esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Experimental Setup  
Based on the modeling research and algorithm design mentioned above, to validate the effectiveness of the proposed algorithm in this paper, simulations were conducted using Matlab 2022a. Matlab was employed to simulate the operation of unmanned aerial vehicles (UAVs) in a post-disaster mountain rescue scenario. The experiments were conducted using an Intel(R) Core(TM) i9-13900 HX CPU @ 2.2 GHz processor, 32 GB of RAM, and a 1 TB hard drive.  
To closely resemble the actual flight process of the UAV in post-disaster rescue scenarios, the specific parameter settings in this study are as follows:  
1. The path planning task space is 10,000 m \(\times\) 10,000 m \(\times\) 10,000 m, and the corresponding parameters for setting up the benchmark terrain obstacle peaks are provided in Table 2.
2. The coordinates for the initial takeoff and landing point are set at (0, 0), and the coordinates for the final takeoff and landing point are set at (100, 100, 100). The maximum range of the UAV is set to 5000 m.
3. The crowd density at ground impact \(\rho\) was set to 0.02 persons/m\({}^{2}\).
4. The parameter settings for the IGWO algorithm are presented in Table 3.
5. The parameter settings for the IAPF algorithm are presented in Table 4.
6. The fitness weights \(b_{1},b_{2},b_{3}\) are set to 0.5, 0.2, and 0.3, respectively.
# Automated Test Creation Using Large Language Models: A Practical Application
## 2 The Evolution of Software Applications for Test Creation
Other approaches include forming questions by selecting an appropriate Wh-word [56], analyzing the relationships between the subject, verb, and object [57], dependency-based patterns [58], syntactic transformation [59], and others. The scientific literature describes various methods for generating distractors for test questions. These include techniques based on parts-of-speech information [60], word frequency [61], domain ontology [62], semantic analysis [44], and others. Detailed reviews of test generation technologies are presented in [55; 63; 64; 65]. The classic approaches to test creation have some significant limitations:  
* Traditional systems are highly dependent on predefined rules and templates, which limits the ability to generate diverse questions, especially when more complex or adaptive test formats are required. * These systems require significant manual configuration, both in creating rules and selecting appropriate keys and distractors, making them time-consuming and labor-intensive. * Adaptation is limited, usually based on prior answers and not easily adjustable to dynamically changing conditions. * The applications use limited semantic information processing, which reduces their ability to understand and interpret deeper contexts and relationships within the text. Modern technologies based on artificial intelligence and large language models (LLMs) provide a rich set of tools that allow for easy overcoming of the aforementioned limitations. Thanks to these technologies, the process of automated creation of personalized tests can be fully innovated, increasing not only efficiency but also the quality of the tests themselves. Artificial intelligence and LLMs can analyze and understand texts at a deeper level, enabling the generation of questions with a high degree of accuracy and relevance. They can create diverse questions tailored to the individual needs and knowledge levels of learners by utilizing contextual information and semantic relationships within the text. Interesting possibilities in this direction are offered by AI-powered chatbots such as ChatGPT, Gemini, Claude, and others. They can be integrated into educational platforms and provide dynamic tests. These chatbots not only generate questions but also offer distractor answers that are sufficiently challenging and relevant, increasing the difficulty level and preventing easy guessing.
