esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Initial Turbulent Conditions  
The initial turbulent conditions are a critical factor influencing jet behavior during its air trajectory and dissipation in a plunge pool [4; 11]. In experimental studies, this is quantified by the turbulence intensity, \(T_{i}\). In numerical simulations, it is represented by the initial turbulent kinetic energy, \(k\), and the dissipation rate, \(\omega\).  
In the present numerical simulations, turbulent kinetic energy was defined as a function of turbulence intensity, as described in Equation (6), where \(\overset{\rightarrow}{V}\) represents the velocity magnitude at each cell. The dissipation rate was defined as a function of turbulent kinetic energy and the mixing length, \(L\), as indicated in Equation (7). Here, \(\mathrm{C}_{\mathrm{p}}\), an empirical constant was set to 0.09, and the mixing length was defined as \(0.07D_{i}\).  
\[k=\frac{3}{2}\left(T_{i}\overset{\rightarrow}{V}\right)^{2} \tag{6}\]  
\[\omega=\frac{\sqrt{k}}{\mathrm{C}_{\mathrm{p}}^{0.25}L} \tag{7}\]  
Simulations were conducted using four different turbulence intensity values to assess the impact of inlet turbulence intensity. The results were compared with experimental data, as illustrated in Figure 8. The analysis indicated that a turbulence intensity of 7% provided the best match with the experimental results across all measurement points.  
As expected, higher turbulence intensities led to lower bottom pressures due to increased turbulent kinetic energy, which enhances energy dissipation in the plunge pool. When comparing the mean dynamic pressure along the jet centerline for simulations with turbulence intensities of 10% and 5%, a difference exceeding 26% was observed. However, these discrepancies were less pronounced far from the jet stagnation point, \(r/D_{i}>0.50\).  
Further work on this topic is underway, involving a detailed 3D CFD simulation of the flow along the upstream pipe system to assess whether the jet's initial turbulence and velocity conditions at a pipe section far upstream of the nozzle influence the resulting jet velocity profile and turbulence intensity.
# Operational Risk Assessment of Engineering Vehicles Considering Driver Characteristics
## 4 Model Network Training and Testing
### Model Testing  
A total of 20 sets of sample data are selected to test the model's generalization ability. The real samples and network output values are shown in Figure 10, with a loss value of 0.330 on the test set. The visualization of the prediction results in relation to the true values indicates that the output of this evaluation model has a high degree of fitting accuracy with the real samples.  
The model's test results are evaluated using mean absolute percentage error (MAPE), root MSE (RMSE), and the coefficient of determination (R\({}^{2}\)). The formula for each evaluation index is as follows:  
\[MAPE=\frac{1}{n}\sum_{n=1}^{N}\frac{\Big{|}O_{pred}-T_{real}\Big{|}}{|T_{real}| }\ *\ 100\%, \tag{6}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{n=1}^{N}\big{(}O_{pred}-T_{real}\big{)}^{2}}, \tag{7}\]  
Figure 10: Test results of the model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Round** & **fold1** & **fold2** & **fold3** & **fold4** & **fold5** & **fold6** & **fold7** & **fold8** \\ \hline \hline MAE & 0.0331 & 0.0463 & 0.0367 & 0.036 & 0.0388 & 0.043 & 0.043 & 0.0441 \\ \hline \hline \end{tabular}
\end{table}
Table 5: MAE of the 8-fold cross-validation.  
\[R^{2}=1-\frac{\sum\limits_{n=1}^{N}{\left({{{O}_{pred}}-{{T}_{real}}}\right)^{2}}}{ \sum\limits_{n=1}^{N}{\left({{{T}_{real}}-{{O}_{avg}}}\right)^{2}}}, \tag{8}\]  
where \(O_{pred}\) is the predicted output value, \(T_{real}\) is the real value of the sample, and \(O_{avg}\) is the predicted output mean.  
The calculation results of each index are shown in Table 6. The R\({}^{2}\) result is greater than 0.9, which is a good result.
## 2 Method
### Line Selection Algorithm  
The proposed line selection algorithm is being used in pre-contingency mode. The SIEMENS PSSE 33.12 software version was used for simulations. The fixed slope Newton-Raphson method was used in power flow calculations. Allowable system total absolute mismatch--0,5 MVA, the maximum iteration number--20.  
The following initial data are entered into the PSSE Baltic model: load, generation, voltage level in nodes, electrical parameters of lines, etc. The simulation with the data is performed. The second step is to check whether the model converges, if there are no unacceptable errors, or if the permissible number of iterations is not exceeded.  
