esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Presentation of the Averagely Weighted Iteration Approach
### Averagely Weighted Iteration (AWI) Approach  
To resolve the convergence issue, the averagely weighted iteration approach is introduced into the iterative procedure. Combined with the AWI approach, Cheng's external force term can be recast as:  
\[g_{a}^{(s)}=\frac{s-1}{s}g_{a}^{(s-1)}+\frac{1}{s}\omega_{a}\big{\{}3f^{(s-1)} \cdot\Big{[}\Big{(}e_{a}-\mathbf{u}^{(0)}\Big{)}+3\Big{(}e_{a}\cdot\mathbf{u}^{(0)} \Big{)}e_{a}\Big{]}\big{\}} \tag{22}\]  
where \(s=1,2,3\cdots N\) is the iteration index, and \(N\) is its upper limit. It can be proven that Equation (22) takes the average value of each iteration result with its former ones.  
Figure 4 is the flow chart of the AWI approach. Each iteration cycle (\(s=1\), \(2\),... \(N\)) consists of the following steps: (1) Calculate the Lagrangian force density \(\mathbf{F}_{l,n+1}^{(s)}\) using Formula (5). When \(s=1\), the superscript \(s-1\) indicates that the variable is directly obtained by the explicit method (not obtained through iteration). (2) Spread the IB force \(\mathbf{F}_{l,n+1}^{(s)}\) to the fluid external force \(\mathbf{f}_{i,n+1}^{(s)}\) using Formula (4). (3) According to Formula (22), calculate the force distribution function \(g_{a,n+1}^{(s)}\) from the external force term \(\mathbf{f}_{i,n+1}^{(s)}\) and the last iteration step \(g_{a,n+1}^{(s-1)}\). (4) Calculate the fluid flow velocity in the IB layer using  
Figure 3: Varying history of Lagrange forces at probe points N and S at time step 1 with \(\overline{K}_{s}=13.6\). (**a**) Varying history of Lagrange forces at probe points N. (**b**) varying history of Lagrange forces at probe points S.  
Figure 2: Non-physical knots appears after the calculation diverges.  
Formulas (16) and (19). (5) Interpolate the IB speed \(\mathbf{U}_{l}^{(s)}\) from the local fluid velocity \(\mathbf{u}_{ij,n+1}^{(s)}\) using Formula (3). (6) Stop the iteration step and go to next evolution step when \(s\) reaches the prescribed iteration time N. (7) If \(s<N\), repeat steps (1)-(7) until convergence.  
As shown in Figure 3, by introducing the AWI approach, the iterative results can be successfully stabilized to a fixed value, which avoids the oscillation divergence caused by the explicit method. Figure 5 shows the varying history of the Lagrange force (at point N) obtained by the explicit method and AWI approach. When the AWI approach is used, the non-physical force oscillation at the immersed boundary point is obviously improved.
# SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks
## 4 Performance Analysis  
In this section, we employ the Mininet network simulation tool to evaluate the performance of the BRF protocol. Initially, we recompile the Linux kernel and incorporate the tcp_brf.c protocol file into the net/ipv4 directory of the kernel. Subsequently, we configure the congestion control algorithm of the kernel to utilize the BRF algorithm. In the net/sched directory of the kernel, modifications are made to the sch_htb.c file to incorporate the bandwidth allocation scheme described in this paper, followed by recompiling the kernel. Subsequently, after installing Mininet, we integrate the congestion detection algorithm outlined in this paper into the OpenDaylight source code and proceed with recompiling and installing. After configuring the test environment, we utilize Mininet to construct diverse network topologies for different testing scenarios to ensure the efficacy of our tests.  
We compare the performance of the protocol on five indicators, including throughput, fairness, packet loss rate, bandwidth convergence speed, and queuing delay. The test protocols include CUBIC, BBR, and BRF.