# A Low-Profile Wideband Monocone Antenna Using Bent Shorting Strips
## 2 Antenna Design  
Ideally, an infinite ground plane is required for a monocone antenna to have a monopole-like radiation pattern, but this is usually approximated by a large ground plane in practice. [PERSON] and [PERSON] presented the input impedance of the monocone antenna using the transmission line theory using the following formula [20]. The monocone antenna in the infinite ground plane is shown in Figure 1. The input impedance of a monocone antenna at the feed point for a given half angle (\(\theta\)) of a monocone is,  
\[Z_{in}=Z_{0}\frac{1-\beta/\alpha}{1+\beta/\alpha} \tag{1}\]  
where  
\[Z_{0}=60\ln\alpha\beta\Big{(}\frac{\theta_{0}}{2}\Big{)}\left[\Omega\right], \tag{2}\]  
\[\frac{\beta}{\alpha}=e^{-2\beta l}\frac{1+j\frac{60}{2\zeta}\sum_{n=1}^{\infty }\frac{(2n+1)}{n(n+1)}[P_{n}(cos\theta_{0})]^{2}\zeta_{n}(kl)}{-1+j\frac{60}{2 \zeta}\sum_{n=1}^{\infty}\frac{(2n+1)}{n(n+1)}[P_{n}(cos\theta_{0})]^{2}\zeta_ {n}(kl)}, \tag{3}\]  
and  
\[\zeta_{n}(kl)=\frac{h_{n}^{2}(kl)}{h_{n-1}^{2}(kl)-\frac{n}{2}h_{n}^{2}(kl)} \tag{4}\]  
\(\beta\alpha\) is the reflection coefficient between the terminating impedance of the cone and free space. \(P_{n}^{2}(\cos\theta_{0})\) is \(n\)th order Legendre polynomial, \(h_{n}^{2}\) is the spherical Hankel function of the second kind, and \(\zeta_{n}(kl)\) is helper funcion. The input impedance of a monocone antenna can be calculated according to the '\(kl\)' value where \(l\)' is the slant height of a monocone and \(k\) (=\(\frac{2\pi}{\lambda}\)) is the wave number. Based on above design equations, the dimensions of a monocone operating at 2.7 GHz are determined by the following: half cone angle (\(\theta_{0}\)) = 56\({}^{\circ}\) and slant height (\(l\)) = 45 mm.  
The proposed antenna consists of a monocone antenna, four bent shorting strips, four parasitic sleeves, and inner and outer circular sleeves. By attaching the bent shorting strips from the monocone hat to the ground plane, a reduction in size is achieved. The proposed antenna has a parallel resonance mode that generates the resonance in the low-frequency band. The parallel resonance is generated by an anti-resonance formed by a parallel circuit containing the capacitance between the top monocone hat and the ground plane, and the inductance of the bent shorting strips. Moreover, the parasitic sleeves and circular sleeves are loaded to improve the impedance matching characteristic of the monocone antenna. The monocone antenna and the ground plane are made of a copper with a conductivity of 5.8001 \(\times\) 10\({}^{7}\) S/m. The radius of the monocone antenna is 0.135\(\lambda_{min}\) (\(\lambda_{min}\) is the free-space wavelength at the low frequency) and the antenna height (\(H_{1}\)) is 0.073\(\lambda_{min}\). The height (\(H_{1}\)) is chosen such that the  
Figure 1: Monocone antenna fed by a coaxial line with infinite ground plane.  
total antenna structure can be embedded into the fuselage of an aircraft. The design parameters of the proposed antenna are shown in Figure 2 and the antenna dimensions are given in Table 1.
## 2 Materials and Methods
### Experimental Procedure
#### 2.1.6 SEM Analysis of Mask Surfaces Sprayed with Micro-Particles  
Scanning electron microscopy (SEM) was carried out using a JOEL instrument (JSM IT200, Tokyo, Japan). SEM was operated at an accelerating voltage of 10 kV to study size and surface morphology. The aerosol-sprayed particles (0.05 to 1 um) on each face mask were observed using the SEM microscopy in low vacuum mode at an operating pressure of 50 Pa. For this purpose, the six different face masks (_Surgical_, _Reusable Cotton_, _FFP2_, _FFP3_, _Antiviral_, and _Silk_) were used and assessed for their particle size and morphology and their relative comparison at varying magnifications between 1000 and 5000. The whole mask's layer was carbon-coated to a thickness of \(\sim\)10 nm before SEM analysis.
