esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Deposition of Silica Sphere Monolayer  
Prior to SWSS fabrication, deposition of a uniform and high coverage SS monolayer must be realized. We, therefore, developed a DMF solvent introduced spin-coating method which makes SS solution preparation easy and results in enhanced SS coverage and uniformity even on expanded Si surface areas [27]. Unlike conventional SS deposition techniques such as Langmuir-Blodgett (LB) dip-coating [15; 16] and water-based spin-coating methods [21; 28], our spin-coating process offers greatly reduced environmental sensitivity to, for example, vibration, humidity, and temperature along with reduced processing time. This is because DMF has more spin-coating oriented solvent properties compared to the conventional solvent, water. Figure 1 schematically compares the behavior of DMF and water during the various stages of spin-coating. First, from Figure 1b, DMF can provide outstanding wetting ability on a Si surface. From our contact angle measurements, the contact angle of DMF on a piranha cleaned Si surface was immeasurably small. This is an essential solvent property for fast, uniform, and omni-directional SS spread-out during spinning, which is also followed by less material loss due to its low centrifugal force caused by low surface tension at the liquid/solid interface, schematically shown in Figure 1d. Second, DMF also offers a significantly lower vapor pressure (VP\({}_{\text{DMF}}\) = 2.7 Torr at 20 \({}^{\circ}\)C) compared to that of water (VP\({}_{\text{water}}\) = 17.54 Torr at 20 \({}^{\circ}\)C) providing increased SS deposition and distribution time during spin-coating due to its relatively slow evaporation rate, schematically illustrated in Figure 1f. This is also very important for spin-coating closely-packed SS assembly in a long range order (Figure 1h) since slower solvent evaporation produces intensified convective flux (J\({}_{\text{w}}\)) of SS to the initially assembled SS (Figure 1f inset) [29]. However, water, due to its high vapor pressure (VP), requires humidity and temperature control or additional surfactant mixtures to delay the evaporation rate as previously reported [14; 21]. Combining these two major advantages of DMF provides even less sensitivity to the spin-coating surface area, and we demonstrated more than 95% monolayer coverage even with much smaller SS (310 nm diameter) on a 2-inch round Si wafer, which is more challenging for spin-coating due to the stronger inter-particle interaction [13]. More details of DMF effect on SS spin-coating can be found in our previous study [27].  
Figure 2 shows SEM images and a picture of a uniform spin-coated SS (1.57 \(\upmu\)m diameter) monolayer on the Si surface with more than 95% coverage that has been achieved through the use of the DMF solvent without any modification of the surface [17] or spin-coating environment [21]. Therefore, with our novel solvent-controlled spin-coating method, we provide highly uniform and enhanced SS monolayer coverage even on large Si surface areas just by using a simple and low-cost spin-coating method.
# Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology
## 4 Discussion
### Potential Application to Evapotranspiration  
Evapotranspiration (ET; evaporation + transpiration) plays an important role in a wide range of biogeophysical processes [53]. In arid environments like Dhofar, water availability is of particular biological importance. A wide range of models exist to estimate ET from in situ and remotely sensed data, and improving satellite-based ET measurements is a major focus of ongoing work in the discipline [54]. Applying the spatiotemporal analysis methods that are developed here to time series of spatially explicit ET estimates, such as those expected from the recent ECOSTRESS mission [55],could provide additional insight into monsoonal landscapes. This could be especially informative when viewed from the spectral mixture modeling perspective, as proposed by [56].
# Minimizing the Number of Distrustful Nodes on the Path of IP Packet Transmission
## 3 Results and Discussion
### Scenario C, with Added Subnet and Two Distrustful Nodes  
Let us extend the previous topology with an additional subnet, which includes nodes P, O, J, K, L, M, and N. Now, our network (Figure 10) has 16 nodes, two of them--E and G--are distrustful.  
Let us choose node A as the root of the tree, that is, this node will correspond to the router whose routing table must be obtained. Figure 11a,b show the tree of shortest paths for the classic Dijkstra algorithm and the modified Dijkstra algorithm, respectively. Red shows the part of the path that the packets will travel, after they have been processed in a distrustful node.  