## 1 Introduction
### Motivation and Novelty  
Although previous studies presented different methods to analyze and optimize the effects of drawbacks on sheet metal forming using FEA and AI, they were mostly based on the optimization results of the forming process under ideal initial conditions. Therefore, they cannot reflect changes in the actual production in real time. In particular, if the quality of the drawbacks deteriorates, or defects occur in the production environment, modifications are performed mostly based on the experience of workers. Few studies have predicted and reflected the effects in real time using DT in consideration of this situation.  
Therefore, this study proposed a method to predict the formability in real time with respect to the position and forces of drawbacks based on DT for predicting the quality of automobile exterior parts. Models for predicting formability according to drawbacks were implemented using machine learning. The drawback geometry applied in this study was based on the automobile render geometry. Regarding the training data for the models, formability data based on drawbead forces by position were generated through FEA. AI models were trained using the data to predict and evaluate the formability. Furthermore, a graphical user interface (GUI) that can display the predicted values according to the positions and forces of drawbacks was constructed using DT.  
This paper is structured as follows. In Section 2, the drawbead design and formability judgment methods used in this study for quality control in render forming are described. Additionally, AI models and the DT used to predict the effects of drawbacks are explained. Section 3 explains the drawbead analysis setting method, analysis data preprocessing, and AI learning setting conditions for the real-time prediction models proposed in this study. Section 4 details the analysis results according to the positions and forces of drawbacks, the AI prediction accuracy results, and analysis contents and explains the GUI that reflected the prediction models. Finally, in Section 5, conclusions on the effects of the positions and forces of drawbacks and real-time formability prediction are summarized.
## 3 Experimental Results and Discussion
### Natural Vibration Properties  
Before and after each intensity level test, the white noise wave was utilized to sweep through the test model in order to investigate the change of its vibration properties. The structural natural frequencies of the specimen after each loading case can be obtained by analyzing the time-history acceleration responses derived from the second order differential of the measured displacement, spectrum characteristic, and the transfer function of each story under the white noise excitation. In addition, the corresponding damping ratio at a specific natural frequency can be calculated by adopting the half-power bandwidth method in Figure 17 as follows:  
\[\xi=(f_{2}-f_{1})/(2f), \tag{1}\]  
where \(\xi\) is the damping ratio,\(f\) is the frequency corresponding to the peak amplitude \(A_{\text{max}}\) of the transfer function,\(f_{1}\) and \(f_{2}\) are the frequencies corresponding to the value equal to 0.707 \(A_{\text{max}}\), respectively.  
Table 8 presents the fundamental natural frequencies of vibration and damping ratios of the test model under the different increasing seismic excitation cases. It was found that the fundamental natural frequencies of the specimen were much higher than the dominant frequencies of three seismic waves applied as the earthquake excitations. It reveals that the occurrence of the resonance phenomenon can be avoided due to the significant differences of these frequencies. According to the expression of natural frequency \(f=\sqrt{k/m}/(2\pi)\), the structural stiffness \(k\) is directly proportional to the square of the frequency. The measured initial natural frequency and initial damping ratio of the specimen were 15.68 Hz and 3.17% subjected to the first loading stage of 0.1 g, respectively. Furthermore, the natural frequency remained essentially unchanged before PGA of 0.6 g, which implies that the test model  
Figure 16: Test phenomena of the specimen. (**a**) Fallen wire; (**b**) deflection of energy absorbers; (**c**) fracture of the weld at the beam–column joint; (**d**) fracture of the weld at the LYP plate.  
without the stiffness degradation remains completely elastic during these stages. However, the natural frequency of vibration gradually decreased with the increase of PGA from 0.7 to 1.0 g, demonstrating that the structural stiffness gradually declined, in agreement with the observed loosening of the high-strength bolts at the column bases. On the other hand, the damping ratio slightly increased during these loading cases because the damping of the structure resulted from a variety of sources, including various connection frictions and external aerodynamic frictions, except for the internal elastic material. A considerable amount of bolt connections were adopted in the column base, which was firmly attached to the vibration table surface with high-strength bolts in the test, and, in addition, all the additional mass blocks were connected by the bolts to the steel floor slab in the frame, which contributed to the increase of the damping ratio.
