esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Cubic Symmetry on Optical Activity of Dielectric 8-srs Networks
## 3 Results and Discussion  
### Fabrication  
In this section, we demonstrate advantages of the GD-DLW method over traditional DLW by fabricating cubically symmetric 8-srs PhCs. This fabrication method is described in the Materials and Methods (Section 2.1). A key feature of the GD-DLW method is its ability to increase exposure in lateral dimensions whilst slightly reducing exposure in axial directions, leading to a correction of voxel asymmetry (Figure 2).  
The impact of GD-DLW on symmetry and resolution of PhCs can be seen in Figure 3, which shows SEM images of fabricated structures. To clearly visualize effects of GD-DLW we considered 2-srs networks (two intertwined srs-networks), a structure with a simple geometry compared to 8-srs networks. Figure 3a shows a SEM image along the [011] direction of a 2-srs network fabricated with traditional DLW. Axial and lateral linewidths of \(\Delta\)Z = 1835 nm and \(\Delta\)X = 581 nm produce an aspect ratio of e = 3.159 (Figure S1). In comparison, Figure 3b shows the corresponding SEM image for a 2-srs network fabricated with GD-DLW. Lateral and axial linewidths are \(\Delta\)Z = 651 nm and \(\Delta\)X = 649 nm respectively, corresponding to an aspect ratio of e = 1.003 and a cubically symmetric 8-srs network (Figure S1).  
In addition to the ability to regain cubic symmetry, use of GD-DLW also enables improvements in fabrication resolution. Figure 3c shows a qualitative comparison of 8-srs networks fabricated at various fabrication powers and dithering radius (See Figure S2 for a complete matrix). Under traditional DLW conditions (R = 0 nm) 8-srs networks fabricated with powers from 0.6 to 1.0 mW are significantly distorted, due to bending and collapse of their individual network segments. When GD-DLW isapplied (R = 900 nm), 8-srs networks are mostly undistorted when fabricated with the same fabrication powers, despite the inherent reduction influence that results from dithering.  
These results show that not only can cubic symmetry be achieved with GD-DLW, but that fabrication powers (and hence fabrication line-widths and resolutions) previously inaccessible (e.g., 0.6 mW) can be accessed when GD-DLW is implemented. This agrees with similar observations for simple 1-srs networks [37] and has enabled us to intricately intertwine eight cubically symmetric 1-srs networks (with lattice constants of 3.5 \(\upmu\)m) without intersection, such as that shown in Figure 3d.
# Array Gain of a Linear Array in an Ocean Waveguide
## 4 Numerical Simulation Results and Discussion  
Comparative analyses of \(AG_{\text{U}}\) and \(AG_{\text{W}}\) are conducted in four scenarios. In the first scenario, we assume that the coherence decreases exponentially and \(ATL\)s' fluctuation over the elements follows the standard normal distribution, and compares \(AG_{\text{U}}\) and \(AG_{\text{W}}\). Then, in the next three scenarios, we consider \(AG_{\text{U}}\) and \(AG_{\text{W}}\) of a horizontal uniform linear array (HLA) in three different ocean waveguides, including shallow water, deep sea, and upslope waveguide, respectively. The HLA with 150 elements, and the spacing between adjacent elements in the array is 4 m, approximately equal to half of the wavelength (the source frequency is 190 Hz).
## 3 Design of the Control Scheme
### High-Speed Train Tracking Control
The details are as follows: \(c_{0}+r_{1}\), \(c_{v}+r_{2}\), \(c_{a}+r_{3}\), and \(m+r_{4}\), where \(r_{1}~{}=~{}0.15\times\sin(t)\), \(r_{2}~{}=~{}0.0015\times\sin(2*t)\), \(r_{3}~{}=~{}0.00015\times\sin(3*t)\), and \(r_{4}~{}=~{}0.1\times\sin(4*t)\). The above time-varying coefficients are shown in Figure 12. The HST operating environment and LADRC parameters are consistent with Example 1._  
The speed tracking error of the four carriages is shown in Figure 13. It can be seen that the speed tracking error of the HST fluctuates periodically compared with Example 1. The range of the HST with ALADRC speed fluctuation is \(\pm\)0.005 km/h, and the range of LADRC speed fluctuation is \(\pm\)0.015 km/h. Therefore, ALADRC is significantly better than LADRC in suppressing the adverse effects of time-varying parameters. The displacement tracking error of the four carriages is shown in Figure 14. It can be seen that due to the periodic change of the HST speed error, its displacement error will also have periodic changes. The error variation range of ALADRC is smaller than LADRC, but neither of them will adversely affect the normal operation of HSTs and will not reduce the parking accuracy of HSTs. Figure 11: \(k_{p}\) and \(k_{d}\) adaptive values. Figure 12: Change of time-varying coefficients. Through calculation, the speed mean absolute errors of ALADRC and LADRC are 0.015 km/h and 0.0315 km/h, respectively. ALADRC also has better control performance than LADRC. In conclusion, the LADRC optimized by IPSO has good control performance. On this basis, the introduction of APC can further improve the control performance of LADRC. Therefore, the control scheme combining IPSO and ALADRC is more suitable for the HST speed tracking controller.
