esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 4 Benchmark Execution and Evaluation  
### Experimental Hardware Platforms  
Internationally, the commercial high-performance processor market is mainly dominated by three companies: NVIDIA, AMD (Santa Clara, CA, USA), and Intel (Santa Clara, CA, USA). In terms of processors for high-performance computing, AMD's latest In-sinct MI250X processor has a double-precision floating-point operation capability of up to 95.7 TFlops (trillions of floating-point operations per second), and NVIDIA's latest Jetson Xavier series high-performance processors also achieve peak performance in the tens of the TFlops range [28], while the 4 th Gen Intel Xeon Scalable processors will deliver improved performance, efficiency, and cost savings for targeted workloads of built-in accelerators. Among these three companies, NVIDIA has a more complete software and hardware ecosystem, providing the CUDA (Compute Unified Device Architecture) platform for developers to create parallel computing programs. In addition, NVIDIA provides various acceleration libraries for intelligent computing. High-performance processors mainly include Loongson  
Figure 4: AES encryption algorithm execution process.  
series processors, Shenwei series processors, and Veiglo series processors. The Loongson processor is widely used in computer, server, and embedded systems, and has shown good performance and stability in certain specific application scenarios.  
This article selects NVIDIA Jetson AGX Xavier, the Loongson platform (Beijing, China), and ASUS laptop processors (Taipei, Taiwan) for system evaluation and analysis. Among them, the ASUS laptop is mainly used as a reference for basic performance results. NVIDIA Jetson AGX Xavier is a heterogeneous SoC (system-on-chip), including CPU, GPU, and several other accelerators; the Loongson experimental platform consists of the Loongson 3A4000 CPU and the Veiglo BI-V100 GPU. Based on the benchmark testing methods constructed in Section 3, this performance evaluation experiment is carried out. The basic performance parameters of the experimental platform are shown in Table 1.  
From the table above, we can see the performance parameters of the three experimental platforms, including CPU, GPU architecture, GPU memory, power consumption modes, etc. Comparing the performance parameters of each platform from the table, we can see that the GPU architecture of Jetson AGX Xavier is more advanced, supporting Tensor Cores, which can better meet the needs of large-scale computing. In addition, Jetson AGX Xavier has an optional 10W power consumption mode, consuming less power compared to the other two platforms. The Loongson platform has the highest number of CUDA cores and 32 GB HBM2 GPU memory, but its power consumption is several times that of other platforms.
## 4 Centralized Scheduling Algorithm for the IEEE802.15.4e TSCH Network
### Deepening of the Observed Scheduling Algorithm
#### 4.4.2 Ftsa  
The FTSA is the first comparison for IRByTSA. This algorithm has a simpler structure than IRByTSA, as shown in Figure 21. Table 1 shows that the difference between FTSA and IRByTSA only lies in item 7, particularly in the process of providing transmission opportunities between child nodes to its parent, be it based on turn or not. The sequence of FTSA child nodes that are given the chance to transmit is always fixed or not based on turn. The transmission chance in each cycle always starts from the same node as long as the node has data, while in IRByTSA, the opportunity to transmit is given to the child nodes based on turn.  
Just like the IRByTSA, the FTSA will provide output in the form of matching_link(s) containing the schedule for each node in each cycle. Figure 22 shows both active and idle/sleep nodes in each  
Figure 21: FTSA.  
cycle by leaning on the matching_links(s) generated by the FTSA. Figure 22 enables us to determine the values of parameters C and \(\lambda\) if we use the FTSA:  
\(\black  
The algorithm output is schedule information summarized in the matching_link (s) variable. The schedule information will be announced by the master node to all nodes in the network to inform them about the timeslot when the nodes must be active or idle/sleep. Figure 24 shows the active and idle/sleep nodes in each cycle based on matching_links (s) generated by the FLSA.  
Figure 23: FLSA.  
Figure 24: Network scheduling using FLSA.  
From Figure 24 the values of parameters C and \(\lambda\) can be determined if the TSCH network uses the FLSA:  
\(\bullet\)\(\mathrm{C}_{\mathrm{FLSA}}=5\) cycles;  
\(\bullet\)\(\mathrm{Total}\ \mathrm{ChOff}=\sum\limits_{s=1}^{5}ChOff(s)=6+4+4+2+1=17\ \mathrm{ChOffs}\);  
\(\bullet\)\(\lambda_{\mathit{FLSA}}=\sum\limits_{s=1}^{C_{\mathit{FLSA}}}TS(s)=\mathrm{TS}(1)+ \mathrm{TS}(2)+\mathrm{TS}(3)+\mathrm{TS}(4)+\mathrm{TS}(5)=1+2+3+3+8=17\ \mathrm{timeslots}\).  
