esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Experiments and Results Analysis
### Comparative Analysis  
In order to further verify the performance of the algorithm, OEABC, the genetic algorithm [37] and ant colony [38] algorithms are used to generate the optimal energy consumption path in the above network environment. Two experiments are designed to compare and analyze the three heuristic algorithms. In Experiment 1, we compared the percentage for the appearance of optimal solution, while in Experiment 2 is the convergence rate of the algorithm.  
**Experiment 1.**_The population size and the number of iterations of the three algorithms are the same, 20 and 100, respectively, other parameter settings also choose the most commonly used settings, details as follows: OEABC: The Limit parameter is 30. Genetic Algorithm: crossover probability Pc is 0.8, mutation probability Pm is 0.2. Ant Colony Algorithm: the proving factor ALPHA is 1.0, the expected factor BETA is 2.0, the pheromone volatility coefficient ROU is 0.5._  
We run the above three algorithms 50, 100 and 150 times, respectively, then calculate the percentage for the appearance of the optimal solution. The calculation method is the number of optimal solutions divided by the total number of runs, and the results are shown in Table 7.  
**Experiment 2.**_In order to verify the convergence speed of the algorithm, in the above network environment, set the parameters SN = 30, Limit = 20, Cycle = 50, by recording the fitness of five paths generated by OEABC that form a convergence curve. The algorithm runs five times, and the corresponding convergence curve shown in Figure 8._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Run Times** & **OEABC** & **Genetic Algorithm** & **Ant Colony Algorithm** \\ \hline
50 & 16.00\% & 14.00\% & 18.00\% \\
100 & 15.00\% & 16.00\% & 11.00\% \\
150 & 16.67\% & 13.33\% & 17.33\% \\ Average & 15.89\% & 14.44\% & 15.44\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The percentage for the appearance of optimal solution.  
Figure 8: The convergence curve of the algorithm.  
The convergence rate \(Cv\) is calculated as:  
\[Cv=\frac{f(r_{opt})-f(r_{init})}{Cycle} \tag{12}\]  
where \(r_{init}\) is the initial path, \(r_{opt}\) is the optimal energy consumption path.  
As can be seen from Figure 8, after 50 iterations, the 1 st convergence is from 50 to 75, \(Cv\) is 0.5; the 2 th from 47 converge to 79, \(Cv\) is 0.64; the 3 th from 53 converge to 77, \(Cv\) is 0.48; the 4 th from 49 converge to 79, \(Cv\) is 0.6; the 5 th from 55 converges to 75, \(Cv\) is 0.4, so the arithmetic average of 50 times \(Cv\) is 0.52.  
Comparing the convergence rates of the above three algorithms in the case of population size 20, 30, 40, the convergence rate \(Cv\) is calculated using Equation (12). The number of iterations are 50, the other parameters are the same as in Experiment 1. The experimental results are shown in Table 8.  
Through the comparison results, we can see the OEABC has an obvious advantage in solving the problem of optimal energy consumption path of WBANs, whether the percentage for the appearance of optimal solution or the convergence rate of the algorithm are considered.
## 2 Materials and Methods
### Assessing GPx Activity and TAC  
Changes in TAC in response to 4 HR treatment were evaluated using commercial kits. The culture conditions for the primary cultured dental pulp cells and H\({}_{2}\)O\({}_{2}\) treatment were the same as those described in Section 2.1. Resveratrol was used as a positive control. Resveratrol or 4 HR was added to the dental pulp cells at a concentration of 1, 10, or 100 uM for 45 min. The pulp cells were then treated with 0.1 mM H\({}_{2}\)O\({}_{2}\) for 30 min. TAC was evaluated after 24 h of resveratrol or 4 HR treatment using the total antioxidant capacity assay kit (CAT#: ab65329, Abcam, Cambridge, UK) according to the manufacturer's instructions. After 90 min of incubation at room temperature, the optical density was measured at 570 nm. The GPx assay was performed using a commercial kit (CAT#: ab102530, Abcam) as per manufacturer's instruction. After adding cumen hydroperoxide, the optical density was measured at 340 nm in the kinetic mode.  