Next, it is checked whether the voltage level in the nodes corresponds to the permissible safe limits, and whether the design capacities of the power lines are not exceeded.  
If the first steps were successful, then the ranking procedure initially uses the three lists based on previously described criteria--Sn, Ratio, LF. From each of the three lists, 20% of the lines are selected to form the initial list of ranked lines. Depending on the number of duplicate lines (same lines appearing in more than one list), the length of the initial list can contain from 20% to 60% of the number of all lines in the electrical network. The list should be shortened if the number of lines is >30% of all modeled lines. A block diagram of the algorithm is provided below in Figure 3.  
A list-creation algorithm is needed to minimize the number of possible combinations. It should be understood that the single universal list may not exist and there will always be cases when a small part of the lines, which are significant for a certain mode, may not be included in the compiled list.  
The disconnection of generators is not explored, and therefore the power lines connecting generators and distribution buses are also not disconnected. There are no isolated islands, so no load transfer (load throwover) function is used. Therefore, if a node (load) is connected to only one line, we do not include this line into any list of combinations. The generation and load nodes of the model are distributed in a way that ensures the redistribution of all power flow mostly at the 330 kV level. Only a negligible part of the power flow can be redistributed through lower voltage lines (110 kV). Thus, in the case of the N-2 simulation, only those combinations which ensure that no 330 kV node remains on an isolated island (in the case of their disconnection) were considered. Hence, if a node has two connected lines, only one can be disconnected.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Number of Lines in the System** & **Full Check** & **10\%** & **20\%** & **30\%** \\ \hline
100 & 4950 & 45 & 190 & 435 \\ \hline
500 & 124,750 & 1225 & 4950 & 11,175 \\ \hline
1000 & 499,500 & 4950 & 19,900 & 44,850 \\ \hline
5000 & 12,497,500 & 124,750 & 499,500 & 1,124,250 \\ \hline
20,000 & 199,990,000 & 1,999,000 & 7,998,000 & 179,970,000 \\ \hline \end{tabular}
\end{table}
Table 2: Combination number in the cases of full check and 10, 20, 30% of lines.
###### Abstract  
The intrinsic muscles of the hand are responsible for finger flexion and extension. The purpose of this study was to investigate the usefulness of stimulating the intrinsic muscles of the hand using repetitive peripheral magnetic stimulation (rPMS). We evaluated angular changes in the finger joints by studying active motion and rPMS. Ten healthy adults were instructed to perform the following tests in random order: (1) maximum active metacarpophalangeal joint flexion; (2) maximum active metacarpophalangeal joint abduction; and (3) repetitive peripheral magnetic stimulation for 2 s at maximum stimulation intensity. A three-dimensional motion analysis system was used to measure angular changes. Pain during stimulation was graded on a numerical rating scale (NRS). The maximum flexion and abduction of the metacarpophalangeal joint were not significantly different between active motion and rPMS. The proximal interphalangeal joint (\(p=0.009\)) and distal interphalangeal joint (\(p=0.005\)) were significantly extended by rPMS. The median NRS score for pain during rPMS was 2. rPMS can produce the same extent of metacarpophalangeal joint flexion and abduction as active movement with less pain. This technique can effectively stimulate the intrinsic muscles of the hand and may be used as a treatment for various diseases that cause immobility of the metacarpophalangeal joints.  
repetitive peripheral magnetic stimulation; metacarpophalangeal joint; intrinsic muscle; three-dimensional motion analysis +
Footnote †: journal: Medical  
muscles without stimulating skin nociceptors, resulting in the activation of the parietopremotor network [12,13]. Furthermore, rPMS drives M1 plasticity and sensorimotor improvements [14]. Pain caused by rPMS is significantly lower than ES-induced pain, even when using the same intensity of stimulation [15]. Therefore, rPMS provides stronger stimulation than ES with limited pain [16, 17, 18]. Pain suppresses neuromuscular activity from the cerebrum [10, 11]. Hence, limited pain during stimulation is a major advantage of rPMS, which has been applied to various somatosensory and motor disorders in recent years [19, 20]. However, there are no reports available on the application of rPMS to movement disorders of the intrinsic muscles of the hand. Here, we evaluated the effectiveness of applying rPMS to the intrinsic muscles of the hand by analyzing the angular changes of the MCP, PIP, and DIP joints with a three-dimensional motion analysis system kinematically.
