esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Results Analysis  
### Methods  
To verify the effectiveness of the proposed controller, simulations were performed using the quadrotor parameters listed in Table 1. The simulations were conducted on a computer with an Intel i5-9400F processor (Intel, Santa Clara, CA, USA) and 16 GB of memory.  
The control parameters are \(T\)s = 0.01 s, with \(N_{\text{P}}=N_{\text{c}}=10\). The range for the three-axis angular velocities are \([-1.5,1.5]\), and the constraints on the three control inputs are \([-1,1]\). The gains of the FLMPC controller are set as \(S=\text{diag}(1,\,1,\,1,\,1,\,1)\), \(Q\) = diag(100, 1, 100, 1, 25, 1), \(R=\text{diag}(0.001,\,0.001,\,0.001)\). A comparison is made with LMPC [15] and adaptive MPC (AMPC) [18], where the gains for both controllers are \(S=\text{diag}(1,\,1,\,1,\,1,\,1)\), \(Q=\text{diag}(100,\,100,\,25,\,1,\,1,\,1)\), \(R=\text{diag}(10,\,10,\,10)\). The parameters of the three controllers remained constant throughout the simulations to ensure fair comparison.  
The simulation starts with the quadrotor UAV in an initial hover state, with attitude angles \(\phi=\theta=\psi=0\). A target pitch angle of 0.2 rad and a sinusoidal roll angle with an amplitude of 0.5 rad were applied to simulate the quadrotor's flight with oscillatory motion, while maintaining the yaw angle at 0 rad.  
The second case aims to evaluate the designed controller's ability in handling sudden changes and assess their step response performance in maintaining stability and accurately reaching the specified target angles. Attitude targets of 0.8 rad for pitch, 0.2 rad for roll and 1.3 rad for yaw are applied while the quadrotor UAV is in the hover state.  
To further validate the robustness and disturbance rejection capability of the designed controller, a step input with an amplitude of 1 rad was applied individually to each attitude angle and each angle was subjected to external white noise disturbances with a standard deviation of 0.01 rad, to simulate the uncertain disturbances encountered in real flight.
# Morphological Analysis of a Collapsing Cavitation Bubble near a Solid Wall with Complex Geometry
## 3 Cavitation Bubble Collapse near a Solid Wall with Complex Geometry
### Verification of the Collapsing Bubble near the Solid Wall  
In previous work, the validity of the pseudopotential MKT-LBM in simulating the cavitation bubble collapse process has been verified from the perspectives of coexistence curves and Laplace law verification [33]. As shown in Figure 4, the experimental and LBM simulation results are presented for comparison. Figure 4 depicts the profile evolutions of the collapsing bubble near a solid wall with complex geometry (\(D=1.03476\)). In the experiments, a metal block is attached to the solid wall sample to prevent the solid wall from moving, because the mass of the sample is light. So the bottom of the solid wall sample needs to be sketched in Figure 4a. It is clearly shown that the numerical result  
Figure 3: Modified model of fractal dimension.  
is in agreement with the experiment. These two groups of figures can present a similar dynamic process of the collapsing bubble. The validity of the numerical model to simulate the cavitation bubble near-wall collapse process is verified from the comparisons with the experimental photos. Owing to the solid wall with complex geometry, the bubble deformation is asymmetric. The evolutions of the physical fields will be analyzed in detail in the following subsections.  
In addition, the grid independence of the numerical model is verified. As shown in Figure 5, three different lattice systems, i.e., 312 lu \(\times\) 312 lu, 412 lu \(\times\) 412 lu, and 512 lu \(\times\) 512 lu, are chosen to carry out a more precise comparison and present asymmetry of the collapsing bubble near the solid wall with complex geometry. According to the same proportion principle, the maximum radii of the cavitation bubble are set as \(R_{0}=50\) lu, \(R_{0}=65\) lu, and \(R_{0}=80\) lu, respectively, and the offset parameters are both \(\lambda=1.5\). Moreover, a non-dimensional time \(T^{*}\) is defined to represent the interval between the initial state and the first collapse. Therefore, Figure 5 extracts the bubble profiles at 0, 0.2\(T^{*}\), 0.4\(T^{*}\), 0.6\(T^{*}\), 0.8\(T^{*}\), and \(T^{*}\) with the acquired coordinate points normalized in the \(x\) and \(y\) directions.  