Evaluating TNF-\(\alpha\) and IL1\(\beta\) Expression in Dental Pulp Cells after 4 HR Treatment  
Primary antibodies against TNF-\(\alpha\) and IL1\(\beta\) (Santa Cruz Biotech, Santa Cruz, CA, USA) were used in all evaluation assays. The cell culture and H\({}_{2}\)O\({}_{2}\) treatment conditions were the same as those described in Section 2.2. Primary cultured pulp cells were treated with 1, 10, or 100 uM 4 HR. The positive control included the H\({}_{2}\)O\({}_{2}\) group without 4 HR pretreatment and the negative control included the no treatment group. Cells were harvested at 8 or 24 h post 4 HR treatment and lysed using radio-immunoprecipitation assay (RIPA) buffer. The lysates were then sonicated for 10 s, following which, their concentration was determined. Total protein concentration was measured using the RC DC(tm) protein assay kit (Bio-Rad Laboratories, Hercules, CA, USA). Equal amounts of protein were then loaded onto the gel and subjected to electrophoresis. After separating the proteins, they were transferred onto a nitrocellulose membrane and subsequent protein blocking was performed, followed by antibody incubation steps as described previously [13]. The dilution ratio of the primary antibody was 1:500. The blot images were obtained using a ChemiDoc XPS system (Bio-Rad Laboratories). \(\beta\)-Actin was used as an internal control.
## 2 Relevant Technologies  
### Distributed Cognition Theory  
Distributed Cognition Theory (DCT) is a cognitive theory proposed and developed by psychologists and cognitive scientists [PERSON] (1995), [PERSON] (1991), and others in the 1990s [10]. It emphasizes that cognitive processes do not solely occur within an individual's mind but are accomplished through interactions with external tools, technologies, and the environment, as well as with others [11]. It represents a new unit of analysis that encompasses all elements involved in cognition. Distributed cognition is a cognitive paradigm that restores the full picture of cognition and reveals its true nature. It is a new development in cognitive science--serving both as a paradigm or perspective for individuals to view cognitive phenomena and as a method or strategy for solving complex problems [12].  
Distributed cognition has broad applications in the field of human-computer interaction. [PERSON] et al. pointed out that designing appropriate external representations on machines can reduce the cognitive load on users. They applied distributed cognition to propose a distributed information resource model for human-computer interaction, clarifying how different systems distribute information between humans and technology. Distributed cognition is regarded as a critical component bridging computer-supported collaborative work (CSCW) and human-computer interaction (HCI) [13]. Distributed cognition provides a theoretical framework for studying how shared information is represented and utilized in computer-supported collaborative work [14]. Digital interactive devices (such as touch-screens, virtual reality equipment, interactive display systems, etc.) are essentially cognitive tools that assist people in completing cognitive tasks and extend individual cognitive abilities. According to distributed cognition theory, cognition is not merely an internal mental activity of individuals but also relies on various physical tools and social support in the environment [15]. Distributed cognition emphasizes \"cognition as distributed.\" Cognition occurs through interactions between individuals and their environment. The environment plays a crucial role in cognitive processes, providing not only necessary information but also support and extension for thought processes.
## 4 Numerical Results
### Analysis of Network Performance
Indeed, as shown in Figure 6, the maximum latencies of midhaul flows are equal to 0 in the scenarios in which joint DU/CU processing is performed for all demands (i.e., for the scenarios reaching 100% of joint processing in Figure 5). Figure 4: Number of active PPs and average overall RU-DU-CU-DC latency in network RING-10 for scenarios with basic links (**left**) and longer links (**right**). Figure 5: Percentage of demands with DU and CU processing performed in the same PP node in network RING-10. The maximum latencies of FH flows shown in Figure 6 are maintained below 100 us, which is the allowable limit in FH. This validates the correct implementation of latency constraints in the MILP model. Moreover, the MH and BH latencies are below 190 us and 360 us, respectively, which is far below the latency limits assumed for these flows (1 ms and 2 ms, respectively), even in the largest network (40 RUs) with longer links. Lower values of maximum BH flow latency for 30 RUs and \(C\geq 1.5\) in the basic scenario (\(M=1\)), when compared to the scenario with 20 RUs, might be due to a particular placement of CU entities with respect to the DC node. Namely, if the most distant CU entity is placed closer to the DC node, then this results in a lower maximum propagation delay of a BH flow in the former scenario than in the latter scenario. Note that for 30 RUs, we have a higher number of active PPs in the network (see Figure 4), which increases the chance to place the CUs closer to the DC. Finally, the MH and BH latencies are higher in the scenario with longer links (\(M=2\)), which is due to larger propagation delays.
## 5 Performance Evaluation
### Experimental Characterization of the Environment  
In order to simulate a realistic situation, we carried out some experiments to characterize the propagation environment of our laboratory using Memsic's MICAz motes [32]. The objective was to check that the models proposed in Section 3 approximately reflect the behavior of our wireless network and, at the same time, to extract the values of some of the model parameters that we need for the simulations.  
