esa-sceva/satcom-chunk-collection huggingface dataset:
# An Alternative Globalization Barometer for Investigating the Trend of Globalization
## 3 Results
### Driving Forces
#### 3.3.2 Driving Forces of de Jure Globalization  
In 2020, the _de jure_ economic Globalization Barometer is falling at 97.85, far below trend; the _de jure_ social Globalization Barometer is 98.1, also \"below trend\"; the _de jure_ political Globalization Barometer is 99.93, in trend.  
First, before the pandemic, populism and protectionism actions by several economies have stirred up hostile emotions in international trade. These emotions were exacerbated by the 2020 global health crisis and its concomitant economic fallout, as economies trying to establish new trade agreements diverted strategies to cope with the ongoing pandemic. Consequently, fewer trade agreements have been signed since the onset of the pandemic. According to the United Nations Treaty Collection, 356 trade agreements were signed in 2020, six less than in 2019; 437 trade agreements entered into force in 2020, 51 less than in 2019.  
Second, when considering _de jure_ social globalization, the clear inconvenience caused by restrictive measures discouraged international human exchange, especially cross-border travel. The construction of new airports also slowed down due to a decrease in international traffic demand, following a decrease in visas issued due to stricter border controls and other relevant COVID-19 countermeasures. An inactive trend in interpersonal infrastructure was one of the major factors that explained the falling trend of _de jure_ social globalization.  
Third, from the perspective of _de jure_ political globalization, international organizations, especially World Health Organization, have been working around the clock to mitigate the global impact of the pandemic with programs, such as the COVAX initiative for worldwide vaccine distribution. Furthermore, relaxed restrictions related to the pandemic lead to a recovery of some UN peacekeeping missions around the world, leading to higher participation in peacekeeping missions and indicating that political globalization is regaining its strengths.
## 2 Components of High-Speed Data Transmission for Lightweight and Small UAV Platforms
### Capture Alignment Tracking Unit  
An optical communication capture, tracking, and alignment unit (ATP system) is a critical technology for visual communication. The ATP system can be divided into coarse- and fine-tracking units, effectively tracking the communication object while simultane  
Figure 1: Composition of laser communication system for light and small unmanned aerial vehicles.  
ously capturing the signal and stabilizing the communication. The crude tracking unit mainly carries out preliminary capture, which usually uses optical components such as beam splitters, mirrors, lenses, and other visual elements to control the beam's propagation direction and the focal point position to carry out tracking. After capturing the signal, the coarse-tracking unit can obtain the corresponding signal and the localization. The fine-tracking unit is based on using crude tracking to carry out high-precision and fast target tracking. The fine-tracking unit mainly carried out the rapid acquisition of the target position and accurate tracking through the use of a high-sensitivity position sensor and a high-speed response control system. The ATP system carried out high-precision and stable target tracking in a complex and changing environment by comprehensively applying the multidisciplinary technologies of optics, electronics, control theory, etc., ensuring free-space optical communication stability. A typical wireless laser communication APT system is shown in Figure 2.
## 4 Discussion
Considering these practical considerations for fixation, further research is needed to refine our understanding and approach. The accuracy of MoS estimation requires further enhancement, although specific criteria have not been established in this study. We propose two potential strategies for improvement. First, considering that MoS values can also be estimated from the angular velocities of the pelvis [27], integrating angular and translational velocities might enhance accuracy. Second, employing multiple IMUs could increase the accuracy. With IMUs now commonly incorporated into smartphones and smartwatches, it is feasible that individuals might carry two IMU-equipped devices in their daily lives. Future research will explore the optimal locations for attaching these two IMUs. A limitation of this study is our reliance on optical motion capture systems rather than IMUs to collect body motion data. Although IMUs are recognized as potential alternatives to optical systems [10; 54; 55; 56], they may entail a loss of accuracy. A challenge with IMU usage concerns securing them to the body effectively. Because of their inertia, including the weight of batteries and communication devices, IMUs may exacerbate noise issues associated with soft tissue movements beneath skin, in contrast to the lightweight reflective markers used in optical systems [57]. Another consideration is the calculation of the center of mass. We approximated the center of mass of the body using the geometric center of three pelvis points because of the lack of inertial data for body segments. This approximation is close to the true center of body mass [58] and is deemed acceptable for MoS calculations. However, this study specifically associates the center of mass with the sacral crest, potentially leading to an overestimation of its effectiveness in MoS estimation. To address these issues and validate the feasibility of IMU-based MoS estimations, future studies should involve experiments that concurrently use optical motion capture systems and IMUs. The definitive potential of IMUs for MoS estimation can be confirmed by further experimental investigations.