# Design and Application Space Exploration of a Domain-Specific Accelerator System
## 3 Acceleration in Application Level
### Comprehensive Analysis  
To illustrate the importance of data transmission on the efficiency of the entire application execution, we go through an analysis of different types of kernels about the ratio of data transmission and accelerator computation. As shown in Figure 6, for each class of kernels, we choose three different sizes, namely small, medium, and large size, from left to right, to acquire test data. As the type and size of measured kernel changes, the proportion of data transmission varies accordingly. The percentage of computation increases with the increase of the algorithmic complexity and scale. It indicates that when executing algorithms with a small size, the accelerators' computation time only occupied 19.4% of the total cycles on average, with the rest of the time spent on preparing and moving data. The numerical value is 62% when the algorithm size is large, which is a more acceptable proportion. This is further evidence that avoiding small chunks of data transmission is the most efficient way to improve the operating speed.
###### Abstract  
The axial force in assembly steel struts with servo systems is a critical indicator of stability in foundation pit support systems. Due to its high sensitivity to temperature variations and direct influence on the lateral deformation of the foundation pit enclosure structure, accurate prediction is essential for safety monitoring and early warning. This study proposes a novel method for predicting the axial force in assembly steel struts with servo systems based on a spatiotemporal adaptive network. The method begins by feeding historical axial force data from multiple steel struts into an LSTM network to extract temporal sequence features. A self-attention mechanism is then employed to capture the global dependencies within the axial force data, enhancing the feature representation. Concurrently, a convolutional neural network (CNN) is utilized to extract local spatial features. Additionally, excavation depth and excavated soil stratification data are processed through convolutional operations to derive stratification-related features. Subsequently, the temporal and spatial features of axial force are fused with stratification-related features derived from excavation data and further refined through a CNN, enabling more accurate predictions. Validation using deep foundation pit data from a metro station in Zhejiang Province demonstrated the method's reliability and improved performance across multiple metrics compared to the existing approaches.  
Keywords:deep excavation; axial force prediction; deep excavation safety early warning; neural network model +
Footnote †: journal: Applied sciences
# The Improved A* Algorithm for Quadrotor UAVs under Forest Obstacle Avoidance Path Planning
## 3 Results
### Performance Simulation Experiment Results
The simulation results show that the improved A* algorithm proposed in this paper significantly reduces the data processing time, the number of traversed nodes, the number of turning points, and the total turning angle of obstacle avoidance, resulting in a shorter and smoother path, which is better suited for efficient, smooth and continuous flights of UAVs in plantation forests. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Environment**} & \multirow{2}{*}{\begin{tabular}{c} **Optimization Steps** \\ **and Methods** \\ \end{tabular} } & \begin{tabular}{c} **Data** \\ **Processing** \\ **Time (s)** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Nodes** \\ **Traversed** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Turning Points** \\ \end{tabular} & \
\begin{tabular}{c} **Path** \\ **Length** \\ \end{tabular} &