The first set of experiments were carried out to determine the \(PRP\) as a function of the \(snr\). These experiments were made with Memsic's MICAz motes. The MICAz is a 2.4 GHz mote with direct sequence spread spectrum radio, which uses an O-QPSK modulation scheme. Two motes were used for the experiments, one as transmitter and one as receiver. The experiments were carried out in a laboratory environment in which there were obstacles and people sitting or walking and interfering signals from other motes and other wireless systems working at the same frequency (WiFi). In each experiment, the transmitter node transmitted a packet of 8 bytes every second and the receiver node measured the RSSof the received packets, from which we obtained the \(snr\) at the receiver. The experiment was repeated for different distances between transmitter and receiver and for different transmission powers in order to have different values of \(snr\) at the receiver. The experimental results are shown in Figure 7, together with the theoretical model given by Equation (1). As it can be noticed in Figure 7, the experimental data adjust quite well to the model, with the exception of some outliers. From this experiment we obtained a value of \(N_{RX}=-87\) dBm for the noise level at the receiver.  
We also made some experiments to characterize our radio channel, that is, to establish a relation between transmission and received power in terms of the distance. In these experiments, two MICAz motes were used again, with the same configuration as in the \(PRP\) experiments, and the tests were carried out in different rooms. The RSS of the received packets was measured for different distances between transmitter and receiver. At each position, several measurements of RSS were obtained, in order to average their values. In these experiments the transmitter used \(P_{TX}=0\) dBm. The results from one of the experiments are shown in Figure 8. The curve in Figure 8 represents the fitting of the mean of Equation (2) to these experimental data and was calculated using the Levenberg-Marquardt algorithm, taking \(d_{0}=1\) m, \(P_{TX}=0\) dBm and \(\eta\) and \(A\) as free parameters. From this experiment we obtained a value for the path loss exponent and the constant \(A\) (\(\eta=2\) and \(A=-64\) dB). For other experiments, the estimated values of \(A\) were between \(-60\) dB and \(-70\) dB and the estimated values of \(\eta\) were between 2 and 3.  
Figure 7: Relation between \(PRP\) and \(SNR\).
## 3 Results  
### Static Study  
The [PERSON]'s moduli extracted from the different static experiments described in the previous section are input into a numerical model created in ANSYS (Figure 10) using structural mechanics to verify the consistency of these experimental results. The cylinders are rigid bodies and their contacts with the sample is frictionless. The two external cylinders are fixed while the centre cylinder is moving down the vertical axis. The meshing elements are tetrahedral.  
Due to the fabrication process, the thickness of each plate varies slightly. Therefore, in order to visualize a linear relationship between the applied displacement \(Y_{c}\) and the resulting force \(F\), the force normalized by the sample thickness raised to the third power \(\frac{F}{B^{3}}\) versus \(Y_{c}\) is plotted, instead of \(F\) vs. \(Y_{c}\) (see Equation (4)). Figure 11 shows the comparison between experimental and simulation results for each dataset.  
Two conclusions can be drawn out of the graph in Figure 11. Firstly, both experimental and simulation results are consistent, which confirms the methodology used here to extract [PERSON]'s moduli of the different plates. Secondly, one of the handmade plates exhibits similar mechanical properties than the industrial MP plate. It concerns the one containing 25% of white glue. We therefore selected this formulation as the most suitable one to fabricate our MP geometries, including the ones made for the following dynamic studies.  
Figure 10: Simulation example of a MP sample under a 3-point compression test: geometry (**a**) and calculated deformation along the vertical axis, in mm (**b**).
## 5 Energy Consumption
### Scenario I: Without Retransmission  
From Equation (13), the energy consumption model for a sensor without retransmission can be expressed as  
\[\begin{array}{ll}E_{non}=&[(\beta_{1}+\beta_{2}d^{\alpha})-\beta_{4}]N_{t, non}m_{data}\\ &+(\beta_{3}-\beta_{4})N_{r,non}m_{data}+\beta_{4}tPm_{data},\end{array} \tag{15}\]  
where \(N_{t,non}\) and \(N_{r,non}\) are the numbers of packets transmitted and received without retransmission, respectively.
