esa-sceva/satcom-chunk-collection huggingface dataset:
# Global Power Dynamics in the Contemporary Space System
## 6 Systems Structure--New Balance of Power in Space
### Potential Scenarios
#### 6.6.3 Multipolar Structure  
A multipolar structure with more than two space powers is considered a likely scenario in the space system. A wide range of scholars and some world leaders have begun to predict a return to a multipolar world [100]. The current space system is marked by a significant increase in the number of space powers, and while significant disparities remain among these powers, there is a discernible trend of China, India, Brazil, and the EU rapidly expanding their presence, catching up to, and potentially overtaking the United States.  
These emerging space powers have demonstrated a growing interest in space activities, leading to increased government planning, organization, and execution of space-related initiatives. This has been evidenced by the development of national space policies, robust space budgets, and influential national space agencies. This shift has resulted in a redistribution of power among the various actors within the space system. This shift suggests the emergence of a multipolar system, comprising new space powers with global ambitions and a willingness to compete with the United States, as well as a new and diverse group of space coalitions and alliances.
# Effects of Lithium Chloride and Nitric Oxide Inhibitor on Orthodontic Tooth Movement in the Rat
## 3 Results
### Thyroid Hormone Levels  
Daily application of LiCl but not L-NAME (\(p>0.99\)) significantly lowered the serum levels of T3 and T4 (Figure 2). Compared to the untreated rats with a T3 of 0.7 pmol/L in median (min 0.5, max 0.79), the daily gavage of 300 mg/kg and 600 mg/kg LiCl decreased T3 to 0.36 pmol/L (min 0.24; max 0.50; \(p=0.042\)) and to 0.21 pmol/L (min 0.15; max 0.35; \(p=0.001\)), respectively. The T3 levels were lightly reversed by the presence of L-NAME to 0.43 pmol/L (min 0.3; max 0.59) and 0.39 pmol/L (min 0.25; max 0.42); however, not significantly (both \(p>0.99\)).  
Figure 1: Orthodontic tooth movement. The orthodontic force was applied to move the right upper first molar towards the incis. Fourteen days following appliance insertion orthodontic tooth movement was measured directly in the mouth to reveal the distance between the first and second right molars and confirmed on a plaster replica. Multiple comparisons were based on Kruskal-Wallis test and Dunn’s correction. \(p\)-values show the changes compared to the normal non-OTM group. Comparison of L300/L600 with L300/L600 + LNAME are all \(p>0.99\) and thus not shown in Figure 1. Data are shown as dot blot with median and 95% CI.  
The same was true for T4; here, 300 mg/kg and 600 mg/kg LiCl decreased T4 from 48.23 pmol/L (min 41.25; max 55.26) to 26.69 pmol/L (min 21.95; max 31.25; \(p\) = 0.005) and 18.84 pmol/L (min 14.52; max 21.99, \(p\) < 0.0001), respectively. Again, L-NAME partially reversed T4 lowering effects of LiCl to 30.23 pmol/L (min 24.89; max 40.66, \(p\) > 0.99) and 35.17 pmol/L (min 30.55; max 40.12, \(p\) = 0.149) but not significantly. Taken together, LiCl but not L-NAME significantly reduced T3 and T4 levels and the decreases were not significantly reversed by L-NAME.
# Delamination Localization in Multilayered CFRP Panel Based on Reconstruction of Guided Wave Modes
###### Abstract  
Multi-layered composite materials are being used in various engineering fields, such as aerospace, automobile, and wind energy, because of their superior material properties. Due to various impact loads during the service life of composite structures, different types of defects can occur, such as matrix cracking, fiber breakage, delaminations, etc. In this research, a novel SHM technique for delamination detection and localization using a minimum number of sensors is proposed. The analytical, numerical, and experimental analysis of GW was performed to increase the probability of detection and localization of delaminations in CFRP material. A new analytical model was developed, which enables identifying converted and transmitted modes in the presence of multiple GW modes. A 2D FFT-based spatial filtering was used to filter the GW modes. The dominant A\({}_{0}\) mode was separated to inspect the delamination. Phase velocity is one of the important features in GW inspection to localize the delamination. A phase spectrum approach was developed to reconstruct the phase velocity dispersion of the GW modes in case material properties are unknown.  
