esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study aimed to compare two female field hockey teams of different competitive levels by analyzing kinematic variables in repeated sprint ability (RSA) tests and the force-velocity profile (FVP). Twenty-five female hockey players representing the elite and subelite levels from the same club volunteered to participate. The RSA protocol included six 30 m maximal sprints with a 30 s recovery. Kinematic variables, such as sprint time, step frequency, and step length, were analyzed for each sprint. Additionally, players performed counter-movement jumps (CMJs) and CMJs with 50% body weight (CMJ50s) to calculate the FV50 using the Bosco Index. The elite players showed better (\(\approx\)2%; \(p<0.05\)) fatigue indexes in sprint time (0-30 m and 0-10 m sections), step length (0-10 m, 10-20 m, and 20-30 m sections), and step frequency (20-30 m section) during the RSA test, as well as greater values (>10%; \(p<0.05\)) in the CMJ50 and FV50 tests. In addition, these RSA (sprint time, step frequency, and step length) and jumping (CMJ, CMJ50, and FV50) variables showed a moderate, significant, or very significant relationship with each other. Therefore, it seems that both strength and speed capacities can be used either in conjunction or as a complementary approach to enhance the overall RSA performance.  
sprint performance; monitoring and evaluation; team sports; fatigue index; sprint time; Bosco Index +
Footnote †: journal: Applied sciences
# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 3 Modeling Process
### Markov Decision Process Modeling
#### 3.3.2 Action Space  
The actor-network will generate action destroy nodes \(\mathbf{\Omega}\) and destroy coefficients \(\mathbf{\Lambda}\). Then, the tasks will be rescheduled when the removeing nodes are calculated according to action destroy nodes and the coefficients. The architecture of the actor-network is depicted in Figure 3. Graph feature extraction was conducted using a multi-layer GAT network. Specific network parameters refer to [40].  
\[A_{t}=(\mathbf{\Omega}^{t},\mathbf{\Lambda}^{t}) \tag{16}\]  
In the \(t\)-th iteration, \(A_{t}\) represents the action, \(\mathbf{\Omega}^{t}\) represents the removing action nodes, and \(\mathbf{\Lambda}^{t}\) is the corresponding removing coefficients.  
For the \(n\)-dimensional decision vector in the \(t\)-th iteration, \(X_{t}=\{x_{1}^{t},x_{2}^{t},\dots,x_{n}^{t}\}\). If the scheduled embedding task vector \(s_{i}^{t}\) is removed from \(S_{t-1}\), the corresponding value \(x_{i}^{t}\) will be set to 0.  
According to the defined state and action space, the state transitions with a given action by adhering to the dynamic process:  
1. Initialize all the parameters, given a feasible initial decision vector \(X_{t}\) and state \(S_{t}\).
2. \"Destroy\" process: We determined the final set of removal points \(RemovingNodes_{t}\) using \(\mathbf{\Omega}^{t}\) and \(\mathbf{\Lambda}^{t}\). The length of the removal point set, denoted as \(M^{t}\), is also influenced by the predetermined destroy rate \(r^{d^{\prime}}\). Similar to Equation (11), the computation process is as follows: \[M^{t}=\left\lfloor\frac{n\times r^{d^{\prime}}}{M}\right\rfloor\times M\] (17) where both \(n\) and \(M\) are defined in Equation (11). Taking the example of destroy action node \(\Omega_{t}^{t}\), we performed sampling among its neighboring nodes (including the \(\Omega_{t}^{t}\)). The sampling space is defined as the set of \(NeighborNodes_{i}\), with the sampling rate set to \(\Lambda_{t}^{t}\), and the number of sampled points set to the product of \(|NeighborNodes_{i}|\) and \(\Lambda_{i}^{t}\). The \(RemovingNodes_{t}\) is generated by sampling \(M^{t}\) points from the entire set of destroy points. According to the \(RemovingNodes_{t}\), the corresponding value of \(X_{t}\) is set to 0, and the corresponding nodes are removed in \(S_{t}\).
3. \"Repair\" process: The repair process involves re-planning, where the repair operator [PERSON] is employed to include task nodes that adhere to the constraints in \(S_{t}\), setting the corresponding decision variables in \(X_{t}\) to one, thereby generating a new state \(S_{t+1}\). [PERSON], which is a designed repair operator, will improve the destroyed solution by inserting new tasks into it, following the time sequence of the task's arrival [19].
