esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### TRFM Trajectory Prediction Model
#### 3.3.2 Encoder  
The encoder primarily encodes the input temporal features. The input temporal features first pass through an encoding layer, where the value encoding of temporal point features is performed (as per Equation (26)). This is then combined with the encoding of temporal positions to form the input features of the encoder (as per Equations (27) and (28)). At this point, the input temporal point features contain both their own high-dimensional information and temporal position information.  
\[VE=Relu(XW+B) \tag{26}\]  
Here, \(VE\) represents value encoding, \(X\) represents the input temporal sequence, \(W\) represents the encoding weight matrix, and \(B\) represents the bias matrix. The encoding results are activated using the \(Relu\) function, introducing non-linearity.  
\[PE_{(pos,2i)}=sin\Big{(}\frac{pos}{10000^{2i/d}}\Big{)} \tag{27}\]  
\[PE_{(pos,2i+1)}=cos\Big{(}\frac{pos}{10000^{2i/d}}\Big{)} \tag{28}\]  
Figure 5: The Self-Attention calculation structure.  
\(PE\) represents position encoding, where \(pos\) denotes the position of the current temporal point in the input sequence. \(2i\) and \(2i+1\), respectively, represent the positions of odd and even features, and \(d\) represents the feature dimension of the sequence points.  
The encoder is composed of multiple stacked TRFM units. The input features of the encoder are replicated three times and multiplied by the corresponding trainable weight matrices \(W_{Q}\), \(W_{K}\), and \(W_{V}\) to generate the \(Q\), \(K\), and \(V\) matrices, which are then fed into the TRFM unit layer. The output dimensions of the temporal features from the multi-layer TRFM units remain consistent with those of the input. The output of the encoder is the temporal features.
## 6 Experimental Verification
### Control Logics Used for Experimental Comparisons
#### 6.1.2 DNN  
DNN structure from [31] was used for comparative purposes because of its efficient control of linear and non-linear MIMO systems. A modelled system is controlled by the proposed adaptive controller, where the algorithm uses the system outputs to tune the controller's parameters. Another reason for using DNN intelligent controller is the capability of the mechanism to cancel disturbances utilizing an adaptive filter. Dealing with disturbances was one of the main goals of this overall research paper and the OENN + OAG system, and it would be informative to see a comparison of performances achieved.  
Figure 5: LQ tracking control diagram in Simulink environment.
# Designing and Simulating the Control Process of a Feed Pusher Robot Used on a Dairy Farm
## 4 Discussion  
The study examined how effectively the simulated and developed wheel drive system and the automatic positioning system interact with each other. The criterion for evaluating the effectiveness was the deviation of the robot's center of mass from the given trajectory of motion.  
Figure 13 shows simulated trajectories of the x and y coordinates of the gravity center of the mobile robot. The dashed lines represent the specified trajectories, the solid lines represent the results of modeling the developed intelligent algorithm. It is apparent that the maximum mismatch error between the set value and the result of modeling the positioning of the mass center of the movable platform reaches \(\sigma\approx 30\) mm.  
The main factor influencing this error is the accuracy of the feedback sensors and the interaction quality of the kinematic pairs.  
When conducting field tests, in one case, inductive sensors mounted in the inner part of the robot body and connected to the control board were used as a feedback system.  
Figure 12: Physical analogue of a robot pusher; 1, outlet of the feed additives dispenser; 2, pusher auger; 3, filler with a closer.  
Figure 13: Trajectories of x and y coordinates of the mobile robot’s gravity center.  
In turn, a metal strip mounted on the feed table surface was used to guide the motion trajectory. An error has been minimized due to the reliability of the communication system.  
When a binocular vision system served as a key tool for controlling the navigation system, there was a positioning error of the robot of up to 100 mm, since the system is sensitive to the movements of animals located at the fence of the feeding table. In this regard, the distance sensor telemetry provides the primary feedback for disorienting the robot on the plane (going off track).  
Thus, when using stepper motors and rigidly articulated drive wheels with an electric drive, it was possible to minimize the inaccuracy of the operation of kinematic pairs. In turn, the positioning systems must be made reliable since the robot may be out of animals' reach during feed dispensing, contributing to the inaccessibility of the distributed feed.