To compare the performance of IRByTSA, FTSA, and FLSA, we recapitulated the results of the algorithm test for small networks in Table 3. In terms of the number of active timeslots (\(\lambda\)) and number of cycles, the table indicates that the performance of FLSA was better than that of IRByTSA and FTSA, while IRByTSA's performance was slightly better than FTSA's. However, to draw conclusions about which of these scheduling algorithms has the best performance in terms of the \(\lambda\) or duty cycle parameters and the number of cycles, we need further testing on networks with larger dimensions. Therefore, we continued performance testing of these three algorithms by measuring their performance on a larger network. The testing used the TLS-VaD to hasten the process.
## 4 Results and Analysis
### Analysis of Compliance Behavior
#### 4.1.5 Management Behaviors and Compliance  
The information security of any organization depends on the management behaviors towards information security policy implementation. Reference [48] created a typology for ISB for security managers to understand different security behaviors. They argued that discipline and agility shape different security behaviors in an organization. Reference [69] analyzed the factors influencing computer security behaviors in organizations. They proposed a research model to evaluate computer security behaviors and concluded that organizational norms, moral obligation, and attitude toward computer security behavior have significant effects on employees' behavioral intentions. Reference [70] worked on health information systems. They developed a behavioral ISPC framework for health employees. Their result shows that leadership, management, and security awareness positively affect the ISB of health employees. It is believed that an employee's behavior is effected by his social circle. Social pressures, norms, and activities have a significant influence on employees' behavior. Reference [71] stated that high working experience employees have more absorbing capacity regarding new tools and technology adaptation, which causes a reduction in risks related to information security. The results of the study also showed that management support and security awareness have significant effects on employees' information security compliant behavior. The effect of the status (hierarchal rank) of an employee on perceived behavioral control (self-efficacy and controllability) was explored by [72]. The study focused on investigating the effects of different status employees' perceived behavioral control over interactive security threats and controls, including explicitly tailgating (unauthorized access to a restricted area). The findings of the study clearly showed the effect of status on the perceived behavioral control of employees. Employees who have more control over their coworkers have positive perceived behavioral control effects on tailgating behaviors; on the other hand, low-status employees with less control over their coworkers adversely perceived behavioral control effects on tailgating behaviors. Reference [73] introduced the psychological contract factor in ISPC research. A psychological contract means an explicit (give and take) contract between an employee and his organization. They argued that if an employee feels that his organization is not fulfilling the psychological contract, the employee may reduce his contribution to the organization. On the other hand, if an employee develops an over fulfillment behavior, then there is a good chance that he will put his extra efforts towards the organization. They created a research model consisting of security countermeasures (ISP and awareness) and rational choice theory (perceived cost and perceived benefits) with the perceived psychological
## 3 Results and Discussion
### Electron Microscopy, Profimometry and Resistivity
Additionally, Figure 5a shows the GaN polycrystalline that was obtained for the hexagonal structure. Figure 5b shows the electron diffraction pattern for the sample of Figure 5a (002), which demonstrates the GaN hexagonal structure and agrees with the results obtained in Figure 2 for the (002) crystalline orientation. Figure 4: XPS spectra for the GaN films doping with Mg or Zn; (**a**) Ga \(2\mathrm{P}_{3/2}\) and Ga \(2\mathrm{P}_{1/2}\), (**b**) N 1s, (**c**) Mg 1s, (**d**) Zn \(2\mathrm{P}_{3/2}\), and (**e**) O 1s. To measure the thickness of the Mg- or Zn-doped GaN films, the profilometry technique was used. Before making the deposition of the Mg- or Zn-doped GaN films, carbon tape was placed as steps on the substrates to obtain the correct thickness by profilometry measurements. Once the Mg- or Zn-doped GaN films were deposited, the carbon tape was removed, and the sensor head was placed on the film at a short distance from the step. These measurements had a negative value range due to the sensor head dropping down the step. Figure 6 shows the average thickness obtained for the GaN films. Figure 6a presents a gradual step and thickness of 6.6 \(\upmu\)m for the Mg-doped GaN films. Figure 6b demonstrates an abrupt change in the step for the Zn-doped GaN films as compared to Figure 6a. Additionally, Zn-doped GaN films had a thickness of 6.3 \(\upmu\)m. Comparatively, the resistivity obtained by the four-point probe measurement method for the Mg-doped GaN films had a value of 0.57 \(\Omega\)cm, while the Zn-doped GaN films had a value of 0.45 \(\Omega\)cm. These resistivity results are characteristic of Mg- or Zn-doped GaN with a value of 0.59 \(\Omega\)cm. Figure 5: **(a)** TEM micrograph of the Zn-doped GaN films; **(b)** Electron diffraction pattern of the Zn-doped GaN films which demonstrated the GaN hexagonal structure. Figure 6: Surface profilometer reading to measure the thickness of the films deposited by radio-frequency magnetron sputtering: (a) Mg-doped GaN films; (b) Zn-doped GaN films.
