esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Hybrid RF-VLP for High-Accuracy Indoor Localization with Sparse Anchors
## 4 Results and Discussions
### Summary of Experimental Results  
The proposed hybrid RF-VLP positioning system outperforms the traditional BLE-only system, achieving a 28.5% improvement in localization accuracy with an RMSE reduction from 36.5 cm to 26.1 cm. Since the receiver layout in this positioning system is random, we verify the system's positioning performance under different receiver arrangements. Experiments show that the square arrangement yields the best results (RMSE of 26.1 cm), while random and triangular configurations still maintain reliable accuracy, averaging around 23 cm. Although smaller receiver spacing can lead to poorer positioning accuracy due to increased GDOP, the proposed system remains highly effective in large-scale positioning scenarios, where receiver spacing is typically larger. In these scenarios, the system continues to deliver accurate results, making it suitable for practical, real-world applications.
## 4 SSO, Crowding Distance, and Elite Selection
### The Elite Selection  
Among numerous different selection policies, the elite selection is the simplest and is thus adopted in the proposed tSSO. The elite selection chooses the best solutions in the current generation to generate and update solutions for the next generation [34]. For example, let \(\mathbf{N}_{\text{sol}}\) = 50, \(X_{1}\), \(X_{2}\),..., \(X_{100}\) be the solutions required for selection. Elite selection ranks and selects the best 500 solutions among \(X_{1}\), \(X_{2}\),..., \(X_{100}\) and renumbers these selected solutions as \(X_{1}\), \(X_{2}\),..., \(X_{50}\).  
In the tSSO, these best solutions are temporary nondominated solutions. Due to the fact that the number of temporary nondominated solutions may be less than or larger than the number of parents, a new elite selection called the hybrid elite selection is developed and used in the proposed tSSO, and the details are discussed in Section 5.3.
## 4 Results
### Considerations on the Analyzed Circuits  
In the three considered scenarios, both circuits C2 and C3 achieve the goal of avoiding dirty gas discharge into the atmosphere through the BF top bleeder.  
The advantages and disadvantages of the two circuits are:  
* The circuit C3 discharges cleaner gas into the atmosphere: Circuit C2 discharges directly all the gas that bypassed the cyclones, while circuit C3 mixes the bypassed gas with the clean gas before discharge it.
* Circuit C3 introduces partially dirty gas into the network. However, the simulation results show that when the bleeder B3 is open, about 50% of the flow is introduced into the network and the part that bypasses the cyclones is 24%. In conclusion, the quantity of partially dirty gas fed into the network is acceptable (about 12%).
* In case of low and medium overpressure, circuit C3 reduces the network pressure compared to the operating conditions. The solution is presented in Section 5.
* Circuit C3 offers the huge advantage of having to provide only about 10 m of DN800 pipe for cyclone by-pass instead of 100 m for the emergency line.
# Resource Allocation in Unmanned Aerial Vehicle (UAV)-Assisted Wireless-Powered Internet of Things
## 3 System Model and Problem Formulation
In this paper, we use \(\varepsilon_{H}^{H}\) and \(\varepsilon_{H}^{L}\) to denote the information transmission interference of the wireless nodes to the HEC nodes, and use \(\varepsilon_{L}^{L}\) and \(\varepsilon_{L}^{H}\) to denote the information transmission interference of the wireless nodes to the LEC nodes. The UAV that is responsible for the energy transfer of the two categories of wireless nodes should maximize the energy transfer profit minus the cost during the wireless information transmission and power transfer process. The objective functions of the UAV for the two categories of wireless nodes are given by  