## 3 Results and Discussion
### Device Structure Dependence of RTN
#### 3.6.3 MOSFETs with Atomically Flat Gate Insulator/Si Interface  
The roughness of the interface between the gate insulator and Si is essential for MOSFETs. The interface roughness degrades not only electron mobility [66; 67; 68; 69; 70; 71] and gate dielectric reliability [72; 73; 74], but also noise generation [75; 76; 71]. An atomically flat interface [77; 78; 79; 80; 81; 82; 83; 84] is effective for reducing low-frequency noise [79; 83; 84; 85; 86; 87].  
Figure 31 shows images of an atomically flat surface and as-received Si(100) measured by atomic force microscopy (AFM). The atomically flat surface was formed in the active region with shallow trench isolation and was measured after the gate oxide formation and following oxide stripping [84]. The average roughness (Ra) of the conventional surface is 0.12 nm, which is the same as the initial surface of Si(100). In an atomically flat surface, a step and terrace structure can be obtained, and the step height is the same as the monoatomic step length of Si(100) of 0.135 nm. The terrace width (L) is defined by the following equation using the off-angle (\(\theta\)) to the just (100) orientation. The average roughness in the trace of the atomically flat interface was less than 0.04 nm, which is the detection limit of our AFM system.  
\[\text{L}=\frac{0.135}{\tan\theta}\;(\text{nm}) \tag{16}\]  
Figure 30: Gumbel plots of \(V_{\text{RMS}}\) for trapezoidal and rectangular MOSFETs. \(V_{\text{DS}}\) was varied from 0.1 V to 1.4V for constant \(I_{\text{DS}}\) of 10 \(\mu\)A and \(V_{\text{BS}}\) of \(-1.9\) V.  
Figure 32 shows the Gumbel plot of V\({}_{\text{RMS}}\) for the atomically flat and conventional SiO\({}_{2}\)/Si interface [84]. The noise of the atomically flat interface is less than that of the conventional interface. This means that introducing the atomically flat interface is extremely effective for reducing RTN as well as 1/f noise [86; 87; 88]. The atomically flat surface was formed before gate oxidation in this experiment [84].  
To implement the surface flattening process, a low temperature of less than 900 \({}^{\circ}\)C and low oxidation species, such as O\({}_{2}\) and H\({}_{2}\)O, must be required [81; 82; 85]. There is another method for flattening the surface first and keeping it during the process steps preceding gate oxidation [85; 87; 88]. Other problems exist, such as STI edge shape and dopant segregation, and the solutions to these problems may affect not only MOSFET characteristics, but also noise [84]. The flattening process just before the gate oxidation is superior to the flattening process in the first step for introducing interface flattening between SiO\({}_{2}\) and Si, and this can be obtained by low temperature Ar annealing by reducing oxidation species.
## 3 Harvester Structure and Concept
### Experimental Evaluation of the Converter Output  
The open-circuit voltage through the converter for acceleration from 0.1 g to 0.2 g for a frequency range from 8 Hz to 30 Hz with a step of 1 Hz was evaluated. The used coil for the experimental results was based on a coil wire thickness equal to 0.1 mm and has 1300 turns with a resistance equal to 175 \(\Omega\). The results show that the converter was able to generate a minimum output voltage of 0.42 V at an acceleration equal to 0.1 g and a frequency equal to 8 Hz, as illustrated in Figure 7. A maximum open-circuit output voltage of 3.98 V was reached for a frequency equal to 15 Hz and acceleration equal to 0.2 g. For a bandwidth frequency equal to 3 Hz and an acceleration of 0.1 g, the converter showed a minimum output voltage of 0.77 V. For an acceleration equal to 0.2 g, the frequency bandwidth reached 5 Hz, where the voltage was 2 V. This enables storing the energy through the proposed circuit in Section 4, not only at resonance, but also for bandwidth frequency.  
To conclude the experimental evaluation of this section, the converter had a structure that was flexible to be adjusted to the required resonant frequency based on different  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Magnet radius & 7.5 mm \\ \hline Magnet height & 8 mm \\ \hline Magnet material & NdFeB 42 \\ \hline Magnet mass & 23.3 g \\ \hline Inner coil radius & 8 mm \\ \hline Outer coil radius & 9 mm \\ \hline Coil wire diameter & 0.1 mm \\ \hline Coil height & 15 mm \\ \hline Coil turns & 1300 \\ \hline Coil resistance & 175 \(\Omega\) \\ \hline Spring thickness & 0.2 mm \\ \hline Spring diameter & 50 mm \\ \hline Spring material & FR4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key parameters for the design of the converter.  