## 2 Proposed Method
### Simulation of the GNSS-R LEO Constellation Orbit  
Monitoring of the sea surface winds is one of the most important and mature applications of GNSS-R. CYGNSS is the first LEO constellation in the world that focuses on monitoring of tropical cyclones using reflected GPS signals. Therefore, to ensure that our work is both feasible and meaningful, the main orbit parameters used for the GNSS-R LEO constellation are the same as those of CYGNSS. All the microsatellites are in repeated ground track orbits with 13 day periods, and an inclination of 35\({}^{\circ}\) was selected to ensure coverage of most of the areas in which cyclones and typhoons occur [24]. In addition, the orbit altitudes are approximately 500 km to ensure that the GNSS reflected signals have sufficient power.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **GALILEO** & **BDS/MEO** \\ \hline altitude & 23,222.0 km & 21,528.0 km \\ eccentricity & 0.001 & 0.001 \\ inclination & 56\({}^{\circ}\) & 55\({}^{\circ}\) \\ argument of perigee & 0\({}^{\circ}\) & 0\({}^{\circ}\) \\ longitude of the ascending node & 0\({}^{\circ}/120^{\circ}/240^{\circ}\) & 0\({}^{\circ}/120^{\circ}/240^{\circ}\) \\ satellite number & 30 & 27 \\ number of orbit planes & 3 & 3 \\ satellite number in each plane & 10 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The orbit parameters of the simulated full GALILEO and BDS MEO constellations.  
Equation (1) is constrained to design the repeat ground track orbits.  
\[D\frac{2\pi}{\omega_{E}-\dot{\Omega}}=N\frac{2\pi}{\dot{M}+\dot{\omega}} \tag{1}\]  
where \(D\) is the number of nodal days and \(N\) is the number of nodal revolutions in a complete cycle. \(D\) and \(N\) are relatively prime. \(\dot{M}\) is the satellite's mean motion. \(\dot{\omega}\) is the rate of the argument of perigee. \(\dot{M}+\dot{\omega}\) is the rate of the mean argument of latitude. \(\omega_{E}\) represents the Earth's rotation rate in the inertial frame, and \(\dot{\Omega}\) represents the rotation rate of the satellite's line of node. \(\dot{\Omega}\), \(\dot{M}\) and \(\dot{\omega}\) are the functions of the orbital semi-major parameter, the inclination and the eccentricity, respectively. Because the CYGNSS satellites are in a near-circular orbit at an altitude of approximately 500 km, \(N\) can be solved to give a value of 194 using Equation (1). The orbital parameters can then be solved according to the principles of repeat ground track orbits [38; 39]. Table 2 lists the orbital parameters of the GNSS-R LEO constellation used in this simulation, including the nodal day and the number of revolutions. The solutions of a, e and \(\omega\) are elaborated in Appendix A. Longitude of the ascending node can be set arbitrarily according to the real situations and we set it to 0 in this paper. In addition, the eight micro-satellites are equally spaced in a single orbit plane. After these orbital parameters have been determined, we can then use orbit generation software to generate the complete constellation. The orbit generation software is developed using the Gauss-Jackson method of numerical integration [40].
## 4 Simulation Results
### Results of Pattern 1  
The performance of the ROA in solving the MPPT problem is evaluated in pattern 1 (STC). In addition, the ROA results are compared with MRFO and PSO. The PV power, current, voltage, and duty cycle curves obtained using the ROA, MRFO, and PSO are showed in Figure 9. The peak power of pattern 1 is 113.42 W. According to the obtained figures, it is clear that the steady-state fluctuations of the ROA-based MPPT are very low compared to those of the MRFO and PSO methods. The MPPT method based on the ROA is a fast method that reaches the global peak value with small transient fluctuations. In contrast, the transient fluctuations of the MRFO and PSO are high compared to the ROA.  
Table 4, shows the numerical results of ROA, MRFO, and PSO performance in MPPT solving for the PV system in view of output power, efficiency, and tracking speed for different methods. As seen in Table 4, extracted PV power equal to 113.39 W, 113.36 W, and 113.32 W were obtained for the ROA, MRFO, and PSO as well as a tracking efficiency equal to 99.97 %, 99.94 %, and 99.91 % also for these methods. On the other hand, it is obvious that the ROA obtained higher peak power value with higher tracking efficiency at a higher tracking speed compared with the MRFO and PSO. The tracking rate achieved was 0.66 S, 1.12 S, and 3.01 S for the ROA, MRFO, and PSO, respectively. Therefore, the results proved ROA superiority for MPPT solving in pattern 1, with higher capability for obtaining the GMP with higher tracking efficiency and rate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter/Method** & **ROA** & **MFRO** & **PSO** \\ \hline Global power (W) & 113.42 & 113.42 & 113.42 \\ Extracted power (W) & 113.39 & 113.36 & 113.32 \\ Tracking efficiency (\%) & 99.97 & 99.94 & 99.91 \\ Tracking rate (S) & 0.66 & 1.12 & 3.01 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numerical results of the ROA, MRFO, and PSO methods in pattern 1.  