The normalized profiles of the collapsing bubble near the solid wall with complex geometry almost coincide with one another under different grid resolutions. In other words, the collapse processes of the cavitation bubble near the solid wall under different grid resolutions are the same. Given the efficiency and accuracy of the numerical model, the computational domain is established in a 512 lu \(\times\) 512 lu lattice region in this manuscript.  
Figure 4: Comparison of the profile evolutions of the collapsing bubble between (**a**) the experiments (\(\lambda\approx 1.6\), \(R_{0}=5\) mm) and (**b**) LBM simulations (\(\lambda=1.6\), \(R_{0}=50\) lu).  
Figure 5: Grid independence verification of the cavitation bubble collapse model near a solid wall with complex geometry.
## 3 Results and Discussion
### The Annual Effective Dose for Food Ingestion  
To evaluate the human health risk, the estimation of the annual effective dose due to the ingestion of investigated samples, as calculated by Equation (2), for the age category higher than 17 years, was performed and experimental results are reported in Table 2. They take into account the human body daily need of about 10 g of salt, and the precautionary hypothesis that this need was satisfied from a single type of salt and the effective \({}^{40}\)K half-life in a human being strictly correlated with the potassium metabolism.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Activity concentration (Bq/g)** & **D\({}_{\text{ing}}\) (mSv/y)** \\ \hline
**Sample ID** & \({}^{40}\)**K** & \({}^{137}\)**Cs** & \\ \hline S1 & 1.76 \(\pm\) 0.16 & \textless{}1.6 & 0.040 \\ S2 & 0.89 \(\pm\) 0.07 & \textless{}1.9 & 0.020 \\ S3 & 0.79 \(\pm\) 0.07 & \textless{}1.6 & 0.018 \\ S4 & 1.06 \(\pm\) 0.09 & \textless{}1.7 & 0.024 \\ S5 & 12.1 \(\pm\) 1.1 & \textless{}1.8 & 0.27 \\ S6 & 1.47 \(\pm\) 0.12 & \textless{}1.5 & 0.033 \\ S7 & 0.93 \(\pm\) 0.08 & \textless{}1.7 & 0.021 \\ S8 & 1.51 \(\pm\) 0.13 & \textless{}1.9 & 0.034 \\ S9 & 4.55 \(\pm\) 0.32 & \textless{}1.8 & 0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The specific activity of \({}^{40}\)K and \({}^{137}\)Cs in the nine investigated food salts and the annual effective dose due to their ingestion.  
Assuming a total yearly consumption of about 3.65 kg, the annual effective dose due to the food salt ingestion was found to be 0.040 mSv/year for Cyprus black, 0.020 mSv/year for Himalayan pink, 0.018 mSv/year for Hawaii red, 0.024 mSv/year for iodized, 0.27 mSv/year for hyposodic iodized, 0.033 mSv/year for Maldon smoked sea, 0.021 mSv/year for common sea, 0.034 mSv/year for Breton sea and 0.10 mSv/year Persia blue, respectively.  
These values represent about the 1.6%, 0.8%, 0.7%, 1%, 11%, 1.4%, 0.9%, 1.4% and 4.3%, respectively, of the total natural radioactivity value (external + internal) for humans, expressed in terms of effective dose (2.4 mSv/year) [25].  
The obtained annual effective dose is under allowable levels (1 mSv/year) [26] for all investigated samples and therefore, there is no risk of ionizing radiation effects on humans.  
The evaluation of human health risk due to the ingestion of investigated samples for age categories lower than 17 years was not performed, because for the age category lower than 24 months, the ingestion of food salt is not recommended, and for the age category between 24 months and 17 years, the daily intake depends on the child needs, but regardless, it is lower than 10 g [27].
## 4 Performance Analysis of the BOC Signal and BPSK Signal
### The Performance of the Fast Switching Algorithm  
The system requires fast switching among different devices. The simulation scenario is designed as follows:  
(1) Device A performs uniform acceleration;  
(2) Device B performs sinusoidal motion; and  
(3) In the fifth second, the device communicates with the master device from A to B.  
Figures 12-14 show the switching time when \(C/N_{0}=65\) dB-Hz at different BOC and BPSK ratios. Furthermore, Figures 15 and 16 show the switching times when \(\alpha:\beta=0.1:0.9\) at different \(C/N_{0}\)s. The simulations show that the switching time is 0.1 s in different situations. However, the switching time in software display is constrained by data refresh rate. In the hardware which we designed, we adopted 80 MHz as the sampling rate of hardware. Considering that synchronization of the reacquisition results with each module will cost 10 clock cycles, the whole requisition time is \((1+10)/80\times 10^{6}=1.375\times 10^{-7}\) s.  