## 3 Proposed Clustering Routing Protocol
### Operation Phase
#### 3.2.1 Selection of Optimal Cluster Heads  
In the proposed protocol, the YSGA algorithm determines the number of CHs and which nodes will be selected as CHs. In the selection, two main criteria are considered: the distance from CH to the base station and the residual energy of CH.  
Regarding the first criteria, establishing the most efficient connection will promote energy savings. Therefore, the selection of CHs considers reducing the length of the links. Additionally, it is assumed that links work at the same frequency, and crosslinks are eliminated because they generate interference during data transmission.  
Concerning the second criterion, the proposed method selects sensor nodes as CHs if its residual energy is higher enough. Cluster heads spend more energy than normal nodes because, according to the energy consumption model, CHs consume energy when receiving, aggregating, and transmitting data. Hence, sensor nodes with the highest residual energy must be selected as CHs for load balancing.  
In contrast to other clustering routing protocols, the number of CHs is not fixed. Instead, the number of CHs is dynamically changed in order to build the best network configuration in every round. Furthermore, the selection of CHs is not random or probabilistic, such as in LEACH or DEEC protocols. In contrast, in our method, the YSGA algorithm automatically finds the optimal number of CHs and selects the best sensor nodes to become CHs in every round. This strategy allows the implementation of the proposed protocol for a wide range of WSN applications without concern about determining the number of clusters.  
Once the optimal cluster heads are selected, each sensor node is incorporated into the nearest cluster head. Nevertheless, if the distance from the sensor node to the BS is shorter than the length to the CH, then the sensor node is not clustered, so the information of this node is transmitted directly to the base station. After completed the clustering process, a potential network configuration is prepared  
A smaller value of \(f(C)\) indicates that the selected cluster heads have generated a better cluster network configuration. In that sense, the TSGA algorithm will choose a set of cluster heads to build different cluster networks in every iteration in order to find the optimal network configuration. The proposed algorithm is summarized in Algorithm 1.  
```
Input: number of alive nodes Output: a set of \(k\) clusters while\(t\leq t_{max}\) Select cluster heads according to the search strategy of TSGA if the distance from a sensor node to the nearest cluster head is shorter than to the base station  Assign the sensor node to the nearest cluster head else  The sensor node will transmit directly to the base station end if else  Calculate the objective function of the current cluster network configuration \(f\big{(}C^{t}\big{)}\) if\(f\big{(}C^{t}\big{)}<f\big{(}C^{opt}\big{)}\)  Update the optimal cluster network configuration as: \(\big{|}\)\(f\big{(}C^{opt}\big{)}\gets f\big{(}C^{t}\big{)}\) end if end while
```  
**Algorithm 1** Proposed routing protocol.
# RAPT: A Robust Attack Path Tracing Algorithm to Mitigate SYN-Flood DDoS Cyberattacks
### Motivation  
Upon the design of the Internet Protocol (IP) by the Advanced Research Projects Agency Network (ARPANET) in the 1970s, the focus was majorly on achieving seamless communication between different entities. Therefore, at that time, security was not an objective by its own in the protocol design. This eventually has made the communication vulnerable to attacks that exploit loopholes in the TCP/IP protocol stack. Nowadays, Internet has become an indispensable communication and service provision platform in our daily life. Unfortunately, we are witnessing a dramatic increase of attack incidents where every host machine connected to the Internet is liable of becoming attacked. It is not only the Internet users that are annoyed and prone to cybersecurity attacks causing failures to the Internet service access, but the commercial/governmental entities are also suffering from financial losses, privacy issues and bad publicity. DDoS attacks, in particular, have caused severe disruptions to the Internet services in recent years [6]. These kinds of attacks are considered among the hardest security threats to address because they are easy to deploy and implement; moreover, they are hard to prevent and trace [7]. Since multi-source DDoS attacks are much more complicated than a single-source DoS attack, DDoS attacks are considered as the target problem in this work. Recent incidents have demonstrated so many vulnerabilities and breaches to the IP exploiting DDoS attacks [8]. Therefore, there is a pressing need to conduct research in the field of cybersecurity to devise an efficient and robust scheme to defend against DDoS attacks.
