esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Discussion and Prospection  
The above experiments confirm the improvement of traditional beamforming by the FWMB algorithm. However, the FWMB algorithm still has some issues that need to be addressed in future research.  
* The leak hole that was used in this paper was a circular hole. In subsequent research, irregular holes that are caused by real collisions will be tried to more comprehensively evaluate the effectiveness of the method.  
Figure 11: Result statistics for different sensor positions (A–I). **(a)** The results statistics of a 1.5 mm hole, and **(b)** the results statistics of a 2.0 mm hole.  
* This paper evaluated the positioning performance at different angles, and further experimental and theoretical analyses of the effective distance of the method and the key influencing factors will be made in subsequent research centers.
* Boundary reflection is also an important factor that causes mispositioning, and no relevant experiments were done in this paper. For large spacecraft structures, the attenuation of waves reduces the effects of reflections. However, the above issues are still worthy of discussion and research. In subsequent experiments, a non-reflective case by adding sound-absorbing cement can be tried.
* In an actual application environment, the structure may have complex structures such as curved surfaces or stiffeners. The effectiveness or compensation method of the FWMB algorithm is also worthy of attention and research.
* The ability to locate leaks at different locations needs to be further evaluated, especially for leaks that are close to the boundary. Though the FWMB algorithm's multi-layer overlay scheme can obtain more accurate results, there will still be multi-peak situations, and the algorithm needs to be improved in the follow-up.  
In summary, for continuous leakage, the FWMB algorithm provides significant improvements. In subsequent developments, optimization and further testing are needed based on actual applications.
# Numerical Investigation of Jet Angle Effect on Airfoil Stall Control
## 3 Results and Discussion
### Static Airfoil Simulations
#### 3.1.2 Flow Control
Figure 9: Time-averaged flow field around the OA209 airfoil with and without steady-blowing jets. \(Ma=0.3\), \(Re=1.15\) M, \(c_{\mu}=0.06\) and \(\alpha=17^{\circ}\). Pressure profiles are shown in Figure 11 for the two angles \(\alpha=14^{\circ}\) and \(15^{\circ}\). At \(\alpha=17^{\circ}\) which is \(3^{\circ}\) higher than the baseline stall angle, the tangential jet reduces the suction pressure in the first half of the suction side, increasing the lift by \(\Delta C_{l}\approx 0.2\) (see Figure 7). The augmentation of the lift in the first half is related to the pitch-up moment \(\Delta C_{m}\approx 0.03\) which is observed in Figure 7. The chord-normal jet impairs the suction pressure, losing the aerodynamic lift for an angle \(\alpha>4^{\circ}\). Additional computations are conducted for the tangential jet actuation using a coarse grid (see Figure 12). The total grid count of the coarse grid is about only a quarter of the grid count of the initial fine grid. Only selective angles near and beyond the baseline stall angle are simulated on a coarse grid. The coarse grid provides similar aerodynamic forces obtained from the fine grid, particularly beyond the stall angle. Near the stall, computational results vary slightly due to the spatial resolution, which is not surprising because the boundary layer separation from a smooth surface is indeed sensitive to numerical methods including the spatial resolution. Figure 11: Comparison between static OA209 with and without steady-blowing jets. \(Ma=0.3\), \(Re=1.15\) M, \(c_{\mu}=0.06\) at \(\alpha=14^{\circ}\) (**left**) and \(17^{\circ}\) (**right**). Figure 10: Shape factor of the suction-side boundary layer on the OA209 airfoil with and without steady-blowing jets. \(Ma=0.3\), \(Re=1.15\) M, \(c_{\mu}=0.06\) at \(\alpha=14^{\circ}\) and \(17^{\circ}\).
## 4 Architecture of Software Receiver
### Software Architecture
#### 4.1.3 Acquisition/Tracking and Positioning  
These functions adopt open source codes from Greenberg and Ebinuma [22] by replacing its interface which controls hardware correlator by our software correlator. Additionally, the WAAS signal processing and Viterbi decoder for messages are added to support positioning with the WAAS GEO. The structure of carrier tracking loop of software receiver for the specific formats of code and carrier table is described in [PERSON] and [PERSON] [23]. And, the phase compensation for carrier wipe-off is implemented using a technique in [PERSON] _et al._[11].
