esa-sceva/satcom-chunk-collection huggingface dataset:
# Biomechanical Analysis of Posterior Ligaments of Cervical Spine and Laminoplasty
## 2 Material and Methods
### Model Validation  
The intact C2-C7 model was validated using the in vitro loading protocol documented by [PERSON] et al. [25]. A pure moment of 1.5 Nm was applied to the odontoid process of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **Material Properties** & **Constitute Relation** & **Element Type** & **Area (mm\({}^{2}\))** \\ \hline \multicolumn{5}{c}{**Bone**[25]} \\ \hline Vertebral cortical bone & \begin{tabular}{c} E = 10,000 MPa \\ v = 0.3 \\ \end{tabular} & Isotropic, Elastic & C3D8 & - \\ \hline Vertebral cancellous bone & \begin{tabular}{c} E = 450 MPa \\ v = 0.25 \\ \end{tabular} & Isotropic, Elastic & C3D8 & - \\ \hline Vertebrae-Posterior & \begin{tabular}{c} E = 3500 MPa \\ v = 0.25 \\ \end{tabular} & Isotropic, Elastic & C3D10 & - \\ \hline Artificial bone & \begin{tabular}{c} E = 10,000 MPa \\ v = 0.3 \\ \end{tabular} & Isotropic, Elastic & C3D8 & - \\ \hline \multicolumn{5}{c}{**Intervertebral Disc**[24]} \\ \hline Ground substance of & \begin{tabular}{c} C10 = 0.7 \\ annulus fibrosis \\ \end{tabular} & \begin{tabular}{c} Hyper-elastic, Mooney-Rivlin \\ C01 = 0.2 \\ \end{tabular} & C3D8 & - \\ \hline Nucleus pulposus & \begin{tabular}{c} C10 = 0.12 \\ C01 = 0 \\ D1 = 0 \\ \end{tabular} & \begin{tabular}{c} Incompressible Hyper-elastic, \\ Mooney-Rivlin \\ \end{tabular} & C3D8 & - \\ \hline \multicolumn{5}{c}{**Ligaments**[23]} \\ \hline Anterior Longitudinal & 15.0 (\(<\)12\%), 30.0 (\(>\)12\%) & Non-linear, Hypo-elastic & T3D2 & 6.1 \\ Ligament & v = 0.3 & & & \\ \hline Posterior Longitudinal & 10.0 (\(<\)12\%), 20.0 (\(>\)12\%) & Non-linear, Hypo-elastic & T3D3 & 5.4 \\ Ligament & v = 0.3 & & & \\ \hline Capsular ligament & 7.0 (\(<\)30\%), 30 (\(>\)12\%) & Non-linear, Hypo-elastic & T3D4 & 46.6 \\ \hline Ligamentum Flavum & \
\begin{tabular}{c} 5.0 (\(<\)25\%), 10.0 (\(>\)25\%) \\ v = 0.3 \\ \end{tabular} & Non-linear, Hypo-elastic & T3D5 & 50.1 \\ \hline Interspinous Ligament & 4.0 (20–40\%), 8.0 (\(>\)40\%) & Non-linear, Hypo-elastic & T3D6 & 13.1 \\ \hline \multicolumn{5}{c}{**Facet Joints**[21]} \\ \hline Apophyseal Joints &
\begin{tabular}{c} Non-linear \\ Soft contact, \\ GAPPUNI elements \\ \end{tabular} & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties assigned to the finite element model [21; 22; 23; 24; 25].  
Figure 1: The intact (C2–C7) finite element model.  
C2 verteb in flexion/extension, lateral bendings, and axial rotations [25]. Furthermore, the caudal endplate of the C7 verteb was fixed by suppressing all six degrees of freedom. The effects of muscular contractions and the weight of the skull were replicated using the follower load method by applying a connector force of 100 N. The ROM, intervertebral nucleus stress, and facet contact forces were computed for each level in flexion/extension, lateral bendings, and axial rotations and were compared with the published in vitro data in the literature by [PERSON] et al. [25], [PERSON] et al. [24], [PERSON] et al. [26], and [PERSON] et al. [27].