\begin{tabular}{c} **Total Turning** \\ **Angle** \\ \end{tabular} \\ \hline \multirow{4}{*}{\(20\times 20\)} & Traditional A* & 1.52 & 161 & 14 & 36.49 & 810\({}^{\circ}\) \\  & Optimization 1 & 0.49 & 75 & 14 & 36.49 & 810\({}^{\circ}\) \\  & Optimization 1 + 2 & 0.53 & 75 & 8 & 31.62 & 190.50\({}^{\circ}\) \\  & Optimization 1 + 2 + 3 & 0.54 & 75 & 0 & 30.58 & 187.84\({}^{\circ}\) \\ \hline \multirow{4}{*}{\(30\times 30\)} & Traditional A* & 3.20 & 456 & 35 & 62.38 & 2070\({}^{\circ}\) \\  & Optimization 1 & 1.06 & 195 & 35 & 62.38 & 2070\({}^{\circ}\) \\  & Optimization 1 + 2 & 1.42 & 195 & 20 & 55.55 & 975.95\({}^{\circ}\) \\  & Optimization 1 + 2 + 3 & 1.43 & 195 & 0 & 54.01 & 895.78\({}^{\circ}\) \\ \hline \multirow{4}{*}{\(40\times 40\)} & Traditional A* & 4.77 & 719 & 33 & 84.38 & 2250\({}^{\circ}\) \\  & Optimization 1 & 1.01 & 218 & 44 & 86.48 & 2870\({}^{\circ}\) \\  & Optimization 1 + 2 & 2.97 & 218 & 29 & 79.18 & 1475.90\({}^{\circ}\) \\  & Optimization 1 + 2 + 3 & 2.99 & 218 & 0 & 75.13 & 1424.28\({}^{\circ}\) \\ \hline \multirow{4}{*}{\(50\times 50\)} & Traditional A* & 7.77 & 1166 & 48 & 100.70 & 2925\({}^{\circ}\) \\  & Optimization 1 & 1.43 & 243 & 50 & 102.18 & 2925\({}^{\circ}\) \\ \cline{1-1}  & Optimization 1 + 2 & 4.53 & 243 & 33 & 97.25 & 1755.03\({}^{\circ}\) \\ \cline{1-1}  & Optimization 1 + 2 + 3 & 4.55 & 243 & 0 & 91.14 & 1525.22\({}^{\circ}\) \\ \hline \hline \end{tabular} Note: Where optimization 1 is the segmented evaluation function with dynamic heuristics and weighted processing, Optimization 2 is the redundant turning point removal strategy, and optimization 3 is the smoothing optimization of quasi-uniform cubic B-sample curves.  
\end{table} 
Table 2: Performance of the algorithms with different environments and optimization steps.
# Towards Lunar In-Situ Resource Utilization Based Subtractive Manufacturing
## 2 Methods and Materials
### Lithium Silicate  
A crystallizable lithium silicate glass (Table 13) was chosen as a feasibility material for developing glass and ceramic materials using in situ resources on the surface of the Moon.  
Like most glass ceramic materials, a lithium silicate glass ceramic is produced in three steps:  
* Cast of the initial glass from a glass building melt;
* Nucleation;
* Crystal growth of the nuclei.  
To build a glass ceramic with about 50% crystalline content, in the case of lithium silicate, first, lithium metasilicate crystallizes, which can be converted into lithium disilicate. For dental applications, this effect is used to yield, in the first step, glass ceramic with rather weak mechanical properties. Here, the glass ceramic can be easily machined to shape, for example, a dental crown. In the second step, by a short temperature process,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Amount in wt-\%** \\ \hline SiO\({}_{2}\) & 59.4 \\ Li\({}_{2}\)O & 18.8 \\ P\({}_{2}\)O\({}_{5}\) & 5.9 \\ K\({}_{2}\)O & 2.0 \\ Al\({}_{2}\)O\({}_{3}\) & 2.0 \\ ZrO\({}_{2}\) & 9.9 \\ CeO\({}_{2}\) & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Composition of lithium silicate glass and glass ceramic.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aloy Element** & **Min Amount [wt. \%]** & **Max Amount [wt. \%]** \\ \hline Al & Rem. & \\ Si & 0.4–0.8 & 0.8 \\ Fe & - & 0.7 \\ Cu & 0.15 & 0.4 \\ Mn & - & 0.15 \\ Mg & 0.8 & 1.2 \\ Cr & 0.04 & 0.35 \\ Zn & - & 0.25 \\ Ti & - & 0.15 \\ Other each & - & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Chemical composition for AA6061 according to alloy specification EN 573-3 10.2019 [86].  
the material is converted to a high-strength material. The crystallization processes for the lithium metasilicate and the lithium disilicate glass ceramics are listed in Table 14.  
Therefore, the lithium silicate glass ceramic is predestinated as a feasibility material because it can be investigated in three stages:  
* Glass, representing other glasses;
* Lithium monosilicate as the main crystal phase, representing rather weak ceramics;
* Lithium disilicate as the main crystal phase, representing high-strength ceramics.  
This is of special interest for lunar applications because it was shown that it is possible to manufacture a variety of basaltic glasses using lunar a regolith simulant and heating it within a susceptor-assisted microwave oven [88]. Either way, from these glasses, glass ceramics can also be built. Application scenarios for the use of glasses, on the other hand, are provided by [PERSON] [89]. For the experimental investigations, a separating cut in the direction of the thickness was selected (cut thickness 7 mm, cut length 15 mm).
## 2 Advanced Persistent Threat
### Methods and Techniques  
APTs use a variety of methods and techniques. The attack process begins with a study of the victim; in many cases, spear-phishing or emails are used together with social engineering, with the aim of the victim downloading an infected file. Then, the attacker compromises the computer and gains access to other computers within the organisation through the network.  
