esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### What Is the Impact of the Hybrid Workflow in Conducting SRs?  
The effectiveness of a hybrid workflow that combines the computational prowess of LLMs with the critical oversight of human reviewers can be substantial in reducing the workload of human reviewers while ensuring comprehensive and accurate data extraction. LLMs can quickly scan through thousands of articles, identifying and extracting relevant data based on predefined criteria. This can drastically reduce the time the first author spends on the initial screening of articles, which is typically the most time-consuming part of the SR process.  
By rapidly processing and narrowing down the selection of articles, LLMs enable a much faster review cycle. For example, the dramatic contrast between the months it took for a human-only method to complete full-text screening and the two weeks required for the first author to double-check the results is a testament to the efficiency of LLMs. By implementing a hybrid workflow, articles (\(t=169\)) identified for exclusion had a perfect agreement with the human-only screening results. Furthermore, conflicts that were not originally identified by the human-only screening were reviewed by our team, resulting in altered decisions (\(n=6\)). The hybrid workflow, with LLMs handling the initial data extraction, alleviates this burden by ensuring that human reviewers are not overwhelmed with information, allowing them to focus on more complex and ambiguous cases that require human interpretation.  
Furthermore, this effectively addressed the temporal lag that often undermines the timeliness of conventional SRs. With this accelerated process, the most recent and relevant research can be incorporated into the SR, ensuring that the findings reflect the latest developments in the field. This rapid inclusion of up-to-date research is crucial for maintaining the relevance and utility of SRs in informing current practice and policy decisions.  
The aim of this hybrid workflow was to improve the paper classification procedure while extracting all the meaningful information needed to write a full SR article. The evaluation showed that the information extraction and inclusion/exclusion classification for writing an SR paper was at least equivalent and possibly better for the hybrid workflow SR compared with the traditional SR, as shown in Table 5, i.e., an improvement in classification accuracy. The human decision on what to do with the information extracted and the included articles, as part of the hybrid SR (or traditional SR), was out of the scope of this paper.
## 4 Case Study
### Trajectory Data Collection  
In order to construct a PCP against CCF in the CEPR-CPS using the PCMD, it needs to collect the offline trajectory data {\(i\)\(\in\)\(Tna\) {\(g_{i^{\prime}\Delta t}\), \(a_{i^{\prime}\Delta t}\), \(r_{i^{\prime}\Delta t}\), \(g_{(i+1)^{\prime}\Delta t}\)}\)} from the CEPR-CPS. Where \(Tna\) {0,1,2,..., \(T\)\(/\Delta t\)}, \(T\) denotes the duration of a trajectory. According to Definition 1, the data are collected in the CEPR-CPS at discrete time \(I\) * \(\Delta t\) (\(i\geq 0\)), and the data collected include the state data \(g_{i^{\prime}\Delta t}\), the control action data \(a_{i^{\prime}\Delta t}\), and the reward data \(r_{i^{\prime}\Delta t}\) of CEPR-CPS.  
The state data \(g_{i^{\prime}\Delta t}\) of CEPR-CPS include the three-phase voltage and the three-phase current values (complex number, per unit) of the 80 nodes, the rotor mechanical angle (deg) and the rotor speed (per unit) of a hydraulic turbine node, the active and the reactive power values (complex number, per unit) of two wind power generation nodes, and the three-phase voltage values and the three-phase current values (complex number, per unit) from inverters of one photovoltaic node, one micro-gas turbine node, and three energy storage nodes.  
According to different operating conditions, leading conditions and subsequent constraints, the control action data \(a_{i^{\prime}\Delta t}\) of CEPR-CPS include the phase voltage of the controllable voltage source of energy storage nodes, the reactive power of wind power generation nodes, the three-phase voltage of the controllable voltage source of micro-gas turbine nodes and photovoltaic nodes, and the active power of a hydraulic turbine node.  
The reward data \(r_{i^{\prime}\Delta t}\) include a reward \(r_{1}\) reflecting the voltage stability, a reward \(r_{2}\) for preventing the CCF caused by the current overload, a reward \(r_{3}\) reflecting the frequency stability, a reward \(r_{4}\) reflecting the control cost, and a reward \(r_{5}\) reflecting robustness against CCF.  