guidelines: delamination; numerical simulation; CFRP; SHM; aerospace
# Emotion, Respiration, and Heart Rate Variability: A Mathematical Model and Simulation Analyses
###### Abstract  
Although the generation mechanism of the low-frequency (LF) component of heart rate variability (HRV) is controversial, HRV is a potential candidate in designing objective measurement methodologies for emotions. These methodologies could be valuable for several biosignal applications. Here, we have conducted a simulation analysis using a novel mathematical model that integrates emotion, respiration, the nervous system, and the cardiovascular system. Our model has well reproduced experimental results, specifically concerning HRV with respiratory sinus arrhythmia and LF, the relation between HRV total power and the respiration frequency, and the homeostatic maintenance by the baroreflex. Our model indicates the following possibilities: (i) The delay in the heart rate control process of the parasympathetic activity works as a low-pass filter and the HRV total power decreases with a higher respiration frequency; (ii) the LF component of HRV and the Mayer wave are generated as transient responses of the baroreflex feedback control to perturbations induced by an emotional stimulus; and (iii) concentration on breathing to reduce the respiration frequency can reduce LF/HF and the reduction can be fed back to the emotional status.  
emotion; heart rate variability; baroreflex feedback theory; computer modeling; Mayer wave
## 6 Numerical Analysis
### Mid-Span Acceleration  
Table 5 and Figure 13 show and compare the maximum mid-span acceleration in the non-isolated, LRB isolated, and three SMA-LRB isolated benchmark bridges. As reflected, both isolation systems, i.e., LRB isolators and SMA-LRBs substantially decrease the peak acceleration under all ground motion records, compared to the non-isolated state. By introducing isolators in the central pier of the benchmark bridge, its stiffness and load transferred from the base to the superstructure reduce, so the acceleration reduces too. After wrapping SMA wires around the LRB isolators, however, the stiffness and the acceleration increase slightly. This increase is the least for SMA-LRBs with cross wires and the largest for the double-cross arrangement of the wires, and still far less than the figures for the non-isolated  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Maximum Base Shear (N)**} \\ \hline
**Earthquake** & **Non-Isolated** & **LRB Isolated** & \begin{tabular}{c} **DC-SMA-LRB** \\ **Isolated** \\ \end{tabular} & \
\begin{tabular}{c} **S-SMA-LRB** \\ **Isolated** \\ \end{tabular} &
\begin{tabular}{c} **C-SMA-LRB** \\ **Isolated** \\ \end{tabular} \\ \hline RSN 8161 & \(7.09\times 10^{6}\) & \(1.58\times 10^{6}\) & \(1.96\times 10^{6}\) & \(1.83\times 10^{6}\) & \(1.71\times 10^{6}\) \\ Synthetic \#1 & \(7.07\times 10^{6}\) & \(1.30\times 10^{6}\) & \(1.70\times 10^{6}\) & \(1.59\times 10^{6}\) & \(1.46\times 10^{6}\) \\ Synthetic \#2 & \(6.82\times 10^{6}\) & \(0.91\times 10^{6}\) & \(1.06\times 10^{6}\) & \(1.04\times 10^{6}\) & \(1.02\times 10^{6}\) \\ Synthetic \#3 & \(6.80\times 10^{6}\) & \(1.22\times 10^{6}\) & \(2.02\times 10^{6}\) & \(1.87\times 10^{6}\) & \(1.35\times 10^{6}\) \\ Synthetic \#4 & \(6.61\times 10^{6}\) & \(0.88\times 10^{6}\) & \(1.69\times 10^{6}\) & \(1.57\times 10^{6}\) & \(1.49\times 10^{6}\) \\ Synthetic \#5 & \(8.25\times 10^{6}\) & \(1.18\times 10^{6}\) & \(1.75\times 10^{6}\) & \(1.64\times 10^{6}\) & \(1.25\times 10^{6}\) \\ RSN 8606 & \(7.17\times 10^{6}\) & \(0.64\times 10^{6}\) & \(0.94\times 10^{6}\) & \(0.84\times 10^{6}\) & \(0.79\times 10^{6}\) \\ Synthetic \#1 & \(6.29\times 10^{6}\) & \(0.97\times 10^{6}\) & \(1.07\times 10^{6}\) & \(1.06\times 10^{6}\) & \(1.01\times 10^{6}\) \\ Synthetic \#2 & \(6.39\times 10^{6}\) & \(0.84\times 10^{6}\) & \(1.18\times 10^{6}\) & \(1.13\times 10^{6}\) & \(1.10\times 10^{6}\) \\ Synthetic \#3 & \(7.90\times 10^{6}\) & \(0.72\times 10^{6}\) & \(0.96\times 10^{6}\) & \(0.94\times 10^{6}\) & \(0.90\times 10^{6}\) \\ Synthetic \#4 & \(6.71\times 10^{6}\) & \(0.66\times 10^{6}\) & \(0.91\times 10^{6}\) & \(0.84\times 10^{6}\) & \(0.71\times 10^{6}\) \\ Synthetic \#5 & \(6.29\times 10^{6}\) & \(0.60\times 10^{6}\) & \(1.04\times 10^{6}\) & \(0.96\times 10^{6}\) & \(0.83\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Maximum base shear for the non-isolated, LRB-isolated and three SMA-LRB isolated bridges.  