The methods that characterise the most \"advanced\" APT groups are the use of zero-day exploits and unknown previously unidentified infection vectors. This methods engages several government organisations in several countries to successfully steal confidential information for a long time without first being discovered.  
The techniques commonly used to carry out an APT attack are adapted or combined depending on the target. Some examples of these techniques are the following:  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Feature** & **APT Attacks** & **Common Malware Attacks** \\ \hline Definition & APT is a sophisticated, targeted and highly & Malware is malicious software used to attack \\  & organised attacks. (e.g., Stuxnet) & and disable any system. (e.g., ransomware) \\ Attacker & Government actors and organised criminal & A cracker (a hacker in illegal activities) \\  & groups & \\ Target & Diplomatic organisations, information & Any personal or business computer \\  & technology industry and others sectors & \\ Purpose & Filter confidential data or cause damage to a & Personal recognition \\  & specific target & It ends when it is detected by the security \\ Attack life cycle & Maintain persistence as possible using & actions (e.g., anti-virus software) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Differences between an advanced persistent threat (APT) attack and common malware attacks [2].  
* Social engineering: Getting a user to compromise information systems. This technique is directed to people with privileged access, manipulating them to divulge personal information to carry out a malicious attack through control and persuasion, instead of involved aleatory attacks on systems [15].
* Spear-phishing: This technique is an attempt that primarily targets a specific organisation in order to collect user credentials, financial information, or other confidential information [16].
* Watering hole: It is similar to spear-phishing in cyberespionage. The attacks are adapted to the needs of the victims. In order to do this, attackers try to obtain information about the victim considering his/her personal interests [17].
* Drive-by-download: this technique performs the unintentional download and execution of malicious software when a malicious web page is visited [18]. The malware is downloaded \"stealthily\" without the knowledge of users, taking advantage of security breaches, browser exploits, or integrated plugins such as ActiveX, Java/JavaScript, or Adobe Flash player [19].
## 3 Antimicrobial Activities of p-C
### Antifungal Effects of p-C Based on Fermentation Characteristics  
p-C has been shown to be effective against _Pseudomonas aeruginosa_, _Escherichia coli_, and _Staphylococcus aureus_, and p-C is a bioactive compound like eugenol and thyme essential oil [85]. It was also found to have no effect on total volatile fatty acid (VFA) production or ratio but could alter fermentation by reducing the production of methane by 30%. The use of p-C has complex effects on animals due to the fermentation process involved, and it is important to find the right concentration, method, and conditions of use [86]. Antibacterial effect is the effect of inhibiting bacteria, and antifungal effect is the effect of acting on fungi. In particular, eucalyptus extract with p-C as the main component has excellent antifungal and antibacterial effects and can inhibit the growth of fungi, even at low concentrations [87].  
In kombucha, p-C is one of the organic compounds (VOCs) that play an important role in the fermentation process. Synthesized mainly in the early stages of fermentation, p-C is a terpene-like compound that is associated with yeast activity. In addition, p-C contributes to flavor changes that occur early in the fermentation process. As the fermentation process progresses, the flavor of kombucha changes from initially citrus, floral, and sweet due to p-C, to citrus, herbal, lavelander, and bergamot, and finally to sweet, floral, and honey [88]. The role of p-C in wine, the quintessential fermented beverage, is also manifested through fermentation. Linalool is the main monoter produced in grapes, and p-C is an oxidized derivative of grape and wine volatiles and its precursor, linalool [89; 90]. The fermentation and aging of wine is driven by volatile compounds, including p-C, and flavors are linked to evolution [91]. In addition, p-C is responsible for odor and p-C imparts odor characteristics through the aging of the compound [92; 93]. It has a pencil-like odor of thyme and oregano with phenolic, earthy, and weak modifications by p-C, which leads to a distinctive cedar-like odor. Thymoquinone, which exhibits a distinctive pencil-like aroma, is reported to determine the structural motif of the aroma by synthesizing and characterizing its p-C derivatives and showing the highest odor threshold of the compound in thymohydroquinone [94].
## 5 Conclusions  
1. From 2001 to 2020, the vegetation cover in the Shanxi section of the Yellow River Basin has consistently improved, with a spatial distribution showing higher values in the central and eastern regions and lower values in the western and southern regions. The trends in average NDVI values in the mining areas are generally in line with the overall trend in the Shanxi section of the Yellow River Basin. All eight mining areas have experienced an increasing trend in vegetation cover. Notably, the NDVI values in the Hebaopian mining area have increased by 100% over the 20-year period. The Xiangning, Liulin, Lishi, and Huozhou mining areas have also exhibited NDVI increases greater than the overall increase in the Shanxi section of the Yellow River Basin, with the QinYuan mining area showing the lowest increase at 21%.