## 4 Thermodynamic Data of the Surface Complexation Model and Case Study
### Application of Thermodynamic Data in SCM  
The total dissolved concentration of Np\({}^{4+}\) (Figure 2) was used as the concentration of the nuclide released from the waste disposal site. PHREQC was used to simulate the surface complexation reaction of the neptunium-237 radionuclide on montmorillonite for the groundwater quality (Table 4) in the subsurface system of the potential disposal site in Taiwan. MX-80 bentonite is a buffer material used in radioactive waste disposal facilities; it is composed of 65-75% montmorillonite. [PERSON] et al. [53] developed equilibrium constants for the surface complexation reactions of the sorption of Np\({}^{4+}\) nuclides on MX-80 bentonite (Table 5). The equilibrium constants of these surface complexation reactions were used as the parameters of the chemical reactions in this study. No currently available database contains the SCM parameters that are defined in the NEA TDB (NEA_TDB_phreeqc_Nov2018.dat) that we used in the PHREEQC modeling.  
Therefore, the thermodynamic data for Np nuclide adsorption to montmorillonite (Table 5) were included in the input file of the PHREEQC. With a complete database of chemical thermodynamics, we can use the PHREEQC to simulate the complexation reaction of Np\({}^{4+}\) nuclide sorption on montmorillonite and other geochemical reactions for the groundwater quality of the potential disposal site in Taiwan.  
The results showed that neptunium with a valence of 3 forms various chemical species due to redox reactions. However, there was no solid nuclide precipitation, only nuclide adsorption on montmorillonite. Neptunium with a valence of 3 forms trivalent, tetravalent, pentavalent and hexavalent ions. The main dissolved ions are as follows: trivalent neptunium, i.e., Np(CO\({}_{3}\))\({}_{3}\)\({}^{-}\), NpOH\({}^{2+}\), and Np\({}^{3+}\); tetravalent neptunium, i.e., Np(OH)\({}_{4}\), Np(CO\({}_{3}\))\({}_{4}\)\({}^{-}\), Np(OH)\({}_{2}\)\({}^{2+}\), Np(CO\({}_{3}\))\({}_{6}\)\({}^{-}\), NpOH\({}^{3+}\), NpSO\({}_{4}\)\({}^{2+}\), Np(SO\({}_{4}\))\({}_{2}\), Np\({}^{4+}\), NpCl\({}^{3+}\) and NpNO\({}_{3}\)\({}^{3+}\); pentavalent neptunium, i.e., NpO\({}_{2}\)CO\({}_{3}\)\({}^{-}\), NpO\({}_{2}\)\({}^{+}\), NpO\({}_{2}\)(CO\({}_{3}\))\({}^{2-}\), NpO\({}_{2}\)OH, NpO\({}_{2}\)SO\({}_{4}\)\({}^{-}\), NpO\({}_{2}\)(CO\({}_{3}\))\({}_{2}\)OH\({}^{4-}\), NpO\({}_{2}\)(OH)\({}_{2}\)\({}^{-}\), and NpO\({}_{2}\)(CO\({}_{3}\))\({}^{5-}\); and hexavalent neptunium, i.e., NpO\({}_{2}\)(CO\({}_{3}\))\({}_{2}\)\({}^{-}\), NpO\({}_{2}\)(CO\({}_{3}\))\({}^{4-}\), NpO\({}_{2}\)CO\({}_{3}\), NpO\({}_{2}\)OH\({}^{+}\), NpO\({}_{2}\)\({}^{2+}\), NpO\({}_{2}\)SO\({}_{4}\), NpO\({}_{2}\)Cl\({}^{+}\), NpO\({}_{8.996 and a pe of 4.068 according to the adsorption capacity of the surface complexation reaction and the total dissolved concentration (Table 6).
