esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 UAV Flying Base Station Properties  
A UAV flying base station combines the functionalities of a base station (to provide the last-mile hop wireless communication link to the user equipment) and UAV drone (to move its location). However, a UAV flying base station is distinguishable from each of these underlying technologies and introduces novel security vulnerabilities and threats previously unseen in stationary base stations and generic UAV drones.
# Dynamic Graph Representation Learning for Passenger Behavior Prediction
## 5 Experiments
### Parameter Sensitivity  
The historical interaction sequences included the attributes of the nodes themselves and the related information from their neighbors. These sequences had a significant impact on the subgraph construction and node feature representation. Therefore, the length of the interaction sequence was a key factor in determining the prediction accuracy. We tested the parameter sensitivity of DyGPP's prediction results under different sequence lengths in the BJSubway-40K dataset, and the results are depicted in Figure 5. As seen from the curve, the accuracy tended to improve with increasing sequence length and gradually stabilized. This is because longer sequences provided more information for tracking passenger and station interactions. However, excessively long interaction sequences exceeded the maximum interaction value for passengers, resulting in no further increase in accuracy once a threshold had been reached.  
\begin{table}
\begin{tabular}{c c c} \hline
**Methods** & **AP** & **AUC** \\ \hline w/o eE & \(0.9681\pm 0.0005\) & \(0.9562\pm 0.0005\) \\ w/o tE & \(0.9685\pm 0.0010\) & \(0.9570\pm 0.0016\) \\ w/o coE & \(0.8877\pm 0.0072\) & \(0.8601\pm 0.0085\) \\ w/o cosE & \(0.9672\pm 0.0014\) & \(0.9543\pm 0.0022\) \\ w/o coE & \(0.8824\pm 0.0114\) & \(0.8533\pm 0.0135\) \\
**DyGPP** & \(\mathbf{0.9699\pm 0.0008}\) & \(\mathbf{0.9591\pm 0.0015}\) \\ \hline \end{tabular}
\end{table}
Table 6: Effects of different components in DyGPP.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Methods** & **AP** & **AUC** & **Time Cost (s)** \\ \hline Transformer & \(0.9692\pm 0.0006\) & \(0.9578\pm 0.0010\) & \(4707.03\) \\ LSTM & \(0.9693\pm 0.0009\) & \(0.9580\pm 0.0018\) & \(4008.01\) \\ DyGPP-mamba & \(0.9690\pm 0.0010\) & \(0.9581\pm 0.0015\) & \(5641.16\) \\
**DyGPP** & \(\mathbf{0.9699\pm 0.0008}\) & \(\mathbf{0.9591\pm 0.0015}\) & \(\mathbf{3604.43}\) \\ \hline \end{tabular}
\end{table}
Table 7: Performance on different temporal encoders.
## 3 Performance Evaluation
### Experimental Environment
#### 3.1.3 Testbed Details  
The Testbed details are shown in Table 2.  
In our experimental testbed, we ran several applications simultaneously. In order to do performance analysis of CPU utilization, we monitored three hardware components such as processor time of CPU, disc time of storage, and paging of RAM. We considered that the utilization of RAM and storage are effects on CPU utilization.
## 2 Materials and Methods
### Degradation Analysis  
In order to confirm if the presence of the aloe vera coating increases the degradation rate of the base material, an enzymatic degradation test was carried out. The enzymatic degradation test was preferred over a more common hydrolytic degradation study in PBS, since the first one offers the possibility of accelerating the test while obtaining comparative results. The enzymatic degradation test was carried out using _proteinase K_ enzymes from _Tritirachium album_ (30 units per mg of protein, Merck) diluted in Tris-HCl buffer pH 8.6 (BioReagent, Merck), at a concentration of 0.2 mg\(\cdot\)mL\({}^{-1}\). Sodium azide (ReagentPlus(r), 299.5%, Merck) was added at the same concentration, in order to diminish the probability of bacterial contamination. Several examples can be found in the literature about the use of this type of enzymes to accelerate the degradation study of PLA samples from several months to a few days or even hours [33; 34; 35; 36]. Under enzymatic degradation and according to the literature, PLA structures are able to retain their properties almost unchanged for up to 8 months [37].  