## 3 Results and Discussions
### Total Pressure Loss of Different Inlet Structures  
Besides the mixing characteristic of reactant, the total pressure loss produced by mixing process is also a key aspect to be investigated thoroughly. Total pressure recovery parameter \(\eta_{\text{rec}}\) is defined as following where \(\overline{p}_{t}\) denotes the average total pressure on the plane perpendicular to \(x\) axis, \(\overline{p}_{t,inj}\) is the average total pressure of reactant at the entrance of combustor, \(\rho\) is the density of reactant, \(\overline{p}_{t,air}\) and \(\overline{p}_{t,\text{CH4}}\) are average total pressure of air and CH\({}_{4}\) at the entrance of combustor respectively.  
\[\eta_{rec}=\frac{\int\overline{p}_{t}\rho dS}{\overline{p}_{t,inj}\int\rho dS} \tag{3}\]  
\[\overline{p}_{t,inj}=\frac{\dot{m}_{air}\overline{p}_{t,air}+\dot{m}_{\text{ CH4}}\overline{p}_{t,\text{CH4}}}{\dot{m}_{air}+\dot{m}_{\text{CH4}}} \tag{4}\]  
Figure 10 displays the variations of total pressure recovery parameter with axial length of combustor. According to the trend of curves of all cases, \(\eta_{rec}\) drops monotonously with axial length increases. Please note that \(\eta_{rec}\) decreases rapidly at the head end of the combustor and major total pressure loss is produced here compared to the slight drop downstream. This can be explained by the turbulence intensity of the reactant due to the violent interaction between CH\({}_{4}\) and air when entering the combustor initially. To specify the turbulence characteristic of different regions within combustor, Figure 11 shows the contours of Reynolds number (\(Re\)). As expected, with the total mass flow rate decreases (from Case 1 to 3), the value of \(Re\) drops within the combustor. \(Re\) implies the interaction of CH\({}_{4}\) and air. Higher \(Re\) means more violent interactions of CH\({}_{4}\) and air.  
Figure 10: Total pressure recovery parameter distribution along axial direction.  
We can infer that larger mass flow rate of CH\({}_{4}\) and air might produce more total pressure loss because of the severer turbulent interaction of flow streams. The total pressure loss of each inlet structures is 28%, 32%, 27%and 26%, respectively in Case 1. Therefore, the value of \(\eta_{rec}\) is approximately 0.3 in Case 1, it arises to about 0.45 in Case 2 and reaches above 0.9 in Case 3. Even though the curves are interlaced each other at initial stage, a specific sequence of \(\eta_{rec}\) is observed after \(x>200\) mm, Structure 2 \(>\) Structure 1 \(>\) Structure 3 \(>\) Structure 4. This sequence is unchanged till the reactant flows to the outlet. This means air radial injections scheme produce lower total pressure compared to air axial injection due to its more violently turbulent flow of reactant when encountering the sharp corner of inlet and inner wall.
## 3 Results
### Case Study: Starlink Constellation (Inter-Satellite LEO-LEO Link)
\begin{table}
\begin{tabular}{c c c c c} \hline
**Starlink-4115** & **Value** & **Link** & **Value** & **Starlink-4541** & **Value** \\ \hline Tx feeder loss [dB] & 2 & Frequency [GHz] & 11 & Interference loss [dB] & 3 \\ Other Tx losses [dB] & 2 & Bandwidth [MHz] & 200 & Rx G/T [dB/K] & 15 \\ Tx HPA power [dBW] & 10 & Bit rate [Mbps] & 100 & Rx feeder loss [dB] & 5 \\ Tx HPA OBO [dB] & 5 & Required Eb/No [dB] & 8 & Other Rx losses [dB] & 1 \\ Tx antenna gain [dBi] & 40 & Availability [\%] & 99.9 & \\  & & Polarization mismatch [deg] & 5 & \\  & & Implementation loss [dB] & 2 & \\  & & Antenna mispointing loss [dB] & 3 & \\  & & Radome loss [dB] & 0 & \\ \hline \end{tabular}
\end{table} 
Table 13: The Starlink satellite transmitter, receiver, and link proprieties. Figure 13: Starlink constellation [29].
