esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Cushion Gas  
The studies emphasize the importance of maintaining a minimum pressure in underground H\({}_{2}\) reservoirs using \"cushion gas\" to prevent geomechanical issues, reduce seismic risks, and ensure brine saturation [77; 78]. Cushion gas improves hydrogen recovery, with [PERSON] and [PERSON] [79] noting a 7% increase in recovery with its use. Salt caverns, requiring less cushion gas, are ideal for pure hydrogen storage, as demonstrated by successful projects in the UK and US [80; 81].  
In porous reservoirs, cushion gas can range from 30% to 70% of stored gas volume and may include CO\({}_{2}\), CH\({}_{4}\), He, or N\({}_{2}\)[79]. However, [PERSON] et al. [82] argue that cushion gas is the costliest component of underground gas storage. Using CO\({}_{2}\) as cushion gas is an option but may cause hydrogen losses [79; 83], while CH\({}_{4}\) has shown superior performance, improving hydrogen permeability and recovery rates [84; 85].  
N\({}_{2}\) offers cost benefits and efficient water displacement but leads to hydrogen mixing during storage cycles, reducing efficiency [84; 86; 87; 88]. Overall, selecting the appropriate cushion gas is critical for effective storage-recovery systems, requiring numerical simulations and geochemical studies to understand the gas-structure interaction. More details on gas mixtures and experiments can be found in the Supplementary Materials, Table S1 and Figure S4.
## 3 Results and Discussion
### Thermal Conductivity Test
#### 3.2.2 Moisture Content  
Figure 11 illustrates the impact of water content variation on soil thermal conductivity, showing that as the water content increased, the thermal conductivity of the carbonized solidified loess rose by 16-24%, while that of the untreated loess increased by 31%. This indicates that carbonized solidified loess effectively suppresses the rate of thermal conductivity growth. The thermal conductivity was tested using one-way analysis of variance (ANOVA) to determine whether there were significant differences between the different moisture content levels. Statistical calculations were performed for each dosage, and the average \(p\)-value obtained for all dosages was 0.00854, which is less than 0.05. This indicates that there were statistically significant differences in the average thermal conductivity values among the different water content levels. This phenomenon can be attributed to changes in material composition and heat conduction mechanisms at both macroscopic and microscopic scales. From a compositional perspective, the samples consisted of three distinct phases: soil particles, pore water, and gas. Under normal temperature conditions, the thermal conductivity of water (0.6 W/(m\(\cdot\)K)) is significantly higher than that of air (0.026 W/(m\(\cdot\)K)). As water content increases and dry density rises, the low-thermal-conductivity air in soil pores is gradually replaced by water, leading to an overall increase in soil thermal conductivity. Therefore, the replacement of air by water is a key factor driving this increase. Additionally, higher water content facilitates the formation of \"water bridges\" [3] between soil particles. These water bridges act as pathways for heat conduction, enhancing heat transfer efficiency at the microscopic scale. This water-mediated heat conduction mechanism significantly improves the soil's ability to transfer heat, further contributing to the rise in  
Figure 10: Thermal conductivity of different mixing amounts.  
thermal conductivity with increasing dry density. Furthermore, during the carbonation process, magnesium hydroxide (Mg(OH)\({}_{2}\)) colloids react with carbon dioxide (CO\({}_{2}\)) to form hydrated carbonates, primarily magnesium carbonate (MgCO\({}_{3}\)) [47]. This reaction consumes a portion of the initial water content, thereby inhibiting the overall heat transfer capacity of the material. In summary, the interplay between air displacement by water, the formation of water bridges, and the production of hydrated carbonates contributed to the increase in thermal conductivity. However, the carbonized solidified soil effectively suppressed the rate of thermal conductivity growth, demonstrating its potential for thermal performance regulation.
# The Reproduction and Evaluation of Star Fields with the Milky Way in a Planetarium
## 2 Experiment
### Experimental Results  
The significance of the evaluations given for each pattern was verified using a \(t\)-test after excluding outlier data using the Smirnoff-Grubbs test and verifying the distribution equality of evaluations using an F-test. Here, we assumed the normality of the evaluated data. These tests were conducted separately for all 66 pattern combinations (12 \(\times\) 11/2) for each evaluation index (faithfulness, preference, and depth feeling). Using parametric statistical techniques on data generated from the Likert scale is still controversial [16; 17]. However, we used the rating score directly obtained from the Likert scale for naive observers who did not have experience of psychophysical experiments. Furthermore, in addition to the above-mentioned analysis, we carried out an analysis of variance (ANOVA) to confirm the influence of the interaction by the combination of physical factors.  
