esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Route Determination for Ship Passage Using Capability Plots
### The Concept of the Algorithm
Next, for each point, all neighboring points are determined, the transition weights between them are calculated, and the values are entered into the appropriate locations in the transition matrix. To illustrate the method for determining transition weights, Figure 9 shows a section of the waypoint grid with designated weights between points. In calculating the individual values, a wind direction of 20 degrees relative to north was assumed. A section of the table with completed transition weights is shown in Figure 10. Due to limited space, the numerical values in the table have been rounded to two decimal places. The full values are available in Figure 7.  
The table should be interpreted as follows:  
_Starting from point 0 (row 0), the transition cost to point 1 is 0.3095, to point 11 is 0.0533 and to point 12 is 0.1937. The transition cost to all other points is 10, as there is no connection between point 0 and those points. The transition cost from point 0 to itself is also 0._  
Figure 7: Conversion of the capability plot to a transition weight matrix. Figure 8: Numbering of individual points in the waypoint grid. Figure 5:0 Waypoints with assigned transition weights. With the weight table defined, the ship's passage route can be determined using [PERSON]'s algorithm. The Python code for the algorithm is presented as Algorithm 1:  
```
defdijkstra(graph, start): # Initialization of weights to all vertices as 10 distances = [vertex: 10 for vertex in graph] # Weight to itself set to 0 distances[start] = 0 # Initialization of the priority queue priority_queue = [[0, start]] whilepriority_queue: # Retrieving the vertex with the smallest sum of weights current_distance, current_vertex = heapq.heappop(priority_queue) # Skip if the current sum of weights is bigger if current_distance > distances[current_vertex]:  continue # Iteration over the neighbors of the current vertex for neighbor, weight in graph[current_vertex].items():  distance = current_distance + weight # Updating the sum of weights if a shorter path is found if distance < distances[neighbor]:  distances[neighbor] = distance  heapq.heappush(priority_queue, (distance, neighbor)) return distances
```  
**Algorithm 1** Dijkstra algorithm
## 4 Numerical Results
When the SNR is 20 dB, Figure 9 illustrates the BER vs. the number of UAVs for the various fading channels. As the number of UAVs increases, the BER increases for all fading channel types. More packets will compete for transmission, as there are more UAVs, raising the BER. The maximum BER is attained by the system with a Rayleigh fading channel, while the lowest BER is attained by the system with a Nakagami-m fading channel. When the number of UAVs is low, there is a noticeable difference between the fading channels of the BER, but when the number of UAVs is high, there is a very small difference. Figure 2: Bandwidth versus achievable rate. Figure 4: Average capacity versus SNR with different THz frequencies. Figure 3: Bandwidth versus transmission power optimization. Figure 5: Outage probability vs. transmission distances. Figure 6: Outage probability versus SNR. Figure 8: Average BER versus SNR. Figure 7: Outage probability versus number of UAVs.
# A Novel Consistent Quality Driven for JEM Based Distributed Video Coding
## 5 Conclusions  
In this paper, a novel JEM-DVC solution has been proposed to take into account the recent achievements of JEM/FVC upgrades. In the proposed JEM-DVC solution, several advanced techniques of JEM Intra coding have been employed to effectively improve the performance of the JEM-DVC codec. For efficient quality consistency control, two novel methods, named KF-Q and WZF-Q, are introduced and applied for JEM-DVC, which can determine the optimal values of QP and QM that are assigned for the key and WZ frame coding, respectively. The proposed JEM-DVC codec is the first DVC codec in the literature, including JEM coding, and thus all of the performance results that are presented in this paper are new. The experimental results show that the proposed JEM-DVC codec significantly outperforms the relevant DVC benchmarks, notably the recent H.265-DVC and DISCOVER DVC codec. In future works, several issues, such as adaptive GOP control for JEM Intra coding, complexity reduction for JEM-DVC coder, and scalable coding for JEM-DVC can be considered to further improve the performance of the JEM-DVC codec and adapt this codec to practical video applications.  
Conceptualization, D.T.D. and X.H.V.; Data curation, H.P.C.; Investigation, D.T.D., H.P.C. and X.H.V.; Methodology, D.T.D. and X.H.V.; Software, H.P.C.; Writing--original draft, D.T.D. and X.H.V.; Writing--review & editing, D.T.D., H.P.C. and X.H.V.  
This research is supported by Vietnam National University, Hanoi (VNU), under Project No. QG.17.42.  
The authors declare no conflict of interest.