After the state data \(g_{i^{\prime}\Delta t}\), the action data \(a_{i^{\prime}\Delta t}\) and the reward data \(r_{i^{\prime}\Delta t}\) are obtained at the discrete time \(i^{*}\Delta t\) (\(i\geq 0\)), then the transition data (\(g_{i^{\prime}\Delta t}\), \(a_{i^{\prime}\Delta t}\), \(r_{i^{\prime}\Delta t}\), \(g_{(i+1)^{\prime}\Delta t}\)) can be constructed. After obtaining the transition data in a successively dependent manner, trajectory data can be obtained. Then, multiple different trajectory data form an offline trajectory dataset {\(|i\)\(\in\)\(Tna\) {\(g_{i^{\prime}\Delta t}\), \(a_{i^{\prime}\Delta t}\), \(r_{i^{\prime}\Delta t}\), \(g_{(i+1)^{\prime}\Delta t}\)}\)}.
# Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN
## 5 Experimental Results
### Performance Comparison
#### 5.4.1 Node Death Rate  
In this section, we compared the performances of different algorithms in terms of node death rate. Specifically, in the MODDPG experiment, we designated the node with the least remaining energy as the target node. The experimental results are shown in Figure 7.  
Figure 7(a) demonstrated a clear disparity in node death rates between D-JERDG and Random. Initially, MODDPG exhibited lower node death rates than D-JERDG for up to 200 nodes. This can be attributed to the advantage of the greedy-based node charging strategy in scenarios with a small number of nodes and low density. However, after  
Figure 6: Learning curve with the episode = 400.  
Figure 7: Experimental results of node death rate.  
reaching 200 nodes, D-JERDG demonstrated a lower node death rate than MODDPG, with a declining trend beyond 300 nodes. When the number of nodes is 250, compared with the MODDPG and random, D-JERDG can decrease the node death rate by about 17% and 44%, respectively. This improvement can be attributed to the clustering algorithm employed in D-JERDG, where several nodes closest to the CH node are selected as CM nodes. This approach ensures that the UAV covers more nodes during each hover, enhancing charging efficiency. The effectiveness of the proposed CH node selection mechanism was also validated, as it increased the likelihood of selecting CM nodes farther from the current CH node as new CH nodes. This enabled energy replenishment and reduced the node death rate of remote area nodes, making D-JERDG more suitable for large-scale networks aiming to maintain network connectivity. From Figure 7b, we examined the variations in node death rates across different algorithms while considering different UAV flight speeds, with 200 nodes in the experiment. Overall, increasing the UAV flight speed resulted in reduced fluctuations in the node death rate, and D-JERDG consistently outperformed MODDPG and random by maintaining lower node death rates. When the speed of the UAV was 15 to 20, compared with MODDPG and random, the average decline rate of D-JEGDG in terms of node death rate was about 10% and 45.5%, respectively. Furthermore, as the flight speed increased, the node death rate exhibited a significant decrease. This highlights the advantageous performance of D-JERDG in terms of node death rate when compared with the other algorithms.
# Roto-Translational Control of Spacecraft in Low Earth Orbit Using Environmental Forces and Torques
## 1 Introduction
The University of Florida Advanced Autonomous Multiple Spacecraft (ADAMUS) laboratory has designed the Drag Maneuvering Device (DMD) [21]. The capabilities provided by the DMD have been previously studied for controlled re-entry, as well as independent orbital and attitude maneuvering applications. In this paper, relative orbit and attitude adaptive controllers are integrated to perform a propellant-less roto-translational maneuver involving DMD-equipped CubeSats. The capability of changing the aerodynamic torques as well as the gravity gradient torque are considered in the attitude dynamics, whereas the effect of differential drag is exploited in the spacecraft relative dynamics. The adaptive controllers compensate for uncertainties in environmental and physical parameters such as atmospheric density, drag/lift coefficients, location of the Center of Mass (CoM) and inertia matrix. Uniformly ultimately bounded convergence is obtained through Lyapunov-based stability analysis for the integrated roto-translational system. The paper is organized as follows: Section 2 presents the translational and attitude spacecraft dynamics, Section 3 shows the individual adaptive controllers and Lyapunov-based stability result when considering attitude-orbit coupling. Sections 4 and 5 present results from a simulation example of two-spacecraft along-orbit formation with simultaneous attitude control, and concluding remarks, respectively. The foremost contributions of this paper are:  
* Design and verification through numerical simulation of an adaptive controller capable of simultaneously achieving three-axis attitude stabilization and in-plane relative maneuvering. * Compensation for physical and environmental uncertainties such as drag/lift coefficients, atmospheric density and CoM location. * Guaranteed ultimately bounded stability through Lyapunov-based analysis in the presence of uncertainties and perturbations.