Scaffolds with a diameter of 9.8 mm and a height of 7 mm were used for the enzymatic degradation test. Four replicas were analyzed per group: PLA, PLA plasma, PLA aloe vera pH 3, PLA aloe vera pH 4 and PLA aloe vera pH 5. The samples were placed in a non-treated 24-well plate and 2 mL of degradation media were added to each well. In addition, non-treated PLA samples immersed in Tris-HCl solution without enzymes were used as control samples. The buffer-enzyme solution was replaced daily to maintain a high enzymatic activity, as the reduction of the pH value due to the release of lactic acid would cause the denaturation of the enzyme [37]. The weight loss of the structures after 5 days of degradation at 37 \({}^{\circ}\)C was assessed by using an analytical balance (\(\pm\)0.1 mg, A&D Scales Gemini Series, GR-200, Germany), to measure the weight of the scaffolds before and after the test. In addition, the pH value of the media was measured (\(\pm\)0.01) in order to follow up the evolution of this parameter during the enzymatic degradation process. The morphology of the scaffolds' surface before and after degradation was evaluated by scanning electron microscopy (SEM; Hitachi TM 3030 at an acceleration voltage of 15 kV).
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 4 Applications of Small Satellite SAR Data  
Biospheric monitoring has been considered as the primary scientific objective in large SAR missions such as NISAR, Biomass, and Tandem-L, which are planned for launch in the near future. Small-satellite SAR has its own advantages of quick revisit time and near real-time coverage, which complement these planned missions and existing Earth-observation assets both in optical or microwave spectra.
## 3 Extended Kalman Filter-Based Relative Transfer Function Estimation
### Vector Taylor Series Approximation  
The eKF is based on applying a linearization over the prediction and observation models using first-order vector Taylor series (VTS). In our case, the prediction model is linear, so the linearization is only applied to function \(\mathbf{h}\) in the observation model. The first-order VTS approximates the model of Equation (14) as  
\[\mathbf{y}_{2,t}\simeq\mathbf{h}\left(\widehat{\mathbf{a}}_{21,t|t-1}, \mathbf{0};\mathbf{y}_{1,t}\right)+\mathbf{H}_{t}\left(\mathbf{a}_{21,t}- \widehat{\mathbf{a}}_{21,t|t-1}\right)+\mathbf{J}_{n_{1,t}}\mathbf{n}_{1,t}+ \mathbf{n}_{2,t}, \tag{26}\]  
where  
\[\mathbf{H}_{t}=\left.\frac{\partial\mathbf{h}}{\partial\mathbf{a}_{21,t}} \right|_{\mathbf{n}_{1,t}=\mathbf{0}}=\begin{bmatrix}Y_{1,t}^{\prime}&-Y_{1,t} ^{\prime}\\ Y_{1,t}^{\prime}&Y_{1,t}^{\prime}\end{bmatrix}, \tag{27}\]  
\[\mathbf{J}_{n_{1,t}}=\left.\frac{\partial\mathbf{h}}{\partial\mathbf{n}_{1,t}} \right|_{\mathbf{a}_{21,t}=\widehat{\mathbf{a}}_{21,t|t-1}}=-\begin{bmatrix} \widehat{A}_{21,t|t-1}^{\prime}&-\widehat{A}_{21,t|t-1}^{\prime}\\ \widehat{A}_{21,t|t-1}^{\prime}&\widehat{A}_{21,t|t-1}^{\prime}\end{bmatrix} \tag{28}\]  
are the Jacobian matrices required for the VTS approach.  
Finally, using (26), the noisy speech statistics can be estimated as [29]  
\[\mathbf{\mu}_{y,t}\simeq\mathbf{h}\left(\widehat{\mathbf{a}}_{21,t|t-1}, \mathbf{0};\mathbf{y}_{1,t}\right), \tag{29}\]  
\[\mathbf{S}_{y,t}\simeq\mathbf{H}_{t}\mathbf{P}_{t|t-1}\mathbf{H}_{t}^{\top}+ \mathbf{J}_{n_{1,t}}\mathbf{\Sigma}_{n_{1,t}}\mathbf{J}_{n_{1,t}}^{\top}+ \mathbf{\Sigma}_{n_{22,t}}+\mathbf{J}_{n_{1,t}}\mathbf{\Sigma}_{n_{12,t}}+ \mathbf{\Sigma}_{n_{21,t}}\mathbf{J}_{n_{1,t}}^{\top}\quad, \tag{30}\]\[\mathbf{C}_{ay,t}\simeq\mathbf{P}_{t|t-1}\mathbf{H}_{t}^{\top}. \tag{31}\]
## 2 The Models
### A Modified Model
For (b), \(p=2\); \(N=3\); \(A=0.1\); \(\omega=0.9\); \(g_{1}=0.01\); \(g_{2}=0.02\); \(g_{3}=0.015\), \(M(t_{0})\) has no roots (see Figure 9b). From Propositions 2 and 3, the reader may formulate the Melnikov's standard for the emergence of crossing the perturbed and unperturbed separatrix. The reader can explore the relevant approximation issue for arbitrarily selected \(p\) and \(N\). One Possible Application that Melnikov Functions May Find in the Modeling and Synthesis of Radiating Antenna Patterns  