As shown in Figure 11, when using the classical algorithm, nine nodes (P, O, J, K, H, I, L, M, and N) will receive data through distrustful node E. When using the proposed  
Figure 11: The shortest path tree for the (**a**) classical Dijkstra algorithm and (**b**) modified Dijkstra algorithm.  
Figure 10: Network topology with 16 nodes and 2 distrustful nodes.  
algorithm, we have excellent results and there are no distrustful nodes on the path to any node. We would like to note that this is not a purely theoretical case; in real networks, there may well be topology variants with such results.  
The obtained results were achieved because router A, as a Gateway, chooses node C, not B, as is the case with the classic Dijkstra algorithm. Figure 12 illustrates this as a Gateway change if the Destination network is P, O, J, and K nodes.
## 3 Experimental Results and Discussion
### THz Generation Efficiency and Spectral Coverage
Despite the abundance of considered parameters, a significative discrepancy between the predicted values and measured ones can be noticed. In particular, the high frequency cut-off at 7.3 THz, limiting the achieved spectral coverage, is not reproduced by the simulation. In our opinion, such a discrepancy can be ascribed to the THz absorption in LN, which was so far neglected because the generated radiation went through a very small thickness in the surface waveguide. Remarkably, the absorption profile of MgO:LN is dominated by a transverse-optical phonon resonance centered at 7.5 THz, being strongly dependent on crystal temperature and the MgO-doping concentration. In order to include such an effect in our simulation, the absorption coefficient has been calculated by combining the four-resonances phonon-polariton dispersion model developed by [PERSON] et al. [54], with the measured parameters provided by [PERSON] et al. for 6.1% MgO:LN at room temperature [46]. A significant improvement in the matching between the predicted and observed efficiency is represented by the red solid curve in Figure 4b. This suggests that the presence of the phonon mode in MgO:LN plays a dominant role, limiting the maximum achievable spectral coverage of our source, even for less than 10 \(\upmu\)m THz propagation within the nonlinear medium. A residual discrepancy at the higher frequencies can still be noticed. In our opinion, this could be ascribed to the presence of a thin air gap \(\Delta\) between the waveguide surface and the silicon prism. Indeed, since the Cherenkov angle is not sufficient to allow for THz transmission at the LN-air interface, in this case the amplitude of the extracted THz wave would be attenuated by an exponential factor \(e^{-\beta\Delta}\), where the cut-off parameter increases as \(\beta\propto n_{\text{{THz}}}^{2}\omega_{\text{{THz}}}\)[55]. In our setup, the high IR power levels used in the THz generation process hamper the possibility to use an additional matching material between the LN crystal and the prism. For this reason, we are not comfortable in providing a quantitative value for the cut-off frequency, the \(\Delta\) parameter being substantially unknown. It is important to stress that, in principle, even an air gap in the order of a few hundreds of nm could be sufficient to justify both the high-frequency limitation and the overall efficiency of the CW THz source. The previous analysis suggests considerable room for improvement, both in terms of absolute THz power and of spectral coverage. In particular, the dimensions of the optical guiding structure allow for a PM bandwidth as wide as 1-8 THz, that can be further enhanced by optimizing the optical contact between the waveguide surface and the HR-Si prism. At the same time, the phonon-induced THz absorption can be minimized by inducing a fast thermo-electric heat dissipation of the LN crystal, preserving the room-temperature operation of our source.
# UAV Communication Recovery under Meteorological Conditions
## 2 System Model
### Line-of-Sight Probability  
In UAV communications with ground users, LoS links can be obstructed by ground obstacles such as buildings, trees, vehicles, pedestrians, and other impediments. Our previous research conducted ray tracing simulations for four distinct urban environments in New York City [32], yielding LoS probability data for UAV-ground user communications, as depicted in Figure 1. The four solid lines in Figure 1 represent the fitting curves, with the corresponding LoS probability equation  
\[P_{LoS}(\theta)=i\cdot\sin(j\theta+k)+l\cdot\sin(m\theta+n),(0\leq\theta\leq 70 ^{\circ}), \tag{2}\]  
where \(\theta\) is the elevation angle of the UAV, and \(i,j,k,l,m,n\) are the fitting parameters. These parameters for the four urban environments are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Regions/Parameters** & \(i\) & \(j\) & \(k\) & \(l\) & \(m\) & \(n\) \\ \hline Region 1 & 4.983 & 0.03925 & \(-\)0.7442 & 4.077 & 0.04385 & 2.148 \\ \hline Region 2 & 0.9833 & 0.01213 & 0.2867 & 0.03389 & 0.09113 & \(-\)0.5077 \\ \hline Region 3 & 1.718 & 0.0317 & \(-\)0.3507 & 1.132 & 0.04341 & 2.362 \\ \hline Region 4 & 3.659 & 0.006567 & \(-\)0.1354 & 0.6815 & 0.03597 & 2.128 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the fitted curves in four regions.  