## 5 Results and Discussion
### Strain Response of the Intensity-Modulated DWDM PM-PCF Sagnac Loop Sensor
Moreover, an easy multiplication of PM-PCF Sagnac loops is possible in order to create a sensor network as its operation relies on DWDM. It is also necessary to account for possible error resulting from temperature effects. This constraint had already been thoroughly investigated in the literature regarding this type of fiber, [16; 21; 22] where almost inherent insensitivity to ambient temperature had been found in PM-PCF (\(\sim\)0.3 pm/\({}^{\circ}\)C). Assuming a temperature variation of 50 \({}^{\circ}\)C (0-50 \({}^{\circ}\)C), the induced error could be approximately 15 \(\mu\epsilon\). It evidently shows that thermal effect could be negligible in the proposed setup. Nevertheless, the advantage of this setup is the utilization of cost-effective optical power measurement instead of spectral analysis. The measurement range is related to physical parameters of PM-PCF. An increase of PM-PCF length causes relatively smaller spacing of interferometer fringes, which reduces the measurement range of the sensor setup.
## 3 Results and Discussion
### Performance Comparison
From Figures 9-11, we can see that the joint angle curve predicted by LS-TCN is closer to the real curve, especially in movements 5 and 6 (the last four peaks/valleys). In addition, we can see that the estimation for movement 4 (i.e., the Power Sphere Grasping) was the worst and vibrated most among the six movements for all the three models (including the RNN, the TCN and the proposed LS-TCN). This was caused by the fact that the sampled joint angle of movement 4 varied dramatically between different repetitions, and this problem may be solved by adding a real-time smoothing algorithm at the end of the methods. For other movements, since the sampled joint angle was much more stable than that of movement 4 in different repetitions, the performance was far better. This paper explored the influences of the kernel size and the network depth of TCN on accuracy. We found that the convolutional kernel size we chose allows the minimum effective information of sEMG, and small numbers of layers are beneficial to continuous motion estimation based on sEMG. Then we proposed LS-TCN based on the experimental results and verified the performance of LS-TCN, TCN, RNN and SPGP when extracting the continuous motion information from sEMG. Although LS-TCN improved the accuracy by 6.6% compared with TCN, there are still several problems to be solved in practical applications of human-computer interaction. First, the current model leverages personal data for training and lacks generality. One possible solution is to train the general model with a large number of subjects and adjust it with transfer learning, which we will try in our future work. Second, collecting stable and high-quality EMG signals is still difficult for current studies. For example, dry electrodes are prone to displacement and wet electrodes are not easy to wear. Third, the stability of the prediction angle needs to be further improved, which may be solved by adding an implementation smoother. Forth, we plan to find optimal parameterization of our network by leveraging advanced technologies in our future work, such as nature-inspired optimization algorithms [37] and multi-objective optimization [38].
## 3 Multi-UGV Path Planning Modeling
### Multi-UGV Collision Avoidance Cooperation Mechanism  
In the path planning problem for multiple UGVs, each UGV must avoid collisions with other UGVs in the process of finding the path. In the experiment of this paper, the initial position of each UGV is initially generated, only the initial position of each UGV is considered not overlapping, and then a multi-intelligent motion cooperation method is proposed to prevent UGV from colliding with other UGVs in the process of movement, and the final goal is that multiple UGVs can quickly find the target point in the same one, and the path generated by each UGV reaches the shortest.  
