esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient SC-FDM Modulation Technique for a UAV Communication Link
## 5 Experiments and Performance Measures
### Performance Measures
#### 5.2.2 Ber  
Another important parameter for measuring the performance of a wireless channel of a UAV communication system is the bit error rate (BER). When data is transmitted over a wireless link between the vehicle and GCS, the BER specifies the number of errors that appear in the received data. The environmental conditions and changes to the propagation path are the fundamental reasons for the communication channel degradation and the respective BER. To achieve an acceptable BER, i.e., for the transmission of control commands on the uplink, a typically acceptable BER is around \(10^{-6}\)-\(10^{-9}\)while that acceptable value for the transmission of the payload data on the downlink is \(10^{-3}\)-\(10^{-4}\)[39]. All the available factors must be balanced. Usually, it is difficult to achieve all the requirements and some compromises are required. Moreover, a higher level of error correction is needed in order to recover the original data. This can help to fix the effects of any occurred bit error. It results in the fact that the overall BER can be improved. If the bidirectional communication between the transmitter and receiver is established very well and the signal-to-noise ratio (SNR) is high, then the BER will be potentially insignificant and will not have an observable effect on the whole UAV communication system. The BER is equal to the number of bit errors (\(N_{E}\)) divided by the total number of transmitted bits (\(N_{T}\)), as expressed by the following equation:  
\[BER=\frac{N_{E}}{N_{T}}. \tag{8}\]  
The \(N_{E}\) can be computed by comparing the transmitted signal with the received signal. The BER is most often expressed in terms of SNR. The SNR can be defined as the ratio of bit energy (\(E_{b}\)) to the noise power spectral density (\(N_{o}\)), which is a power per Hz and it is expressed as follows:  
\[SNR=\frac{E_{b}}{N_{o}}, \tag{9}\]  
where \(E_{b}\) is a measure of energy and can be defined by dividing the carrier power by the bit rate.  
The probability of bit error (\(P_{b}\)) represents the probability that the error rate arises in the received signal. The \(P_{b}\) for M-ary PSK can be defined as:  
\[P_{b}\cong 2Q\Bigg{[}\sqrt{\frac{2E_{av}}{N_{o}}\sin\Bigl{(}\frac{\pi}{M} \Bigr{)}}\Bigg{]}, \tag{10}\]  
where \(E_{av}\) is the average energy of transmitted symbol. \(Q\) represents the scaled form of the complementary error function (erfc) and it is given by:  
\[Q(x)=\frac{1}{2}erfc\biggl{(}\frac{x}{\sqrt{2}}\biggr{)}. \tag{11}\]  
It is necessary to note that each different kind of modulation scheme has its own value for the error function. This is due to the fact that each type of modulation scheme executes in different ways in the presence of noise.  
Finally, we can calculate the \(P_{b}\) for M-ary QAM by using the following equation:  
\[P_{b}\cong 4\biggl{(}1-\frac{1}{\sqrt{M}}\biggr{)}Q\Bigg{[}\sqrt{\frac{3E_{av}} {(M-1)N_{o}}}\Bigg{]}. \tag{12}\]
## 2 Proposed Model: QPE Analytical Approximation Model
### Coordinate Systems for Modeling  
Two main coordinate systems are used in the derivation of the analytical approximation model. These two coordinate systems can be helpful in generating a clear description of the relative movement of the spaceborne SAR and the point of interest on the surface of the Earth.