# Extensible Hook System for Rendezvous and Docking of a CubeSat Swarm +
## 2 Extensible Hook System Design  
The proposed EHS is based on a scissor boom structure with different DoF according to the mission requirements and CubeSat structures. Figure 2 shows an initial design of the proposed system. It has been chosen due to its mechanical simplicity and capacity to be extended a greater distance than other structures, such as simple manipulators. However, analysis of the lateral stability is pending according to the materials used and the particular application. The proposed system is initially provided with three joints (3 DoF): The first joint allows the system to extend and retract the end effector, actuating on the bar that holds the scissor structure. The second joint allows the main scissor structure to be re-oriented. Finally, the whole EHS can be rotated by the third joint. These three DoF allow one to place the end effector at any position within the EHS workspace.  
Figure 1: Deployed CubeSat swarm with the extensible hook system.  
According to this configuration, Equation (1) represents the position of the end effector (\(x\)) w.r.t. the prismatic joint (\(y\)) on the actuation bar, which depends on the half length of the link (\(L_{L}\)), the distance between the actuation bar to the first link (\(L_{B}\)), the distance between the last link and the contact point of the end effector (\(L_{E}\)), and the number of pairs of intermediate links (\(N\)). It is worth mentioning that this equation includes non-linearities that should be taken into consideration to design the joint controller.  
\[x(y)=L_{E}+L_{B}+(2N+3)\bigg{(}\sqrt{L_{L}^{2}-y^{2}}\bigg{)} \tag{1}\]  
Based on this configuration, the EHS has been adapted for mounting on a CubeSat with a 4U total size, i.e., 10 cm \(\times\) 10 cm \(\times\) 45.4 cm. This configuration allows the EHS to be extended up to 5 m, allowing up to four EHS (two per side) in a 16U CubeSat. Figure 3 illustrates the configuration described previously, in which two EHSs are placed on two sides. The EHS could be adapted to different applications by reducing its size and, therefore, its reachability. However, in this paper, the main objective of this EHS is to be able to connect the CubeSat swarm from a 10 m distance, keeping them connected in a formation flight.  
The end effector will be based on a configuration with permanent and electromagnets, allowing them to connect EHSs between them, using the electromagnets to increase the power during the docking stage or to compensate the permanent magnets for undocking both CubeSats. A clear example of this configuration has been published with applications on drones [12], where they were equipped with permanent magnets that attracted each other to create a unique structure in the air. The main drawback of using magnetic fields is that it could affect CubeSat sensors, such as magnetometers, which are commonly used for CubeSat navigation.  
Figure 3: CubeSat configuration with four EHSs folded and deployed.  
Figure 2: Proposed scissor boom-based extensible hook system with 3 joints.
# Adaptive Sensor Array Error Calibration Based Impact Localization on Composite Structure
## 4 Evaluation on Carbon-Fiber Composite Panel  
### Experimental Setup  
A 610 mm \(\times\) 310 mm \(\times\) 310 mm carbon-fiber composite panel, which was a part of a real aircraft oil tank structure, was selected for evaluation, shown in Figure 6. The composite panels were fixed by two rows of rivets with the width of 45 mm to the rid at four sides. The integrated structural health monitoring scanning system (ISHMS) was adopted here as the monitoring equipment, which was used to control the excitation and sensing of the PZT sensor array. Figure 6a shows the experimental set up. Uniform linear sensor arrays were used in the experiment including 7 PZT sensors bonded on the structure. The diameter of the PZT sensor was 8 mm, and the thickness was 0.48 mm. These sensors were arranged with a space of 10 mm, which were labeled as PZT1, PZT2 \(\dots\) PZT7, respectively. However, the sensor array always had a certain array element position error; this was defined as an imprecise PZT sensor array, seen in Figure 6b.