Let us dwell on the functions \(M(t)\) generated by Propositions 2 and 3. Thus, for example, typical diagrams are visualized in Figure 10 with the following selection of parameters:  
1. \(p=2\); \(N=2\); \(A=0.13\); \(\omega=0.6\); \(g_{1}=-0.09\); \(g_{2}=-0.05\) (from Proposition 2);
2. \(p=2\); \(N=3\); \(A=0.011\); \(\omega=0.9\); \(g_{1}=-0.01\); \(g_{2}=-0.02\); \(g_{3}=-0.03\) (from Proposition 3). Of course, the complex problem related to choosing an optimization approach to minimize lateral radiation in the specified class of diagrams can be considered as open. The reader can find additional information in [27]. **3. One More Note on the Subject: Generating Chaos via \(x|x|\)**  
In this section, we will explore the dynamics of the following \"mixed\" model:  
\[\left\{ \begin{array}{rcl}\frac{dx}{dt}&=&y\\ \frac{dy}{dt}&=&x-x|x|-\epsilon\left(A\sum_{p=1}^{n}y|y|^{p-1}-\sum_{j=1}^{N}g_{ j}\sin(j\omega t)\right)\end{array} \right. \tag{16}\]  
where \(0\leq\epsilon<1\), \(A\) is the damping level, \(N\) is natural number, and \(p\geq 1\) is the damping exponent. Particularly, we investigate the next differential system  
\[\left\{ \begin{array}{rcl}\frac{dx}{dt}&=&y\\ \frac{dy}{dt}&=&x-x|x|-\epsilon\left(Ay|y|^{p-1}-\sum_{j=1}^{N}g_{j}\sin(j \omega t)\right).\end{array} \right. \tag{17}\]  
The total energy of this system (\(\epsilon=0\)) is \(H(x,y)=\frac{1}{2}y^{2}-\frac{1}{2}x^{2}+\frac{1}{3}x^{2}|x|\). Some details can be found in [13]. The trajectory is obtained by (see Figure 11):  
\[ \begin{array}{rcl}x_{0}(t)&=&\pm\frac{3}{1+coshl}\\ y_{0}(t)&=&\mp\frac{3 sinht}{(1+coshl)^{2}}\end{array}  \tag{18}\]  
Figure 11: The orbits \((x_{0}(t),y_{0}(t))\) (thick) and energy potential (red). Figure 10: Melnikov functions \(M(t)\) as a typical diagrams (in confidential intervals): (**a**) case I; (**b**) case II. Figure 14: (**a**) The solutions of the system; (**b**) \(x\)–time series; (**c**) \(y\)–time series; (**d**) phase space (Example 7). Figure 13: (**a**) The solutions of the system; (**b**) \(x\)–time series; (**c**) \(y\)–time series; (**d**) phase space (Example 6).
## 3 Samples and Methods
### Automatic Mineral Identification and Characterization  
The samples were first subjected to a pretreatment process involving grinding with progressively finer grit sandpapers to achieve a smooth surface, followed by further surface polishing using argon ion milling to create a sample surface suitable for imaging using electron microscopy. The samples were examined using a Zeiss Crossbeam 550 scanning electron microscope (Zesis, Oberkochen, Germany), in both secondary electron (SE) imaging and backscattered electron (BSE) imaging modes at 20 kV and 3 nA, respectively. The AMICS software (3.0.0.45) was used to obtain mineral compositions and their spatial distribution on mosaicked large-field BSE images by integrating the strong stereoscopic effect of SE imaging with the mineral brightness contrast from BSE imaging and the elemental distribution data from EDS analysis.
## 2 Results and Discussion
### 2D Periodic PLAs Made of W-SiN-W Membranes
Thus, it is found that the perfect-absorption band is realized by the combination of local electric-field scattering by the on-top W patch and magnetic-field localization at the SiN-W interfaces. The local scattering can be interpreted as local plasmon at the rectangular W nanostructures and includes substantial longitudinal components that are usually excited as continuum [12] and are considered to realize the perfect-absorption continuum. In terms of the structures, the 1D structures in Figure 4 do not have the rectangular corners in the \(xy\) plane and therefore do not tend to form perfect-absorption continuum. Figure 7: Absorbance spectra of 2D periodic W-SiN-W membranes at the normal incidence. (**a**,**b**) on-top rectangular (side \(W_{d}=450\) nm) and circular patches (diameter \(D_{m}=504\) nm), whose schematics are illustrated in Figure 1e,f, respectively. The black and green curves denote the 2D membranes of SiN thickness (\(t\)) 100 and 200 nm, respectively. The periodicity \(P\) was set to \(P=1000\) nm in these cases.