# Method for Wind-Solar-Load Extreme Scenario Generation Based on an Improved InfoGAN
## 4 Case Study
### Timing Autocorrelation Analysis  
Since the wind, solar power, and load scenarios generated in this paper form a continuous time series process, they exhibit a strong temporal correlation. Therefore, the autocorrelation function (ACF) is employed to capture and describe this temporal dependency. The calculation formula for the ACF is provided in Equation (26):  
\[ACF_{k}=\frac{\sum\limits_{t=1}^{n-k}\left(X_{t}-\overline{X}\right)(X_{t+k} -\overline{X})}{\sum\limits_{t=1}^{n}\left(X_{t}-\overline{X}\right)^{2}} \tag{26}\]  
where \(ACF_{k}\) is the autocorrelation function (ACF); \(K\) indicates the number of time intervals; \(X(t)\), \(X(t+k)\) denote the load values at time \(t\) and time \(t+k\), respectively. \(\overline{P}\) indicates the daily average of load data. The value of \(ACF_{k}\) ranges from \([-1,\,1]\); the closer the value is to \(1\), the higher the positive correlation of the time of the variable force; the closer it is to \(-1\), the higher the negative correlation, and \(0\) means no correlation  
The generated scenarios were analyzed using the autocorrelation function (ACF). The ACF results for the scenario data produced by the three models in the comparative experiments are illustrated in Figures 4-6.  
\begin{table}
\begin{tabular}{c c c} \hline
**Model** & **RMSE** & **MAPE** \\ \hline Case 1 & 0.9710 & 7.82 \\ Case 2 & 0.9176 & 6.59 \\ Case 3 & 0.8914 & 5.17 \\ \hline \end{tabular}
\end{table}
Table 6: Comprehensive error scores of scenario sets under Cases 1–3.  
As illustrated in Figures 4-6, the scenarios generated in Case 3 exhibit trends similar to those of historical scenes, demonstrating the effectiveness of the improved InfoGAN  
Figure 4: ACF diagram of the wind power scenario under different models.  
Figure 5: ACF diagram of the solar power scenario under different models.  
Figure 6: ACF diagram of the load scenario under different models.  
model proposed in this paper. Overall, Case 3 shows a significant advantage over the other two methods in terms of error range and temporal correlation.
## 2 Background
### Virtualization of Sensor Network and its Business Model  
Unlike wireless sensor networks, the VSN environment has a collection of multiple heterogeneous sensor network resources that coexist in the same physical space. In Figure 1, there are different types of physical sensor networks existing in the same domain. There are many Sensor Infrastructure Providers (SInPs), indicated by different circles in the lower layer of Figure 1. There are two Sensor Virtualization Network Service Providers (SVNSPs) in the model. Each SVNSP hires resources from one or more SInPs to form VSNs, and deploys customized protocol and services.  
In traditional wireless sensor networks the infrastructure provider and service provider are same entity, but increasingly diversified applications of sensor networks in different fields such as battlefield surveillance, habitat monitoring, disaster recovery and building smart homes, make it necessary to differentiate between the WSN infrastructure providers' and service providers' perspective. The objective behind this is to minimize the cost of establishment and to reduce the manageability cost. The main difference between the participants in the sensor network virtualization model and the traditional model is the presence of two different roles, SInP and SVNSP, as opposed to the WSN provider as a whole.  
**SInP** It deploys and manages the substrate physical sensor network resources. They offer their resources through programmable interfaces to different SVNSPs. SInPs distinguish themselves through the type of services they provide and the sensor node of which vendor and communication protocol they used. Different Vendor Companies can deploy sensor nodes and make their individual infrastructure which can be used by the company or can be leased to different virtual service providing companies to run their individual applications. It helps the effective utilization of the physical sensor node on a broader scale.  
**SVNSP** It leases resources from multiple SInPs to create and deploy VSNs by sharing allocated virtualized network resources to offer end to end application user services. A SVNSP can achieve network services from multiple InPs. The resources used by the SVNSP can be reused by the other SVNSPs in a recursive fashion.  
**ALU** Application level users (ALUs) in the VSN model are similar to those of the existing WSNs, except that the existence of multiple SVNSPs from competing SInPs provides a wide range of choice. Any end user can connect to multiple SVNSPs from different SInPs for using multiple applications.