## 4 Discussion  
This study aimed to develop an interpretable predictive model using machine learning to investigate the contribution of different types of digital technology tools to university students' engagement in PE classrooms. Furthermore, SEM was employed to analyze the pathways and mechanisms by which digital technology usage frequency, PU, PEU, and ASE influence student engagement. The findings demonstrate that the RF model achieved the highest predictive performance, identifying subject-specific software, management software, websites, and mobile devices as key factors influencing classroom engagement. Notably, subject-specific software and management software showed significant synergistic effects. Additionally, SEM results revealed that digital technology usage frequency not only directly influenced classroom engagement but also exerted indirect effects through PU, PEU, and ASE. This study further confirmed a chain-mediated effect, wherein PU and ASE, as well as PEU and ASE, sequentially contributed to engagement enhancement. By integrating machine learning with SEM, this study introduces a novel approach that enables both the precise prediction of classroom engagement and a systematic exploration of its underlying mechanisms. These findings provide valuable insights for optimizing digital learning environments and improving instructional strategies in PE classrooms. This study confirms the significant role of digital technology in enhancing student engagement, aligning with prior research [12]. However, unlike previous studies that \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Index** & **x\({}^{2}\)/df** & **GFI** & **AGFI** & **CFI** & **TLI** & **RMSEA** & **NFI** & **IFI** & **RFI** \\ \hline Model 1 & 5.754 & 0.972 & 0.950 & 0.947 & 0.924 & 0.064 & 0.937 & 0.947 & 0.909 \\ Model 2 & 5.436 & 0.974 & 0.953 & 0.949 & 0.926 & 0.062 & 0.938 & 0.949 & 0.911 \\ Ideal value & \textless{}3.0 & \textgreater{}0.9 & \textgreater{}0.8 & \textgreater{}0.9 & \textgreater{}0.9 & \textless{}0.08 & \textgreater{}0.9 & \textgreater{}0.9 & \textgreater{}0.9 \\ \hline \end{tabular}
\end{table} 
Table 4: Fit indices for structural equation modeling. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Mean** & **SD** & **1** & **2** & **3** & **4** & **5** \\ \hline
1. Digital technology & 29.827 & 10.707 & — & & & & \\
2. Classroom participation & 41.586 & 7.139 & 0.519 *** & — & & & \\
3. Perceived usefulness & 12.434 & 2.220 & 0.506 *** & 0.774 *** & — & & \\
4. Perceived ease of use & 12.476 & 2.186 & 0.487 *** & 0.752 *** & 0.874 *** & — & \\
5. Academic self-efficacy & 83.867 & 12.400 & 0.478 *** & 0.628 *** & 0.566 *** & 0.572 *** & — \\ \hline \end{tabular}
\end{table} 
Table 3: Descriptive statistics and correlation coefficients of variables (total n = 1158).
## 3 Results
### Characteristics of the Water Chemical Indicators for Four Typical Water Bodies
The contributions of phytoplankton sources of organic matter to the four types of water bodies have the order of River (90.11%) > Canal (86.08%) > Lake (53.22%) > Pond (46.15%). Terrestrial sources of organic matter are mainly found in lakes (34.18%) and ponds (44.60%), but contribute minimally to canals and rivers. Notably, the contributions of sewage source organic matter \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**C and N Contents of Sedimentary** & & & & \\
**Organic Matter** & & & & \\ \hline \multirow{3}{*}{Canal (n = 8)} & Range & 0.14–0.36 & 1.17–3.36 & 6.96–9.24 \\  & Mean & 0.23 & 2.00 & 8.27 \\  & SD & 0.09 & 0.85 & 0.72 \\ \hline \multirow{3}{*}{Lake (n = 8)} & Range & 0.10–0.43 & 0.81–4.65 & 7.49–10.90 \\  & Mean & 0.29 & 3.01 & 8.79 \\  & SD & 0.12 & 1.32 & 1.12 \\ \hline \multirow{3}{*}{Pond (n = 8)} & Range & 0.16–0.34 & 1.34–3.54 & 7.87–10.72 \\  & Mean & 0.25 & 2.20 & 8.90 \\  & SD & 0.08 & 0.80 & 0.96 \\ \hline \multirow{3}{*}{River (n = 8)} & Range & 0.14–0.43 & 1.09–4.01 & 7.35–9.16 \\  & Mean & 0.29 & 2.52 & 8.30 \\ \cline{1-1}  & SD & 0.12 & 1.10 & 0.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristics of C and N contents of sedimentary organic matter of canals, lakes, ponds, and rivers. to the four types of water bodies have the order of River (32.32%) > Canal (27.31%) > Lake (12.60%) > Pond (9.25%). In general, the contribution of phytoplankton and sewage sources to the sediments of rivers and canals is greater than that of terrestrial sources, while the contribution of terrestrial sources to the sediments of lakes and ponds is greater that of phytoplankton and sewage sources.