Figure 12: Graphical demonstration of the base shear for the non-isolated, LRB-isolated and three SMA-LRB isolated bridges.
## 6 Scenario and Methodology  
For the experimentation, we have created a scenario example in NS-3, taking the idea from the base paper. The NS-3 scenario for DCWO algorithm consists of maximum of 50 nodes connected to a single AP. Simulations in NS-3 used a simple topology and following settings.  
* IEEE 802.11 ax
* Single-user transmission  
Figure 3: DCWO Flow Chart.  
* Error-free radio channels
* 1024-QAM with 5/6 coding rate
* 20 MHz channel
* 1500 B packets
* CBR UDP uplink traffic to a single access point  
In the considered topology, the nodes transmit data to AP. The AP calculates the observed probability of collision and selects an optimal CW value. The optimal CW value is calculated considering the network behavior and a new CW value is broadcasted. We have assumed immediate and perfect transfer of information from the state to the agent and also immediate CW configuration at each node.  
All the hyperparameters used for DCWO are listed in Table 1.  
The Neural Network of DDQN is implemented in tensorflow. TensorFlow is an open-source library designed for fast numerical computing. An illustration of our proposed approach is shown in Figure 4. Our proposed approach considers channel, number of nodes, data to be transferred, Contention Window(CW), and collisions, and decides an optimal CW value. Every observation in the scenario is defined as the current probability of collision in the network. This probability is calculated based on the number of successfully received frames and total transmitted frames.
# Experimental Study on Shear Mechanical Properties of Pile-Soil Interface Under Freezing Conditions
## 3 Experimental Scheme and Testing Process
### Testing Process  
In order to study the variation characteristics of temperature and water content in the process of the pile-pressing test, the soil temperature and water content were monitored at different locations and the data were analyzed. Take a specific test condition (soil temperature \(-5\)\({}^{\circ}\)C, water content 15%) as an example to illustrate the process. Figure 3(a) shows the temperature-time curves at different depths along the pile-soil interface. Figure 3(b) shows the temperature-time curves at burial depths of 15 cm, with different distances from the pile.  
Figure 4: Temperature–time curve during cooling process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Test Number**} & \multicolumn{2}{c}{**Experimental Factors**} \\ \cline{2-3}  & **Water Content/\%** & **Soil Temperature/\({}^{\circ}\)C** \\ \hline
1 & 15 & \(-2\) \\
2 & 15 & \(-5\) \\
3 & 20 & \(-2\) \\
4 & 20 & \(-5\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test conditions.  
During the process of soil freezing, the variation trends of soil temperature could be divided into four stages, including the precipitous drop stage, stable phase transformation stage, soil freezing stage, and stable stage, respectively. It was observed that the cooling of the soil was characterized by a top-down process, and the soil started to freeze from the periphery inward. When the freezing time exceeded around 50 h, the temperature of the pile and surrounding soil varied little and stabilized near the target temperature.  
As shown in Table 4, the final stable temperatures at each measuring point were compared, and the deviation of each value was within \(\pm\)0.2 \({}^{\circ}\)C. The loading could not be implemented until the temperature at different positions of the organic glass barrel reached a steady state.  
In this paper, moisture content before and after a pile-pressing test was compared. The remolded soil samples were prepared according to the target moisture content, then the initial moisture content of the soil was measured before the soil was packed in the organic glass barrel. Soil was placed in four layers, each 50 cm deep. After the pile-pressing test had been completed, three groups of soil samples were collected around the pile at 5 cm, 10 cm, and 15 cm depths, respectively. Then, the moisture content was re-measured and the comparison with the initial moisture content was conducted. Table 5 shows the comparison of soil moisture content at different depths before and after pile-pressing test, under the condition of \(-5\)\({}^{\circ}\)C and a moisture content of 15%. With the decrease in temperature, the soil experienced an in situ freezing process. The water content in the soil varied from 1.62% to 7.25% and the average value was 4.85%, which indicated that the water migration during the soil freezing process in this experiment can be ignored.