First, we checked whether there was a tendency for rating faithfulness based on astronomical observations. However, we could not find any relationship between rating scores and observers' characteristics. Therefore, we treated all answers of 37 observers as experimental results without categorizing the observer's prior information such as age, gender, and experience of astronomical observation. The average rating value for each pattern is shown in Figure 4. The projection pattern with the highest rating was Std-L1 for faithfulness and Std for preference and depth feeling, respectively (\(p<0.01\)). In addition, D2-L2 for all indices was rated as the lowest score (\(p<0.01\)). For all three indices, we found a tendency of ratings for each pattern. We focused on the luminance represented by the projected patterns and calculated the total luminance.  
Figure 5 shows the average rating value with the standard error among all 37 observers for each pattern in luminance, ordered from low (left) to high (right). We confirmed the tendency from the peak (high rating) to the valley (low rating) along the luminance order for each evaluation index. In the faithfulness evaluation, there was a broad peak from Std-L1 to S1-L2 and a valley for the brighter pattern D2. This valley result was almost identical for the other two indices of preference and depth feeling. However, the tendency of the peak range differed, as shown in Figure 5b,c. There were two peaks for preference evaluation, and the peak range for the evaluation of depth feeling was rather narrow (from  
Figure 4: Evaluation results for all observers for each projection pattern. (**a**) Faithfulness; (**b**) Preference; (**c**) Depth feeling.  
D2-L1 to D1-L2). This result indicates that both luminance and other factors affect the judgment of preference and depth feeling evaluations.
## 3 Results and Discussion  
### Effect of Oils on the Peak Current of AMPARs  
To inquire direct effects of the essential oils on amplitude generated by AMPARs, Heterologous expression (GluA1/2 and GluA2/3) and Homologous expression of GluA1 and GluA2 subunits in HEK293 cells were individually and separately examined via whole-cell patch-clamp with and without the administration of the compound of interest. The peak currents of AMPA receptors subunits with glutamate alone and no treatment are \(834\pm 45\) pA, \(1330\pm 77\) pA, \(520\pm 39\) pA, \(443\pm 45\) pA, for GluA1, GluA2, GluA1/2, and GluA2/3, respectively as shown in Figure 1A. The essential Citronellol oil was applied to the cell and resulted in a drastic reduction in the peak current of the AMPARs previously mentioned to \(185\pm 23\) pA, \(380\pm 26\) pA, \(121\pm 19\) pA, and \(153\pm 32\) pA. Likewise, the effect of Geraniol oil had a similar impact on the peak current of AMPAR subunits. Hence, the peak current of GluA1, GluA2, GluA1/2, and GluA2/3 were reduced to the following readings; \(232\pm 31\) pA, \(429\pm 35\) pA, \(163\pm 17\) pA, and \(164\pm 35\) pA Figure 1B showcases whole-cell current traces from GluA1 channels (most significant) expressed in HEK-293 cells in the absence (left) and presence (right) of Citronellol oil.  
The whole-cell current recording was conducted at \(-60\) mV, pH 7.4, and \(22\)\({}^{\circ}\)C. Graphs summarize weighted time constants for activation. Data shown are mean \(\pm\) SEM; n = 4 (number of patch cells in the whole-cell configuration). A one-way ANOVA test was used to measure the significance: \({}^{*}p<0.05\); \({}^{**}p<0.01\); \({}^{***}p<0.001\); ns, not significant.
## 4 Cooperative Stochastic Transmission Planning
### WSN with Two-Dimensional Distribution  
We are now going to study the case of a WSN with two-dimensional spatial distribution, since theoretically it would lead to a greater gain (ideal case). If we have a distribution of \(N_{x}\) = four motes along a horizontal line, and \(N_{y}\) = four motes along the perpendicular horizontal line, assuming the motes are identical and the separation is the same, we can expect an ideal maximum gain of \(N_{x}\times N_{y}\) = 16.  