## 4 Results and Discussions
### Peak Factor  
The peak factor is usually utilized for the estimation of gust factor, which plays an important role in determining the wind load on structures [43]. The estimated peak factors of typhoons and hurricanes are respectively presented in Figures 5 and 6, together with the corresponding probability distributions. For typhoons, the average value of the peak factor is 2.5211 and the standard deviation is 0.0198. The fitted PDF is shown in Figure 4(b). For hurricanes, the average value of peak factor is 2.5123, slightly smaller than that of typhoons, and the standard deviation of peak factor is 0.0102, significantly smaller than that of typhoons. The probability distribution of peak factors in hurricanes follows t location-scale distribution with location parameter of 2.5174, scale parameter of 0.0022 and shape parameter of 0.9845.  
Neither the expression nor the value of peak factor is explicitly prescribed in ASCE7-10. By matching the gust factor over open terrain ( \(G_{u}=1.53\)) and the turbulence intensity \(I_{u}\) of  
Figure 4: Longitudinal integral scales and their PDF for hurricanes.  
Figure 3: Longitudinal integral scales and their PDF for typhoons.  
0.2 in Equation (34), a peak factor of 2.65 could be obtained. It should be noted that this calculation is based on an averaging time of 1 h. For a duration of \(t_{g}\), the gust wind speed can be expressed as:  
\[\Omega\Big{(}t_{g},T\Big{)}=\overline{U}(T)+g_{u}\Big{(}t_{g},T\Big{)}o_{u}. \tag{38}\]  
Suppose the turbulent wind fluctuations follow the Gaussian distribution, the peak factor will be associated with the exceedance probability of the standard normal distribution. The probability of exceedance of wind gust with a duration of \(t_{g}\) within an observation period of \(T\) could be calculated as [47; 48]:  
\[P\Big{[}U>\Omega\Big{(}t_{g},T\Big{)}\Big{]}=\frac{t_{g}}{T}. \tag{39}\]  
Thus, the peak factor should satisfy:  
\[g_{u}\Big{(}t_{g},T\Big{)}=\Phi^{-1}\Bigg{(}1-\frac{t_{g}}{T}\Bigg{)}. \tag{40}\]  
With the gust duration of 3 s and the averaging time of 10 min, the peak factor is around 2.575, which is slightly higher than the measured values in typhoons and hurricanes. AIJ-RLB-2004 carries out a performance-based wind resistant design procedure. Accordingly, the peak factor is included in the required performance of wind load level and the return period of wind speed. Hence, the comparison of the measurement results with AIJ-RLB-2004 is not discussed here.
# Randomized Feature and Bootstrapped Naive Bayes Classification
## 2 Related Theory
### Parameter Estimation and Implementation  
The naive Bayes model requires an estimation of the prior probabilities, means, and variances for each class and feature.  
* Prior Probability: The proportion of training samples in class \(y_{k}\) is used: \[P(Y=y_{k})=\frac{N_{k}}{N_{\text{tr}}},\] (14) where \(N_{k}\) is the number of training samples in class \(y_{k}\) and \(N_{\text{tr}}\) is the total size of the training set.
* Conditional Probabilities \((P(X_{i}|Y=y_{k})):\) For continuous features, the Gaussian likelihood is parameterized using the estimated means \(\mu_{ik}\) and variances \(\sigma_{ik}^{2}\).  
The model is trained on \(N_{\text{tr}}\) samples, and predictions are made on \(N_{\text{test}}\) samples using the decision rule \(\underset{y_{k}}{\text{argmax}}P(Y=y_{k}|X)\). Performance metrics such as accuracy, precision, and recall are computed to evaluate the model's effectiveness [30].  
Figure 1 illustrates the decision boundary of a Gaussian naive Bayes classifier applied to a three-class classification problem with two features. The dataset is divided into training and test sets, demonstrating how the model generalizes unseen data. The decision regions are formed based on Gaussian distributions fitted to each class, with contour lines marking the boundaries between them. The challenge in this classification task arises from overlapping distributions and non-linearly separable regions, which influence the model's ability to assign correct class labels.
## 4 Results
### Simulated Data
#### 4.1.2 Second Experiment  
In the second scenario (Figure 4), the first target moves horizontally from left to right, the second target moves vertically from bottom to top and the third target moves vertically from top to bottom. This time, the initial coordinates of the targets are target one: x = \(-\)80 m, y = \(-\)70 m, target two: x = 0 m, y = \(-\)140 m and target three: x = 3 m, y = 0 m. The measurements corresponding to the three targets, selected by the Hungarian data association algorithm, are shown in Figure 4a. The trajectories of the three targets generated by Kalman filters associated with each target can be seen in Figure 4b. After the application of the explicit polynomial fitting procedure, the trajectories represented together in Figure 4c and separately in Figure 4d are obtained.  
The estimated trajectories are identical to the initial trajectories [6].