## 6 Experiments
### Experiment IV: Stability Comparison with Branch Models  
In Experiments II and III, we tested the time-point performance and accuracy of the branch models. In this experiment, we evaluated the stability of the proposed combined model by comparing the standard deviation of the MAPE (STD-MAPE) values (as shown in Table 6). Because there is no randomness in the mathematical models, we just tested CS-BPNN and DE-OSELMNN of branch models in terms of stability. Here, we show the wind-speed data from Sites 1 and 2, and the results of MAPE and STD-MAPE from 100 time experiments.  
The results in Table 6 show the following:  
(1) CS-BPNN performs better than DE-OSELM in accuracy on most test days.  
(2) In terms of stability, CS-BPNN obtained lower STD-MAPE than DE-OSELM in Site 1, but in Site 2, DE-OSELM was better than CS-BPNN.  
(3) Our proposed combined model reached the best accuracy and stability values compared to the two other models.  
(4) The lowest STD-MAPE value of CS-BPNN was achieved on Wednesday on Site 2; at the same time, the STD-MAPE of DE-OSELM and our combined model were 0.2430 and 1846, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Site 1**} & \multicolumn{3}{c}{**Site 2**} \\ \cline{3-8}  & \multicolumn{1}{c}{**CS-BPNN**} & \multicolumn{1}{c}{**DE-OSELM**} & \multicolumn{1}{c}{**Combined Model**} & \multicolumn{1}{c}{**CS-BPNN**} & \multicolumn{1}{c}{**DE-OSELM**} & \multicolumn{1}{c}{**Combined Model**} \\ \hline \multirow{8}{*}{MAPE} & Mon & 5.91\% & 5.77\% & 5.69\% & 4.55\% & 4.80\% & 4.20\% \\  & Tue & 3.73\% & 3.73\% & 3.70\% & 4.11\% & 4.23\% & 3.74\% \\  & Wed & 4.08\% & 4.10\% & 4.08\% & 4.07\% & 4.19\% & 3.67\% \\  & Thu & 5.17\% & 5.44\% & 5.17\% & 5.29\% & 5.50\% & 4.81\% \\  & Fri & 5.73\% & 5.75\% & 5.69\% & 6.15\% & 6.10\% & 5.38\% \\  & Sat & 6.63\% & 6.55\% & 6.44\% & 5.70\% & 5.79\% & 5.04\% \\  & Sun & 5.10\% & 5.09\% & 5.08\% & 4.38\% & 4.40\% & 3.93\% \\ \cline{2-8}  & Mon & 0.4910 & 0.5220 & 0.4617 & 0.3704 & 0.3492 & 0.3448 \\  & Tue & 0.2555 & 0.2676 & 0.1871 & 0.2668 & 0.2434 & 0.2001 \\  & Wed & 0.2389 & 0.2752 & 0.2176 & 0.2051 & 0.2430 & 0.1846 \\  & Thu & 0.7592 & 0.8261 & 0.6506 & 1.1536 & 0.9245 & 0.8039 \\  & Fri & 0.3601 & 0.3754 & 0.3243 & 0.4756 & 0.4489 & 0.3702 \\  & Sat & 0.3714 & 0.4134 & 0.3636 & 0.3705 & 0.3519 & 0.3470 \\  & Sun & 0.3162 & 0.3562 & 0.2797 & 0.2827 & 0.2759 & 0.2048 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The stability and accuracy from three model.  
**Remark 3**.: _From the results shown in Table 6, our combined model obtained the lowest MAPE and STD-MAPE, which means that our combined-model functions could achieve high accuracy and strong stability compared with the single models._
# Biohydrogel Based on Dynamic Covalent Bonds for Wound Healing Applications
## 2 Materials and Methods
### Fourier Transform Infrared Spectroscopy  
Fourier transform infrared (FTIR) spectroscopy was used to investigate the chemical interaction between the hydrogel and freshly prepared acid-soluble collagen--an abundant component of human skin and bones [21]. Small quantities of the PBA-Alg hydrogel complex, collagen, and mixtures of hydrogel and acid-soluble collagen were scanned over a wavelength range of 4000 to 650 cm\({}^{-1}\) at a resolution of 8 cm\({}^{-1}\) using FTIR (Agilent Technologies, Santa Clara, CA, USA) in transmittance mode.