# Energy Blockchain for Public Energy Communities
## 3 Field-Test Implementation and Results
### LEC Planning Results  
Another feature of the proposed platform is the exploitation of the open data available in the blockchain for the planning of possible improvements in management approaches or infrastructure upgrades. As an example, the proposed KPIs have been calculated by varying the PV installed capacity in each building, in the case of no-LEC management and LEC management, as described in Section 2.2. The proposed KPI calculation methods have been shared on the blockchain. In addition, an automated calculation has been performed and written on the blockchain.  
The results, shown in Figure 8 show that 30 kWp cumulative installed PV capacity would provide a compromise between initial costs and sales earnings. In particular, as referred in Equation (1) with the purpose of maximizing self-consumption and profit for the whole community, such configuration allows getting a cost reduction of 22.67% with respect to the current no LEC scenario. Even better, the cost reduction can increase to 32.72% when the buildings are federated into an LEC. The optimized size for each building are reported in Table 3.  
Figure 8: Cost analysis for no LEC and with LEC scenario.  
Figure 7: Economic and environmental impact of the existing infrastructures in the no renewable, current, and EC scenario.  
The KPIs show further improvements with the introduction of a BESS, as described in Section 2.2. The results are depicted in Figures 9 and 10. The best results, shared and retrievable by potentially everyone on the blockchain, take into account both KPIs. They have been observed for installed capacities between 45 kWp and 75 kWp, with a power to capacity ratio of 2. Within this interval, the smart LEC management would yield a cost reduction from 60% to 80%, when installing BESS sizes of 2 kW/4 kWh. Moreover, this configuration highlights an emission reduction between 50% and 60%, with respect to the current scenario.
# Analysis of Load Transfer and the Law of Deformation within a Pile-Supported Reinforced Embankment
## 4 FEM Numerical Analysis
### Equal Settlement Plane  
The vertical displacement on the multiple cross sections in the embankment is, in the test of M1, M2, and M4, extracted for analysis, as shown in Figure 15. The planar diagram of the extracted path of the data is also shown in Figure 15. In addition, the data from M2 are omitted because of the similar deformation rules with M1, M3, and M4. The characteristics of vertical displacement can be seen in Figure 15.  
1. The variability of the filling cohesiveness has not essentially altered the deformation pattern of pile-supported reinforced embankment. Meanwhile, note that the above conclusion is related to the fact that the embankment is composed of granular material. Although the cohesion of the embankment filling has been increased, the granular material's properties have not been fundamentally changed. The equilibrium condition between the soil particles at the bottom of the embankment will be broken because of the subsoil's settlement. The cooperative movement of nearby particles will result in the uneven settlement inside the embankment.
2. The change in filling cohesion has little impact on the displacement of soil particles, and when the filling cohesion increase, there is a slight reduction in the differential settlement at the same height in embankments. For example, the maximum settlement values in the M1, M3, and M4 are 2.42 mm, 2.37 mm, and 2.35 mm, respectively, when the embankment height h = 0.5.  
Figure 14: Schematic diagram of area division in the embankment. (**a**) Low embankment height, (**b**) High embankment height.  
3. The progressive formation of equal-settlement planes during the filling of embankments is only marginally impacted by changes in filling cohesion. The equal settlement surface heights monitored from the tests M1, M3, and M4 are 0.36-0.4 m, 0.30-0.33 m, and 0.30-0.33 m, respectively. In addition, combined with the soil-arching height mentioned in the previous section, the heights of the soil-arching crown from tests M1, M3 and M4 are 0.36 m, 0.31 m, and 0.302 m, respectively. Altogether, the soil arching and the equal-settlement plane are approximately identical in height.
## 8 Relevant Applications of Melt Electrospinning  
Similar to the fibers obtained by solution electrospinning, melt electrospun fibers can be appealed in several applications including filtration and separation, food packaging, tissue engineering scaffolds, etc. Especial interests concern industrial scale production and the possibility to avoid solvents [9; 15; 16]. However, control of fiber size and porosity brings, as previously discussed, some challenges in each melt electrospinning technique and polymeric systems that may be also dependent on the specific application.