## 3 Electro-Hydraulic Servo System Modeling
### Modeling of the Pressure Servo System  
In the pressure control loop, a servo valve is used to regulate the cylinder's back pressure for a given signal. Similarly, the valve's dynamics is regarded as a proportional component. The relationship between the displacement of the spool and the input signal can be expressed as follows:  
\[x_{v2}=k_{s2}u_{2}. \tag{8}\]  
Since the natural frequency of the valve is much higher than the natural frequency of the system, the relationship between the spool displacement and the input signal is represented using Equation (9):  
\[x_{v2}=k_{s2}u_{2}, \tag{9}\]  
where \(x_{v2}\) is the spool's displacement, \(k_{s2}\) is the valve's gain and \(u_{2}\) is the signal input to the valves. The flow at the outlet \(q_{2}\), which is related to the opening of the valve's orifice \(x_{v2}\), is given by Equation (10):  
\[q_{2}=k_{v2}x_{v2}\sqrt{\Delta p_{2}} \tag{10}\]  
where \(k_{v2}\) is the flow coefficient, and \(\Delta p_{2}\) is the pressure drop of the valve [27], which is given by Equation (11):  
\[\Delta p_{2}=\left\{\begin{array}{l}p_{s}-p_{2},\ x_{v2}\geq 0\\ p_{2}-p_{o},\ x_{v2}<0\end{array}\right.. \tag{11}\]  
For the chamber at the outlet, according to the flow continuity equation, Equation (12) is obtained and shown as following:  
\[\dot{p}_{2}=\left[q_{2}+A_{2}\dot{x}_{L}-C_{cp1}p_{2}+C_{ip2}(p_{1}-p_{2}) \right]\frac{\beta_{e}}{V_{2}-A_{2}x_{L}}+\Delta e_{1}, \tag{12}\]  
where \(\beta_{e}\) is the effective oil bulk modulus, \(p_{2}\) is the pressure of the rod chamber, \(A_{2}\) is the effective area of the rod, \(x_{L}\) is the displacement of the rod, \(V_{2}\) is the initial volume of the cylinder chamber, \(C_{cp2}\) is the external leakage coefficient, \(C_{ip2}\) is the internal leakage coefficient and \(\Delta e_{1}\) is the uncertainty due to parameter perturbation, modeling errors, and disturbance.  
Considering that \(u_{2}\) is the system input and \(p_{2}\) is the system output, according to Equations (9)-(12), the state space equation of the system can be written as Equation (13):  
\[\dot{x}_{v1}=d_{1}u_{2}+d_{2}+\theta_{o1}, \tag{13}\]  
where \(x_{o1}\) is the state variable, \(d_{1}=\frac{\beta_{e}}{V_{2}-A_{2}x_{L}}k_{v2}k_{s2}\sqrt{\Delta p_{2}}\), \(d_{2}=\frac{\beta_{e}}{V_{2}-A_{2}x_{L}}A_{2}\dot{x}_{L}\), and \(\theta_{o1}\) is the uncertainty due to modeling error, parameter uncertainties, and disturbance. Similarly, an NDO is designed to compensate for \(\theta_{o1}\).
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
### Research Aim  
The main aim of this research is to develop new techniques for producing realistic mobility and world models that could be used to evaluate MANET protocols in two different contexts: modelled battlefield and shipping movement. Its other goal is to show that different MANET application scenarios require different routing protocols. This is demonstrated by the fact that these realistic models yield significantly different results from commonly used simulation models, such as the random waypoint model (RWP). Their performance also varies when applied to common protocols. Thus, there is no single solution to the MANET routing problem, and different application areas will have different suitable protocols.
# An Efficient Relayed Broadcasting Based on the Duplication Estimation Model for IoT Applications
## 4 Simulation Study
### The Effect of the Network Scale  
The simulation described in this section was performed to investigate the effect of the network scale. The network radius in the previous simulations was \(2D_{tx}\), which was extended to \(3D_{tx}\) and \(N_{node}\) was set between 160 and 240 in this simulation (Considering the possible applications of relayed broadcasting, the radius of a network will typically be two or three times the transmission range of the source node). Figure 16a shows that as \(N_{node}\) increased, the \(\overline{F}_{val}\) of BASE decreased from 817 to 762, but the \(\overline{F}_{val}\) of DBF increased from 856 and 885, which was greater than that of BASE by 5-16%. Except for these two mechanisms, \(\overline{F}_{val}\)s of the other mechanisms were largely constant even though \(N_{node}\) changed. PBF maintained the highest value of \(\overline{F}_{val}\), which was around 998 regardless of \(N_{node}\). The average values of \(\overline{F}_{val}\) for DRBF and ADBRF were 970 and 947, respectively, but they increased to 998 and 991 when the re-queuing scheme was applied in DRBFRq and ADBRFRq.  