\[V^{H}(t)=\max\int_{0}^{T}\left[ \begin{array}{c}\sum\limits_{i=1}^{N_{H}} \left[\left[\gamma_{t,i}(p_{H,i}(t))-\gamma_{th}\right]^{2}-\pi_{H}(t)p_{H,i}( t)\right]-c_{H}p_{H}^{2}(t)\\ -\varepsilon_{H}^{H}\sum\limits_{i=1}^{N_{H}}p_{H,i}(t)-\varepsilon_{H}^{L} \sum\limits_{i=1}^{N_{L}}p_{L,i}(t)-\mu_{H}x(t)\end{array} \right]e^{-rt}dt+s_{ H}x(T)e^{-rT} \tag{5}\]  
\[V^{L}(t)=\max\int_{0}^{T}\left[ \begin{array}{c}\sum\limits_{i=1}^{N_{L}} \left[\left[\gamma_{L,i}(p_{L,i}(t))-\gamma_{th}\right]^{2}-\pi_{L}(t)p_{L,i}( t)\right]-c_{L}p_{L}^{2}(t)\\ -\varepsilon_{L}^{L}\sum\limits_{i=1}^{N_{L}}p_{L,i}(t)-\varepsilon_{L}^{H} \sum\limits_{i=1}^{N_{H}}p_{H,i}(t)-\mu_{L}x(t)\end{array} \right]e^{-rt}dt+s_{ L}x(T)e^{-rT} \tag{6}\]In (5) and (6), \(c_{H}p_{H}^{2}(t)\) denotes the cost of energy transfer to the HEC nodes, and \(c_{L}p_{L}^{2}(t)\) denotes the cost of energy transfer to the LEC nodes. \(\mu_{H}\) and \(\mu_{L}\) are the natural energy attenuation rate. \(s_{H}x(T)e^{-rT}\) and \(s_{L}x(T)e^{-rT}\) are the terminal cost for the two categories of wireless nodes. Based on (3), we can find the optimal solutions for information transmission, which are mainly varied based on the unit price of transferred energy. After the UAV announces the unit energy price for the two categories, the wireless nodes can make optimal decisions. The unit price of the transferred energy is based on the optimal solution of (5) and (6), combined with (4).
## 3 Antenna Design and Analysis
### Initial DR Dimensions and Parametric Study
Similarly, the effect of the cylindrical air-gap radius on transmission coefficient (S\({}_{12}\) or S\({}_{21}\)) is shown in Figure 4. It is clearly observable from this figure that, by increasing the radius of the air-gap, the isolation at 2.6 GHz improves considerably and its value reaches almost to 35 dB for rad = 6 mm. At 1.8 GHz, the isolation decreases with the increase in _rad_ but at a very low rate and has a value of 13 dB at _rad_ = 6 mm. Figure 3: \(|S_{11}|\) for different values of cylinder radius. Figure 2: S-Parameters Plots. The effect of the metallic sheet strip length \(sl\) on the reflection and transmission coefficients is shown in Figures 5 and 6, respectively. From Figure 5, it is quite apparent that the length of the strip has negligible effect on the reflection coefficient at both the frequency bands. A considerable effect of \(sl\) can be observed on the isolation of the ports as shown in Figure 6. The isolation at 1.8 GHz for \(sl\) = 10 and 15 mm is almost 12 and 13 dB while the isolation improves to 19 dB when the strip length increases to 20 mm. At the 2.6 GHz band, the length of the strip shifts isolation depth towards the lower frequency giving a value of isolation of 38 dB at 2.6 GHz for a value of \(sl\) = 20 mm. Figure 4: \(|\mathrm{S_{12}}|\) for different values of cylinder radius. Figure 5: \(|\mathrm{S_{11}}|\) for different value of sheet height. Figure 6: \(|\mathrm{S_{12}}|\) for different value of sheet height.
# GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks
## 7 Conclusions and Future Works
### Limitations and Future Directions  
A number of possible improvements of this study can be possible, which can be subjected to future works. Some of them are as follows:  
* The study presented in this paper is solely simulation based, and we have not ported the proposed algorithm onto a real sensor based embedded system and thus not tested it in a real environment. Whereas a simulation environment can assume perfect channel estimation and network synchronization, the real environment introduces various challenging tasks.
* If a WSN can run for an enhanced lifetime, it will definitely be cost effective in the long run because various network elements such as sensor nodes and batteries will be utilized for a longer period of time, and the number of fresh network deployments will be reduced. However, the cost estimation from an economical viewpoint was beyond the scope of our present study. A complete cost analysis can be performed to get better insights into the relationship between an extended network lifetime from an en-route filtering perspective and overall network cost.