Figure 8: Characteristic curves of PV panel for pattern 3: (**a**) P-V, (**b**) I-V.  
Figure 7: Characteristics of PV panel for pattern 2: (**a**) P-V, (**b**) I-V.
## 2 Related Works
### Constellation State Adaptive Routing  
Traffic scheduling needs to be adaptive due to dynamic satellite motion. Some studies [24; 25] have implemented traffic scheduling and resource management using SDN controllers. However, the use of a centralized controller leads to increased latency and decreased accuracy with network scale and topology changes. Therefore, there is a need for more flexible algorithms that can handle dynamic topologies. To address the limitations of centralized controllers, [PERSON] et al. [26] have proposed a multipath routing strategy based on a hidden Markov model. This routing strategy predicts the future network connection state of a mobile node by analyzing its historical connection state. However, the limited resources of satellites, such as communications and packet processing capacity, pose challenges [27]. Therefore, reasonable traffic guidance is necessary to retard network congestion. [PERSON] et al. [28] have developed a dynamical traffic scheduling strategy to implement multipath finding. This strategy calculates paths by estimating the topology and distributes traffic.  
[PERSON] et al. [29] proposed a routing approach that considers a mixed potential field based on depth and energy to make routing decisions. This approach shows good performance in stable networks. However, it needs a long convergence time when the network undergoes changes because its routing decision parameters rely on information from neighboring nodes. [PERSON] et al. [30] proposed a routing algorithm that combines a static topology with a real-time link state. Based on the periodicity of satellite networks and the predictability of topology changes, priority is given to calculating static topology sequences. Once the link state changes, the routing forwarding table is recalculated using a breadth-first search algorithm. This updated table is then broadcast across the entire network. [PERSON] et al. [31] proposed a routing algorithm for LEO satellite networks based on random geometry. As an effective mathematical tool, random geometry is very suitable for the system-level analysis of satellite network topologies. This algorithm can effectively ensure that data packets are always forwarded in the established reference direction within a reliable range. [PERSON] et al. [32] proposed a dynamic routing algorithm based on mobile ad hoc networks. In the article, the satellite network is modeled as a multi-hop wireless network cluster to avoid frequent topology changes causing broadcast storms. The authors combined the advantages of static routing and dynamic routing, making this algorithm highly autonomous and functionally compatible in satellite networks.
# A Novel and Adaptive Angle Diversity-Based Receiver for 6G Underground Mining VLC Systems
## 3 Adaptive Orientation Receiver
### Adaptive Receptor Orientation Scenario
Considering these details and based on the receptor hemidodecahedron geometry, \((x^{H},y^{H},z^{H})\)=\((0,0,0)\), \((x_{j}^{H},y_{j}^{H},z_{j}^{H})\) and \(\mathbf{n}_{j}^{tilt(H)}\) are represented as  
\[(x_{j}^{H},y_{j}^{H},z_{j}^{H})=\left(ap\Big{[}1+cos(\alpha_{j}^{H})\Big{]} cos(\beta_{j}^{H}),ap\Big{[}1+cos(\alpha_{j}^{H})\Big{]}\sin(\beta_{j}^{H}), \frac{ap\Big{[}1+cos(\alpha_{j}^{H})\Big{]}}{tan(\alpha_{j}^{H})}\right), \tag{15}\]  
\[\mathbf{n}_{j}^{tilt(H)}=\Big{[}\sin(\beta_{j}^{H})\cos(\alpha_{j}^{H}),\sin( \beta_{j}^{H})\sin(\alpha_{j}^{H}),\cos(\beta_{j}^{H})\Big{]}, \tag{16}\]  
\[\alpha_{j}^{H}= \begin{cases}0^{\circ},&\text{if $j=1$}\\ \alpha_{j},&\text{if $j=2,\dots,6$},\end{cases}  \tag{17}\]  
\[\beta_{j}^{H}=\frac{2\pi(j-1)}{5}\qquad for\ \ 2\leq j\leq 6, \tag{18}\]  
where \(ap\) is the apothem of the hemidodecahedron and is represented by \(ap=\frac{L}{2 tan(\omega/2)}\), with \(L\) as the length of the sides of the hemidodecahedron lateral surfaces and \(w\) as its central angle. The angles \(\alpha_{j}^{H}\) and \(\beta_{j}^{H}\) are are the angles of rotation and tilt of each PD \(j\) with respect to the secondary coordinate system, respectively.