## 3 Experimental Results
### Effects of Main Variables on Bearing Capacity  
#### 3.3.1 Lap Length  
As shown in Figure 13, with the increase in lap length, the bearing capacity of CHBC is improved. It is known that bond behavior together with the concrete compression at the head contributes to the bearing capacity. The bond capacity shows a positive correlation of the increase in lap length, so the increase in lap length improves the bearing capacity of CHBC.  
The range of the bearing capacity of the specimens with a lap length varying from six to nine times the steel bar diameter is between the yield and ultimate tensile strength of the headed bar. Referring to Chinese design specification GB 50010-2020 [37], the required lap length is equal to about 32 times the steel bar diameter for straight rebar under the specimen design parameters in this paper. However, through the additional heads, a lap length of six times the steel bar diameter can guarantee the yielding of the steel bar. Lap length is saved in large quantities which is economically beneficial in construction since it can significantly reduce the length of reinforcement connection area and the on-site construction work is hence reduced.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**No.** & **Specimen ID** & \(F_{u}\) **(kN)** & **Average of \(F_{u}\) (kN)** & \(F_{u,s}\) **(kN)** & **F\({}_{u,s}\) (kN)** & **Failure Mode** \\ \hline
1 & & 53.6 & & & & \\
2 & M4-D12-6d & 53.9 & 54.6 & 50.7 & 69.8 & Steel bar connector failure \\
3 & & 56.2 & & & & \\ \hline
4 & & 59.5 & & & & \\
5 & M4-D12-7.5d & 60.0 & 60.0 & 50.7 & 69.8 & Steel bar connector failure \\
6 & & 60.6 & & & & \\ \hline
7 & & 61.1 & & & & \\
8 & M4-D12-9d & 61.9 & 62.0 & 50.7 & 69.8 & Steel bar connector failure \\
9 & & 63.1 & & & & \\ \hline
10 & & 53.3 & & & & \\
11 & M3-D12-7.5d & 55.2 & 54.6 & 50.7 & 69.8 & Steel bar connector failure \\
12 & & 55.3 & & & & \\ \hline
13 & & 63.4 & & & & Steel bar connector failure \\
14 & M6-D12-7.5d & 66.4 & 65.8 & 50.7 & 69.8 & Steel bar connector failure \\
15 & & 67.5 & & & & Steel bar rupture failure \\ \hline
16 & & 66.4 & & & & \\
17 & M8-D12-7.5d & 66.7 & 66.7 & 50.7 & 69.8 & Steel bar rupture failure \\
18 & & 67.0 & & & & \\ \hline \end{tabular} Note: \(F_{ys}\) and \(F_{u,s}\) are the yield and ultimate tensile strength of the headed bar in the material test.  
\end{table}
Table 4: Experimental data of bearing capacity.
## 2 Materials and Methods
### CFD Simulations
#### 2.3.4 Grid Details and Grid Independence Study  
The computational domain was discretized using ANSYS ICEM CFD (ANSYS Workbench 2019 R2) by structural hexahedral cells with lower densities allocated at outer boundaries and higher densities distributed around and within the building model where steep velocity gradient occurs. The optimal grid distribution of the computational domain with 1:100 scale model of NVD building in and around is shown in Figure 6a,b.  
Grid independence was analyzed to ensure that the resolution of the mesh did not influence the results by using three different density grids (867,082, 2,345,564, and 4,885,144 cells, respectively) (Figure 7a,b). The optimum grid distribution was achieved by completing a grid-independence control at the inlet boundary velocity of 4 m\(\cdot\)s\({}^{-1}\), during which three different simulations were run with different meshes, until the velocity distribution was constant for the specific point.  
Figure 7 shows the results of the grid independence study. The vertical \(U\) velocity profile at the center (x = 0.165 m, y = 0.478 m) of the NVD barn, and the horizontal \(U\) velocity profile at the 0.02 m height started from 0.015 m from the windward side sidewall and ended after 0.05 m of the leeward side of the NVD building were compared. There was a slight difference (max difference 0.146 m-s\({}^{-1}\)) between the solutions of the medium and low density meshes, while no considerable differences between medium and high density meshes were observed. Therefore, in this study, the medium density mesh (2,345,564 cells) was used for further simulation.