As shown in Figure 16b, \(\overline{T}_{dis}\) for all of the mechanisms linearly increased with \(N_{node}\). The \(\overline{F}_{val}\) of PBF was the highest, which was greater than that of BASE by 3.9-5.1 times. The average values of \(\overline{T}_{dis}\) for the other mechanisms were not much different: 3.55, 4.38, 4.78, 4.57, 6.32, and 5.99, in CBF, DBF, DRBF, ADBRF, DRBFRq, and ADBRFRq, respectively. It was interesting that when compared to DRBF and DRBFRq, \(\overline{T}_{dis}\) somewhat decreased in ADBRF and ADBRFRq. Compared to PBF, the average value of \(\overline{F}_{val}\) for ADBRFRq was 99.3%, but that of \(\overline{T}_{dis}\) remarkably decreased by 5.5-6.3 times. This result supports the conclusion that the approximated duplication ratio along with the re-queuing scheme is very effective at decreasing the dissemination time without compromising the reliability. By comparing the results in Figure 16 with those in Figure 10 where the network radius was \(2D_{tx}\), we can confirm that the outstanding performance of the proposed mechanism is maintained regardless of the network scale.  
Figure 16: The performance comparison of the flooding mechanisms when the network radius was extended to \(3D_{tx}\): (**a**) the number of valid frames \(\overline{F}_{val}\) and (**b**) the dissemination time \(\overline{T}_{dis}\).  
Figure 15: The performance comparison of the proposed flooding mechanisms under the moving scenario: (**a**) the number of valid frames \(\overline{F}_{val}\) and (**b**) the dissemination time \(\overline{T}_{dis}\).
## 3 Proposed Methodology
### Feature Derivation Process  
Following feature selection, a unique Feature Derivation Process (FDP) was used to develop advanced features that improve dataset representation. This stage enhances the chosen features to capture the model's hidden data linkages, interactions, and temporal dynamics. The FDP combines NDS, TEC, and JAA to provide valuable characteristics [34]. Nonlinear Dependency Synthesis finds complicated feature pair interactions. A new interaction feature, \(\theta_{i,j}\), is derived from two features, \(p_{i}\) and \(p_{j}\), using the synthesis function in Equation (10).  
\[\theta_{i,j}=\log(|p_{i}\cdot p_{j}|+1) \tag{10}\]  
This modification emphasises multiplicative connections and suppresses noise to emphasise important interactions. The logarithmic component normalises excessive values. Temporal Evolution Capturer detects DDoS-related anomalies and patterns by analysing feature trends. Equation (11) calculates the temporal evolution rate, \(\kappa_{k}\), for a feature, \(q_{k}\), seen throughout a temporal frame, \(\Xi\):  
\[\kappa_{k}=\frac{\sum_{t=1}^{\Xi}(q_{k}^{t}-q_{k}^{t-1})}{\Xi-1} \tag{11}\]  
where \(q_{k}^{t}\) represents the feature value at time \(t\), whereas \(\kappa_{k}\) measures the average change rate inside the temporal window, \(\Xi\).  
Joint Attribute Aggregator summarises many features' behaviour into a single statistic. To calculate a joint metric, \(\zeta_{m,n,o}\), for features \(r_{m},r_{n},r_{o}\), use the aggregation formula in Equation (12).  
\[\zeta_{m,n,o}=\sqrt{\frac{(r_{m}^{2}+r_{n}^{2}+r_{o}^{2})}{3}} \tag{12}\]  
Our approach estimates the root mean square of characteristics to balance representation and preserve variability.  
The FDP creates new features (\(\Theta\)) via interactions, temporal evolution, and joint aggregation. The final feature set is \(\mathcal{F}_{\text{final}}=\mathcal{F}_{\text{selected}}\cup\Theta\), where \(\mathcal{F}_{\text{selected}}\) comprises selection process features and \(\Theta\) represents newly derived attributes.  
These advanced features capture complex patterns and relationships, boosting the model's multi-label DDoS attack identification. The FDP integrates crucial information into learning by gradually extending the feature set.
# SCCDNet: A Pixel-Level Crack Segmentation Network
## 2 Methods
### Loss Function and Hyperparameters  
Since the crack detection task can be equivalent to the semantic segmentation problem of two classes, that is, the output of the network is a single-channel probability map of crack prediction. Therefore, we chose the cross-entropy function with sigmoid as the loss function of the model. The formula is as follows:  
\[l_{n}=-w_{n}[y_{n}\cdot log\sigma(x_{n})+(1-y_{n})\log(1-\sigma(x_{n}))], \tag{4}\]  
where \(x_{n}\) represents the predicted image output by the model, \(y_{n}\) represents the ground truth of the input image, \(n\) represents the n-th image in the batch size; \(\sigma(\cdot)\) represents the sigmoid function, which can assign the predicted image to a value between 0-1; \(w_{n}\) represents the weight of each loss. When a pixel in the ground truth image belongs to a crack, then \(y_{n}=1\). If the value of the pixel in the predicted image is closer to 1, then the value of \(l_{n}\) is closer to 0, and the training loss is smaller; on the contrary, the value of the pixel is closer to 0, the greater the training loss. Therefore, sigmoid cross entropy is suitable as the loss function of the crack detection task.  