We assume that three UGVs start from the starting point at the same time and move towards the target point at the same speed, and each UGV first observes whether it can move. If it cannot be moved, wait at the origin until it can be moved; if it can be moved, check whether it has encountered an obstacle, such as UGV1:UGV1, which determines whether it has encountered a fixed obstacle (that is, whether it has moved to the black area of the environment), and if it encounters an obstacle, it selects the action with the maximum Q value according to the improved \(\varepsilon-greedy\) strategy to execute; if UGV1 does not encounter a fixed obstacle during this movement, it is determined whether the current state of UGV2, UGV3 and UGV1 is collusion-free: if there is no collision, UGV1 will select the action with the maximum Q value according to the improved \(\varepsilon-greedy\) strategy to execute, if the state among the three is any of the upward collision, downward collision, left collision, right collision, upper left collision, lower left collision, upper right collision, lower right collision, at this time, if UGV1 waits in place for more than 1 s, continue to judge the relationship among the three UGV states, and select the action execution according to the relationship among the three, otherwise, wait in place until the waiting time is greater than 1 s. The specific cooperation mechanism is shown in Figure 4, and the same cooperation mechanism is followed in the process of each UGV movement.  
Figure 3: UGV movement direction.  
Figure 2: Schematic diagram of UGV motion.
## 2 Single Molecule Electrical Measurements: Biomolecule Detection
### Glycans, and Biocompatible Polymer (PEG) Sensing  
Glycans are an important biopolymer, belong to group of compounds consisting of various sugars connected by glycosidic bonds. Glycans can bind not only to other sugars but also to proteins, lipids, and other small molecules to produce a variety of molecules. These glycoproteins and glycolipids are important biopolymers that play important physiological roles in living organisms, and their detection has attracted much attention. Such glycosides and its related molecules have been reported by single-molecule detection [34; 35; 36]. Besides this, biocompatible polymers have attracted interest as nanodevice modification substances. Among them, polyethylene glycol (PEG) is often used in the chemical modification of nanodevices for avoiding the non-specific sticking of protein on the devices. In the reports of single-molecule measurements, the degree of polymerization of PEGs have been detected [37; 38; 39].
## 5 Simulation Experiments
### Packet Delivery Ratio Analysis  
We explore the packet delivery ratio of DHD-MEPO versus EMSCOLER and IERP, experimenting for two network environments of each size. The packet delivery ratio is defined as the ratio of the quantity of packets received by the BS to the total quantity of packets sent. The analysis of Figure 19a,b shows that the packet delivery rate using the DHD-MEPO algorithm is always in the leading position. The reason for this is that in the initial stage of the algorithm, the network is divided into cells and group nodes are selected for the group management of the cells, and the group nodes are responsible for collecting and aggregating the data information within the cells they manage before sending it to the BS center for further processing. IERP and EMSCOLER do not take into account this aspect of the packet management network area, but the reason why the packet delivery rate is higher with EMSCOLER than with IERP is that EMSCOLER uses a grid concept and selects relay nodes for data transmission in the steady state of the link.  
Figure 18: The \(100\times 100\times 100\) m\({}^{3}\) area connectivity map: (**a**) Initial node location distribution; (**b**) Distribution of node positions after algorithm optimization; (**c**) Network connectivity.  
By comparing it with other algorithms, we demonstrate that DHD-MEPO performs well in the aspect of coverage and uniformity of energy consumption. (1) In the aspect of energy performance, the deficiencies of 3D-VPCA, a derivative algorithm of the Virtual Force Algorithm (VFA), affect the virtual movement of mobile nodes and the energy consumption of the movement. Meanwhile, the C-CICHH's total energy consumption is slightly worse than that of DHD-MEPO, because C-CICHH transforms it into a maximum weighted exact-match matter by designing a weighted complete dichotomous chart, with the aim of finding the allocation strategy that minimizes the total energy consumption while solving the network coverage problem. Furthermore, 3D-VPCA and C-CICHH only take maximizing coverage and minimizing total energy consumption as the optimization objectives, ignoring the uniformity of the remaining energy of the nodes. In contrast, DHD-MEPO optimizes the remaining energy equalization as the major factor in the process of repairing holes. Therefore, the algorithm in this paper outperforms 3D-VPCA and C-CICHH in the performance of residual energy equalization. (2) In terms of coverage enhancement, the essence of 3D-VPCA and DHD-MEPO is to separate redundant mobile nodes to fill the unmonitored region. However, the final movement effect of the repair nodes in 3D-VPCA will inevitably be affected by the attraction between the nodes and the uncovered points being monitored threshold, the repulsive force threshold between the nodes, as well as the step distance during each round of virtual movement. Although these parameters are iteratively adjusted to ensure optimal performance, the results are not significantly improved. This is the reason why the final coverage after the 3D-VPCA repair is lower than the algorithm in this paper.