## 4 Evaluation of Model Developments
### Evaluation of Updated Land Cover and Vegetation
Given the longer time scales of the land surface processes compared to the atmosphere, and to overcome spin-up issues caused by unstable surface state, the surface initial conditions of each forecast run are obtained from the corresponding surface offline simulations. The synoptic situation is in the predictable range when focusing on short-range forecasts and an impact on near-surface atmosphere through sensible and latent heat flux and radiative forcing is also triggered by changes in land cover and LAI. Figure 9: Mean diurnal range (drange) bias of Land Surface Temperature (LST) in the control simulation (CTR) during June-August (JJA, (**a**)) and September-November (SON, (**b**)). Difference of the mean absolute bias in the simulation with new ESA-CCI LU/LA map and LAI disagradation (CLAI) in respect to the control simulation for JJA (**c**) and SON (**d**). Note that the color bar for differences in (**c**,**d**) has a range that is only half of the range in (**a**,**b**). The statistics were computed for the period 2004 to 2015 considering only clear-sky conditions. Screen-level temperature and dewpoint can be verified using the analysis, which draws closely to the surface synoptic observations. Figure 11 shows the 2 m temperature normalised RMSE difference between forecasts based on the new ESA-CCI LU/LC map with LAI disaggregation operator and forecasts based on the current operational maps with regards to the operational analysis for the 4 seasons. A mixed signal is seen for different areas with a marked reduction of the 2 m temperature rmse (bluish colour) when using the updated vegetation especially in the northern hemisphere during MAM and JJA (panels (b,c) respectively), while JF and SON (panels (a,d) respectively) are marked with a mainly neutral impact. Although the overall results of introducing these updated vegetation maps are neutral to positive, a few areas still show a degradation in some atmospheric parameters pointing to the need for additional investiga  
Figure 11: A 2 m temperature normalised RMSE difference between 36-h forecasts using ESA-CCI LU/LC with the new LAI disaggregation operator (CLAI) and a control simulation using the operational configuration (CTR) for JF (**a**), MAM (**b**), JJA (**c**) and SON (**d**). Figure 10: Annual mean evapotranspiration relative bias (%) with respect to GLEAM (**a**) and FluxCom (**c**); and the absolute relative bias difference (%) between the ESA-CCI + LAI (CLAAI) experiment and the control experiment (CTR), where GLEAM is used for verification in (**b**) and FluxCom in (**d**). Note the different range of the color-bars between (**a,c**) (\(-\)100 to 100) and (**b,d**) (\(-\)50 to 50). tions and a parameter optimization with respect to the complex boundary layer turbulence interactions. This development is crucial for an accurate land surface representation and is therefore part of the operationally viable research for upcoming cycles.
# Route Optimization for Open Vehicle Routing Problem (OVRP): A Mathematical and Solution Approach
## 3 Materials and Methods
### Experiment Design  
The study adopts a multidimensional approach to model and solve the OVRP. Initially, AMPL (A Mathematical Programming Language) is used. This tool is ideal for tackling a range of optimization problems, based on linear and integer programming [25]. To ensure consistent and comparable results, all tests were performed on a 2.9 GHz Intel Core i5 10400F processor with 32 GB of DDR4 RAM at 3200 MHz. The use of AMPL and hardware specificity are fundamental to the quality, consistency and efficiency of the research. They ensure that the results are accurate, reproducible and comparable, providing a solid basis for evaluating and improving optimization techniques applied to OVRP.