# Effect of Solid Particles on Droplet Size Applying the Time-Shift Method for Spray Investigation
###### Abstract  
This study investigated the influence of solid particles on primary breakup and resulting droplet size for different process parameters. Two sets of Newtonian fluids (each consisting of one pure liquid and one suspension at the same respective viscosity) were used, for isolated investigation of solid particles on spray formation independent of liquid viscosity. The spray was recorded by a high-speed camera and a SpraySpy(r) system based on the time-shift effect, while a commonly used Spraytec(r) laser diffraction analyzer was employed for validation. An external-mixing twin-fluid atomizer was operated at different gas velocities and corresponding GLR at constant liquid mass flow. For the investigated suspensions an increased Sauter mean diameter was detected, compared to the pure liquids with identical dynamic viscosity. This effect was explained by the tensile strength stabilizing the suspension droplets.  
Keywords:twin-fluid atomization; suspension; time-shift method; particle; spray formation; droplet size +
Footnote †: journal: Applied
# Research on a Fast Image-Matching Algorithm Based on Nonlinear Filtering
## 2 Theoretical Analysis
### Adaptive RANSAC Algorithm
Since different images contain different content and features, if the \(c\) value is a definite value, it will lead to a decrease in the correctness of the matching; in this paper, to solve this problem, we use the adaptation and inspection process, as shown in Figure 2.  
In the first step, three groups of feature band point pairs are randomly selected from all the feature band point pairs, which are subjected to linear validation and region comparison validation [18], and the affine transformation operator \(n_{1}\) is established for the selected feature point pairs. In the second step, updating of the \(c\) value is carried out; if \(n_{1}\) is greater than the \(c\) value, the value of \(n_{1}\) will be changed to \(c\), and at the same time, the remaining feature band points will be verified, and vice versa The three groups of feature point pairs will be re-selected. Updating the \(c\) value through this process can effectively improve the robustness of the algorithm. For the model \(H_{0}\), after passing the pre-test, all the remaining feature points will be detected, and the logarithmic number of filtered feature points \(n_{2}\) will also be derived; then, the sum of \(n_{1}\) and \(n_{2}\) will be the number of points within the feature points of the algorithm \(N_{found}\). If \(N_{best}\) is less than \(N_{found}\), then \(N_{found}\) will be assigned to \(N_{best}\) and the value of \(M\) will be updated based on Equation (7), where \(\varepsilon\) is  
\[\varepsilon=\frac{N_{best}}{N} \tag{8}\]  
where \(N\) is the total number of feature points; conversely, we return to re-random sampling. The loop is ended when the affine transform operator \(c\) is greater than \(M\), and the optimal model \(H\) is obtained. The RANSAC algorithm needs to manually set the sampling times, thresholds, etc., and the settings of these parameters have a great impact on the effectiveness of the algorithm. In this paper, by introducing adaptive updating of the logarithm of the feature points \(c\) and the number of interior points \(N_{best}\), this algorithm can improve the model's robustness and adaptability to different images.
# Kinematics and Performance Analysis of the 2T1R/1T2R Reconfigurable Decoupled Parallel Mechanism
## 5 Optimization Design
Similarly, take the same plane and draw the distribution of the optimized mechanism performance indexes, as shown in Figure 12. In both 1T2R and 2T1R motion modes, the volume of the workspace increases, and the LTI in the whole plane is greater than 0.6, and the kinematic performance is significantly improved compared with that before optimization.
# Reducing Q-Value Estimation Bias via Mutual Estimation and Softmax Operation in MADRL
## 5 The Multi-Agent Particle Environment Experiment
### Q Value Estimation Bias Experiment  
This article conducted a Q-value estimation bias experiment based on a cooperative navigation task in the particulate environment. By sampling from the experience replay pool to obtain samples of actions, states, and reward information to calculate the real Q-value and the estimated Q-value. In state s and action a, the average of 200 rounds of 100 steps' cumulative rewards calculation is taken as the estimated value of the real Q-value. The comparison of the approximate value obtained through the Q network output with the real Q-value can reflect the situation of Q-function estimation bias of different algorithms.  
As shown in Figure 5, the lines marked with crosses represent the Q-value estimated by the neural network, and the lines marked with triangles represent the real Q-value. In the MADDPG algorithm, the blue line with the triangle logo's Q-value is higher than the orange line, implying that the MADDPG algorithm has an over-estimation, which is more apparent in the early training; under the MATD3 algorithm, the blue line with the triangle logo's Q-value is lower than the orange line, indicating that the MATD3 algorithm actually has an under-estimation. Under the M2 ATD3, MASTD3, and other algorithms, the blue line with the triangle logo's Q-value is lower than the orange line, which means that M2 ATD3, MASTD3, and other algorithms actually have an under-estimation, and the degree of underestimation is similar to that of the MATD3 algorithm, and it can be observed that the real Q-value of MATD3, M2 ATD3, MASTD3, and other algorithms are higher than the real Q-value of the MADDPG algorithm, reflecting from the side that these three kinds of algorithms' policies are better.  