* In addition to effective re-clustering, optimized sink mobility can further enhance the network lifetime. An interesting question in sink mobility is when and where to relocate the sink. In order to answer \"when\", the time-instant, in terms of how many nodes were depleted (or events), can be determined by optimizing fuzzy membership  
Figure 13: Detection capacity.  
functions for the sink relocation fuzzy system using GA. To address the \"where\" issue, we would evaluate the aforementioned trajectory as well as the energy-aware sink trajectory. Determining the optimal trajectory under a particular network condition, e.g., size of a network, sparse, or dense networks, would also be worthy of investigation
* In addition to re-clustering and optimized sink mobility, balanced dynamic routing can be investigated with the aim of a generalized framework to maximize the network lifetime.  
Conceptualization, M.K.S. and S.M.R.I.; Data curation, M.K.S., S.M.R.I., and M.H. ([PERSON]); Formal analysis, M.K.S., S.M.R.I., M.H. ([PERSON]), M.A.-A.-W. and M.H. ([PERSON]); Funding acquisition, A.A.; Investigation, M.K.S., S.M.R.I., and M.H. ([PERSON]); Supervision, A.A.; Writing--original draft, M.K.S. and S.M.R.I.; Writing--review and editing, M.K.S., S.M.R.I., and M.H. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by King Saud University in 2020.  
The authors are grateful to the Deanship of Scientific Research at King Saud University for funding this work through Vice Deanship of Scientific Research Chairs: Chair of Pervasive and Mobile Computing.  
The authors declare no conflict of interest for publishing in this journal.
# Acceleration of a Production-Level Unstructured Grid Finite Volume CFD Code on GPU
## 5 Performance and Discussion
### Overall Performance  
Finally, we tested the parallelized GPU code by coarse and fine grids of CHN-T1 and counted the performance of the acceleration ratio of each sub-module compared to the serial CPU version, as shown in Figure 13. It can see that the acceleration effect is very obvious, and the overall speed-up ratio of the program can reach 127.7. In the flux calculation part, the acceleration ratio of the subprogram can reach more than 400, demonstrating the effectiveness of the proposed parallelization method and the memory access optimization technique.  
In practical CFD engineering calculations, it is usually necessary to compute until the residual converges to a specified level to obtain stable aerodynamic parameters. Next, this section evaluated the actual performance of the implemented parallel algorithm in terms of convergence speed indicators. Figures 14 and 15 show the residual convergence history of complete calculations for M6 and CHN-T1 with grid size orders of more than 10 million, respectively. This experiment uses CPU+GPU heterogeneous computing cluster with six computing nodes. It is obvious that the colouring algorithm has an impact on convergence efficiency due to the degradation of the implicit property of LU-SGS. It requires more iterative steps to reduce the residual to the same order and even does not converge for the M6. However, the concurrency of colouring algorithms is high, requiring less time for every single iteration, which can compensate for the loss of algorithm convergence efficiency. The concurrency of hyperplane algorithms is not as high as colouring, but it improves the convergence efficiency of LU-SGS because it reduces the bandwidth of the system matrix by cell reordering. By comprehensive comparison, the hyperplane method is more stable  
Figure 12: Speed up of LU-SGS subroutine for different grid level of M6 wing: (**a**) Compared with the same method running on CPU. (**b**) Compared with the original LU-SGS running on CPU.  
Figure 13: Overall speed-up of parallel computing the CHN-T1 on GPU: (**a**) Grid level 1. (**b**) Grid level 2.  
and efficient. With the same number of processes, CPU+GPU heterogeneous computing can speed up more than 30 times compared with pure CPU computing.
# Space-Ground Remote Sensor Network for Monitoring Suspended Sediments in the Yellow River Basin
## 2 Data and Methods
### Data  
#### 2.2.1 Measured Spectra and Suspended Sediment Concentration  
In this study, water reflectance spectral data were acquired using a ZK-UVIR-I in situ hyperspectral spectral water quality monitoring instrument. The spectral range of the instrument is 350-950 nm, with data from the commonly used range of 400-900 nm selected for analysis. The spectral resolution is 0.5 nm. Data collection took place from 26 August 2022 to 26 September 2022 at the Wangtuan station in the Qingshui River basin, Ningxia, China. The actual suspended sediment concentration (SSC) in the water body was determined through manual field sampling and laboratory filtration. Water sampling was conducted using the \"single-line waterfront and one-point surface\" approach, with sampling times synchronized with the acquisition of hyperspectral data. Regarding the sampling frequency, when the river flow was high, at least seven samples were collected per day, while under normal conditions, a minimum of one sample per day was taken. The determination of SSC using the filtration method involves the following steps: (1) A specific volume of water sample is collected using a graduated cylinder and allowed to settle; (2) The supernatant is decanted, and the sediment is poured onto filter paper, allowing the water to percolate; (3) The sediment wrapped in filter paper is dried in an oven; (4) The dry sediment is weighed using a balance with a precision of 0.001 g; (5) The SSC is calculated as the ratio of the dry sediment mass to the volume of the water sample.