## 2 Experimental  
Methyl vinyl silicone rubber (SR) with a vinyl content of 0.16 wt% was purchased from Wynca Co., Ltd., China. POE (grade 8150), with a melt flow index of 0.5 g/10 min at 190 \({}^{\circ}\)C/2.16 kg, was purchased from Dow Chemical Co., Ltd., USA. Conductive carbon blacks (CBs) with a BET surface area of 83 m\({}^{2}\)/g were purchased from Evonik Degussa Co., Ltd., Germany. Multi-walled carbon nanotubes (CNTs, 10-20 nm in internal diameter and 10-30 \(\upmu\)m in length) were supplied by Nanjing Xianfeng Nano Material Technology Co., Ltd., China. The 1-vinyl-3-ethylimidazol bromide ILs were supplied by Lanzhou Greenchem ILs Co., Ltd., China. 2,5-dimethyl-2,5-di(tert-butylper-oxy)-hexane (DBPH) as curing agent was purchased from AkzoNobel Peroxide Co., China. EVA (ethylene-vinylacetate copolymer, grade 265, 28 wt% VA content) was purchased from Dupont Co., USA, and was used as a non-reactive compatibilizer to enhance the interfacial adhesion between SR and POE.  
The SR/POE/CB-CNT-IL composites were prepared as follows. First, CBs, CBs, and CNTs, ILs-modified CBs (CB-IL), ILs-modified CBs and CNTs by (CB-CNT-IL) were ground in an agate mortar. Then, the fillers were added to SR by a two-roll mill at 125 \({}^{\circ}\)C, and POE and DBPH were introduced and mixed for 15 min. Finally, the composites were vulcanized under 10 MPa pressure for 10 min at 180 \({}^{\circ}\)C, and then post-cured in a drying oven at 190 \({}^{\circ}\)C for 3 h. The amounts of components prepared for the composites are shown in Table 1.  
The morphology of the composites was observed by scanning electron microscopy (SEM, JEOL, JSM-6490 LV, Japan) at an accelerating voltage of 20 kV and field-emission scanning electron microscopy (FESEM, JSM-6700F) at an accelerating voltage of 5 kV. The electrical conductivity was determined using a multi-function digital electric meter (Victor Tech, Victor 86-e). The EMI shielding effectiveness (SE) data were obtained by using nanocomposite slabs (22.86\(\times\)10.16\(\times\)0.6 mm\({}^{3}\)), which were fitted into a waveguide sample holder with a vector network analyser (Agilent Technologies, N5247A, America) at 8.0 to 12.0 GHz (X band). EMI shielding properties of the samples were evaluated from the S parameters, which could be calculated calculate SE\({}_{\text{T}}\) (total shielding effectiveness), SE\({}_{\text{R}}\) (reflection loss) and SE\({}_{\text{A}}\) (absorption loss) [15].
## 1 Introduction
A droop-based fully decentralized architecture is proposed in [15], where an adaptive droop control is employed in a multi-source DC grid to manage both voltage and power. The study implemented an algorithm that takes into consideration the energy pricing and the state of charge, without the use of communication lines. In [16], an EMS was implemented in a 1 KW prototyped MG. The primary goal of this study revolved around robustness and optimization of energy production. Another stability-focused study in [17] conducted an observation on the stability of a DC microgrid based on a pre-planned transaction schedule on a university campus in Korea. In [18], A robust current control based on Lyapunov theory was implemented using a multiport converter for hybrid renewable PV-Wind energy systems. The study deployed a dSPACE-DS1104 effectively to implement the proposed control. All of the aforementioned studies conducted the work on a DC microgrid, either focusing one particular aspect, be it stability related or energy management related but seldom both at in tandem, with bulk of the testbed developed being concerned more with the step response effect on stability. There are few articles that discusses the effect of energy management on the stability of the MG. The work done in paper is primarily concerned with the design of a stable multi-source microgrid in purview of energy management with the following contributions:  
* The generalization of the regulation and the optimization problem in the MG
* Creation and Implementation of a versatile simulated model of a MG
* Real practical application of the model in an experimental testbed  
The paper is organized as follows; Section 2 compiles the theoretical notions used for the formulation of the model and control structure, serving as a precursor for energy analysis. Next in Section 3, a graphical interpretation of the consumption profile, projected onto the optimization objectives, is conducted. This approach provides a clear understanding of the effective strategies employed for subsequent analysis. Section 4 provides a detailed account of the simulation model constructed from the previous finding in Sections 2 and 3, afterwords the results are displayed and interpreted. Section 5 discloses the experimental results obtained and components deployed in light of the theoretical study. Finally, the paper ends with a conclusion that summaries the undertakings of this paper and experimental notes regarding the nuances of the realization process.