## 1 Introduction
Subsequently, research conducted in 2017 was developed for a simple ORC and ORC with recuperator configuration as a WHR from a V12 stationary diesel engine, which produces 1500 kW at 1500 rpm, and exhaust gas temperatures between 466.8\({}^{\circ}\)C and 484.5 \({}^{\circ}\)C, using different working fluids such as acetone, n-hexane, n-octane. Toluene, ethanol, and MDM. Both cycles use a thermal oil heat exchanger which is the proposed in this study, and the backpressure effect of the bottoming cycle is studied in simulation allowing to find a reduction of specific brake fuel consumption of 17.7 g/kWh and 19.7 g/kWh [16]. In this sense, to improve the thermoeconomic indicators, an ORC cycle with a screw expander was evaluated in 2018 integrated to a marine diesel engine ZLC-6210-5 with 6 cylinders in line that produces 662 kW at 750 rpm. The results allowed the recovery of power of R11 (83.6 kW), R141b (97 kW) and cyclohexane (97.8 kW), where a thermoeconomic analysis was carried out for the variables evaporating temperature and pressure ratio at an exhaust gas temperature of 340 \({}^{\circ}\)C [17]. Recently, a thermal evaluation was performed only for a simple ORC cycle, where bioethanol from selected microalgae was evaluated as working fluid in the operation of a 6 cylinder in-line marine diesel engine, which generates 996 kW at 1500 rpm, obtaining recovered power of 5.1 kW [18]. The main contribution of this research is the present thermoeconomic analysis of WHR configurations as bottoming cycles of a 2 MW natural gas engine, where an exergetic analysis is combined with economic considerations, in order to determine the design of each system with the best cost-efficiency ratio [19; 20]. Each of the exergy flows, exergy losses, and exergy destruction rates are assigned with their costs to identify those unprofitable processes, and therefore to propose the appropriate modification of the system to achieve the best economic performance, through the analysis of the variables that influence the exergy performance of the systems, such as the relative cost [21; 22], exergoeconomic factor [23], and the cost rates of exergy destruction and exergy loss [24]. These indicators enable to evaluate the viability and feasibility of implementing the proposed configurations in the different engine operating conditions.
# Influence of Linear Springing on the Fatigue Damage of Ultra Large Ore Carriers
## 3 Calculation of Fatigue Damage Accounting for the Effect of Wave
### The Total Structural FATIGUE Damage Including the Effect of Springing  
The ITTC two-parameter spectrum is used to describe the sea state. The stress response spectra of wave induced vibration exhibit distinct broad band characteristics, as shown in Figures 1 and 2, and they both contain two peaks. The first peak is caused by the low-frequency wave load when being treated as a rigid body, and the second is resulted from springing. [PERSON] and [PERSON] [28], based on the rain flow coefficient and Gauss process, devised a method for fatigue damage analysis that is capable of dealing with separated double-peaked spectra. By combining a narrow band approximation and the traditional spectrum analysis method for fatigue assessment, a method is proposed in the present study for calculating the fatigue damage due to wave induced vibrations.  
Figure 1: Stress spectrum of 400,000 DWT ore carriers (OC) (\(T_{Z}\) = 6.5 s, \(H_{S}\) = 6.5 m, \(U\) = 11.25 kn, \(\beta\) = 0\({}^{\circ}\)).  
The stress response spectrum is divided into two components: a low frequency component and a high frequency one, which can be described by separate statistical characteristics:  
\[\alpha=\frac{\sqrt{m_{0l}}}{\sqrt{m_{0h}+\sqrt{m_{0l}}}} \tag{16}\]  
\[\zeta=\frac{T_{02l}}{T_{02h}}=\frac{\sqrt{m_{2l}m_{0l}}}{\sqrt{m_{2l}m_{0h}}} \tag{17}\]  
\[\varepsilon_{l}=\sqrt{1-\frac{m_{2l}^{2}}{m_{0l}m_{4l}}} \tag{18}\]  
where \(m_{il}\) and \(m_{ih}\) are the \(i\)th moments of low frequency and high frequency components, respectively.  