# Clustering Method of Large-Scale Battlefield Airspace Based on Multi A * in Airspace Grid System
## 5 Experimental Simulation and Analysis
### Algorithm Comparison Experiment  
In this part of the experiment, Dijkstra algorithm, A * algorithm, and Best First Search (BFS) algorithm are selected as three mature intelligent path planning algorithms. By replacing A * algorithm, the battlefield airspace is clustered according to the steps of the large-scale battlefield airspace clustering method based on Multi-A * introduced in this paper. By comparing the results of airspace clustering, the length of airspace clustering lines, and the number of spatio-temporal clusters generated by airspace clustering lines, the advantages and disadvantages of different algorithms in achieving airspace clustering tasks are evaluated. The length of the airspace clustering line can be used as an important indicator to measure the complexity of airspace association clustering in specific projects. In general, the longer the airspace clustering line is, the more steps and links are required in the specific engineering application and algorithm implementation. Similarly, if the battlefield airspace changes, the airspace association clustering will also change accordingly. The longer the length is, the more difficult and complex the clustering line adjustment will be. The airspace clustering time is an important indicator to reflect the efficiency of airspace association clustering. The shorter the time consumed by airspace association clustering, the higher the efficiency of airspace association clustering and the higher the degree of adaptation to the  
By comparing the length of clustering lines, it was found that under the clustering requirements of different airspace clusters, the A * algorithm can plan shorter clustering lines to achieve the airspace clustering requirements proposed by the task. The overall task amount is smaller, and the overall task amount is sorted from small to large, as follows: A *, Dijkstra, BFS. By comparing the airspace clustering time, it was found that under the clustering requirements of different airspace clusters, the A * algorithm can spend less time to achieve the airspace clustering requirements proposed by the task, and the task completion efficiency is higher. The task completion efficiency is sorted from high to low: A *, BFS, Dijkstra. Therefore, the A * algorithm is more adaptable to the airspace grid system and more suitable for airspace clustering.
## 2 New Field-Oriented Control (FOC) Technique
To address this, we propose an enhanced method that integrates the PLL concept with conventional indirect FOC, and introduces an angle switching module, as illustrated in Figure 11.  
Figure 11: The functional blocks of \(\theta_{rf}^{s}\) angle Switch. Figure 10: The functional blocks of \(\hat{\theta}_{rf}^{s}\) calculator. Since our derivation is based on the final value theorem, which implies steady-state operation, the structure shown in Figure 11 includes a mechanism to determine whether the system has reached steady state and to coordinate the corresponding transient mode of operation. The actual rotor speed, denoted as \(\omega_{r}\), is obtained from a dedicated speed sensor. This signal is passed through a low-pass filter to extract the low-frequency component. Based on this filtered signal, an adaptive tolerance band is defined by the upper and lower bounds \(g_{up}=1+\Delta\) and \(g_{down}=1-\Delta\), where \(\Delta\) represents a tunable parameter that determines the allowable bandwidth. When the dynamic behavior of \(\omega_{r}\) exceeds this predefined range, the system identifies it as a transient state deviation. Consequently, a control signal is generated to reset both the output \(\delta\omega_{rf}^{s}\) and the integrator state of the PI regulator to zero, thereby reverting the control scheme back to the conventional Indirect Field-Oriented Control (IFOC) mode. The proposed module is built upon the conventional indirect FOC structure and incorporates the developed PLL-based framework. Under non-steady-state conditions--such as motor startup, acceleration, deceleration, or during significant torque disturbances--the rotor speed often falls outside the predefined steady-state range. When this occurs, the detection mechanism activates a low-level \"Level_Rst\" signal, which suppresses the output of the proportional-integral (PI) regulator, effectively holding it at zero. At this stage, the PLL is disabled and unable to operate, and the system reverts to a traditional indirect FOC scheme to estimate the rotor flux angle (\(\theta_{rf}^{s*}\)), as depicted in Figure 3a,b. Once the system re-enters the predefined steady-state range, the \"Level_Rst\" signal returns to a high level, thereby activating the proposed control strategy. The phase-locked loop (PLL) utilizes its output (\(\delta\omega_{rf}^{-s}\)) to adjust the estimated angle (\(\widetilde{\theta}_{rf}^{s}=\theta_{rf}^{s*}\)), ensuring close tracking of the desired angle (\(\theta_{rf}^{s}\)). Through this repeated switching mechanism, the system leverages the robustness of the traditional indirect FOC during transients and transitions to a more accurate estimation mode during steady-state. Consequently, the initial architecture shown in Figure 8 is further refined into the second-stage structure illustrated in Figure 12.  