## 2 Results and Discussion
### Differences in VOC Profiles Composition
faecalis_ and _K. pneumoniae_) increased in profiles after bio-AgNPs treatment. Aldehydes are often reported as products of lipid peroxidation and could be generated due to cell membrane damage. The interaction of fatty acids with reactive oxygen species (ROS) produces radicals which tend to be stabilized through scissions, giving rise mainly to alkanes, alcohols and aldehydes [30]. For both _K. pneumoniae_ and _P. mirabilis_, the percentage of acids raised with the addition of nanoparticles to the medium. Free fatty acids may occur due to some release from cell wall. However, the production of an increased variety of this class of compounds can be connected with alterations of plasma membrane composition. Previous research investigated alterations in plasma membrane composition of silver ions and silver nanoparticles: _Pseudomonas putida_ modified the _trans/cis_ ratio of unsaturated fatty acids in order to decrease membrane fluidity and counteract the wall permeability occasioned by silver ions [31]. A shift in the composition of lipids from bacterial membrane is also reported, in the sense that the portion of saturated fatty acids tends to increase, creating a \"rigidifying effect\" intended to stabilize the membrane after AgNPs incursion [32]. Considering that the metabolisms of _K. pneumoniae_ and _P. mirabilis_ appeared to be the most susceptible to the studied nanoparticles, an elevation in the number of fatty acids is possibly a consequence of cell wall degradation. Studies describing ROS-mediated mechanisms confirmed the occurrence of lipid peroxidation as a consequence of secondary damage during stress caused by the administration of bactericidal agents [33; 34]. More specifically, ciprofloxacin was found promoting DNA and lipid oxidation in _Staphylococcus aureus_; lipid peroxidation was attested by the formation of malondialdehydedehyde, a product of ROS interaction with unsaturated fatty acids and plasmatic marker of oxidative stress [35]. An investigation on _Lactobacillus helveticus_ showed that fatty acid composition patterns were altered as a response to environmental stress conditions, possibly leading to ROS production [36].
# Optimization for an Indoor 6G Simultaneous Wireless Information and Power Transfer System
## 1 Introduction
The objective function used in this paper is the weighted sum of the individual objective functions, such as the antenna pattern, antenna sidelock, and scattering s-parameters. None of the BER is considered in these cases. A lot of low-power sensors for 6G SWIPT/WPT IoT are placed in modern communication systems. SWIPT has an advantage compared with the conventional time-division multiplexing mechanism, where the transmissions of power and information are separate. In addition, past SWIPT power-splitting has used fixed splitting coefficients, but in fact, this may lead to excessive waste of transmission energy. Thus, if we can control the adjustment of the splitting coefficients dynamically, the harvest power efficiency can be improved a lot. Our proposed system can switch beams according to the sensors in the environment. As a result, overall high performance can be achieved. Our novelty is to present beamforming technique optimization for SWIPT and WPT in 6G terahertz broadband communication systems. The beamforming technique is used to adjust the phase of the transmitter to find the maximum power harvesting efficiency under the constraint of the preset lowest bit error rate and lowest harvesting power. The bit error rate of the broadband signal is affected by the multipath and the power level at the same time, as the bit error rate is a nonlinear function and, thus, one cannot find the best solution by the differential method. The evolution algorithms are used to optimize the different requirements at the same time by the designed multi-objective function. The ray-tracing method is used to simulate the environment. Then, SADDE is used to optimize the synthetic field pattern of the antenna array to improve the system performance. The structure of this article is listed as follows: Section 2 introduces the system model and the deployment of the antenna array. In Section 3, the evolution algorithm and objective function are presented. Numerical results are given in Section 4, where we compare the SWIPT and WPT using SADDE. The final section summarizes the contributions of this paper.
## 5 MATLAB-Simulink System Model
### Battery Stack  
The Li-ion battery energy storage system (LiBESS) model is an adaptation of the generic battery model available in the SimPower Systems library of Simulink, initially proposed in ([PERSON] and [PERSON], 2009 [24]). This specific LiBESS model was chosen for its user-friendliness, accuracy (despite its simplicity), and dynamic performance.  
The dynamic (runtime) and thermal behavior of the model have been modified, and a detailed explanation and validation is included in [25]. The version presented here differs from the one in [24] in that it sacrifices some generality for simplicity because it is specifically designed for lithium-ion batteries. Additionally, the internal resistance is represented by two distinct electrical resistances: a constant ohmic resistance and a variable polarization resistance that depends on the state of charge (SOC).  