According to the joint narrow band approach that was proposed by [PERSON] and [PERSON] [28], the fatigue damage calculation due to wave induced vibration can be obtained as,  
\[\begin{array}{ll}D_{T}&=D_{l}+\frac{1}{\xi\left(\sqrt{1-\alpha^{2}}\right)^ {m-1}}\sqrt{(1-\alpha^{2})+\alpha^{2}\varepsilon_{l}^{2}}\\ &\times\bigg{[}\left(1-\alpha^{2}\right)^{m/2+1}\left(1-\alpha^{2}-\alpha\sqrt {(1-\alpha^{2})}\right)+\alpha\sqrt{\pi(1-\alpha^{2})}\frac{m\Gamma\left( \frac{m+1}{2}\right)}{\Gamma\left(\frac{m+1}{2}\right)}\bigg{]}D_{h}\end{array} \tag{19}\]  
where \(D_{T}\) is the total fatigue damages, \(D_{l}\) and \(D_{h}\) are the contributions of the low frequency and high frequency wave components, respectively, as given by Equation (11). Once \(D_{T}\) and \(D_{l}\) are obtained, the contribution of wave induced vibration to the fatigue damage can be described by the damage ratio of \(\lambda\),  
\[\lambda=\frac{D_{T}}{D_{l}} \tag{20}\]
## 4 Results
### On-Body Access Point Network Architecture
#### 4.1.4 Finding Optimal Protocol Parameter Using Off-Line Computer Simulation  
Due to the similarities between the off-line simulation conditions and the on-line hardware implementation (both processed real channel data), simulation results matched hardware results. The only difference was that in hardware implementation the RSSI table is a reduced version of that used for simulation due to the use of power control in the real-time implementation. Since each ED only uses enough transmission power to reach a specific ED, other EDs might not receive the message and so RSSI data would not be obtained. We observed a negligible disparity between hardware and simulation results due to this factor.  
Figure 10 presents a simulation sweep over values of \(M\) using RSSI data obtained using the experimental setup for the on-body AP network architecture (Section 3.2). The NL improvement ratio is presented as a function of \(M\). It is clear that increasing \(M\) improves performance substantially up to \(M=60\). For \(M>60\) performance is still better than for \(M=0\) but to a diminishing extent as \(M\) increases. Choosing \(10<M<110\) obtains a NL improvement of around 40%. Similar trends were observed for other network architectures. Results are omitted for brevity.  
Figure 9: Energy per Bit ratio for \(M=100\)_vs._\(M=0\). Note that the proposed system exhibits an average 0.4 dB increase in energy per bit.
# High Voltage Ride through Strategy of Wind Farm Considering Generator Terminal Voltage Distribution
## 2 Voltage Distribution Characteristics under a Wind Power Transmission Fault  
### Analysis of Voltage Swell under DC Blocking  
The topology of wind power transmission via HVDC is shown in Figure 1, and its equivalent circuit is shown in Figure 2. The basic structure of wind power transmission via HVDC includes the sending-end grid, wind farm, reactive power compensation device, HVDC transmission, and the receiving-end grid. In Figure 2, \(P_{\mathrm{L}}\) and \(Q_{\mathrm{L}}\) are the active and reactive power output by the sending-end grid, respectively; \(X_{\mathrm{L}}\) is the equivalent reactance of the sending-end AC system; \(P_{\mathrm{F}}\) and \(Q_{\mathrm{F}}\) are the active and reactive power, output by wind farm, respectively; \(X_{\mathrm{F}}\) is the equivalent reactance of the wind power system; \(Q_{\mathrm{C}}\) is the reactive power output of the reactive power compensation equipment; \(P_{\mathrm{T}}\) and \(Q_{\mathrm{T}}\) are the active and reactive power transmitted to the converter station at the sending-end grid, respectively.  
In normal operation, reactive power should satisfy the follow equation:  
\[Q_{\mathrm{T}}=Q_{\mathrm{L}}+Q_{\mathrm{F}}+Q_{C} \tag{1}\]  
The active and reactive power transmitted to the converter station at the sending-end grid satisfy the following equation [22]:  
\[Q_{\mathrm{T}}=P_{\mathrm{T}}\frac{(\pi/180)\mu-\sin\mu\cos(2\alpha+\mu)}{\sin \mu\sin(2\alpha+\mu)} \tag{2}\]  
where \(\alpha\) is the trigger angle of the rectifier, and \(\mu\) is the commutation angle.  
Figure 1: The topology of the wind power transmission.  
Figure 2: The equivalent circuit of the wind power transmission.  
When a DC blocking fault occurs, the active power absorbed by the sending-end converter station drops rapidly, so that \(Q_{\rm T}\) drops rapidly. The reactive power accumulates at the sending-end converter station, which forms a voltage swell at the grid-connection point. The magnitude of the voltage swell can be calculated as [22]:  
\[\Delta U_{\rm r}\approx\Delta Q_{\rm dr}/S_{\rm sr} \tag{3}\]  
where \(\Delta U_{\rm r}\) is the transient voltage swell of the sending-end converter station; \(S_{\rm sr}\) is the short-circuit capacity of the sending-end converter station; \(\Delta Q_{\rm dr}\) is the surplus reactive power of the converter station after the DC blocking fault.  