Figure 7: Open-circuit voltage output of the electromagnetic converter for an applied frequency between 7 Hz and 30 Hz for an acceleration of 0.1 g and 0.2 g.  
parameters, principally the planar spring structure and the used mass. This makes the converter a good solution to be adjusted to the desired defect that needs to be detected based on its characteristic frequency and acceleration. Further, the converter showed a good performance, with a minimum output equal to 0.42 V, even under weak acceleration equal to 0.1 g, which is interesting for several applications.
## 4 Results and Discussions
### Skeleton Curves  
The load-displacement skeleton curves of the specimens are illustrated in Figure 12. The loads of the main characteristic points are summarized in Table 4, where \(P_{\text{cr}}\), \(P_{\text{y}}\), \(P_{\text{max}}\), and \(P_{\text{u}}\) are the cracking, yield, peak, and damage loads of the specimen, respectively, and \(P_{\text{u}}\) = 0.85 \(P_{\text{max}}\). Figure 12 and Table 4 demonstrate the following:  
(1) The skeleton curve of the pure steel frame is relatively smooth. The skeleton curves of the specimens are S-shaped when the cast-in-place RACSWs are infilled. Compared with SPE2 infilled RACSWs with a 100% replacement rate of recycled coarse aggregate, the cracking load of SPE1 infilled ordinary concrete wall increases by 37%, the average yield load decreases by 22%, and the bearing capacity is nearly the same.  
(2) The bearing capacity of SPE2 is 2.4 times higher than that of the pure steel frame. The load decreases faster in SPE2 and SPE3 than in the pure steel frame after the peak load, demonstrating that the ductility of the specimens of infilled cast-in-place RACSWs decreases slightly.  
(3) The comparison of SPE2 and SPE3 demonstrates that the concrete cracking load of the specimen is approximately 1.25 times higher in end-plate joints than in welded-bolted joints. The yield and peak loads decrease by 13% and 8%, respectively, showing that the connecting stiffness of BCJs slightly influences the bearing capacity of the specimens of infilled cast-in-place RACSWs.  
(4) The bearing capacity is 1.44 times higher in SPE4 than in the pure steel frame when the prefabricated RACSWs are infilled, thereby indicating that the prefabricated RACSWs can effectively improve the bearing capacity of the structure.  
(5) The skeleton curves of SPE4 and SPE5 are coincidental at the initial loading stage, and the peak load is only 4% lower in SPE5 than in SPE4, emphasizing that the connecting stiffness of BCJs slightly influences the bearing capacity of the specimens of infilled prefabricated RACSWs. The load of SPE4 and SPE5 decreases smoothly after the peak load, thereby indicating that the structure of the infilled prefabricated RACSWs has a high safety reserve.
# Performance Evaluation of IM/DD FSO Communication System Under Dust Storm Conditions
###### Abstract
Collectively, these investigations underscore the important role of controlled laboratory environments in systematically analyzing the performance of FSO communication systems under sandstorm conditions, enabling consistent and repeatable assessment necessary for deployment in dust-prone regions. In this work, we introduce a novel contribution to the field of FSO communications by developing a probabilistic model for the irradiance fluctuation of the received signal under conditions of dust storms. To our knowledge, this is the first study in the literature to provide a statistically grounded model of signal irradiance fluctuation caused by dust particles. Our approach is based on controlled experimental measurements that capture the variability of the optical signal received during simulated dust storm events. Using the developed model, we derive closed-form expressions for several key performance metrics of FSO communication systems. These include closed-form expressions for the average BER, average channel capacity, and outage probability. Using these expressions, we can assess the reliability and operational feasibility of the FSO system in dust-prone environments. The remainder of the paper is organized as follows. Section 2 presents the experimental setup and the proposed model. Section 3 discusses the system model and the analysis of the performance metrics. In Section 4, we discuss the performance results. Finally, in Section 5, we conclude the article by summarizing the main findings and describing the directions for future research.
## 3 Proposed RL-HCPV  
Reinforcement learning (RL) is an ML computational technique in which an agent attempts to increase the overall reward by executing actions and engaging with an anonymous environment. The suggested RL-based data-driven technique in this research is divided into two sections, as seen in Figure 2, (i) the appropriate DL algorithms are trained for predicting the future PV generation, appliance consumption, EV SOC, EV availability, and electricity prices (ToU and RTP); (ii) a fuzzy Q-learning algorithm is created for generating efficient one-hour-ahead DR decisions. The next subsections go into the specifics of this data-driven solution strategy. The one-hour-ahead prediction of PV generation, appliance consumption, EV SOC, EV availability, and electricity prices allows for the effective scheduling for charging/discharging the energy storage units, including SB and EV batteries, using a V2H unit. To ensure the optimal and intelligent management of the DR of this smart MG, an RL-HCPV algorithm is proposed in Algorithms 1 and 2 and Figure 3. The predicted data are used as input parameters in the fuzzy Q-learning model to create the ideal schedule for energy storage units charging and discharging while taking into account all energy and transport demand restrictions. This section explains the process of predicting the input parameters of the model. Several DL algorithms, including artificial neural networks (ANN), long short-term memory (LSTM), and multi-target regression, are used to predict the uncertainty parameters; PV generation, appliance consumption, EV SOC, EV availability, and electricity prices from historical time-series datasets. Figure 2: Schematic of the RL-HCPV system.