## 5 Analysis of the Influence of Piled Soil on Foundation Pits
### Influence of Foundation Pit Accidents on the Elevator Shaft  
While the soil pile is beneficial for the deformation of the supporting structure of the foundation pit, it also generates stress on the soil around the elevator shaft, so it is necessary to discuss its influence on the foundation pit of the elevator shaft accident in this project. This analysis is performed from the perspective of deformation of the bottom of the foundation pit and the change in groundwater flow.  
Figure 17a shows a diagram comparing the displacement of the elevator shaft position in the foundation pit. Under the action of the pile soil, the maximum deformation of the elevator shaft near the pile soil side is reduced from 18.91 mm to 17.66 mm, corresponding to a reduction of approximately 6.6%, and the displacement of other positions is reduced to varying degrees. According to the analysis of the mechanism of back pressure soil action, the displacement of the soil under the pile soil is limited under the action of the pile soil in the pit, reducing the deformation of the soil inside the foundation pit. Figure 17b compares the groundwater seepage flow at the location of the elevator shaft in the foundation pit. Because the foundation pit is equivalent to the pile load outside the pit, the seepage velocity around the elevator shaft near the side of the pile is slightly increased, by less than 1%. Therefore, because the position of the soil pile is far from the elevator well, it has little impact on the deformation at this position and has almost no impact on groundwater seepage.  
In summary, the piled soil plays a supporting and reinforcing role in this foundation pit. Under the action of the soil pile, the displacement of the soil inside the foundation pit is significantly reduced, which also limits the seepage velocity and seepage flow of groundwater in the whole foundation pit but has little effect on the foundation pit of the elevator shaft. Therefore, a sudden surge accident still occurred at the location of the elevator shaft in the foundation pit, which was not directly related to the accumulation of soil. The main reasons for this foundation pit inrush accident are the lack of depth of the water-resisting curtain, the quality defects of construction method piles and the unreasonable emergency disposal scheme.  
Figure 16: Comparison of the displacement and seepage flow at the piled soil position: (**a**) displacement in the absence of piled soil; (**b**) displacement under the action of piled soil; (**c**) seepage flow without piled soil; (**d**) seepage flow under the action of piled soil.
## 5 Trade-Off between Communication Rate and Convergence Performance  
### Communication Rate with Unknown Packet Loss Probability  
**Theorem 3**.: _Under the condition that the probability of packet loss is unknown, the communication rate of the improved CSTCS (23) and (24) is_  
\[\frac{1}{N}\sum_{k=1}^{N}\gamma_{k}^{\epsilon}\rightarrow\overline{\gamma}= \alpha+\frac{1-\alpha}{mn}\sum_{i=1}^{n}F_{i},\ N\rightarrow\infty. \tag{43}\]  
Proof.: From (35), it is known that  
\[\frac{1}{N-N_{0}}\sum_{k=1}^{N}\gamma_{k}^{\epsilon}I_{\{k\in\overline{\cal O }_{N,i}^{0}\}}-\frac{1}{m(N-N_{0})}\sum_{k=1}^{N}s_{k}I_{\{k\in\overline{\cal O }_{N,i}^{0}\}}\to 0,\ N\rightarrow\infty. \tag{44}\]  
Combining with (30), we can obtain  
\[\frac{1}{N-N_{0}}\sum_{k=1}^{N}\gamma_{k}^{\epsilon}I_{\{k\in\overline{\cal O }_{N,i}^{0}\}}\rightarrow\frac{F_{i}}{mn},\ N\rightarrow\infty. \tag{45}\]  