## Materials and Methods
### Theoretical Method
#### 2.3.3 Global Sound Pressure Level Estimation
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{‘**Car’ (Road Traffic)**} & \multicolumn{2}{c}{‘**Ambulance’ (Ambulance Siren)**} \\  & \multicolumn{2}{c}{Lw (dBA) = 110.2} & \multicolumn{2}{c}{Lw (dBA) = 122.6} \\ \multirow{2}{*}{**Scenario**} & **LdBAx** & **LdBAx** & **LdBAx** \\  & \multicolumn{2}{c}{‘**Person’**} & \multicolumn{2}{c}{‘**Balcony’**} & \multicolumn{2}{c}{‘**Person’**} \\  & \multicolumn{2}{c}{**Conf 1**} & **Conf 2** & **Conf 3** & **Conf 4** \\ \hline Free path, without barrier & 87.8 & 85.1 & 99.5 & 97.9 \\ Plain screen barrier without caps & 63.7 & 66.7 & 78.6 & 89.2 \\ Y-shaped double cap on top of barrier & 50.0 & 60.4 & 64.6 & 88.2 \\ Y-shaped double cap on top of barrier & 47.7 & 58.9 & 63.6 & 88.2 \\ w/o ground reflection & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Global sound pressure level (SPL) (dBA) at receiver positions. Figure 28: A-weighted SPL spectrum in 1 Hz bands in Configuration 4 (Ambulance–Balcony) for ‘Y’cap with and without ground reflection. The screen barrier SPL spectrum is shown as a reference. Figure 27: A-weighted SPL spectrum in 1 Hz bands in Configuration 3 (Ambulance–Person) for the ‘Y’ cap with and without ground reflection. The screen barrier SPL spectrum is shown as a reference. (for the 'Ambulance-Balcony' configuration, it has previously been verified that the modeling of the top barrier is not a relevant consideration). The performance of the barriers can also be enhanced (by 2.3 dB and 1.5 dB for person and balcony cases, respectively) if the ground is absorbing and the reflection from the side of the source can be discarded. Additionally, when the source is a vehicle siren (ambulance), the Y-shaped double cap improves the performance of conventional plain screen barriers by about 14 dB in the case of a person at street level as a receiver, and by a further 1 dB if the ground reflection is properly absorbed. However, again, the improvement in terms of _IL_ seems to be largely negligible when caps are attached to the top of conventional screen barriers where a LoS exists between the source and the receiver ('Ambulance-Balcony' case) with just 1 dB of additional attenuation and even less if the reflection on the ground is canceled out. It should be noted that canceling out the ground reflection on the listener side of the barrier and its impact on _IL_ was also analyzed, but the simulations revealed only a negligible impact on final SPL since the _IL_ was quite similar to scenarios where the ground reflection at this side was present.
# Energy-Saving Breakthrough in the Point-to-Point Control of a Flexible Manipulator
## 5 Simulation and Experimental Results
tracking control and manipulator modeling in the experimental setup. The dashed lines in Figures 8c,d and 9c,d represent the experimental results for the cycloidal motion (Equation (4)). The feedforward control of the proposed method effectively mitigates residual vibration after positioning, even under varying driving conditions, confirming the method's efficacy in vibration suppression. Next, the energy-saving effects of the proposed method are discussed. Table 2 compares the experimental drive energy values for three different driving conditions: (\(\theta_{E}=\pi/2\) rad, \(T_{E}=0.8\) s), (\(\theta_{E}=\pi/4\) rad, \(T_{E}=0.7\) s), (\(\theta_{E}=\pi/6\) rad, \(T_{E}=0.6\) s). In the table, \"Cyc\" refers to the cycloidal motion, with simulation results presented in parentheses. The experimental results confirm that residual vibration is suppressed without  
Figure 8: Comparison of the simulation and experimental results obtained by the proposed method (\(T_{E}=0.7\) s and \(\theta_{E}=\pi/4\) rad): (**a**) joint angle, (**b**) angular velocity, (**c**) tip displacement, and (**d**) motor torque. Figure 7: Comparison of simulation and experimental results obtained by the proposed method (\(T_{E}=0.8\) s and \(\theta_{E}=\pi/2\) rad): (**a**) joint angle, (**b**) angular velocity, (**c**) tip displacement, and (**d**) motor torque. inducing higher-order vibration modes under all three driving conditions in the previous method. For reference, a comparison of the simulation and experimental results from the previous method under these conditions is shown in Figures A1 and A2 in Appendix A. The experimental drive energy values were higher than the simulation values, likely due to unaccounted factors such as motor friction. As indicated in Table 2, the values for the proposed method are lower than those of the previous studies for all driving conditions, demonstrating substantial energy savings. Therefore, it can be concluded that the proposed method generates an optimal trajectory that not only suppresses residual vibration but also significantly reduces energy consumption. The effectiveness of the proposed method in reducing drive energy is also explored. The drive torque, as outlined in Equatuon (2), comprises three components. To illustrate, consider the driving conditions (\(\theta_{E}=\pi/2\) rad, \(T_{E}=0.8\) s). A comparative analysis of the time histories of these three components is shown in Figure 10. Figure 10a,b show the simulation results of the proposed method and the previous study, respectively. Figure 10a reveals that \(\alpha_{1}\bar{\theta}\) and \(\alpha_{2}\bar{W}\) in the present method have an opposite phase relationship, with \(\tau=0\) at the center. This opposite phase relationship is not present in the previous method during the time interval of approximately 0.1 to 0.7 s. Although the proposed method increases the magnitude of angular acceleration, it effectively reduces the overall drive torque due to this opposite phase relationship, thereby achieving energy savings. As