Additionally, the selected four rectangular-shaped environmental regions in New York City in our previous work are suburban, urban, dense urban, and high-rise urban. The average building heights of the four selected regions are 6.5 m, 12.2 m, 22.7 m, and 55.0 m, respectively. The ratios of the land area covered by buildings to the total land area are 0.26, 0.43, 0.94, and 0.78, respectively. The average numbers of buildings per unit area (square kilometer) are 2582.7, 1100.9, 2243.9, and 844.0, respectively.
# Communication Technologies in Emergency Situations
## 4 Emergency Communications Technologies--A Systematic Literature Review
### Methodology
#### 4.1.2 Screening  
After removing duplicates, 47 articles were screened by reading their titles and abstracts. The screening criteria were:  
1. only articles addressing the technological deployment of ECS;
2. only articles about the communication systems during the emergency (no prevention and neither post-emergency recovery);
3. only articles reporting ECS technologies from the last five years (2017-2021).  
During the screening process, 13 articles were discarded, leaving 34 articles for the eligibility phase. Out of these 13 excluded articles, 2 articles referred to ECS from 2009 to 2013, 10 articles did not include any technologic contribution but focused on the social aspects of the communications during emergencies, and 1 article did not refer to the deployment of an ECS during the emergency but focused on the post-disaster communication network recovering.
## 2 Methods  
### Apparatus  
We used a mouth simulator (AWA6150, Hangzhou Aihong Instrument Co., Ltd., Hangzhou, China) to play the audios in a specified mask condition and the data acquisition system (BBM MKII, Muller-BBM VibroAkustik Systeme GmbH, Munich, Germany) to save the sound recorded by the microphone (BSWA MPA201, BSWA Technology Co., Ltd., Beijing, China) in the semi-anechoic chamber. The mouth simulator simulated the sound field near the human mouth. The mouth simulator was placed inside a customized dummy head and driven by a USB sound adapter (ADI-2 DAC, RME Audio, Haimhausen, Germany). The microphone was placed on a tripod directly opposite the mouth simulator at the same height as the lip ring of the mouth simulator. The distance between the microphone and the mouth simulator was set as 0.2 m and 0.6 m. The recordings were conducted in 7 mask conditions: without a mask (M0), with a surgical mask [27] (M1), an activated-carbon mask [28] (M2), with a hanging-ear medical protective mask [29] (M3), with a headwear medical protective mask [29] (M4), with an anti-particulate mask (with a breather valve) [30] (M5), and with a cloth mask (M6). M1, M3, and M4 are medical masks that can prevent virus. M2 and M5 are often used in laboratories and factories, and M5 is also known as the KN95 mask. M6 is used for warmth. Figure 1 shows the connection set-up of the apparatus and 7 mask conditions.  
Figure 1: Connection set-up of apparatus and 7 mask conditions. (**a**) Connection set-up of apparatus; (**b**) No mask (M0); (**c**) Surgical mask (M1); (**d**) Activated-carbon mask (M2); (**e**) Hanging-ear medical protective mask (M3); (**f**) Headwear medical protective mask (M4); (**g**) Anti-particulate mask (M5); and (**h**) Cloth mask (M6).
## 5 Conclusions  
The following main conclusions can be drawn from this study.  
1. Medium-grained low-cohesive soils can be effectively stabilized with chitosan solutions.
2. Adding chitosan to the tested soil specimens improves their shear modulus \(G\) substantially (even up to 3 times).
3. Soil-chitosan mixtures show better dynamic properties (dynamic shear modulus) even before the curing process. Therefore, the soil dynamic characteristics are noticeably enhanced even when the optimal curing conditions cannot be met.
4. Even relatively low chitosan concentration solutions (1.5 g of chitosan per 1 kg of dry silica sand) can be a very effective stabilizer.