Therefore, in the scenario that wind power is transmitted via HVDC, the DC blocking fault will cause a voltage swell at the grid-connection point. To deal with the swell, wind turbines' HVRT capability must be considered to avoid trip-off accidents.
# Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems
## 6 Conclusions  
In this paper, the user pairing and power allocation problems for the NOMA downlink systems have been studied, with the consideration of BS power transmission and minimum user rate constraints. First, the sum rate maximization problem was formulated, then the optimal power allocation in closed-form solutions for the multiplexed users was derived using KKT conditions. Second, the energy efficiency maximization problem was formulated in a fractional programming form, in which the global optimal power allocation solution was not guaranteed. Accordingly, the optimal power allocation solution was obtained using the DKL algorithm. Moreover, to get the optimal user pairing, the HNG algorithm paired two users in the subchannel to maximize their sum rate and energy efficiency performance. The results of the simulation have shown that the proposed NOMA-PKKT-HNG achieves high sum rate and provides more fairness. In comparison, NOMA-PDKL-HNG achieves higher energy efficiency performance and better fairness among users when compared to the other techniques. It can be concluded that NOMA-PKKT-HNG and NOMA-PDKL-HNG techniques are promising solutions for 5G NOMA systems.  
Conceptualization, Z.J.A., N.K.N. and A.S.; Data curation, Z.J.A., F.H.; Formal analysis, Z.J.A. and A.S. Investigation, Z.J.A., Methodology, Z.J.A., F.H. and M.B.; Project administration, N.K.N.; Resources, N.K.N.; Software, Z.J.A. and N.K.N.; Supervision, N.K.N., A.S. and F.H.; Validation, Z.J.A., M.B. and F.H.; Writing--original draft, Z.J.A., M.B., and F.H.; Writing--review & editing, Z.J.A., N.K.N., A.S., F.H. and M.B. All authors have read and agreed to the published version of the manuscript. This research was funded by UPM PUTRA BERIMPAK under fundamental research grant (9584300). The authors declare no conflict of interest.
# Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol
###### Abstract
The best way to monitor regions, minimize cost, and maximize network efficiency. 2. Cut down the adverse impacts of the high mobility of UAVs. 3. Traditional routing protocols cannot efficiently manage flying networks due to high node mobility and topology changes. The range and mobility of UAVs are considered the second concern after energy consumption, and these are also assumed as the primary influencers for UAV network topologies [4; 5]. Therefore, it is essential that a new routing protocol be created, which takes these factors into account and handles all the topological changes in the network. Because of the many problems faced by FANETs and the air-network settings, such a paper provides a pac for routing the UAV communication mechanism known as UAV-to-UAV communication. Moreover, with the help of this framework based on the Flying Ad-hoc network protocol, it facilitates optimal contact between UAVs. LEACH [3] is one of the notable WSN clustering routing protocols. In this sensor, nodes are collected in groups called clusters, and each one chooses one cluster as the leading cluster, called the cluster head, and the remaining nodes in a cluster are called cluster members [4]. Nodes collect information from their surroundings and relay it to their CH. Data from cluster participants obtained by CH are grouped, compressed, and delivered to the sink once they have been aggregated. By decreasing the number of transmission packets through cluster construction, the LEACH protocol improves the lifespan of WSNs. However, it does not take the distance or current node energy into account while selecting the cluster head, which is considered one of the various downsides. The CH only uses a single hop when communicating with the sink, which is another downside that makes LEACH irrelevant for vast networks. This paper proposes a protocol using LEACH, which collects inputs consisting of gathered information about UAVs. Based on such knowledge, the selection of CH and contact routes that stay active for a long time may be developed. The main aim is to find highly autonomous routes (longer operating life), residual energy, and a better probability of delivery success. The best path, best connections, and improved network capacity can be achieved from the proposed routing protocols. This paper is disseminated as follows: Section 2 presents the background of the flying Ad Hoc Network (FANET) and the LEACH Protocol. Section 3 consists of the related work. In Section 4, our newly proposed routing protocol and the specifics of the implementation system are outlined. Section 5 covers the performance evaluation of the suggested routing protocol. Section 6 presents the conclusion and the future work.