The model parameters we tuned are: the size of input images (\(448\times 448\times 3\)), the size of ground truth (\(448\times 448\times 1\)). In this paper, we choose SGD optimizer to optimize the entire model and the mini-batch size is 4. The momentum variable is set as 0.9 and it is helpful for stabilizing network training. The weight decay is 0.0001 while the initial learning rate is set as 0.005. We use CosineAnnealingLR [39] strategy to adjust the learning rate during the training and set T_max as 5, which means that the change period of learning rate is 10. This strategy is helpful for training loss to escape from local minimum point. In order to ensure that the model is fully trained, we have to choose appropriate stopping criteria for the training process. The specific strategy is: if the loss of validation set dose not decrease in 10 epochs, the model is considered to be fully trained. Further, the maximum epoch is set as 100.
# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
## 2 Materials
### Performance of IPSO  
To validate the performance of the IPSO algorithm, the CEC2022 benchmark test functions were used for performance evaluation. In the CEC2022 set, Function 1 is a Unimodal Function, Functions 2-5 are Basic Functions, Functions 6-8 are Hybrid Functions,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Iteration** & **Standard PSO** & \
\begin{tabular}{c} **Exploration** \\ **Group** \\ \end{tabular} &
\begin{tabular}{c} **Exploitation** \\ **Group** \\ \end{tabular} & **IPSO** \\ \hline
1–25 & 1417.24 & 4043.52 & 1455.71 & 3467.72 \\
26–50 & 1052.74 & 4418.87 & 896.49 & 1859.96 \\
50–75 & 658.23 & 3921.64 & 508.65 & 895.33 \\
76–100 & 372.30 & 2912.25 & 264.68 & 366.09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average Variance of Inter-Particle Distances Across Different Iterations.  
and Functions 9-12 are Composition Functions [18]. For this study, Functions 1, 2, 7, and 9 were selected as representative test cases, as shown in Table 2.  
The IPSO algorithm and the PSO algorithm were run on each test function, with the optimization constraints as shown in Table 3.  
The results were plotted as fitness curves, as shown in Figure 1. It can be observed that the IPSO algorithm outperforms the PSO algorithm in terms of both convergence speed and accuracy.  
However, both algorithms exhibit a degree of randomness during the optimization process. To obtain more reliable data, both algorithms were executed 30 times on each test  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sequence** & **Function** \\ \hline Func1 & \(f_{1}=\sum_{i=1}^{n}x_{i}^{2}+\left(\sum_{i=1}^{n}0.5 ix_{i}\right)^{2}+\left( \sum_{i=1}^{n}0.5 ix_{i}\right)^{4}\) \\ Func2 & \(f_{2}=\sum_{i=1}^{n-1}\left[100(x_{i+1}-x_{i}^{2})^{2}+(x_{i}-1)^{2}\right]\) \\ Func7 & \(f_{7}=\left|\left(\sum_{i=1}^{n}x_{i}^{2}\right)^{2}-\left(\sum_{i=1}^{n}x_{i} \right)^{2}\right|_{0.5}+\left(0.5\sum_{i=1}^{n}x_{i}^{2}+\sum_{i=1}^{n}x_{i} \right)/n+0.5\) \\ Func9 & \(f_{9}=\prod_{i=1}^{n}\left(1+i\sum_{j=1}^{n}\frac{2^{j}x_{i}-round(2x_{i})}{2^ {j}}\right)^{n\frac{1}{1.2}}-1\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: CEC2022 benchmark test functions.  