Then, it follows that  
\[\frac{1}{N}\sum_{k=1}^{N}\gamma_{k}^{\epsilon} = \frac{1}{N}\sum_{k=1}^{N}\Bigl{(}\gamma_{k}^{\epsilon}I_{\{k\in \overline{\cal Z}_{N}\}}+\gamma_{k}^{\epsilon}I_{\{k\in\overline{\cal Z}_{N}\} }\Bigr{)}\] \[= \frac{1}{N}\Biggl{(}N_{0}+\sum_{i=1}^{n}\sum_{k=1}^{N}\gamma_{k}^{ \epsilon}I_{\{k\in\overline{\cal O}_{N,i}^{0}\}}\Biggr{)}+\frac{1}{N}(\gamma_{ nL_{N}+1}^{\epsilon}+\ldots+\gamma_{N}^{\epsilon})\] \[= \frac{N_{0}}{N}+\sum_{i=1}^{n}\frac{N-N_{0}}{N}\Biggl{(}\frac{1}{ N-N_{0}}\sum_{k=1}^{N}\gamma_{k}^{\epsilon}I_{\{k\in\overline{\cal O}_{N,i}^{0}\}} \Biggr{)}+\frac{1}{N}(\gamma_{nL_{N}+1}^{\epsilon}+\ldots+\gamma_{N}^{\epsilon})\] \[\rightarrow \alpha+\frac{1-\alpha}{mn}\sum_{i=1}^{n}F_{i},\ N\rightarrow\infty.\]  
The proof is completed.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 5 Recent MM Techniques and 5G Architecture
### Considerations for SDN-based MM Solutions
#### Advantages of cross-layer approach  
* Allows for collaboration among multiple OSI layers, thus enabling multi-homing support in respect of efficient utilization.
* The distribution of network information among the different OSI layers is allowed [99].
## 3 Discussion
### BADH2-Independent 2 AP Synthesis in P. amaryllifolius?  
Considering the functionality of _PaBADH2_ in _P. amaryllifolius_,along with high 2 AP synthesis, raises the question of how 2 AP is synthesized in _P. amaryllifolius_. Interestingly, [PERSON] et al. [34] reported that in aromatic rice, the upregulation of _P5 CS_ might be associated with the higher levels of delta-1-pyrroline-5-carboxylic acid, thus showing accumulation of a high amount of 2 AP. The analysis of the expression level of \(\Delta^{1}\)-pyrroline-5-_carboxylic acid synthetase_ and the enhanced concentration of its product among fragment and nonfragment varieties exhibited that 2 AP is synthesized by the direct reaction of methylglyoxalwithdelta-1-pyrroline-5-carboxylate independent of BADH2 [37]. In another study, [PERSON] et al. [34] studied an in vitro model system and demonstrated that methylglyoxal was one of the main precursors for 2 AP synthesis. Methylglyoxal might react directly with \(\Delta^{1}\)-pyrroline-5- carboxylic acid, derived from L-proline by purified recombinant B. subtilis ssp. natto PRODH, and lead to the formation of 2-acetyl-1-pyrroline. The over-produced \(\Delta^{1}\)-pyrroline-5-carboxylic acid may be detoxified by methylglyoxal and results in the biosynthesis of 2 AP. In our analysis, we recorded comparatively higher levels of P5 CS and methylglyoxal in _P. amaryllifolius_. Therefore, ultimately, this mechanism might be existing in _P. amaryllifolius_, which might be responsible for high 2 AP synthesis independent of _PaBADH2_ (Figure 8).  
Moreover, fragrance is a trait of high value in rice. The Discovery of the recessive fragrance gene in rice [PERSON] et al. [10] has facilitated the breeding of fragment rice. The same gene has been linked with fragrance in sorghum and other plant species. The loss of function of the _BADH2_ gene [10] that results in fragrance is associated with a loss of performance that may be due to stress susceptibility caused by the disruption of a pathway that is a key stress response mechanism in plants [12]. Pandanus leaves have the same fragrance but are much more resistant to stresses, such as salt [38], than fragrant rice varieties. Understanding the biochemical processes in Pandanus may support the breeding of higher-yielding stress-tolerant rice and other crops that are also fragrant.