## 4 Simulations
### Case without the In-Track Control  
For the case without in-track control, the LSMC is designed as  
\[\mathbf{U}_{L2}=-m_{0}((A_{23}e_{2u}+A_{24}e_{2u})+P_{22}(A_{21}\mathbf{\bar{e}}_{2u}+A_{ 22}\mathbf{e}_{2a})+c_{2}(P_{22}\mathbf{\bar{e}}_{2u}+\mathbf{e}_{2a})+\mathbf{K}_{21}\mathbf{s}_{L2} +\mathbf{K}_{22}\text{sig}^{\zeta_{2}}(\mathbf{s}_{L2})). \tag{44}\]  
The sliding mode surface is designed as \(\mathbf{s}_{L2}=\mathbf{e}_{2a}+\mathbf{P}_{22}\mathbf{\bar{e}}_{2u}+c_{2}\mathbf{\bar{e}}_{2u}\), where \(c_{2}>0\) is the controller parameter. The other control parameters for SNNC and LSMC are shown in Table 4. Similar to the case without the radial control, the controller parameters are selected on the principle that similar control energy is consumed to complete the formation reconfiguration mission.  
Time histories of the relative position errors and velocity errors of the SNNC and LSMC are shown in Figures 6 and 7, details of the trajectories since T are also enlarged in the right side of Figures 6 and 7, respectively, from which it can be seen that the steady-state relative position error for both types of controllers is in the order of \(10^{0}\) m. The steady-state error is smaller and the control accuracy is higher with the SNNC compared to the LSMC controller.  
Figure 8 shows the comparison of the SNNC and LSMC control inputs, with a magnitude of \(10^{-3}\) m/s\({}^{2}\) and a steady-state time of about 0.8 period. The reconfiguration trajectory of the spacecraft is shown in Figure 9, which shows that the SNNC is capable of managing the formation reconfiguration mission and demonstrating the feasibility of formation reconfiguration with the loss of in-track thrust.  
A comparison of the performance indices for the two controllers is shown in Table 5. Similar to the case with the loss of radial thrust, for the same amount of control energy consumed, the SNNC has faster convergence and a reduction in steady-state error of approximately 33.2% compared to the LSMC controller, providing higher control accuracy.  
Figure 8: Time histories of control inputs for the case without in-track control.  
Figure 7: Time histories of relative velocity errors for the case without in-track control.
# Design of Adits for People Passing Spacing in High Altitude Highway Tunnels in Cold Regions
## 2 Materials and Methods
### Verification of Numerical Simulation Reliability  
We attempted to compare the simulation results with the experimental results in order to ascertain the reliability of the simulation results. [PERSON] et al. [14] conducted six full-scale tunnel fire experiments in a tunnel at a high altitude of 4100 m. The smoke-spreading characteristics, maximum temperature, and longitudinal temperature distribution were studied. In this research, at an altitude of 4100 m, the ambient pressure is 62.63 kPa, the ambient temperature is 9-11 \({}^{\circ}\)C, and the ambient air density is 0.84 kg/m\({}^{3}\). The longitudinal ventilation velocity of Test 6 varies from 0 to 1 m/s. The fire area is 2.0 m\({}^{2}\) and the fire scale is 1.55 MW. A special test was conducted to compare with the experiment results based on the above environmental conditions. The comparison results shown in Figure 3 show that the FDS predictions are in good agreement with the experimental results.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Test No.** & **Altitude, m** & **Ambient Pressure, kPa** & **Ambient Temperature, \({}^{\circ}\)C** & **Longitudinal Ventilation Velocity, m/s** \\ \hline
1–3 & 0 & 101.325 & 26.0 & 0, 2, 4 \\
4–6 & 1000 & 89.876 & 24.7 & 0, 2, 4 \\
7–9 & 2000 & 79.501 & 22.2 & 0, 2, 4 \\
10–12 & 3000 & 70.121 & 18.3 & 0, 2, 4 \\
13–15 & 4000 & 61.66 & 16.8 & 0, 2, 4 \\
16–18 & 5000 & 54.048 & 15.0 & 0, 2, 4 \\ \hline \end{tabular}
\end{table}
Table 1: Summary of all tests.  
Slight discrepancies are observed near the fire source, with slightly higher temperatures in the experimental results compared to the FDS results. However, these discrepancies become negligible away from the fire source due to complex turbulent and heat transfer phenomena that occur in close proximity to fire sources. Additionally, a constant longitudinal wind velocity was set in the FDS model, which continuously varied during [PERSON]'s experiment. Overall, the results of the comparison prove the reliability of the FDS simulation.