Additionally, the aging model included in the Simulink library has been replaced by a more accurate model, which was previously developed and validated in [26]. This aging model measures the LiBESS lifetime in terms of capacity decay and is valid for both LFP and NMC chemistries within Li-ion. The model considers both cycling and calendar aging, and it has been implemented in an online manner, meaning that the dynamic and thermal parameters of the model vary as the LiBESS ages. The main equation of the model is included as follows:  
\[Q_{loss}=\left(a\cdot T^{2}+b\cdot T+c\right)\cdot e^{(d\cdot T+e)\cdot C_{ meta}}\cdot\text{AH}+\text{f}\cdot e^{g\cdot SoC}\cdot e^{\frac{b}{T}}\cdot\text{t} ^{z} \tag{5}\]  
where \(Q_{loss}\) denotes the final capacity degradation in percentage, \(T\) represents the battery temperature in Kelvin, \(AH\) stands for the ampere-hour throughput in Ah, \(C_{rate}\) is the charge/discharge rate, \(t\) represents the time in days, z is the power law factor, and the constants a through h are parameters that depend on the battery's chemistry.  
The models, all dynamic, thermal, and aging, have been parameterized based on the LiBESS cell ANR26650M1 from A123. The main parameters are included in Table 1, where parameters a to h correspond to the aging model.
# Performances of the Synergy of Silica Fume and Waste Glass Powder in Ternary Blended Concrete
## 3 Discussion of Results  
### Effect of Glass and Silica Fume Synergy on the Concrete Workability  
It is apparent that the presence of silica fume (10 wt.%) significantly reduces the workability of the concrete due to its fineness, as shown in Figure 4. This is not different from what has been previously reported in the literature ([PERSON] and [PERSON], 2004). It is worthy to note that the presence of waste glass powder (WGP) in synergy with silica fume at 1:3 and 1:1 denoted as C\({}_{90}\)G\({}_{2.5}\)S\({}_{7.5}\) and C\({}_{90}\)G\({}_{5}\)S\({}_{5}\), respectively, reduced the consistency in comparison with C\({}_{100}\)G\({}_{0}\)S\({}_{0}\) by 55.6% and 11.11%, respectively. This suggests that the combination of silica fume (SF) and GWP waste influences the workability of the mixture. The impact of the spherical shape of SF (Figure 3c) in the presence of WGP contributed to the concrete flow as noticed in C\({}_{90}\)G\({}_{2.5}\)S\({}_{7.5}\) (75 mm slump), compared to the sample in which it was absent (C\({}_{90}\)G\({}_{2.5}\)S\({}_{0}\)), for which the slump value was 55 mm. The reduction in the consistency of SF-free concrete in the presence of WGP was due to the angularity of its shape (Figure 3b), thereby generating more interparticle frictional resistance. Despite such an effect, the consistency of GWP-based concrete was still higher than that of the ordinary Portland cement (OPC) concrete [21] for which the slump was 45 mm. The resultant effect of SF and GWP in the ternary blended concrete can therefore be said to contribute to its consistency and cohesiveness.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mixes**} & \multirow{2}{*}{**Percentage**} & \multirow{2}{*}{**Cement**} & \multirow{2}{*}{**Glass**} & \multirow{2}{*}{**SF (kg/m\({}^{3}\))**} & \multicolumn{2}{c}{**Initial**} & \multirow{2}{*}{**SP (kg/m\({}^{3}\))**} & \multirow{2}{*}{**Fine Agg**} & \multirow{2}{*}{**Coarse**} \\  & & & & & & **(kg/m\({}^{3}\))** & & **(kg/m\({}^{3}\))** & \\ \hline C\({}_{100}\)G\({}_{0}\)S\({}_{0}\) & 0.0\% & 350 & 0 & 0 & 147 & 1.75 & 798 & 1093 \\ C\({}_{90}\)G\({}_{0}\)S\({}_{10}\) & 0.0\% & 315 & 0 & 35 & 147 & 1.75 & 786 & 1093 \\ C\({}_{90}\)G\({}_{2.5}\)S\({}_{7.5}\) & 2.5\% & 315 & 8.75 & 26.25 & 147 & 1.75 & 787 & 1093 \\ C\({}_{90}\)G\({}_{5}\)S\({}_{5}\) & 5.0\% & 315 & 17.5 & 17.5 & 147 & 1.75 & 788 & 1093 \\ C\({}_{90}\)G\({}_{7.5}\)S\({}_{2.5}\) & 7.5\% & 315 & 26.25 & 8.75 & 147 & 1.75 & 789 & 1093 \\ C\({}_{90}\)G\({}_{10}\)S\({}_{0}\) & 10.0\% & 315 & 35 & 0 & 147 & 1.75 & 790 & 1093 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mix design for glass-blended silica-fume concrete.