# Design of Multi-Cell Cooperative Control Algorithm Based on Fuzzy Brain Emotional Learning
## 3 Single-Cell Manipulation Control
### Convergence Analyses  
In the above adaptive learning algorithm of neural networks, (20) and (21) adopt the gradient descent method to learn and optimize their parameters. The FBEL neural network is iterated several times, and the model parameters are estimated by the loss function at each step. The learning rate is the progress controlling the model learning in the iterative process, which determines whether the objective function can converge and when it converges to the minimum value. If the learning rate is too large, the step length of each learning will be long. Gradients may oscillate around the minimum or fail to converge. When the learning rate is too small, convergence is slow. It is very likely to enter a local minimum and fail to reach a better solution.  
Suitable learning rates \(\eta_{\mu}\) and \(\eta_{\sigma}\) are therefore found by the Lyapunov stability theorem to ensure stable convergence of the system in order to achieve the fastest convergence and optimal solution. It is worth mentioning that the convergence analysis is the analysis of the FBEL neural network under the system model. Not only for single-cell control, but also for multicellular control.  
The Lyapunov function \(P\) is chosen as:  
\[P(i)=\frac{1}{2}e^{2}(i) \tag{26}\]  
Figure 4: Structure diagram of a closed-loop system based on the FBEL neural network.  
Then the change is as follows:  
\[\Delta P(i)=P(i+1)-P(i)=\frac{1}{2}\Big{[}e^{2}(i+1)-e^{2}(i)\Big{]} \tag{27}\]  
The prediction error can be expressed as:  
\[e(i+1)=e(i)+\Delta e(i)\cong e(i)+\frac{\partial e(i)}{\partial\varepsilon_{ nm}}\Delta\varepsilon_{nm} \tag{28}\]  
where \(\Delta\varepsilon_{nm}\) represents the change of \(\varepsilon_{nm}\).  
In (28), \(\frac{\partial e(i)}{\partial\varepsilon_{nm}}\) can be obtained from (20)  
\[\frac{\partial e(i)}{\partial\varepsilon_{nm}}=\tau_{e}=(D_{nm}-B_{nm})\cdot G _{nm}\cdot\frac{2(I_{n}-\varepsilon_{nm})}{\omega_{nm}{}^{2}} \tag{29}\]  
Substituting (20) and (29) into (28), we can obtain:  
\[\begin{array}{c}e(i+1)=e(i)-\eta_{t}e(i)\Big{[}(D_{nm}-B_{nm})\cdot G_{nm} \cdot\frac{2(I_{n}-\varepsilon_{nm})}{\omega_{nm}{}^{2}}\Big{]}^{2}\\ \qquad\qquad\qquad=e(i)-\eta_{t}e(i)\tau_{e}^{2}\end{array} \tag{30}\]  
Therefore, the change in the Lyapunov function is:  
\[\begin{array}{c}\Delta P(i)=\frac{1}{2}\Big{[}e^{2}(i+1)-e^{2}(i)\Big{]}\\ =\frac{1}{2}e^{2}(i)\Big{[}\big{(}1-\eta_{\varepsilon}\tau_{e}^{2}\big{)}^{2}- 1\Big{]}\end{array} \tag{31}\]  
According to (31), when \(0<\eta_{\varepsilon}<\frac{2}{\tau_{e}^{2}}\), \(P(i)>0\) and \(\Delta P(i)<0\). So the system is stable and convergent.  
Similarly, it can be proved in this way. When \(0<\eta_{\omega}<\frac{2}{\tau_{\omega}^{2}}\)\(\tau_{\omega}=(D_{nm}-B_{nm})\cdot G_{nm}\cdot\frac{2(I_{n}-\varepsilon_{nm})^{2}}{ \omega_{nm}{}^{3}}\), the system is stable and convergent.
