencies.
3. The larger the radius, the more pronounced the effect of temperature on the STL. As the opening angle increases, the STL will experience an aggregation phenomenon at (1, 1)-order modal frequencies.  
Conceptualization, G.Z.; methodology, G.Z.; software, S.C.; validation, T.Z.; formal analysis, G.Z.; investigation, S.C.; resources, J.G.; data curation, G.Z.; writing--original draft preparation, G.Z.; writing--review and editing, G.Z.; visualization, T.Z.; supervision, J.G.; project administration, J.G.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the \"13 th Five-Year Plan\" National Key R & D Plan of China, grant number 2016 YFB1200602, and Major Strategic Projects of The Chinese Academy of Egineering, grant number 2018-ZD-16.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 14: Effect of the different loss factors on STL.