Figure 1: Fitness curve.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sequence** & **Dimension** & **Number of Particles** & **Number of Iterations** & **Bounds** \\ \hline Func1 & 20 & 30 & 10 & \([-100,100]\) \\ Func2 & 20 & 30 & 100 & \([-100,100]\) \\ Func7 & 20 & 30 & 100 & \([-100,100]\) \\ Func9 & 20 & 30 & 500 & \([-100,100]\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimization Constraints for IPSO and PSO Algorithms.  
function, and their performance was compared using optimal values, worst values, means, and variances. The test results are summarized in Table 4. Compared to the traditional PSO algorithm, the IPSO algorithm shows improvements in both the optimal and average values, with a significant reduction in variance, indicating higher convergence accuracy and greater stability.
# The State of the Art of Biomechanics Applied in Ergonomic Furniture Design
## 3 Findings and Discussions
### Biomechanics Utilized in Different Types of Furniture
#### 3.1.1 Common Furniture
The results showed that kitchen base cabinets with drawers were more ergonomic than kitchen base cabinets with doors. As an important group in the world, the physical health and comfort of students while studying has also received much scientific attention. Based on the RULA, it was found that most preschool students experience mismatch between the anthropometric dimensions of the students and the dimensions of the chair and desk [37; 38]. This issue is present in primary and secondary schools as well as universities. The primary reason for this is that the furniture provided is largely modular and the classroom areas are limited, leading to a considerable decrease in overall comfort and functionality [39; 40]. Therefore, some researchers aim to find the ideal design parameter dimensions of student desks and chairs based on relevant anthropometric information and considering the biomechanics of students in the sitting position and the design of relevant desks and chairs [41]. The proper design of desks and chairs based on ergonomics increases efficiency, promotes the quality of education, leads to correct posture in students, and reduces the risk of musculoskeletal disorders [42]. Most of the studies above analyze and examine the biomechanics of common furniture from a comfort-oriented perspective, which is advantageous for designing ergonomic and practical furniture. Nevertheless, much of the research still concentrates on seating design. furniture, in fact, has a vast range of applications, and biomechanics can have broad applications in the furniture design procedure. In future research, biomechanics can be conducted to study the human-furniture interactions of various other pieces of furniture, including tables and beds. Previous scientific studies have mainly examined the biomechanics of static postures during use. However, a biomechanical approach that analyzes dynamic movements in relation to the actions performed during use is necessary for a comprehensive understanding.
## Appendix L Description of Mechanisms for Vehicles
### Proof-of-Reputation [623]  
Proof-of-Reputation manifests as a two-layer consensus mechanism for IoV scenarios. The main consensus layer is a permissioned network, and the lower layer (termed 'physical layer' by the authors) follows a reputation-based methodology. In this lower layer, vehicles hold reputation values that dictate the likeliness of any transaction proposed by them being included in a block. This reputation value is, presumably, influenceable by the main consensus layer or other off-ledger means. Due to its goal of serving permissioned systems, this mechanism does not provide Sybil attack resistance.
###### Abstract
However, during the motion process, the fixed Proportion Integration Differentiation (PID) parameters cannot effectively adjust the large position mutation, resulting in excessive overshoot, which causes the controlled ball to vibrate during the step motion. There is severe jittering and even \"falling out of control\" when using larger step sizes for motion control. Even when using a large step size for motion control there is a violent jitter, and also an uncontrolled fall. There is high adaptability of the neural network to complex systems with nonlinear and ambiguous models. In order to improve the stability of the motion process, the PID controller composed of neurons has stronger adaptability. The contribution of the paper is presented as follows:  
1. An active magnetic levitation actuation system is built, and a dynamic model of the actuation system based on force imbalance is established. The relationship between the levitating motion of the electromagnetically actuated object and the external magnetic field is revealed in the model;
2. A controller combining neural network and PID is adopted to control the electromagnetic actuators, in which the PID control parameters are adjusted through a neural network. This controller eliminates the uncertainties of the complex external magnetic field actuation systems and improves the levitating motion tracking performance. Compared with the model-based controller, the combination of neural network algorithm and PID improves the stability of the magnetic levitating motion. The proposed controller is verified in experiments and simulations, and the result proves that the neural network PID control outperforms the model-based controller;
3. The magnetic levitation actuation and motion control system based on force imbalance can realize non-contact stable levitation. Large-displacement levitating motion of the controlled object in non-magnetic slender pipes can be realized by the proposed step-by-step actuation control method, including other detection and operation equipment attached to the sphere. Non-destructive quality inspection and dredging of the inner wall of slender pipes in the industrial fields can be applied by the control system.