## Appendix A  
A list of acronyms most commonly used in the text:  
* 3 GPP--3 rd Generation Partnership Project
* BPSK--binary phase shift keying
* BW--bandwidth* CNIR--carrier-to-noise and interference ratio
* DSR--detection success ratio
* EIRP--equivalent isotropic radiated power
* eNB--evolved node-B
* LPWAN--low-power wide-area network
* LTE--long-term evolution
* LTN--low-throughput network
* MCL--maximum coupling loss
* MCS--modulation-coding scheme
* MISO--multiple input single output
* NB-IoT--narrowband Internet of Things
* NPRACH--narrowband physical random access channel
* NPUSCH--narrowband physical uplink shared channel
* Nrep--number of repetitions
* QPSK--quadrature phase shift keying
* SNR--signal-to-noise ratio
* TDD/FDD--time-division duplex/frequency division duplex
* UE--user equipment
* UNB--ultra-narrowband
## 6 Discussion and Analysis
### Overhead Analysis  
As shown in Figure 16, compared with the baseline scheme, there will be some overhead induced by the proposed scheme (on average 3.32%), but the worst case response time is still much smaller than that of Meta-Cure method, which induces 9.33% on average overhead compared with that of the baseline scheme. As for Meta-Cure, when the redundant write operation for every critical page or selective copy operation occurs during garbage collection process, the write operation time will be greatly prolonged and this time will be taken as the worst-case response time.  
Compared with the baseline scheme, which does not have the request feedback operation, the worst-case response time of Asymmetric Programming is a little longer than that of the baseline scheme. If the request feedback operation is conducted multiple times, the garbage collection process will be prolonged and that will induce some overhead for the worst case response time.  
Figure 16 presents the worst-case response time of Asymmetric Programming with different percentages of read operations and different ratios of random accesses. For our Asymmetric Programming scheme, the request with worst-case response time takes place when the request queue scheduler delays the current write request to serve the next request (as shown in Algorithm 1) and the garbage collection process is triggered at the same time. This request feedback operation may be conducted several times. Thus, the worst-case response time would not be constant with different workloads.
# Numerical Study of Partial Dam-Break Flow with Arbitrary Dam Gate Location Using VOF Method
## 1 Introduction
[6; 11; 29; 30; 31; 32] provided a simplified method to simulate peak discharge value when a total dam collapsed instantaneously, while analytical solutions predicted this value in the case of partial dam-break in [11]. The analytical method was shown to be quick in predicting peak discharge and matched well with 2D SWE results [19]. However, great advances in simulating flood flow propagation, mean the aforementioned methods cannot precisely demonstrate time variation of flow rate at dam sites, because the occurrence of vortex around the dam gate may be of significant influence, particularly on peak discharge. Most importantly, analytical solutions do not account for the impact of dam gate location on outflow hydrographs [6; 19]. On the other hand, to our knowledge, there are few published works estimating this information using a 3D numerical model. Flow 3D was used to compute peak discharge through planar dam breach when there was flow over the top of the dam [31]. While MIKE 3D software was applied to synthesize a discharge time series, in terms of total dam failure [29]. Discharge hydrographs yielded by means of different values of dam breach width were simulated by Flow 3D in [28]. However, they did not account for the impact of the dam gate's position. Therefore, in the second approach, we use a 3D CFD method to simulate maximum flow rate at a reservoir outlet in the same conditions as those of the first purpose. The result showed that this value does not only depend on geometry of reservoir, initial stage, and breach size, but it is also affected by location of the dam's broken block.