## 3 Obstacle Vertex Extraction Algorithm Based on Equivalent Path
### Obstacle Vertex Extraction Algorithm (OVEA)
#### 3.3.3 Oblique-Oblique Movement  
For the oblique-oblique movement, we can consider it as a sequence of oblique-axial-oblique movement. The first part of the oblique-axial movement should be reversed according to the strategy outlined in Section 3.3.2 to construct the equivalent path. Since there are no axial movements, the equivalent path for the entire segment is the original path itself. Similarly, the equivalent path for the second part of the axial-oblique movement is also the original path itself, as shown in Figure 9.  
Therefore, for oblique-oblique movement, we only need to record the obstacle vertices that pass through it and add them to the candidate points set.  
The pseudocode of the obstacle vertex extraction algorithm is shown in Algorithm 1.  
Figure 8: Oblique–axial movement is reversed to axial–oblique movement (where the blue line represents oblique–axial movement and the red line represents axial–oblique movement).  
Figure 9: Oblique–oblique movement directly records the obstacle vertex passed.  
```
Initialize map, \(S=\{B,s_{1},s_{2},\dots,s_{n},E\}\); For Every two continuous movement of \(S\) in different directions  Determine the type of two movements; if they are Axial--oblique For i = 1 to n  While n\({}_{i}\) did not touch any obstacles AND\(\left(x_{1}^{i},y_{1}^{i}\right)\
eq\left(x_{2}^{i-1},y_{2}^{i-1}\right)\)  Move \(n_{i}\) in the opposite direction along the straight movement; END While IF There is only one obstacle grid in \((x_{1}^{i},y_{1}^{i})\), \((x_{1}^{i},y_{2}^{i})\), \((x_{2}^{i},y_{1}^{i})\), \((x_{2}^{i},y_{2}^{i})\) IF candidate point set contains \(\left(\frac{x_{1}^{i}+x_{2}^{i}}{2},\frac{y_{1}^{i}+y_{2}^{i}}{2}\right)\);  Update the position of \(\left(\frac{x_{1}^{i}+x_{2}^{i}}{2},\frac{y_{1}^{i}+y_{2}^{i}}{2}\right)\); ELSE  Add \(\left(\frac{x_{1}^{i}+x_{2}^{i}}{2},\frac{y_{1}^{i}+y_{2}^{i}}{2}\right)\) to the candidate point set; END IF END For
```  
**Algorithm 1**Obstacle Vertex Extraction Algorithm
## 3 Methodology
### Sample Preparation  
The clays were collected from a depth of 0.5-1 m by digging (using auger) into the ground and removing the top soils. After collection, the laboratory soil contamination assessments showed that no herbicide contamination existed in the clays. After that, the natural clays were dried in an oven with a 105 \(\pm\) 5 \({}^{\circ}\)C temperature for 2 h. They were then manually crushed and passed through a sieve No. 100 to have a uniform texture and to remove the clusters and agglomerated particles [31]. Three percentages of 2, 4, and 6% by dry weight of soil was considered to mix with the soil [27; 31]. To contaminate clays, initially, the desired amount of soil was placed in a 50 mm layer in the tray and a predetermined volume of atrazine was sprayed on and thoroughly mixed in. The contaminated soil was then flattened, and the next layer of the soil was placed on the previous soil layer and the previous section repeated. After finishing the mixing process, the soil was placed in a sealed plastic bag and stored in a moisture and temperature control room for 7, 14, and 21 d to reach to an equilibrium condition [28; 31; 46].  
A Bromhead torsional ring shear apparatus with a modified (knurled) top porous platen [47] was employed to investigate the shear strength characteristics of the contaminated soils [48]. This device has a container with an outer diameter of 100 mm, an inner diameter of the 70 mm and a height of 5 mm similar to [47; 49; 50]. To reduce the generation of excess pore water pressure and soil extrusion from the container during the test, samples were sheared at a very slow shearing rate of the 0.018 mm/min [47; 48; 49; 50; 51]. The specimens selectively were placed under three normal stresses (\(\sigma\)\({}_{\mathrm{n}}\)) of 100, 150 and 200 kPa [31; 52; 53; 54] and left until the vertical strains become constant then shearing stage started. Figure 4 illustrates typical prepared specimens of amber, black, and kaolinite clay in this study. Also, a summary of the test design is illustrated in Table 2.  
Figure 3: Particle size distribution (PSD) of the amber clay, black clay and kaolinite clay.