Figure 11: The data transmission error rate of a composite signal and pure signal.  
Figure 12: The switching time when \(\alpha:\beta=0.5:0.5\) and \(C/N_{0}=65\) dB-Hz. (**a**) the Doppler frequency error, and (**b**) the Doppler frequency measurement.  
Figure 13: The switching time when \(\alpha:\beta=0.3:0.7\) and \(C/N_{0}=65\) dB-Hz. (**a**) the Doppler frequency error, and (**b**) the Doppler frequency measurement.  
Figure 14: The switching time when \(\alpha:\beta=0.1:0.9\) and \(C/N_{0}=65\) dB-Hz. (**a**) the Doppler frequency error, and (**b**) the Doppler frequency measurement.  
Figure 15: The switching time when \(\alpha:\beta=0.1:0.9\) and \(C/N_{0}=55\) dB-Hz. (**a**) the Doppler frequency error, and (**b**) the Doppler frequency measurement.
## 3 Results and Discussion
### Chemical Analysis  
The averaged values for the carbonate system speciation are presented in Table 1. This data refers to the carbon parameters measured and calculated at the end of the experiments. The salinity parameter used was 35, and the temperature was 23.5 \(\pm\) 0.5 \({}^{\circ}\)C. As expected, the total inorganic carbon (TIC) was higher as pH was reduced, and the saturation index for calcite (\(\Omega_{\mathrm{Cal}}\)) and aragonite (\(\Omega_{\mathrm{Arag}}\)) decreased as pH decreased and \(p\)CO\({}_{2}\) increased.  
Mussel shells are made of up to 83% agronomic [74]. Aragonite is more soluble than calcite, therefore shell dissolution may be a considerable problem faced by mussels in acidified seawater [75]. The \(\Omega_{\text{Arag}}\) remains favorable for calcification when \(>\)1 [73]. The present study shows a decrease in the \(\Omega\) value related to an increase in protons H\({}^{+}\), being a critical value for calcification when pH \(\leq\) 7.5 (Table 1). According to [76], CaCO\({}_{3}\) is of high ecological relevance, acting on the calcification kinetics and interfering in bivalve shell formation. Its reduction and dissolution depend on \(\Omega_{\text{Arag}}/\Omega_{\text{Cal}}\) availability, which are changed via H\({}^{+}\) variance.
# A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks
## 2 Related Work  
Wireless sensor networks have been extensively applied in many areas. They do not depend on any supporting infrastructure, and therefore have special advantages over other networks in terms of disaster monitoring. The MAC layer plays a vital role in disaster-monitoring systems. Thus far, various relevant MAC protocols for WSNs have been developed, mostly based on contention-based, schedule-based, hybrid, or multiple wireless interface mechanisms, as shown in Table 1. A comparison of the relevant MAC protocols is presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & **Protocol** \\ \hline \multirow{2}{*}{Contention-based MAC} & S-MAC, B-MAC, PMAC, RT-MAC, MaxMAC, ENCO, QoS-MAC, DRX, PW-MAC, CSMA/WSD \\  & & BMA, TRAMA, TDMA-W, ArDez, DMAC \\ \multirow{2}{*}{
\begin{tabular}{c} Hybrid MAC \\ Multiple wireless interfaces MAC \\ \end{tabular} } & CSMA-STDMA, E-hybrid, WirelessHART, Z-MAC, TCS \\  & & MMAC-HR, DSP \\ \hline \hline \end{tabular}
\end{table}
Table 1: MAC protocols for wireless sensor networks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & **Main Scheme** & **Computation** & **Control** & **Adaptivity to** & & **Time Synchronization** \\  & **Overhead** & **Overhead** & **Topology** & **Delay** & **Precision** \\ \hline \multirow{2}{*}{S-MAC} & Virtual cluster, adaptive listening & No & Rather high & Good & Rather high & Low \\  & LPL, clear channel & No & High & Good & Moderate & Moderate \\  & assessment & Pattern exchange, avoid overhearing & Pattern & High & Good & Moderate & Moderate \\  & Feedback control, & Clear channel & & & & \\  & avoid contending & control & & & & \\  & Extra wake-ups, & Duty cycle & & & & \\  & traffic adaptation & control & & & & \\  & Estimated number & Contention & & & & \\  & of contenders & window sizes & Moderate & Good & Moderate & Low \\  & Differentiated service & No & Rather low & Good & Moderate & Low \\  & Priority- and & No & Rather low & Good & Low & Low \\  & delay-aware & & & & & \\  & Predictive wakeup & On-demand & & & & \\  & duty cycle & Prediction & & & & \\  & & Packet loss & & & \\  & Weak signal detection & diagnosis & & & & \\  & & & & & \\  & Clustered network, & & & & \\  & slot assignment & & & & \\  & Winning slot, & & & & \\  & reservation, & & & & \\  & piggy back & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of relevant MAC protocols.