2. The NDVI variation in the Shanxi section of the Yellow River Basin exhibits significant fluctuations, with an overall positive trend. Areas with moderate to high levels of fluctuation account for 54.39% of the total. The Hebaopian mining area has a high proportion of areas with significant fluctuations, accounting for 38.85%, which aligns with the previously mentioned doubling of NDVI values. The Lishi and Xiangning mining areas generally experience moderate fluctuations, while the remaining mining areas exhibit low to relatively low levels of fluctuation.
3. In the Shanxi section of the Yellow River Basin, the changes in vegetation cover are positively correlated with both temperature and precipitation, with a higher correlation observed with precipitation. The NDVI shows a positive correlation with precipitation and temperature, accounting for 94.52% and 70.67% of the total area, respectively. Precipitation is the primary driver behind the increase in the NDVI. The changes in the NDVI and precipitation/temperature exhibit significant spatial heterogeneity.  
Methodology, H.C. and M.X.; Software, Y.Z. and H.X.; Resources, P.G. and M.X.; Data curation, P.G. and Y.Z.; Writing--original draft, P.G.; Writing--review and editing, H.C., J.H., S.G. and Y.D.; Supervision, H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. U22A20620).  
Not applicable.  
The data used in this study can be obtained from the second author at [EMAIL_ADDRESS] with a reasonable request. The data are not publicly available due to ongoing study.  
The authors declare no conflict of interest.
# Performance Analysis of the Shore-to-Reef Atmospheric Continuous-Variable Quantum Key Distribution
## 4 Extinction Effect of Sea Salt Particles
The refractive index of seawater is usually taken as 1.33. The refractive of dry sea salt particles is usually taken as 1.55 - \(i0.0035\). \(\frac{r(H)}{r_{0}}\) is the ratio of the wet particle radius to the dry particle radius, representing the effect of relative humidity on aerosol refractive index. The empirical relationship between \(\frac{r(H)}{r_{0}}\) and relative humidity can be expressed as [26]:  
\[\frac{r(rh)}{r_{0}}=(1-rh)^{-\frac{1}{2}}-(1-60\%)^{-\frac{1}{2}}+1, \tag{27}\]  
where \(d=3.5\). The extinction efficiency factor caused by sea salt aerosol can be obtained as follows:  
\[Q_{ext}=\frac{\lambda^{2}}{2\pi}\sum_{n=1}^{\infty}(2n+1)Re(a_{n}+b_{n}). \tag{28}\]  
Figure 4 indicates the relationship between the radius of sea salt particles and extinction efficiency factor. As depicted in the figure, the extinction efficiency factor increases with the increment of particle radius, where the ocean atmospheric transmission distance is 10 km and the relative humidity is 80%. The extinction coefficient of sea salt particles plays an important role in the analysis of the influence of sea salt particles on ocean atmospheric CVQKD. In accordance with Mie scattering theory, the extinction coefficient is [19]:  
\[\beta_{ext}=\pi\int_{r_{1}}^{r_{2}}r^{2}Q_{ext}f(r)dr. \tag{29}\]  
\(f(r)\) is particle radius distribution function, \(r_{1},r_{2}\) is the minimum and maximum radius of particles respectively. The purpose of this paper is to analyze the effects of size of sea salt particles and relative humidity on ocean atmospheric CVQKD. Therefore, only single particle radius distribution is considered, and then \(\beta_{ext}\) is expressed as:\[\beta_{ext}=\pi r^{2}Q_{ext}N. \tag{30}\]  
\(N\) is the number of particles for per unit volume. In this paper we set it as \(N=10^{15}m^{-3}\)[27]. For ocean atmospheric CVQKD, the extinction of the beam and the distance of transmission act on the transmittance together. The transmittance can be expressed as [8]:  
\[T=e^{-\beta L}. \tag{31}\]  
The total extinction coefficient comprises the aerosol scattering, aerosol absorption, molecular scattering, and molecular absorption terms. Among the many contributors to the extinction in atmosphere, we will focus here only on the sea salt particles, so we set scattering and absorption of the other aerosol and molecular as fixed value. Therefore, the extinction coefficient can be shown as:  
\[\beta=\beta_{ext}+\beta_{0}. \tag{32}\]  
\(\beta_{0}\) is the extinction of other aerosols and molecules, \(\beta_{0}=10.3\times 10^{-5}\)[8].