## 3 Results
### Comparison of CPU Resources for Different Orders
#### 3.5.2 Verification of CPU Resources in the Receiver  
To verify that the receiver can satisfy all task time restrictions while performing high-precision orbit determination, a complete examination of the orbit determination algorithm's computation time under various gravity field models is required [47; 48]. By measuring the calculation time for each model order, we can ensure that all receiver duties work properly, ensuring the system's overall stability. Next, we evaluate the performance of each order model on the spaceborne receiver and investigate how to strike the best balance between orbit determination accuracy and computational efficiency with limited computational resources. Verification results from the receiver are shown in Figure 8 and Table 7:  
\begin{table}
\begin{tabular}{c c c} \hline
**Order** & **Numerical Integration** & **Filter Time** \\ \hline
40 & 305.65 & 5.89 \\
50 & 314.36 & 5.92 \\
60 & 325.47 & 5.92 \\
70 & 355.38 & 5.96 \\
80 & 362.71 & 5.94 \\ \hline \end{tabular}
\end{table}
Table 6: Computation time on PC (unit: milliseconds).  
Figure 8: Computation time in the receiver: (**a**) numerical integration time; (**b**) filter time.  
\begin{table}
\begin{tabular}{c c c} \hline
**Order** & **Numerical Integration** & **Filter Time** \\ \hline
40 & 225.68 & 22.67 \\
50 & 252.95 & 22.84 \\
60 & 286.9 & 23 \\
70 & 323.18 & 23.73 \\
80 & 367.79 & 24.52 \\ \hline \end{tabular}
\end{table}
Table 7: Computation time in receiver (unit: milliseconds).
## 5 Discussion
### Influence of Main Engine Load on System Performance
When the host load reaches 80%, the difference in net output power between the PORC system and the BORC system steadily widens; as the load on the main engine goes from 80% to 100%, the net output power difference between the two systems grows from 34.1 kW to 46 kW. This indicates that with the increase of the main engine load, the improvement of BORC system performance will be more evident by increasing the preheater. Therefore, when the ship mainly operates under high load conditions (80%SMCR or above), the advantage of using the PORC system to reclaim the waste heat from the main engine will be more visible. The increment in the net output power of the preheater incorporated in the BORC system shows a diminishing trend as the main engine load rises. Specifically, as the main engine load increases from 60% to 100%, the rate of net output power increase decreases from 13.73% to 10.33%. This phenomenon occurs due to the accelerated increase in net output power of the BORC system as the main engine load escalates, leading to a reduction in the magnitude of the increment. Figure 5: Effect of main engine load on the difference and increase of net output power of two systems.
## 2 Problem Model in Mission Scenario  
The mission scenario is shown in Figure 1. Our stealth UAV swarm has broken through the enemy's dense defense circle and entered the target search area. At this stage, the mission UAV obtains the real-time position and motion information of enemy high-value targets through radar or Electro-Optical (EO) load, and performs the search mission. Enemy ground vehicles adopt scattered avoidance mode, keep moving and scattered avoidance, which increases the difficulty of the mission. In highly dynamic and highly time-sensitive scenarios, maximizing the ability to strike all enemy mobile ground targets within a limited strike window time places extremely high demands on the autonomous task planning capability of our mission swarm.  
The mission UAVs are controlled by a reinforcement learning algorithm, with each UAV acting as an independent agent. The internal limiting factor is the absence of centralized cooperative command and dispatch, while the swarm's intelligence relies on the emergent intelligence provided by the artificial intelligence algorithm. There are two external limiting factors. First, there is electromagnetic interference in the environment, and there are distance constraints and noise interference in the communication and observation between UAVs, that is, friendly UAVs can only obtain each other's state information within a certain communication range, and can only observe the state information of an enemy UAV swarm within a certain radar or EO visual distance, and random noise is added to the communication information and observation information. Second, the target has maneuverability, adopts expert strategy control, and has the ability of global observation  
Figure 1: UAV swarm autonomous search.  
and spontaneous decentralized defense, which restricts the UAV swarm's ability to carry out the established tasks.  
The goal of the control framework and simulation training is to enhance the intelligence level of the mission UAV swarm and develop its group awareness. When hitting ground targets, swarms can connect target allocation, mission planning and maneuver control, and quickly close the killing chain of \"target-discovery to swarm-maneuver\". In this process, the task itself and the algorithm itself are deeply analyzed. Through the combination of control framework and related algorithms, the swarm has the ability of terminal autonomous confrontation. Through simulation experiments, the feasibility of the method is verified.