## 3 Results and Discussion
### Evaluation of the Composites' Behavior Subjected to Physical Attacks
#### 3.2.3 Freeze-Thaw Attack  
Figure 10 shows the modulus of rupture and toughness for the GRC composites subjected to freeze-thaw cycles. After 10 cycles, a significant drop in the modulus of rupture values for the C composite ([PERSON] et al. [14] reported this behavior after 30-50 cycles) and the pozzolan-containing composites is highlighted. This drop is much more marked for the specimens with pozzolan and SC because they have a lower modulus of rupture for all the tested cycles. These samples drastically lost their properties after 50 cycles. For 100 cycles, they yield around 8 MPa for the GFA50+SSF10 composite, and the GFA60 composite yielded around 6 MPa. This is evidenced because the matrix with pozzolan is not fully developed, a weakening that occurs due to the effect of the freezing water present in pores. For the specimens with an accelerated pozzolan reaction (the Acc series), the drop after 50 cycles is marked, but the modulus of rupture continues to be higher than that for the control composite, C, under the same conditions. All this indicates that the accelerated pozzolanic reaction is a positive factor for freeze-thaw durability. Figure 10b shows the composites' toughness results after exposure to cycles. The toughness value of the control composite C is lower, with values close to zero (brittle fracture). This implies significant damage to fiber integrity due to the physical attack. The pozzolan composites generally present values close to the control by the end of the curing process, and their toughness lowers much less after 50 and 100 cycles. This result suggests that the freeze-thaw process damages the matrix, but a certain fiber integrity remains. The decrease in mechanical properties is generally due to the effect of the water present in composite pores freezing which, given its increasing volume, initiates a progressive cementing matrix deterioration process. This effect is much more noticeable for the composites with pozzolan and standard curing because they present greater water absorption (see Figure 1).  
Figure 10: Modulus of rupture (**a**) and toughness (**b**) of the GRC composites subjected to freeze–thaw tests.
# A Hybrid JADE-Sine Cosine Approach for Advanced Metaheuristic Optimization
## 5 Results and Discussion
### JADESCA Sensitivity Analysis  
The sensitivity analysis heatmaps for the JADESCA algorithm across CEC (F1-F12), as shown in Figure 8, reveals significant insights into the impact of varying the number of search agents and the maximum iterations on the final fitness values. For functions like F1, F6, and F11, a clear trend is observed where increasing the number of iterations results in improved fitness values, especially when the number of search agents is kept low. This indicates that these functions benefit more from longer computational efforts when fewer agents are used. On the other hand, functions such as F2, F4, and F8 exhibit relatively stable performance across different iterations and agent configurations, suggesting a robustness in the optimization process that is less sensitive to these parameters. Particularly, for F5 and F9, the results show that beyond a certain threshold of search agents and iterations, the improvement in fitness values becomes negligible, highlighting an optimal balance that can be achieved without unnecessarily increasing computational cost. These heatmaps provide a comprehensive view of how different parameter settings influence the effectiveness of the JADESCA algorithm, guiding future efforts to fine-tune the algorithm for specific problem instances.  
Figure 7: Box plot analysis over CEC2022 (F1–F12).
## 3 Development of Thermal Condition Monitoring System
### Overview of WSNs and Communication Links Characteristics  
Data transmission over cable systems and lines has been found to have high latency and are susceptible to several challenges which minimize their communication effectiveness. This has led to the development of new technologies for wireless network communication and optic based communication systems. However, in developing rural settings and remote areas optic fiber communication is not practically realistic due to its high cost vis-a-vis human linked breaks in sub-terrain activities. Wireless communication, though known to have marginal challenges in relation to weather conditions, is deemed to have a better yield when optimized through linked budgets. This logic informed the development of the proposed system to incorporate Wireless communication in the DERs grid monitoring management systems.  