# AI-Based RPA's Work Automation Operation to Respond to
## 2 Research Background
### Overcoming Cybersecurity Challenges with RPA and AI  
To overcome the limitations of existing security systems, it is essential to adopt Robotic Process Automation (RPA) and Artificial Intelligence technologies. Traditional, manual security frameworks often require significant human intervention for repetitive and rule-based log processing tasks, which are both time-consuming and prone to human error. RPA addresses these issues by automating such tasks, minimizing human involvement, and significantly improving the speed of security response. Moreover, RPA can standardize and integrate log data generated by various security systems, thereby enhancing the efficiency and consistency of security analysis.  
AI further complements this process by analyzing large-scale log data in real time, detecting emerging threats, and predicting future cyberattacks based on historical patterns. By applying time-series models like Long Short-Term Memory (LSTM), as shown in Figure 1, AI can predict recurring cyberattack patterns based on historical data, enabling proactive responses [17]. LSTM is particularly effective for this purpose because it can remember important past information for long periods, making it well-suited for predicting events based on time-series data. The integration of RPA and AI technologies, therefore, significantly enhances the overall capabilities of security systems by enabling real-time responses and accurate predictions of cyber threats.  
The goal of this research is to develop an automated and intelligent cybersecurity response system by integrating RPA and AI, effectively overcoming the limitations of traditional manual security frameworks. The proposed system aims to minimize the involvement of security practitioners by automating repetitive tasks, enabling real-time data processing, and ensuring immediate responses to threats. Additionally, the system standardizes and integrates log data from various security solutions, improving the accuracy of data analysis and supporting the development of AI models capable of predicting future attack patterns. Notably, this solution offers a cost-effective, highly efficient security framework that can be implemented even in resource-constrained environments, such as government agencies, public institutions, and military facilities. The results of this research are expected to enhance the cybersecurity capabilities of both public and private sectors, enabling faster and more accurate responses in an increasingly dynamic digital landscape.
# Double Additive Margin Softmax Loss for Face Recognition
## 4 Experiment
### Comparison with State of the Art Loss Functions on LFW Dataset  
In this part, we evaluate the performance of the proposed DAM-Softmax loss and the state-of-the-art loss functions. Following the previous experimental setting, we train a ResNet-Face18 model under the guidance of the original softmax, L-Softmax, A-Softmax, AM-Softmax and DAM-Softmax on the training dataset of CAISAWebFace. The experimental results on the test dataset of LFW are shown in Table 4.  
From Figure 6, It can be seen that the verification accuracy of DAM-Softmax loss is over 80% after one epoch while AM-Softmax loss requires 20 epoches to achieve the similar accuracy. In the 40 th epoch, DAM-Softmax loss reaches the best performance which is still superior to the one of AM-Softmax loss. Figure 7 reports the training loss under the variation of epoch. When epoch = 75, the training loss of the original Softmax loss approaches stabilization with the value of about 13 by using softmax, the AM-softmax's training loss reach stabilization at around 10 when epoch is 55, while DAM-softmax get to stabilization at the 40 th epoch and has a lower training loss. Therefore, This can demonstrate that the proposed loss has a faster convergence speed than AM-Softmax loss. As can be seen in Table 4, our proposed DAM-Softmax loss consistently arrives at competitive results compared to the other losses, which demonstrates the effectiveness of DAM-Softmax loss.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Accuracy Rate** \\ \hline Softmax ( resnet-face18, 110 epoch ) & 97.08\% \\ L-Softmax ( resnet-face18, 110 epoch ) [10] & 97.33\% \\ A-Softmax ( resnet-face18, 110 epoch ) [14] & 97.52\% \\ AM-Softmax ( resnet-face18, 110 epoch ) [15] & 97.68\% \\ DAM-Softmax ( resnet-face18, 47 epoch ) & **97.83**\% \\ DAM-Softmax ( resnet-face18, 110 epoch ) & **97.97**\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Some results of comparative testing experiment.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline Parameter m & 0.25 & 0.3 & 0.35 & 0.4 & 0.45 & 0.5 \\ Accuracy Rate & 97.21\% & 97.34\% & 97.45\% & **97.68**\% & 97.57\% & 97.59\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of different values of \(m\) with AM-Softmax.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline Parameter m & 0.1 & 0.13 & 0.15 & 0.17 & 0.18 & 0.2 & 0.22 & 0.25 \\ Accuracy Rate & 97.68\% & 97.74\% & 97.82\% & 97.94\% & **97.97**\% & 97.89\% & 97.81\% & 97.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of different values of \(m\) (110 epoch) with DAM-Softmax.