## 3 Results and Discussion
### Frontier MOs and Electron Mobility
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Molecular Formula** & **233 K** & **263 K** & **273 K** & **300 K** & **313 K** & **333 K** & **363 K** \\ \hline \multirow{4}{*}{HN} & \multirow{2}{*}{OH} & \multirow{2}{*}{\(7.17\times 10^{-23}\)} & \multirow{2}{*}{\(1.72\times 10^{-22}\)} & \multirow{2}{*}{\(2.17\times 10^{-22}\)} & \multirow{2}{*}{\(3.91\times 10^{-22}\)} & \multirow{2}{*}{\(4.88\times 10^{-22}\)} & \multirow{2}{*}{\(6.86\times 10^{-22}\)} & \multirow{2}{*}{\(1.04\times 10^{-21}\)} \\  & & & & & & \\ \cline{1-1} \cline{5-7}  & \multirow{2}{*}{\(8.79\times 10^{-12}\)} & \multirow{2}{*}{\(1.08\times 10^{-11}\)} & \multirow{2}{*}{\(1.15\times 10^{-11}\)} & \multirow{2}{*}{\(1.31\times 10^{-11}\)} & \multirow{2}{*}{\(1.39\times 10^{-11}\)} & \multirow{2}{*}{\(1.49\times 10^{-11}\)} & \multirow{2}{*}{\(1.64\times 10^{-11}\)} \\  & & & & & & \\ \cline{1-1} \cline{5-7}  & \multirow{2}{*}{\(7.43\times 10^{-13}\)} & \multirow{2}{*}{\(9.52\times 10^{-13}\)} & \multirow{2}{*}{\(1.02\times 10^{-12}\)} & \multirow{2}{*}{\(1.20\times 10^{-12}\)} & \multirow{2}{*}{\(1.29\times 10^{-12}\)} & \multirow{2}{*}{\(1.41\times 10^{-12}\)} & \multirow{2}{*}{\(1.58\times 10^{-12}\)} \\  & & & & & & \\ \cline{1-1} \cline{5-7}  & \multirow{2}{*}{\(2.78\times 10^{-16}\)} & \multirow{2}{*}{\(5.42\times 10^{-16}\)} & \multirow{2}{*}{\(6.60\times 10^{-16}\)} & \multirow{2}{*}{\(9.85\times 10^{-16}\)} & \multirow{2}{*}{\(1.24\times 10^{-15}\)} & \multirow{2}{*}{\(1.60\times 10^{-15}\)} & \multirow{2}{*}{\(2.23\times 10^{-15}\)} \\  & & & & & & \\ \cline{1-1} \cline{5-7}  & \multirow{2}{*}{\(4.62\times 10^{-21}\)} & \multirow{2}{*}{\(3.85\times 10^{-20}\)} & \multirow{2}{*}{\(7.24\times 10^{-20}\)} & \multirow{2}{*}{\(2.99\times 10^{-19}\)} & \multirow{2}{*}{\(5.56\times 10^{-19}\)} & \multirow{2}{*}{\(1.26\times 10^{-18}\)} & \multirow{2}{*}{\(3.75\times 10^{-18}\)} \\  & & & & & & \\ \cline{1-1} \cline{5-7}  & \multirow{2}{*}{\(6.92\times 10^{-50}\)} & \multirow{2}{*}{\(1.29\times 10^{-45}\)} & \multirow{2}{*}{\(2.41\times 10^{-44}\)} & \multirow{2}{*}{\(1.76\times 10^{-41}\)} & \multirow{2}{*}{\(3.19\times 10^{-40}\)} & \multirow{2}{*}{\(1.43\times 10^{-38}\)} & \multirow{2}{*}{\(2.37\times 10^{-36}\)} \\ \cline{1-1} \cline{5-7}  & \multirow{2}{*}{\(1.41\times 10^{-22}\)} & \multirow{2}{*}{\(3.70\times 10^{-22}\)} & \multirow{2}{*}{\(4.93\times 10^{-22}\)} & \multirow{2}{*}{\(9.36\times 10^{-22}\)} & \multirow{2}{*}{\(1.24\times 10^{-21}\)} & \multirow{2}{*}{\(1.79\times 10^{-21}\)} & \multirow{2}{*}{\(2.92\times 10^{-21}\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calculated electron mobility (m\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\)) of the studied molecule at a wide temperature range.