There are diverse wireless communication technologies for WSNs application in the commercial markets for different developments, namely home automation, control of agricultural applications, laboratory and experimental research works, and industrial intelligent monitoring and controls [61; 62]. The WSNs application in emerging industrial intelligent monitoring and controls are gaining significance in most energy and power engineering industries and their performance are commendable in the promotion of operational technology [63]. These industrial WSNs are used for monitoring conditions of system performance in order to master the thresholds and operation controls status. The industrial WSNs in their application are mandated to comply with industrial standards and code of practice [63]. The standards emphasize uniformity in operations and adherence of security and safety practice. These characteristics are in accordance with IEC 61850, IEC 60870-5, ISA 100.11a, and IEEE 802.15.4 standards. Although the evolution of wireless communication technologies is envisaged, their application should propose relevance in the industrial wireless sensor network application where deemed possible for deployment.  
The advancement in micro-electro-mechanical systems (MEMS) and digital electronics processing facilitates the development of less costly, low-power, and multifunctional WSNs sensor nodes for miniature sized systems and signal transmission in relatively acceptable distances. The collaborative effort of the WSNs evolution enables system operations to be autonomous, unattended, and adaptive to environment at reasonable cost as compared to traditional wired instrumentation and communication systems. With the said attributions, WSNs technologies propose to be prominent in industrial and automation development to enhance system operational performances. In view of this, the paper presents overview characteristics of wireless communication technologies applicable in practice of the DERs system applications [57; 62; 64].
# Analysis of the Delamination Process with Nitric Acid in Multilayer Composite Food Packaging
## 3 Results
### Contour Plots and Duration  
The findings presented in the contour plot in Figure 5 are noteworthy. The straight lines depicted on the plot indicate that the surface is planar and adheres to a linear model. Furthermore, these findings reveal significant differences among the time zones. Specifically, when the strip widths are 1 cm or 1.5 cm, and ultrasound is not employed, the curves  
Figure 4: Factors and their relation coefficients plot with a 95% confidence interval.  
traverse diagonally across the graph area, which imply that the process duration decreases with an increase of temperature and concentration. Conversely, when the strip width is 0.5 cm, the curves run more horizontally, signifying that the temperature effect is less pronounced. In this instance, the process completion rate depends more on the nitric acid concentration. A similar trend is observed when the strips are 1.5 cm wide, and ultrasound is utilized, with the curves appearing nearly horizontal.  
Intriguingly, the model exhibits a starkly different scenario when the width is 0.5 cm and 1 cm, and ultrasound is applied. Primarily, it is important to note that the curves are nearly vertical, indicating that nitric acid concentration exerts minimal influence. In this situation, the temperature factor assumes a critical role with the reaction time decreasing as the temperature diminishes. Consequently, to achieve optimal layer separation via ultrasound with widths of 0.5 cm and 1 cm, selecting a lower acid concentration and a reduced temperature is recommended.  
This study's findings hold considerable implications for future research in this field. The observed differences among the time zones and the varying effects of factors, such as temperature and concentration, establish a basis for the further investigation of this process. Moreover, these findings can be employed to optimize the layer separation process using ultrasound, which boasts numerous applications across diverse fields, including medicine, chemistry, and engineering. The results suggest carefully considering strip width and ultrasound usage is crucial for ensuring optimal outcomes. Additionally, this study underscores the significance of temperature and nitric acid concentration in this process, which can inform the design of future experiments and the development of more efficient layer separation methods.
## 2 Numerical Model and Experimental Verification
### Results Analysis  
The numerical simulation adopted the same loading method as the experiment; the curve of axial force and displacement of the bottom center column is shown in Figure 5. Through the comparison of curves, it was found that the simulation results in the elastoplastic stage were in good agreement with the test results; the curves were almost the same; in the plastic hinge stage, the error was very small; after the concrete cracks and fails, the flexural bearing capacity of the beam ends was basically lost, that is, the simulation effect after the suspension cable [22] was in good agreement with the test curve, and the error was very small.  
Figure 5: Comparison between bottom column axial force and displacement relationship curve.