# Effects of Short Fibers on the Long-Term Behavior of RC/FRC Beams Aged under Service Loading
## 2 Experimental Program
### Materials Properties  
Three different concrete mixes were prepared: A plain concrete (PC) control mix, two fiber-reinforced concrete (FRC) mixes, embedding respectively steel fibers (ST) with a 0.6% volume fraction, and polyester fibers (POL), with a 0.9% volume fraction. A description of each component material is presented in the following.  
The concrete mix includes the Portland cement type CEM A-LL 32,5 R, which is based on limestone Portland type II with a high initial strength.  
Steel and polyester short fibers were used in the concrete mixes. Hooked steel fibers having a length of 30 mm, a diameter of 0.6 mm and an aspect ratio (L/D) of 50, were adopted. The quality of the steel fibers meets the requirements of the UNI 11037 [37]. Polyester fibers had a length of 30 mm, a diameter of 0.45 mm and an aspect ratio (L/D) of 66. Steel fibers had tensile strength and elastic modulus as equal to 1150 MPa and 210 GPa, respectively, while polyester fibers had a strength of 600 MPa and an elastic modulus of 11.3 GPa.  
Longitudinal and transversal steel reinforcement was provided in the form of bars (\(\Phi\)14 mm) and stirrups (\(\Phi\)8 mm), having a yield strength of 520 MPa and 567 MPa, and a tensile strength of 614 MPa and 600 MPa, respectively.  
The concrete matrix was made by using 300 kg/m\({}^{3}\) of cement type 32.5 R II-A/LL, a water/cement ratio of 0.65 and a maximum aggregate size of 12 mm (see Table 1 for the mix design). The FRC workability was measured with a slump test according to EN 206-1 [38] and the class resulted S5. The maximum aggregate size was chosen in order to allow a uniform and efficient distribution of fibers within the mold. Four cubic specimens (\(150\times 150\times 150\) mm) for each concrete mix (PC, ST and POL) were tested under uniaxial compression for quality control. Table 2 reports the cubic compressive strengths (average value from four samples \(150\times 150\times 150\) mm) after 28 days from casting, and the corresponding elastic moduli obtained according to ISO 6784 [39]. Tests on notched beams subjected to a three-point load were also carried out according to EN 14651 [40]. The main experimental results are reported in Table 2; it can be noticed that the steel fibers were more effective than polyester fibers in terms of toughness, as shown by parameters f\({}_{\text{R1}}\) as defined by EN 14651 [40].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Concrete Mix Components** & **ST** & **POL** & **PC** \\ \hline CEM 32.5 R II-A/LL (kg/m\({}^{3}\)) & 300 & 300 & 300 \\ Superplasticizer CRTV-L (kg/m\({}^{3}\)) & 1.59 & 2.5 & 1.77 \\ Sand (0–4) (kg/m\({}^{3}\)) & 1028 & 1023 & 1037 \\ Gravel (4–10) (kg/m\({}^{3}\)) & 704 & 701 & 710 \\ Water/Cement ratio & 0.65 & 0.65 & 0.65 \\ \hline \end{tabular}
\end{table}
Table 1: Mix design.