## 1 Introduction
FMA2C stratifies the problem and uses the upper layer to obtain more environmental information to assign goals to the lower layer. The lower level makes decisions based on the goals and environmental information it receives. The guidance of the upper layer for the lower layer enables the agent to make long-term decisions and, thus, solve the TSC problem more efficiently. However, FMA2C does not take full advantage of the target. The lower layer only considers the goal and state at the same time when making decisions and does not establish relationships with other agents. Therefore, in this paper, we propose a novel goal-based multi-agent hierarchical model (GMHM) to solve the large-scale TSC problem. We first divide the environment into independent regions, each containing multiple traffic lights. In this region, we abstract an upper agent and use those traffic lights as lower agents. The upper agent assigns specific goals to the lower agents according to the environment information and the mutual position of subordinate agents. After receiving a goal, the lower agent not only considers that when making decisions but also infers its neighbor's actions from the neighbor's goals. The lower agent uses the goal to gain a more comprehensive understanding of the environment and to take full account of its neighbors. In this way, our algorithm ensures effective cooperation between agents. The main contributions of our paper are as follows: First, we investigate the development of multi-agent reinforcement-learning algorithms in TSC problems and propose a goal-based multi-agent hierarchical model for existing problems. Second, we describe our proposed model in detail. For the upper agent, we use a transformer structure to assign specific goals to each lower agent. For the lower agents, we added the concept of goals to the MADDPG algorithm to make it more efficient to cooperate. Finally, we verify the advantages of our proposed algorithm over the state-of-the-art algorithms through several experiments. The rest of the paper is structured as follows: Section 2 briefly elaborates on the background knowledge of reinforcement learning. Section 3 introduces the related work of reinforcement learning in solving TSC problems. In Section 4, we describe our proposed GMHM algorithm in detail. In Section 5, the effectiveness of our proposed algorithm is verified by experiments. Section 6 carries on the simple summary.
## 4 Experiments
### Ablation Studies  
According to the experimental results, it is preliminarily analyzed that the excellent performance of the DESTformer is attributed to the decomposition of time series into seasonal and trend terms through frequency domain decomposition techniques. For the seasonal part, a multi-view attention mechanism is designed to capture the complex periodicity of seasonal changes. For the trend part, a multi-scale attention mechanism is designed to capture sub-trends under different receptive fields and learn the long-term regularity of trend changes. Ablation experiments are specifically designed to verify the roles of the frequency domain decomposition module, MVI-Attention, and MSC-Attention in the model.
# Effectiveness of High-Intensity Therapy in Rehabilitation of Parkinson's Disease Patients
## 4 Results
### Sociodemographic and Clinical Characteristics  
A total of 100 patients with PD were enrolled and randomly assigned to one of four intervention groups: Exergame (n = 25), Cycling (n = 25), Agility Training (n = 25), and Robot-Assisted Training (Cmill) (n = 25). The gender distribution was balanced, with 37 males and 37 females. Participants' disease severity ranged from Hoehn and Yahr stages 2-4, ensuring representation of individuals with moderate PD progression. Baseline assessments confirmed homogeneity across clinical and functional characteristics among groups.
# Biosynthesized Zinc Oxide Nanoparticles Disrupt Established Biofilms of Pathogenic Bacteria
###### Abstract  
Global emergence and persistence of the multidrug-resistant microbes have created a new problem for management of diseases associated with infections. The development of antimicrobial resistance is mainly due to the sub-judicious and unprescribed used of antimicrobials both in healthcare and the environment. Biofilms are important due to their role in microbial infections and hence are considered a novel target in discovery of new antibacterial or antibiofilm agents. In this article, zinc oxide nanoparticles (ZnO-NPs) were prepared using extract of _Plumbago zeyalmica_. ZnO-NPs were characterized and then their antibiofilm activity was tested against Gram-positive and Gram-negative bacteria. The ZnO-NPs were polydispersed, and the average size was obtained as 24.62 nm. The presence of many functional groups indicated that phytocompounds of _P. zeyalmica_ were responsible for the synthesis, capping, and stabilization of ZnO-NPs. Synthesized NPs inhibited the biofilm formation of _E. coli_, _S. aureus_, and _P. aeruginosa_ by 62.80%, 71.57%, and 77.69%, respectively. Likewise, concentration-dependent inhibition of the EPS production was recorded in all test bacteria. Microscopic examination of the biofilms revealed that ZnO-NPs reduced the bacterial colonization on solid support and altered the architecture of the biofilms. ZnO-NPs also remarkably eradicated the preformed biofilms of the test bacteria up to 52.69%, 59.79%, and 67.22% recorded for _E. coli_, _S. aureus_, _P. aeruginosa_, respectively. The findings reveal the ability of green synthesized zinc oxide nanoparticles to inhibit, as well as eradicate, the biofilms of Gram-positive and Gram-negative bacteria.  
zinc oxide nanoparticles; _Plumbago zeyalmica_; biofilm; EPS; biofilm eradication