# A Novel Genetic Algorithm for the Synthetical Sensor-Weapon-Target Assignment Problem
## 3 Description of the Proposed Method
### The Framework of GA-SWTA  
In this paper, a novel genetic algorithm for the S-WTA problem (GA-SWTA) is proposed. This algorithm begins with a proposed problem-specific initialization method to generate the population. Then, it proceeds to the loop phase. Initially, the selection is performed to generate a new generation by some specific selection method, such as the roulette wheel selection and the tournament selection. Afterwards, the crossover and mutation operators are implemented to generate new individuals. Thereafter, in order to handle the constrains of the S-WTA problem, the proposed R-STA operation and R-WTA operation are performed to repair the individuals. At the end of the loop, the potential individuals are selected for the next evolution. The loop will continue until the iterations reaches the maximum number. The framework of GA-SWTA is shown in Algorithm 4.  
```
0: Max generation: \(maxgen\). Population size: \(pop\). Crossover probability: \(pc\). Mutation probability: \(pm\). Other necessary scenario information of S-WTA problem.
0: The best individual \(lbest\). Step 1 Initialization: Generate an initial population \(X=x_{1},x_{2},\cdots,x_{pop}\) by the proposed initialization method. Meanwhile, \(Iter=0\). Step 2 Selection: A proportion of population \(X\) are selected as a new generation \(X^{\prime}=x^{\prime}_{1},x^{\prime}_{2},\cdots,x^{\prime}_{pop}\) by some specific selection method. Step 3 Reproduction: For \(i=1,2,\cdots,pop\) do  3.1 Crossover: Generate a random number \(p\in[0,1]\), if \(p<pc\), randomly select an individual \(x^{\prime}_{j}\) in \(X^{\prime}\), then perform the crossover operator on \(x^{\prime}_{i}\) and \(x^{\prime}_{i}\) to generate two new individuals as \(x^{\prime\prime}_{i}\) and \(x^{\prime\prime}_{i}\).  3.2 Mutation: Generate a random number \(p\in[0,1]\), if \(p<pm\), randomly select an individual \(x^{\prime}_{i}\) in \(X^{\prime}\), then perform the mutation operator on it to generate a new individual \(x^{\prime\prime}_{i}\). Step 4 Repair: Perform the proposed R-STA operation and R-WTA operation for the reproduced population \(X^{\prime\prime}\). Step 5: Let \(X^{\prime\prime}=X^{\prime\prime}\cup X\). Calculate the fitness for all the individuals in \(X^{\prime\prime}\), then select the best \(pop\) individuals as the new generation \(X\). Step 6: \(Iter=Iter+1\), then go to Step 2. If \(Iter\) reaches \(maxgen\), output the best individual \(lbest\).
```  
**Algorithm 4** GA-SWTA
## 2 Materials and Methods--Implementation, Integration, and Testing of the IDEASSat Flight Model
### IDEASSat Satellite Subsystems
#### 2.1.6 Command and Data Handling (CDH)  
IDEASSat used the industrial-grade SmartFusion2 M2S050-FG484I System-on-Module (SOM) from Emcraft as the processing core of CDH. The SmartFusion2 System on Chip (SoC) combines a 32-bit ARM Cortex-M3 processing core, integrated hard IP I/O and FPGA architecture on a flash memory base on a single chip [14]. The SmartFusion2 also incorporates features attractive for use in space operations, especially considering the need for autonomous operation in a high ionizing radiation environment. These features include built-in single error correction/double error detection (SECDED) that can assist in the correction of data corruption from single event upsets, built-in watchdog timer to recover from software hangs [14; 15], as well as past radiation test results indicating resilience to single event effects [16; 17]. The SOM was mounted on a self-made interface card, which provided physical connections with other subsystems through the PC/104 and D-sub interface.  