# Model Based Open-Loop Wind Farm Control Using Active Power for Power Increase and Load Reduction
## 5 Conclusions  
A simple but effective model based open-loop wind farm control algorithm was proposed and validated with a relatively high-fidelity simulation tool in this study. The wind farm model for control was a simplified version of the wind farm model for dynamic simulation, and consisted of a wind turbine model, wake model, wind propagation and superposition model, and a wind farm control model. Unlike the wind farm model for simulation, it had a look-up table based wind turbine model that does not have wind turbine dynamics. The wind farm model was used in the wind farm controller to find optimal power commands to individual wind turbines. With a function minimization algorithm, the wind farm control algorithm was validated with dynamic simulations with turbulent winds. For a virtual wind farm with three wind turbines in series, simulations were performed for both the baseline and the proposed wind farm controllers at the mean wind speed of 8 m/s. As the result, the proposed wind farm controller was found to increase the total power output by about 2.4% and to decrease the load of the most upstream wind turbine in DEL by about 16.7% when the turbulence intensity was 0.075 and the control frequency was 0.1 Hz.  
In addition, parametric studies were performed to understand the power increase of the proposed wind farm controller compared with the baseline controller for different turbulent wind speeds different turbulence intensities and different control frequencies. The power increase was found to decrease as the turbine spacing increases and it decreased to about 1.5% when the turbine spacing was 10 RD at the mean wind speed of 8 m/s. In addition, the power increase was found to decrease as the turbulence intensity increases and it decreased to about 0.83% when the turbulence intensity was increased to 0.21. When the mean wind speed was close to the rated wind speed, the power increase was also found to decrease.  
Although no wind direction change was considered in the current study, the effect of the model based wind farm control algorithm was found to be effective to increase power and reduce load in a wind farm. However, in the current study, the [PERSON]'s eddy viscosity wake model was used to calculate the wind speed in the wake region, and [PERSON]'s frozen turbulence assumption was used for wind speed propagation. Therefore, the results need to be experimentally validated in a wind tunnel or in the field for better understanding.  
This work was supported by the Human Resources Program in Energy Technology and New & Renewable Energy Core Technology program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource form the Ministry of Trade, Industry & Energy, Korea (Nos. 20154030200950 and 20153030023610).  
H.K. developed wind farm simulation tool and farm controller; K.K. constructed wind turbine model and developed wind turbine controller; and I.P. and H.K. analyzed simulation results and wrote the paper.  
The authors declare no conflict of interest.
# Vector Magnetic Anomaly Detection via an Attention Mechanism Deep-Learning Model
## 4 Discussion  
With the above experiments, the attention deep-learning model we proposed enables only one magnetometer to detect magnetic anomaly under Gaussian color background noise.  
In addition to developing a powerful tool for magnetic anomaly detection, our study provides a novel perspective on the observation system. We suggest that two main factors control the performance of detecting moving targets: stations and generalizability. Because most methods need differential calculation by the reference magnetometer to eliminate interference, this is the reason why no comparison with other methods is made in this study. Moreover, different methods have different limitations; for example, OBF is not good at colored noise, MED is limited by SNR, and DeepMAD relies on datasets. We noted that deep learning could learn feature extraction from massive amounts of data to complete the classification task, and attention mechanisms can help the model process such sequential signals.  
Our model carried out single station detection with the benefit of a very deep encoder and attention mechanism, and the generalizability was improved by training with large amounts of simulation data. The model is pioneering; however, it needs the following improvements. The first is multi-target detection: when multiple targets appear at the same time, it is difficult for the model to differentiate. The second is model implantation: the network structure is still too large to be implemented into mobile chips. The third is transfer learning: when going for specific target detection in a particular place, we suggest training the model with a dataset containing local geomagnetic and target anomalies.
# Study the Effect of Operating Temperature on Performance in Dry Scroll Vacuum Pump
## 3 Results and Discussion
### Influence of Operating Temperature on the Pumping Speed  
Figure 8 shows the evolution of instantaneous pumping speed with respect to the rotation angle under different operating temperatures. It can be seen that under different operating temperatures, the pumping speed varies periodically with the rotation angle. The operating temperature obviously affects the pumping speed, and the overall change trend does not change.  
The average pumping speed \(S_{m}\) of the scroll pump is calculated according to Equation (11), and the results of the calculation are listed in Table 6.  
The simulation error is \(-5.7\%\) when the wall temperature is \(338.15\) K. When the wall temperature is \(298.15\) K, the vacuum pump has been fully cooled. Compared with the wall temperature of \(338.15\) K, the pumping speed is increased by approximately \(5.9722\%\).  
The suction process of a dry scroll pump can be regarded approximately as a continuous process where heat is exchanged between gas and the pump wall. During the suction process, the heat transfer (\(Q_{bs}\)) from the pump wall to the gas can be expressed as Equation (12):  
\[Q_{bs}=A_{s}\alpha\Delta T=A_{s}\alpha(T_{wall}-T_{gas}) \tag{12}\]  
where \(\Delta T\) is the temperature difference between the gas and the wall, \(T_{wall}\) is the wall temperature, \(T_{gas}\) is the gas temperature, \(\alpha\) is the convection heat transfer coefficient, and \(A_{s}\) is the surface area of the suction process.  