The Q-value estimation bias comparative experiment can qualitatively reflect whether different algorithms have overestimation or underestimation. However, due to the complexity, randomness, and instability of multi-agent games, it is difficult to accurately calculate the true Q-values. Therefore, it is difficult for us to reliably compare the degree of Q-value underestimation of MATD3, M2 ATD3, and MASTD3 algorithms, and further research is needed through performance experiments.  
Figure 4: The Multi-Agent Particle Environment: (**a**) Cooperation Communication Task. In this example, the speaker needs to inform the listener that the target landmark is red landmark M3, and the listener needs to approach the correct landmark based on the speaker’s information. (**b**) Cooperation Navigation Task. In this example, agent 1 needs to approach green landmark M1, agent 2 needs to approach blue landmark M2, agent 3 needs to approach red landmark M3, and they should try to avoid colliding with each other while moving.
# Cellular Effects of Silver Nanoparticle Suspensions on Lung Epithelial Cells and Macrophages
## 3 Results
### Effects of AgNP Suspension and AgNO\({}_{3}\) Solution on Oxidative Stress  
Intracellular ROS levels were evaluated after exposure to AgNPs for 24 h (Figure 5a). In A549 cells, the intracellular ROS level was increased by exposure to AgNP suspensions. Intracellular ROS levels in AgNP-B-treated cells were approximately 1.5-times higher than those in untreated cells. The intracellular ROS level in AgNP-A-treated cells was 2.6-times higher than that in untreated cells. In dTHP-1 cells, AgNP-B administration increased intracellular ROS level to approximately 1.1-times higher than that in untreated cells. Moreover, intracellular ROS level was 2.2-times higher in AgNP-A-treated cells than in untreated cells. On the contrary, 10 \(\upmu\)mol/L AgNO\({}_{3}\) did not increase the intracellular ROS level in either A549 or dTHP-1 cells. Furthermore, the gene expression of HO-1, an important oxidative stress response protein, in cells exposed to AgNPs was evaluated as an oxidative stress marker (Figure 5b). HO-1 levels were induced by exposure to 10 \(\upmu\)g/mL AgNP-A and AgNP-B suspensions. The HO-1 levels were 7.9- and 10.1-times higher in AgNP-A- and AgNP-B-treated A549 cells, respectively. Furthermore, HO-1 gene expression in AgNP-A- and AgNP-B-treated dTHP-1 cells was 35.3- and 13.9-times higher, respectively. In addition, AgNO\({}_{3}\) exposure at 10 \(\upmu\)mol/L did not increase the HO-1 level in either A549 or dTHP-1 cells.  
Figure 4: Regulation of IL-8 expression by AgNPs and AgNO\({}_{3}\). (**a**) IL-8 protein concentration in culture supernatant. A549 and dTHP-1 cells were exposed to AgNP suspensions at 1 and 10 \(\upmu\)g/mL or AgNO\({}_{3}\) at 10 \(\upmu\)mol/L. After incubation for 24 h, the culture supernatant was collected, and the level of IL-8 was determined by ELISA. (**b**) IL-8 gene expression. A549 and dTHP-1 cells were treated with AgNP suspensions at 10 \(\upmu\)g/mL or AgNO\({}_{3}\) at 10 \(\upmu\)mol/L. After incubation for 24 h, IL-8 gene expression was determined by real-time PCR. **\(p<\) 0.01 (vs. untreated cells, ANOVA and Dunnett’s test).
## 3 Camino Architecture for V2X Communication
### Cellular Long-Range Communication  
In addition to the short-range technologies, CAMINO supports cellular long-range communication using 4G or even 5G modems, also addressed as C-V2X Uu, where Uu refers to the interface used for the communication between the UE and the base station. The cellular interface provides a routable internet uplink. Within CAMINO-Core, an MQTT client enables message transfer towards an MQTT broker that can run on the edge or the core. This way, the same facility layer messages that are generated in the CAMINO-Core services (see Section 4), can also be exchanged via the MQTT broker on specific topics. The MQTT client can subscribe to several topics and direct incoming messages to the proper service. GeoCasting services could be used for distributing the messages to the vehicles using a geo-tiling mechanism, where users automatically receive the data relevant for their tile [15]. Furthermore, the forwarder service in CAMINO-Core allows transparent exchange of external messages via the cellular long-range interface using MQTT.