The self-developed interface card was equipped with a spare real time clock (RTC) as a redundancy for satellite restart; and had two SD memory cards as storage space for scientific data, beacon data, and important parameters. In order to assist in ground testing, the interface card also had a software burn-in function and a test interface for ground support equipment (GSE).  
Table 4 shows the communication protocols that IDEASSat used. The data interface protocols supported by CDH included I2C, SPI, and UART. Due to the limited number of serial interfaces of the SOM itself, some serial interfaces were provided by synthesizing related communication protocols through the FPGA.
## 2 Related Work
This algorithm assumes a static wireless mesh network that is different from our dynamic network topologies. Also, this algorithm is based on multi-radio or multi-channel routing, opposite to our assumptions. A lot of unicast routing protocols [18, 35, 56] and some improvements [57] are related to MANETs. Some of their extensions with gateway discovery are given in [58, 59, 60, 61, 62]. Most of these protocols use hop-by-hop routing, that is completely different from our routing protocol based on multicriteria optimization and a centralized approach. In recent years, more research has focused on the development of dynamic and scalable protocol in WMNs, especially analyzing density [63]. This is very important if we use WMN for multimedia streaming [64]. Some modifications of protocols are caused by _Ad-Hoc_ 802.11s-based networks [65, 66]. In accordance with the application of protocols in mobile devices, and various types of applications, some research was to develop multi path protocols [67, 68]. At the same time, several papers describe cross-layer based proactive routing protocols [69, 70]. Several improvements for AODV based on process algebra are given in [71]. As a concluding remark, existing routing protocols use flooding in order to discover and maintain routes between users and gateways within MANETs. In WMNs, most of the traffic is assumed to flow to and from the gateways connected to the Internet [11]. Based on that, we have designed a new routing protocol without flooding. Furthermore, we propose a new routing metric based on artificial logic and optimal multicriteria optimization.
## 4 Validation Experiments and Results
### Validation Experiment of Resistant Rehabilitation Training Mode of the Controller  
For patients who have recovered their motor ability in the later rehabilitation stage, the strength of the main muscle groups of the arm is relatively weak. Therefore, resistant rehabilitation training is required for these patients to further strengthen their muscle force. To illustrate that the controller proposed in this paper can be applied to resistant rehabilitation training by adjusting the parameters, the parameters of the controller were set as \(R_{W}\) = 25 mm, \(K_{0}\) = [300, 300, 300] N/m, \(K_{1}\) = [500, 500, 500] N/m, \(K_{1}\) = [500, 500, 500] N/m, and \(K_{2}\) = [500, 500, 500] N/m during the experimental testing. The robot's pose remained fixed during the testing, and the subject was required to complete three laps in counterclockwise direction under the guidance of the Unity scene. Figures 20-22 present the actual and predetermined trajectories of the subject's arm, the filtered interaction force and torque between the subject's arm and the handle, and the filtered total interaction force between the subject's arm and handle as well as the deviation between the actual and predetermined trajectories, respectively.  
Figure 19: The total interaction force and the deviation between the handle and the subject’s hand.  
Figure 21: The interaction force and moment between the handle and the subject’s hand. (**a**) is the interaction force between the handle and subject’s hand; (**b**) is the moment between the handle and subject’s hand.  
Figure 22: The total interaction force and the deviation between the handle and the subject’s hand.  