## 4 Discussion
In [PERSON]'s study [26], a hybrid spatial-temporal feature fusion neural network (STFFNN) is introduced, amalgamating extracted features through convolutional neural networks (CNN) for spatial learning and utilizing Bi LSTM for network storage by merging temporal and spatial features. In our study, we also extract features preserving rich spatial and temporal information. However, our approach involves constructing a new spatial network for EEG signals within the framework of complex networks to enhance the extraction of EEG information. Emotion recognition based on EEG signals holds promising applications, including auditory attention research and clinical psychiatric investigations. Despite these prospects, there are inherent limitations in the current research. This article presents a novel complex network achieved through the fusion of phase space reconstruction and visibility graph, thereby retaining the intricate temporal and spatial features of EEG signals. The absence of a standardized criterion for selecting spatial dimensions and time-delay parameters in phase space construction necessitates a discussion tailored to different signals and research contexts. Moreover, emotional stimulation introduces a certain impact on the selection of EEG patterns and features. In [PERSON]'s study [27], a discernible relationship between emotion and cognition was identified in specific regions during emotional interference, encompassing the bilateral dorsal anterior cingulate cortex, anterior insula, left inferior frontal gyrus, and superior parietal lobule, which exhibit sustained effects in these areas. Research affirms the nervous system's involvement in various interference processing types with the regulation of emotional and cognitive interference relying on interactions within extensive distributed brain networks. In [PERSON]'s investigation [28], the pivotal role of the default mode network (DMN) region and executive region in emotional interference processes was demonstrated. Negative emotional interference prompts activity regulation in diverse regions, such as the frontal and parietal lobes, correlating with the regulation of functional connections between these task-activation regions and DMN regions. Both studies highlight that emotional interference triggers engagement in emotional processing activities in specific brain regions, influencing characteristic responses within the brain \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Title 1** & **Dataset** & **Methodology** & **Mean Accuracy** & **StdACC** \\ \hline [PERSON]’s study [20] & SEED & GSCCA & 82.96\% & 9.95\% \\ [PERSON]’s study [21] & SEED & GRSLR & 87.39\% & 8.64\% \\ [PERSON]’s study [22] & SEED & DBN-CRF & 90.40\% & 8.49 \\ Our work & SEED & NEM & 91.39\% & 4.69\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of classification accuracy from [PERSON]’s study, [PERSON]’s study, and [PERSON]’s study. network. Consequently, subsequent EEG emotion classification research should factor in the impact of emotional interference and opt for suitable classification modes and features.
## 4 Conclusions  
In this study, ternary beta-titanium alloys with a tensile strength of 800 MPa or higher were designed by replacing alloy elements, Al and V, with biocompatible Mo and Fe. TiO-Fe alloys were fabricated by applying a powder metallurgy process, and microstructure and mechanical properties according to Mo content were considered. The major results are summarized as follows.  
1. It is possible to secure excellent price competitiveness by successfully developing Ti-Mo-Fe P/M alloys by adding low-cost Mo and Fe, which can fabricate complex parts and reduce costs. The cost of alloying elements is reduced by 81-84% and 63-67% compared to the widely used Ti-6 Al-4V and Ti-13 Nb-13 Zr alloys, respectively. Further, the cost may be further reduced by using HDH powder.
2. In the case of TMF 34 I/M, an \(\alpha+\beta\) lamellar Widmanstatten microstructure was observed, and in the case of TMF 54 I/M, an \(\alpha\) phase precipitating inside the equiaxed \(\beta\) grain was observed. In the equiaxed microstructure, where cracks occur mainly at the \(\alpha/\beta\) interface, deformation can be generated by slips and twins. Further, it is  
Figure 9: SEM images of the fractured surface of (**a**) TMF 34 P/M, (**b**) TMF 54 P/M, (**c**) TMF 34 I/M, and (**d**) TMF 54 I/M.  
reinforced by the precipitated fine \(\alpha\) phase according to an increase in Mo content, showing high tensile strength and elongation in the TMF 54 I/M alloys.
* According to the increase in Mo content, TMF 34 P/M \(\rightarrow\) TMF 54 P/M, the thickness of the Widmanstatten \(\alpha\)-lath decreases (12.289 \(\upmu\)m \(\rightarrow\) 10.547 \(\upmu\)m) and the size of prior \(\upbeta\) grains decrease (226.55 \(\upmu\)m \(\rightarrow\) 211.99 \(\upmu\)m). For this reason, it was possible to obtain excellent tensile strength of 800 MPa or more, even though the pore fraction increased (2.1% \(\rightarrow\) 3.5%) as the Mo content increased.
* Tensile fractures occurred in the elastic region before the yield point due to pores in both the TMF 34 P/M and TMF 54 P/M alloys, and the change in Mo content was analyzed with I/M alloys by the E and \(\sigma\) reinforcing mechanisms.  
Conceptualization, H.-W.H. and D.-G.L.; methodology, H.-W.H. and D.-G.L.; validation, H.-W.H., J.-H.P. and D.-G.L.; formal analysis, H.-W.H., J.-H.P. and D.-G.L.; investigation, H.-W.H. and D.-G.L.; writing--original draft preparation, H.-W.H.; writing--review and editing, D.-G.L.; supervision, D.-G.L.; funding acquisition, J.-H.P. and D.-G.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data are available within the manuscript.  
This work was supported by the Korean government MOTIE (the Ministry of Trade, Industry and Energy), the Korea Evaluation Institute of Industrial Technology (KEIT) (No. 20010047), and the Korea Institute for Advancement of Technology (KIAT) (No. P0002019).  
The authors declare no conflict of interest.