5. Chitosan can be used as an eco-friendly short-term soil stabilizer. The specimens show signs of degradation after 14 days of being submerged in water.  
The findings presented in this study suggest that chitosan solutions can be successfully used as a soil stabilizer. It positively affects soil dynamic properties. This, along with the biodegradability of chitosan makes it a possible solution for stabilizing temporary soil structures (e.g., working platforms) or for regular slope surface stabilization. Chitosan stabilization is a low-cost (as chitosan is a waste material) and easy-to-use (does not require specialized equipment to apply) method for temporary ground improvement. Considering  
Figure 23: Specimen C2-23608 after 14 days of degradation in water.  
high eco-compatibility of the material, its application is compliant with the main goals of sustainable engineering.  
The authors plan to continue their research on chitosan-soil composites. Specifically, more dynamic tests will be conducted and focused on determining other dynamic properties (e.g., the damping ratio) and more in-depth analysis of the underlying binding mechanisms of chitosan solutions will be performed.  
Conceptualization, I.D. and P.S.; methodology, M.B. ([PERSON]) and I.D.; software, P.S.; validation, I.D. and P.S.; formal analysis, I.D. and R.O.; investigation, I.D., M.B. ([PERSON]) and M.B. ([PERSON]); resources, I.D., P.S. and R.O.; writing--original draft preparation, M.B. ([PERSON]) and M.B. ([PERSON]); writing--review and editing, M.B. ([PERSON]) and P.S.; visualization, M.B. ([PERSON]); supervision, I.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, upon reasonable request.  
The authors declare no conflict of interest.
## 3 The Proposed Model
### Feature Selection: LETLBO Algorithm
#### 3.1.3 Poor Student Tutoring Phase  
The basic TLBO can not be used for this stage; the initial purpose of this stage is for enhancing the grades of worse students. A similar procedure was utilized under this stage as well, with learners ranking from better to poor depending on their grades.  
A learner assumes a worse learner when it exists in the bottom 10%. This stage used to is arbitrarily select learner \(x_{T}\) in all the worse students \(\chi_{i}\), whose rank exists at the top 50%, and the learning is dependent upon the subsequent formula:  
\[x_{i,new}^{d}=x_{i,old}^{d}+rand\cdot\left(x_{T}^{d}-x_{i,old}^{d}\right) \tag{6}\]  
If \(x_{i,new}\) is superior to \(\chi_{j,old}\), \(x_{i,new}\) is accepted; if not, \(X_{j,old}\) remains the same. The students with worse grades have a lesser probability of upgrading their position from the type of optimum students, but students with better grades take a comparatively superior probability of upgrading their position. The worse student tutoring stage plays a vital role in enhancing the grades of worse students into that of better students. This technique was appropriate to real-time teaching-learning procedures, but the worst students of all the time require tutorials for its enhancement, more tutorials than if related to other better students.  
The fitness function of the LETLBO technique considers the count of selective features and the classifier performance. It minimises the set size of selective features and maximizes classification accuracy. Thus, the subsequent fitness function is utilized for evaluating individual solutions as follows:  
\[Fitness=\alpha\times ErrorRate+(1-\alpha)\times\frac{\#SF}{\#All\_F} \tag{7}\]  
where \(ErrorRate\) denotes the classifier rate of errors exploiting the selective feature. \(ErrorRate\) can be evaluated as the percentage of inappropriate classifications to the count of classifiers developed in the formula as a value within \([0,\ 1]\). \(\#SF\) implies the selective feature count, and \(\#All\_F\) indicates the overall amount of features from the original data. \(\alpha\) is exploited for controlling the importance of subset length and classifier quality. Here, \(\alpha\) is fixed to 0.9.