Figure 20: The trajectory of subject’s hand during the resistant testing. (**a**) A 3D view of the trajectory; (**b**–**d**) XOY, XOZ, and YOZ view of the trajectory, respectively.  
It can be seen from the experimental results that the trajectory of subject's arm was mainly inside the FTR, as shown in Figure 20. However, the interaction force between the subject's arm and the robot was relatively large, as shown in Figure 21a. Although the subject was moving within the FTR, the controller parameter \(K_{2}\) was set as a constant value to create a resistance force that prevents the subject from moving. Therefore, the subject had to overcome the resistance before they could move their arm actively. To a certain extent, the upper limb muscle groups of patients could be strengthened by resistant rehabilitation training, and resistance training of different intensities can be achieved by adjusting the parameter \(K_{2}\). With the increase in \(K_{2}\), the intensity of the resistance training increases. One can conclude from the experimental results that the performance of the controller could be useful to train a participant's upper limb muscle strength at different intensities by adjusting the values of controller parameters.
# Performance Analysis of Dual-Hop DF Multi-Relay FSO System with Adaptive Modulation
## 5 The Performance Analysis of Dual-Hop Multi-Relay System  
Here, we focus on analyzing the performance of the considered dual-hop multi-relay (DH-MR) system with AM. In this system, only one relay is selected to transmit the data based on a specific RS scheme. If \(\gamma_{I}\) represents the received SNR of the adaptive DH-MR system after applying the selection protocol, the modulation level of the system is chosen based on \(P_{o}\) and \(\gamma_{I}\) as  
\[M=M_{i}=2^{i}\text{ if }\gamma_{i}\leq\gamma_{t}\,<\,\gamma_{i+1},\,i=1, \ldots,N. \tag{19}\]  
For this system, the modulation level selection probability is given by  
\[P_{\text{ms}}^{\text{sc}}(i)=P_{r}\{\gamma_{i}\leq\gamma_{t}\,<\,\gamma_{i+1} \}= \begin{cases}F_{\gamma_{I}}(\gamma_{i+1})-F_{\gamma_{I}}(\gamma_{i}),&i=1, \ldots,N-1\\ 1-F_{\gamma_{I}}(\gamma_{i}),&i=N.\end{cases} \tag{20}\]  
The SE of the adaptive DH-MR system with RS can be expressed in a manner analogous to the adaptive DH-SR system, as follows:  
\[S_{\text{sc}}=\frac{R_{b}}{W}=\sum_{i=1}^{N}b_{i}\log_{2}M_{i}=\sum_{i=1}^{N} i\,b_{i}, \tag{21}\]  
where  
\[b_{i} =P_{r}\{\gamma_{i}\leq\gamma_{t}\,<\,\gamma_{i+1}\}=\int_{\gamma_ {i}}^{\gamma_{i+1}}f_{\gamma_{I}}(\gamma)d\gamma\] \[=F_{\gamma_{I}}(\gamma_{i+1})-F_{\gamma_{I}}(\gamma_{i}). \tag{22}\]  
The SE of the DH-MR system can be represented as  
\[S_{\text{sc}} =\sum_{i=1}^{N}i\left[F_{\gamma_{I}}(\gamma_{i+1})-F_{\gamma_{I} }(\gamma_{i})\right]\] \[=N-\sum_{i=1}^{N}F_{\gamma_{I}}(\gamma_{i}). \tag{23}\]  
The OP of the adaptive DH-MR system occurs when \(\gamma_{t}\) falls below \(\gamma_{1}\), hence  
\[P_{out}^{\text{sc}}=P_{r}\{\gamma_{t}\,<\,\gamma_{1}\}=\int_{0}^{\gamma_{1}}f _{\gamma_{I}}(\gamma)d\gamma=F_{\gamma_{I}}(\gamma_{1}). \tag{24}\]The CDF of \(\gamma_{t}\), \(F_{\gamma(t)}\), dependes on the used RS protocol. Therefore, before obtaining expressions for the performance measures, we first need to derive the CDFs of the three RS schemes.