Since the surface area remains the same and the gas temperature is constant, the higher the wall temperature is, the larger the temperature difference and the heat transfer (\(Q_{bs}\)) are. Figure 9 shows that the higher the wall temperature of the fixed scroll is, the more energy is transferred and thus the more energy is used to raise the gas temperature.  
The worse the cooling capacity of the fixed scroll is, the higher the temperature of the gas in the suction chamber is. Assuming that the pumped gas is an ideal gas, it satisfies the ideal gas equation of state:  
\[PV=NKT \tag{13}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Suction Pressure/kPa** & **Operating Temperature/K** & **Pumping Speed/L/min** \\ \hline
1 & 298.15 & 189.84 \\
1 & 318.15 & 186.59 \\
1 & 338.15 & 179.15 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Pumping speed.  
Figure 8: Instantaneous pumping speed changes with rotation angle.  
The suction pressure is always maintained at 1 kPa. After the pumped gas enters the pump, it receives heat from the surface near the suction port. The gas temperature increases and the volume expansion increases, which causes the change in pumping speed. The greater the heat dissipation capacity of the pump body, the less heat is exchanged by the pumped gas, the gas temperature change is less, the volume expansion is less, and the pumping speed is correspondingly larger.  
Therefore, the purpose of improving the pumping speed under the same geometric design parameters can be realized by improving the cooling capacity of the pump body.
## 1 Introduction  
Rigid intervertebral fixation is the first priority, in the currently used spinal-corrective surgery methods, in terms of achieving final fusion [1, 2, 3]. Fixation devices, including pedicle screws, hooks, wires, plates and interbody cages, have been commonly used in recent decades, and pedicle screws have gained increasingly widespread acceptance [4, 5, 6, 7, 8].  
Failed back surgery syndrome resulting from pseudarthosis, ranging in incidence from 5 to 35%, has been reported in recent years [9, 10, 11, 12, 13]. Improvements in bone graft, the local environment, instrumentation and surgical techniques have all contributed to better fusion rates [13]. In addition to biological factors, studies comparing surgical techniques for managing and preventing pseudarthosis have improved with the use of rigid instrumented fixation [14].  
The screw geometry, consisting of the thread shape, core shape and pitch depth, and insertional factors, such as the pilot-hole size, insertional depth and trajectory, contribute to the fixation stability of pedicle screws and have been discussed in recent years. However, the proper screw geometry and pilot-hole size remain controversial in current biomechanical studies [15, 16, 17, 18, 19, 20, 21]. Inconsistent results arise from the use of allogeneic or xenogeneicvertebrae with different anatomical characteristics and density distributions, various screw materials, mixed screw brands, and uncontrolled partial threads and tapping methods in pilot-hole size studies [15; 17; 21; 22; 23]. To the best of the authors' knowledge, the most effective screw geometry configuration has not yet been determined by nonbiased biomechanical testing [24; 25]. In our study, we tested the pullout strength and insertional torque of six different screw geometry configurations, namely three outer/inner diameter shapes and two thread profiles that are commonly used in clinical spinal surgeries, in standard polyurethane foam test blocks.  
The purpose of the study was to evaluate the effect of bone density and pilot-hole size on the biomechanical performance of various screw geometries, resolve the dilemma of screw loosening and investigate the recommended proper screw geometry for clinical applications.
# Quad-Beam 4 \(\times\) 2 Array Antenna for Millimeter-Wave 5G Applications
## 1 Introduction
The single-element antenna presented in this article is a figure-eight-shaped elliptical ring. The literature has illustrated several elliptical-shaped antennas with defected ground structures for impedance matching over wide bandwidths. For example, in [21; 22], the ground planes were defected for impedance matching over a wide bandwidth at the cost of reduced radiation directivity. However, the single-element antenna structure documented in this article is a novel figure-eight-shaped elliptical ring. Better impedance matching is achieved by altering the geometry of the patch rather than the ground plane so that the antenna can simultaneously achieve better resonance and directivity. Further, the directivity and gain are enhanced by configuring the single-element antenna to a \(4\times 2\) array with a corporate (parallel) feed network, ensuring equal power and phase distribution across all elements. The compact structure has dimensions of \(2.02\lambda_{1}\times 1.82\lambda_{1}\) (where \(\lambda_{1}\) is the wavelength at 30 GHz). The design leverages non-uniformelement spacing to form a quad-beam pattern, with a detailed analysis provided on the radiation characteristics and beam formation mechanism. The proposed antenna achieves a maximum gain of 10.6 dBi, potentially improving further gain by replicating and modifying the feed network. The design is well-suited for 5G wireless communication applications, where high-gain, multi-beam capabilities are critical for a robust performance.