To see if a higher gain is really achieved, the SCB procedure of the previous case is repeated. A Monte Carlo simulation is carried out in which the random delays are introduced as we did before according to [14]. It is considered a WSN with \(N\) = \(N_{x}\times N_{y}\) motes, \(N_{x}\) = 4, \(N_{y}\) = 4 separated \(dx\) = \(dy\) = 70 m in which ideally there would be phase shift \(x\) = \(y\) = 0, with which the maximum pointing would be obtained at, \(\theta\) = 90\({}^{\circ}\), \(\varphi\) = 90\({}^{\circ}\), in the direction perpendicular to the plane that contains the motes, \(k\) = 2 \(f\)/\(c\), (\(c\) = 2.9978 \(\times\) 10\({}^{8}\) m/s). Thus, the expression of the gain is as follows:  
\[G_{2D}=\left[AF\right]_{N_{x}\times N_{y}}=\sum_{n=1,m=1}^{N_{x},N_{y}}a_{nm}e^ {jk(x_{nm}\cos\varphi\sin\theta+y_{nm}\sin\varphi\sin\theta-a_{nm})} \tag{4}\]  
Figure 10 shows the normalized gain in the direction \(\theta\) = 90\({}^{\circ}\), \(\varphi\) = 90\({}^{\circ}\), versus the number of attempts. It is observed that a gain higher than 50% of the maximum theoretical gain is achieved in 3% of the attempts. Figure 10 shows 100 transmissions, where random clock offsets result in a gain greater than 50% of the maximum gain in transmissions \(n\) = 20, 39, and 41. In addition, the maximum stochastic gain is 9.8, achieved in transmission \(n\) = 39, and the average gain value is \(G_{2D}\) = 3.7 \(\pm\) 1.8, (95% CI [3.4, 4.1]). Figure 11 shows the distribution function of the gains where an acceptable adjustment to a Gaussian distribution is observed, and where the right side of the tail is observed with gain values displaced to the right of the mean, approaching the theoretical maximum value.  
Figure 10: Normalized gain in each of the 100 stochastic transmissions in a two-dimensional WSN distribution of \(4\times 4\) motes.  
Table 2 shows the case 5 \(\times\) 5, 6 \(\times\) 6, 7 \(\times\) 7, and 8 \(\times\) 8. The gain increases in increasing the number of motes, however this increase in gain does not follow a linear relationship with the number of motes \(N_{x}\times N_{y}\) similarly to the one-dimesional deployment. What is important in the results is that maximum gain is achieved in a reduced number of transmission trials, which is important in order to make a reduced consumption of energy which in turns is positive for the lifetime of the WSN.
## 2 Related Work  
Previous research studies have focused on the positioning of the flying base station and its interplay and impact on the communication channel to the user. They used the standard path loss model to characterize the channel signal performance with respect to physical distance, e.g., [3; 9; 10; 11; 12], and the corresponding channel capacity, e.g., [13; 14; 15; 16; 3]. These approaches and metrics are similar to what we used in our work to quantify the channel performances with respect to the flying base station's position. However, this previous research focused on the communication link between the flying base station and the user while disregarding the link between the flying base station and the stationary base station, in contrast to our work.  
A relatively small number of studies have focused on the flying base station in relation to the stationary base station. [PERSON] et al. [15] included a stationary base station in their analysis and model, allowing users to connect to the stationary base station instead of a flying base station, similar to our work. However, in contrast to our work, they did not consider the flying base station as a relay to the stationary base station and explored scenarios where the flying base station could operate stand-alone. [PERSON] et al. [17] captured the reliance of the flying base station on the stationary base station but focused on the flying base station's cell selection (which cell/stationary base station to rely on) and the handover between stationary base stations. They assumed flying base station positioning, which only considered the user's location, not the stationary base station's location, similar to the aforementioned previous research. In contrast, we use the stationary base station's location to control the flying base station's positioning and demonstrate how the flying base station channel capacity depends on the stationary base station's location.  