Figure 12: System structure of the proposed method in Stage II.
## 4 Experimental Verification
### Speed of the Acquisition Process  
To verify the speed of the acquisition process, the time required to achieve different success rates was analyzed using telemetry data, as shown in Figure 14. We can observe that the novel acquisition process can achieve a 90% success rate in 0.1 s, while the traditional acquisition process needs 32 s. Therefore, the acquisition process was accelerated by more than 300 times.
## 3 Methods for Closed Path Determination
### Watershed Transformed Maximum Average-Intensity Circle Extraction  
Both WT and MAIC methods are simple and intuitive ways to obtain a closed path for circulation calculation, and they have equally excellent performance when the OV beam has little to no aberration. However, when the OV beam is severely distorted by aberration, the intensity sum map of the OV beam will have a shape far from a perfect circle, and may even split into several parts, resulting in both methods losing their efficiency. To further improve the performance under this severe aberration condition, we here propose a new method that combines WT and MAIC. In this method, we first perform watershed transformation on the intensity sum map, and then search for the ridge from among the elements on the watershed lines based on the MAIC algorithm. With the output of watershed transformation previously obtained, the influence of the element along the ridge and the noise can be significantly reduced, and thereby, a more proper closed path can be extracted. For simplicity, we named this method the watershed-transformed MAIC method (WT-MAICM).
## 3 Results  
### Premaxilla Shape Variation  
We found a shared pattern of divergence in the premaxilla shape (parallel evolution) between morphs in the two systems analyzed (Figure 2). RW1 was related to the width and the angle of the ascending process, with positive values showing a narrower ascending process and a sharper angle corresponding to the elongate-body morph in both systems, with opposite patterns found in the deep-body morph. RW2 showed variation related to the width of the premaxilla and length of the ascending process, with morphs showing no differentiation based on this relative warp. RW1 and RW2 together explained most of the variation (80.2%) of the shape of the premaxilla (Supplementary Material Table S1A for explained variation of all relative warps). In the northern system, Lake Catemaco, morphs showed some overlap in premaxilla shape (Figure 2A), contrasting with the southern morphs from the San Juan River system, which showed a larger divergence, with almost no overlap in premaxilla shape between morphs (Figure 2B), depicting a geographical cline, increasing north to south, in the premaxilla shape differentiation between the morphs.  
The analyses of the premaxilla shape revealed no differences in disparity between morphs in the Lake Catemaco system (Procrustes variance in the deep-body morph: 0.008 vs. the elongate-body morph: 0.01, \(p\) = 0.21), while in the San Juan River system, the elongate-body morph showed a two-fold higher disparity of premaxilla shape than the deep-body morph (Procrustes variance in the elongate-body morph: 0.015 vs. the deep-body morph: 0.009, \(p\) < 0.001). We can appreciate these differences in the graphical representation in the vectors' length toward the elongate-body morph, where the morphs from the San Juan River showed a larger differentiation (Figure 3B,D) than the morphs from the Lake Catemaco system (Figure 3A,C). Thus, we observed a parallel pattern of premaxilla shape evolution in the two systems when plotting the shape differences using the deep-body morph for reference.  
Figure 2: Premaxilla shape variation for (**A**) Lake Catemaco system and (**B**) San Juan River system by morph. Deformation grids show shape variation along RW1 (\(X\) axis) and RW2 (\(Y\) axis).