# Silk Protein-Based Membrane for Guided Bone Regeneration
## 2 Silk Material Proteins
### Fibroin  
SF is hydrophobic protein and forms the core of silk filament [31]. It is approximately 10-25 \(\mu\) in diameter and consists of two different proteins, which are heavy (H) and light (L) chain [32]. H and L chains connect with a single disulfide bond and have molecular weights of 325 and 25 kDa, respectively [33]. The P25 glycoprotein (25 kDa) is linked to the H and L complex with a non-covalent bond [33]. The H and L chains and P25 appear in SF with a 6:6:1 ratio [32]. The amino acid sequence of the H chain contains the repetitive hexapeptide of Gly-Ala-Gly-Ala-Gly-Ser and dipeptide of Gly-Ala/Ser/Tyr, and it forms a more stable anti-parallel \(\beta\)-sheet crystallites structure [34]. While the H chain contains the repetitive peptide and is more hydrophobic, the L chain is non-repetitive and more hydrophilic [35]. The secondary structure of SF is the random coil and the anti-parallel \(\beta\)-sheet structure, which is connected to each peptide by a hydrogen bond [36]. The main crystal structure of SF is silk I and II. Silk I contains the random coil and amorphous structures, and silk II has an anti-parallel \(\beta\)-sheet structure with hydrogen bonds [35]. These strong hydrogen bonds with adjacent chains contributes to the mechanical and tensile strength of SF [16; 35; 37].  
SF is purified from sericin by the degumming process, which consists of boiling silk cocoons in alkaline solution [14]. It provides the mechanical properties of strength and toughness of silk fibers [14]. It also shows excellent biocompatibility, biodegradation, and differentiation of mesenchymal cells [33]. SF was recognized as a biomaterial by the United States (US) Food and Drug Administration (FDA) in 1993 [35]. SF has been used as a natural biopolymer for scaffolding for bone substitute materials [35]. Several types of SF materials have been manufactured for bone tissue engineering scaffolding, such as sponges, films, hydrogels, and membranes [16]. SF membranes are easily processed by casting or an electrospinning technique using a SF solution [27; 38]. Degummed silk cocoons are dissolved in the solvent mixture [16], and the mixture is dialyzed and formed into the aqueous SF solution [16; 27]. SF membranes are obtained by casting this solution on a specific dish or plate, and it is transparent, rapidly soluble, and brittle in its dry state [19]. Electrospinning nanofibers have been used for the manufacture of SF membranes [39]. These membranes show improved mechanical properties and slow degradability [38; 39]. Both SF membranes show excellent osteogenic cell affinity and induce the new bone regeneration on the animal calvarial defect as a barrier membrane [19; 39; 40].
## 5 Simulation and Analysis
### Performance Analysis of Identification Algorithm
It can be seen from Figure 7, similar to Figure 6, the \(P_{IE}\) performance of the one-step algorithm is not sensitive to the variation of the standard deviation of AOA error, but the \(P_{IE}\) performance of the two-step algorithm deteriorates with the growing standard deviation of AOA error. The reason is similar to that of Figure 6 because the second step of the two-step algorithm adopts the scattering signals combination that minimizes the sum of absolute values of the difference between estimates and measurements of AOA, which result in performance deterioration with the increasing of the standard deviation of the TOA. Similar to Figures 5 and 6, the performance of the two-step algorithm is superior to that of the one-step algorithm, and the performance under the condition of the ROS model outperforms that of the DOS model.
# Analytic Hierarchy Process and Multilayer Network-Based Method for Assembly Line Balancing
###### Abstract  
Assembly line balancing improves the efficiency of production systems by the optimal assignment of tasks to operators. The optimisation of this assignment requires models that provide information about the activity times, constraints and costs of the assignments. A multilayer network-based representation of the assembly line-balancing problem is proposed, in which the layers of the network represent the skills of the operators, the tools required for their activities and the precedence constraints of their activities. The activity-operator network layer is designed by a multi-objective optimisation algorithm in which the training and equipment costs as well as the precedence of the activities are also taken into account. As these costs are difficult to evaluate, the analytic hierarchy process (AHP) technique is used to quantify the importance of the criteria. The optimisation problem is solved by a multi-level simulated annealing algorithm (SA) that efficiently handles the precedence constraints. The efficiency of the method is demonstrated by a case study from wire harness manufacturing.  
assembly-line balancing; multi-objective optimization; simulated annealing; multilayer network Article