In more general wireless communication, beyond a telecommunications base station, previous studies have focused on relay networking performances, where a relay or multiple relays are introduced to improve communication. The research particularly relevant to our work considered mobility on the relays and included information-theoretic performance analyses, e.g., [18; 19; 20; 21]. However, these previous works focused on channel resources, power control, or relay selection, given the relay locations and channels. The relay locations and channels can vary but are given, and previous research works did not consider the relay node's mobility control for positioning. In contrast, our work assumes a flying base station dedicated to the user, thus moving and positioning itself at the ideal location to maximize the flying base station's channel capacity.
# Germanium Single Crystals for Photonics
## 13 Conclusions  
It should be noted that germanium (along with silicon) is one of the most studied materials. Therefore, it is often used as a model material in the initial study of little-studied phenomena. Often, after receiving the first positive results, such studies are continued using more accessible materials.  
This study presents the results of many years of research into the optical properties of single-crystal germanium and the applications of this material in IR optics and photonics, carried out mainly by the authors. Germanium is transparent in the spectral range of 1.8-23 \(\upmu\)m (although, there are a number of phonon absorption bands in the range of 11-23 \(\upmu\)m); a transparency window has been discovered in the terahertz (THz) spectral  
Figure 13: Optical transmission of a Ge single crystal doped with antimony. 1—undoped Ge (thickness 1 mm); 2—undoped Ge; \(\rho\): 3—46 Ohm\(\cdot\)cm; 4—20 Ohm\(\cdot\)cm; 5—5 Ohm\(\cdot\)cm; 6—2.7 Ohm\(\cdot\)cm (spectra 2–6 were obtained on samples h = 10 mm). Reprinted with permission from Ref. [12]. 2019, Pleiades Publishing, Ltd.  
range (\(\sim\)100-300 \(\upmu\)m). Two atmospheric transparency windows in the IR ranges of 3-5 \(\upmu\)m and 8-14 \(\upmu\)m practically cover the region of maximum transmission of germanium. Crystalline germanium, due to its physical and chemical properties, is one of the system-forming materials of IR optics, and its use in this area is up to 25-30% of the total consumption structure of this material. The use of crystals in optics is significantly affected by impurities, the specifics of obtaining single crystals, and the presence of structural defects. Information is presented on the effect of isotopic purity on the transmission spectra of all five isotopically pure germanium single crystals.  
Conceptualization, G.K. and V.R.; methodology, V.R. and I.K.; formal analysis, V.R. and I.K.; resources, G.K.; writing--original draft preparation, V.R.; writing--review and editing, G.K., V.R. and I.K.; supervision, G.K.; project administration, I.K.; funding acquisition, V.R. All authors have read and agreed to the published version of the manuscript.  
The work was carried out with the financial support of the Russian Science Foundation RSF grant No. 24-19-00727, [[https://rsfc.ru/project/24-19-00727](https://rsfc.ru/project/24-19-00727)]([https://rsfc.ru/project/24-19-00727](https://rsfc.ru/project/24-19-00727)) using the capabilities of the Shared Use Center of Tver State University.  
No new data were created or analyzed in this study. The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors express their gratitude to [PERSON] for assistance in computer design of the drawings.  
Author [PERSON] was employed by the company Tydex LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Precast Bridges of Bamboo Reinforced Concrete in Disadvantaged Village Areas in Indonesia
###### Abstract  
Bamboo is an inexpensive, environmentally friendly, and renewable building material that thrives in Indonesia. Bamboo has a high tensile strength but also has weaknesses, namely, it is easily attacked by insects and has high water absorption. Utilization of bamboo as a precast concrete bridge reinforcement must be treated first through soaking, drying, and giving a waterproof coating and sand. This research aimed to obtain a precast bamboo reinforced concrete bridge technology with good integrity, with measuring parameters of deformation and deflection according to AASHTO standards. The dimensions of the bridge were a span of 320 cm, a width of 224 cm, and a height of 115 cm. Two bridge frames were connected by four bridge beams. The bridge plate was made of a 10-cm-thick concrete plate. The bridge support of the reinforced concrete is assumed to be the hinge support and the rubber bearing is assumed to be the roller support. The bamboo reinforced concrete frame bridge test was carried out directly with a load of a minibus-type vehicle. The test results show that the precast bamboo reinforced concrete frame bridges have sufficiently good integrity; that is, they can distribute loads with deflection and deformation that do not exceed their permits. The maximum displacement occurs in the bridge frame of 0.25 mm, meeting the requirements based on the AASTHO and RSNI T-12-2004 standards, which is not more than \(\Delta_{max}\) = L/800 = 3.75 mm. The maximum deformation occurs in the bridge beam of 0.20 mm, and the bridge frame of 0.13 mm meets the requirements based on the AASTHO and RSNI T-12-2004 standards, which is not more than \(\delta_{max}\) = L/800 = 3.75 mm.  