# Nonlocal and Size-Dependent Dielectric Function for Plasmonic Nanoparticles
## 2 Methods and Theoretical Framework
[14; 15; 16] were used: \(\omega_{g}=2.1062\text{ eV}\), \(\gamma_{\text{bound}}=0.158\text{ eV}\), \(K_{b}=2.3\times 10^{24}\text{ s}^{-3/2}\), \(E_{\text{F}}=2.5\text{ eV}\). Note that the interband transitions from the d-band to the conduction sp-band near the L point in the Brillouin zone can be taken into account by the size-corrective contribution for bound electrons in the form of Equation (6) [46]. Because of the nonlocal dielectric function, \(\varepsilon_{\text{Nonlocal}}(\mathbf{k},\omega)\) is estimated by fitting Equation (1) to the complex bulk dielectric function, which is the local, frequency-dependent part of the response, taken from optical measurements given in Ref. [33] and exactly the same as the first term \(\varepsilon_{\text{bulk}}\left(\omega\right)\) using the size-dependent dielectric function \(\varepsilon_{\text{SD}}\left(\omega,R\right)\) of Equation (4); we adopt here the former as a substitute for the latter. The resulting nonlocal and size-dependent (NLSD) dielectric function of the metal is thus approximated by  
\[\varepsilon_{\text{NLSD}}\left(\mathbf{k},\omega,R\right)=\varepsilon_{\text {Nonlocal}}(\mathbf{k},\omega)+\Delta\varepsilon_{\text{free}}\left(\omega,R \right)+\Delta\varepsilon_{\text{bound}}\left(\omega,R\right). \tag{7}\]
# Analysis of Vertical Earth Pressure Acting on Box Culverts through Centrifuge Model Test
## 4 Measurement Results of Vertical Earth Pressure Acting on Box Culvert
### Relationship between Theoretical and Measured Vertical Earth Pressure  
Figure 10 shows a plot of the measured vertical earth pressure versus the theoretical vertical earth pressure under the loading and unloading conditions. Apart from the center line, where the theoretical and measured values are equal, most of the data are located in the upper right corner. This means that the theoretical values are under-designed or the measured values are higher than the actual values.  
Figure 9: Measured vertical earth pressure under loading and unloading conditions; cover depth of (a) 4 cm, (b) 8 cm, (c) 12 cm, and (d) average of measured vertical earth pressure.  
Moreover, a linear regression analysis of the data indicated that the value of R-square in the loading condition was higher than that in the unloading condition. Here, R-square generally refers to the coefficient of determination, and its value ranges between 0 and 1. If R-square is 1, all data are perfectly correlated. A low value of R-square indicates that the data measured from the established regression linear line are widely spread, which means that the variability is large.
## 4 Results and Discussions
### Optimal Combination Verification  
Table 4 illustrates the optimal operational parameters of the air compressor under different optimization objectives. The results indicate substantial differences in the optimal operational parameters when varying weights are assigned to total power consumption, isentropic efficiency, and mass flow rate as optimization objectives. In engineering applications, researchers can utilize the grey relational optimization theory to adjust the weights assigned to different objectives, thereby making corresponding adjustments to the operational parameters of the air compressor. This approach aims to achieve high efficiency and energy conservation in the operation of the air compressor.  
As shown in Figure 14, compared to the initial sample average values (isentropic efficiency 41.48%, total power consumption 6.463 kW, 0.0712 kg/s), Optimization A demonstrates a notable 42.18% enhancement in isentropic efficiency and a substantial 44.92% reduction in energy consumption. Optimization B results in a significant 95.1% increase in mass flow rate. Optimization C achieves a remarkable 78.88% elevation in mass flow rate and a 48.23% improvement in isentropic efficiency while only experiencing a 50% rise in energy consumption. The validation results indicate that Optimization A significantly reduces energy consumption aimed at lowering the power consumption of the air compressor, thereby enhancing its energy efficiency. The significant increase in mass flow rate and isentropic efficiency achieved by Optimization B and Optimization C substantiates the air compressor's optimization outcomes under varying requirements. This analysis and discussion thoroughly validate the optimization results for different demands on the air compressor.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Weight** & **Optimization A** & **Optimization B** & **Optimization C** \\ \hline Total power consumption \(\downarrow\) & 0.45 & 0.48 & 0.35 \\ Isentropic efficiency \(\uparrow\) & 0.55 & 0 & 0.35 \\ Corrected mass flow rate \(\uparrow\) & 0 & 0.52 & 0.30 \\ Optimal combination & No. 4 & No. 8 & No. 7 \\ \hline \hline \end{tabular} Note: \(\downarrow\) the smaller, the better. \(\uparrow\) the larger, the better.  
\end{table}
Table 4: Optimum operational combinations of the air compressor under different objectives.  
Figure 13: Multi-objective optimization results in high efficiency, large flow rate, and low energy consumption.