## 4 Discussion  
### Impact of Levels of Force Control on Positional Performance and Spillage Prevention  
The first and primary hypothesis aimed to investigate how task difficulty, represented by maximal (Max) and submaximal (SubMax) levels of force control, affects positional performance and spillage prevention during bimanual lifting tasks in children with neurodevelopmental disabilities. The results supported the alternative hypothesis (H\({}_{a}\)), showing that four out of six positional parameters showed worse performance in the Max zone than in the SubMax zone. The results indicate that maximal levels of force control pose greater challenges in maintaining positional performance and preventing spillage than the submaximal zones. Comparing with the literature, [PERSON]'s findings align closely with our results, showing that deviations in the affected hand significantly affect bimanual task performance in children with USCP, particularly under conditions that strain their capabilities [12]. [PERSON] et al. (2020) found that when exertion levels increase, maintaining stability in bimanual tasks becomes more challenging [23]. Similarly, [PERSON] et al. (2020) reported that maximal exertion leads to greater neuromuscular fatigue and impaired motor control compared to submaximal conditions [24]. These findings suggest that maximal levels of force control conditions impose greater challenges on children with USCP, leading to instability and reduced control, particularly in bimanual tasks. Moreover, [PERSON] et al. (2022) found no significant performance decline between submaximal efforts and lower weight conditions, contrasting with our findings under maximal conditions. A key difference is the study population--ours focuses on children with neurodevelopmental impairments, while [PERSON] et al. (2022) examined healthy individuals [25]. Overall, the findings confirm that levels of force control significantly affect positional performance and spillage prevention, with maximal zones presenting greater challenges than submaximal zones.  
Another point is that the small differences observed in AH down tilt and NAH down tilt across Max and SubMax zones may be due to the following reasons: During bimanual lifting, the body naturally distributes weight evenly, reducing the need for large side-to-side tilts [26]. Since both hands are engaged equally, bilateral coordination minimizes asymmetrical shifts, making left and right tilt less affected by increased effort [27]. Additionally, sagittal plane movements (anterior/posterior tilt) require greater postural  
Figure 7: Scatter plot of all the position variables between the Max and SubMax zones.  
adjustments, as they involve larger neuromuscular activation, leading to more noticeable differences under different levels of force control [28].
# The Dependence of Ultrasonic Velocity in Ultra-Low Expansion Glass on Temperature
## 5 Results and Discussion
### Measurement and Analysis of \(c_{\mathrm{L}}\)-T Data  
#### 5.2.1 Acquisition of \(c_{\mathrm{L}}\)-T Data  
The tested sample and an ultrasonic transducer fixed above the sample were placed into a thermostatic water tank, and the temperature in the tank was steadily increased  
Figure 6: The correlation calculation result of an actual received ultrasonic signal: (**a**) the ultrasonic echo signal; (**b**) the correlation coefficient distribution.  
Figure 7: The change in \(c_{\mathrm{L}}\) with time for samples 1#-6#.  
in the range of 10-30 \({}^{\circ}\)C. Here, the temperature range of 10-30 \({}^{\circ}\)C was chosen for two reasons: (1) this corresponds to the real temperature range that ULE glass is likely to experience when undergoing ultrasonic measurements, i.e., the range of room temperature throughout the year, and (2) it corresponds to the temperature range of the ultrasonic transducer in use. We were utilizing a standard immersion transducer, whose normal operating temperature range is 10-60 \({}^{\circ}\)C, beyond which the piezoelectric action of the transducer would be weakened, making high-amplitude data acquisition difficult. The ultrasonic echo signals were manually sampled and stored by the PC at a temperature interval of 1 \({}^{\circ}\)C, which was chosen to account for the maximum number of temperature points to be sampled and the required modeling time. The temperature was held constant within \(\pm\)0.05 \({}^{\circ}\)C, as measured using a digital thermometer probe in the bath.  