precast bridges; bamboo reinforced concrete (BRC); bridge technology; bridge frame +
Footnote †: journal: Applied Sciences
# Spectroscopic Studies of Laser-Based Far-Ultraviolet Plasma Light Source
## 1 Introduction  
The vacuum-ultraviolet (VUV, with the wavelength \(\lambda\approx\) 10-200 nm) [1], the extreme ultraviolet (EUV, \(\lambda\approx\) 10-121 nm) and X-ray (XR, \(\lambda\approx\) 0.01-10 nm) spectral bands of the electromagnetic radiation have the potential for important applications, especially in the semiconductor industry for high volume manufacturing lithography (HVML) [2; 3; 4; 5], metrology tools [6; 7; 8; 9; 10; 11], and material sciences [12; 13; 14]. Usually, for laboratory applications, a part of the VUV radiation, the so-called far-ultraviolet (FUV), in the energy range from \(\approx\)6 to 10 eV (the corresponding wavelength region of \(\lambda\approx\) 122-200 nm) is obtained from discharge lamps or excimer lasers. The former has drawbacks regarding achievable irradiances, whereas the latter are restricted to a few specific wavelengths. Nowadays, the pulsed-power generator-based Z-pinch type plasmas and the laser-produced plasmas (LPs) are practical high-power radiation sources in the EUV to XR spectrum regions. LPs have been increasingly considered as bright, broadband light sources, due to their intense radiative emission and their small plasma sizes. Particularly, LPs generate spectral emission that includes both various line radiations due to bound-bound transitions of ion charge states and continuum components, extending from the visible to the XR regions and can be selected by the choice of the target material and laser irradiation conditions. To date, the most industrial development of LPs sources has concentrated on microlithography applications in the EUV and XR regions.  
In this study, we present calibrated spectral irradiances and the measured radiation conversion efficiencies (CEs) over the wavelength region from \(\approx\)123 to 164 nm created by a LPP source with a few solid planar targets. Additionally, a relativistic configuration-interaction flexible atomic code (FAC) [15], a developed non-local thermodynamic equilibrium (non-LTE) population kinetics code, the so-called collisional-radiative (CR) model [16; 17; 18], and two-dimensional radiation-hydrodynamics FLASH code [19] were used to investigate the silicon (Si) plasma dynamics created by a 1.064 \(\upmu\)m, 10 nsfull-width-at-half-maximum (FWHM) laser pulse with planar solid target. This study enabled investigating the plasma spectral features of low-ionized charge states of various elements and the CEs, especially in a high-repetition rate low-laser irradiance LPP source, as well as to benchmark the computational model.
## 3 Materials and Methods
### Experimental Program
#### 3.2.3 Structural Monitoring  
Throughout the testing process, structural responses were continuously monitored. Detailed displacement measurements were performed to evaluate the connection's displacement behavior under load.  
Figure 8: Laboratory tests: (**a**) system assembly; (**b,c**) detail of the positioning of the digital indicators.  
High-precision digital dial indicators (\(\pm\)0.01 mm) were used to monitor structural displacements. The displacement at the beam's end was recorded using dial indicators positioned on both sides, ensuring that no torsional effects occurred, which could otherwise compromise the correlation between experimental results and the numerical model. The indicators M1, M3, and M4 shown in Figure 9 also have symmetrical counterparts on the rear side of the frame, further ensuring that no torsional distortion influenced the measurements.  
Additionally, displacements in the frames, both at the beam and the column, were recorded to validate the accuracy of the numerical model. Figure 9 illustrates the monitoring layout and measurement points.  
The hydraulic press used in the experiments was equipped with a digital force gauge, allowing the application of loads according to the conditions predicted by the FEM model. This ensured precise control over the applied loads throughout the testing process.