To ensure that the glass sample was in thermal equilibrium during the TOF measurement, the sample was immersed in a controlled water bath for at least 94 min, which was calculated according to the time \(t\) (hours) to reach equilibrium for a given glass thickness \(d\) (cm), which could be expressed as \(t=d^{2}/16\) in [26]. To reduce measurement errors, the echo signals were acquired three times at each temperature point. By using the described correlation algorithm method, the ultrasonic TOF was obtained, and the average values were used to calculate the \(c_{\mathrm{L}}\) of the tested ULE samples. Nevertheless, the experiment did not consider the change in the \(d\) and \(\rho\) of the tested samples.  
The obtained ultrasonic velocities of samples 1#-6# are presented in Figure 8 in terms of temperature. The ultrasonic velocities are observed to increase as the temperature increases. Changes in \(c_{\mathrm{L}}\) are correlated with temperature changes, regardless of the absolute value of the CTE. Comparing Figure 8a-f, we can also observe that the ultrasonic velocity differs at the same temperature and that the \(c_{\mathrm{L}}\) increases with the CTE of ULE glass.
## 3 Simulation Results
### Changes in Patient Parameters to Demonstrate a Relaxation Test  
Figure 6 depicts the waveforms of the systematic flow (\(Q_{s}\)), IRBP speed (\(\omega(t)\)), stroke volume (SV), left atrium pressure (\(P_{la}\)), aortic pressure (\(P_{ao}\)), left ventricle pressure (\(P_{lo}\)), estimated flow (\(\bar{Q}_{est}\)) vs. the desired reference flow (\(Q_{r}\)), and the correlation between the estimated flow (\(\bar{Q}_{est}\)) and the actual flow (\(Q_{p}\)) generated by the CVS model in the relaxation scenario. When suddenly decreasing the \(V_{total}\) at 60 s for a period of 10 s, the closed-loop control system automatically activated to track the changes in \(Q_{r}\) from (\(4.5+4 sin(2\pi t/T)\)) to (\(2+4 sin(2\pi t/T)\)). During this period, the desired reference signal decreased over an interval of 10 s.  
Figure 5: Hemodynamic characteristics for a controller at constant speed.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Healthy Case** & **Heart Failure Case** \\ \hline \(V_{total}\) & 5300 mL & 5800 mL \\ \(E_{es,lv}\) & 3.54 mm Hg/mL & 0.71 mm Hg/mL \\ \(E_{es,rv}\) & 1.75 mm Hg/mL & 0.53 mm Hg/mL \\ \(R_{sa}\) & 0.74 mm Hg/s/mL & 1.11 mm Hg/s/mL \\ \hline \end{tabular}
\end{table}
Table 3: Baseline of CVS model.  
Figure 6: Results in relaxation test; (**a**) systematic flow (\(Q_{s}\)); (**b**) IRBP speed (\(\omega(t)\)); (**c**) stroke volume (SV); (**d**) left atrium pressure (\(P_{la}\)); (**e**) aortic pressure (\(P_{ao}\)); (**f**) left ventricle pressure (\(P_{lp}\)); (**g**) estimated  
flow (\(\hat{Q}_{\mathit{est}}\)) vs. desired reference flow (\(Q_{r}\)); (**h**) correlation between estimated flow (\(\hat{Q}_{\mathit{est}}\)) and actual flow (\(Q_{p}\)).  
During the relaxation test, the controller responded to the decrease in the systematic flow from 4.5 L/m to 2 L/min (Figure 6a) by reducing the pump speed from 2950 rpm to 2100 rpm (Figure 6b). Based on that, the stroke volume of the LV decreased due to the reduction in \(V_{total}\). These changes were associated with the shift to the left of the LV volume-pressure loop by 300 mL (Figure 6c). As a result, the IRBP successfully reduced \(P_{la}\) and increased \(P_{ao}\) and \(P_{lv}\) as fluctuation signals (Figure 6d, Figure 6e and Figure 6f, respectively).  
The results also demonstrated that \(\hat{Q}_{\mathit{est}}\) accurately tracked \(Q_{r}\) within an error of 0.25 L/min (Figure 6g). Moreover, the correlation between \(\hat{Q}_{\mathit{est}}\) and \(Q_{p}\) was significantly high; the slope was 1.13 for the linear regression, the mean absolute error \(error_{mean}=0.2637\), and the correlation coefficient \(R^{2}=0.9949\).