esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 1 Instruction
As the GNSS is required to maintain the horizontal navigation safety during all phases of flight while the vertical navigation safety supported by GNSS is only required in approach and departure phases, as shown in Table 1. The satellite selection strategy takes horizontal accuracy and integrity as targets in this study while can also be applied to VPL-demanded flight phase through a similar process. The rest of this paper is arranged as follows. Section 2 describes the accuracy and integrity models in the weighted slope-based RAIM algorithm and analyzes the impacts of subset size on the performance to exhibit the importance of satellite selection in the RAIM process. In Section 3, the impacts of a single satellite on the accuracy and integrity of the subset are investigated to recognize the valuable satellite for the current subset, then the performance-requirement-driven fast satellite selection algorithm is proposed according to the above investigation. Then, Section 4 illustrates the performance of the proposed algorithm with comparison simulations. Finally, the conclusion is drawn in Section 5. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of Operation** &
\begin{tabular}{c} **HOR/VERT.** \\ **Accuracy (95\%)** \\ \end{tabular} & **Integrity** & **HAL/VAL** & **Time to Alert** & **Continuity** & **Availability** \\ \hline En Route Oceanic & 3700 m/NA & 1–\(10^{-7}\)/h & 7408 m/NA & 5 min & 1–\(10^{-4}\)/h to 1–\(10^{-8}\)/h & 0.99 to 0.99999 \\ En Route Continental & 3700 m/NA & 1–\(10^{-7}\)/h & 3704 m/NA & 5 min & 1–\(10^{-4}\)/h to 1–\(10^{-8}\)/h & 0.99 to 0.999999 \\ En Route Terminal & 740 m/NA & 1–\(10^{-7}\)/h & 1852 m/NA & 15 s & 1–\(10^{-4}\)/h to 1–\(10^{-8}\)/h & 0.99 to 0.99999 \\ APV-I & 16 m/20 m & 1–\(2\times 10^{-7}\)/App. & 40 m/50 m & 10 s & 1–\(8\times 10^{-6}\)/15 s & 0.99 to 0.9999 \\ APV-II & 16 m/8 m & 1–\(2\times 10^{-7}\)/App. & 40 m/20 m & 6 s & 1–\(8\times 10^{-6}\)/15 s & 0.99 to 0.9999 \\ Category I & 16 m/4 m & 1–\(2\times 10^{-7}\)/App. & 40 m/10 m & 6 s & 1–\(8\times 10^{-6}\)/15 s & 0.99 to 0.99999 \\ Category II & 6.9 m/2 m & 1–\(10^{-9}\)/15 s & 17.3 m/5.3 m & 1 s & 1–\(4\times 10^{-6}\)/15 s & 0.99 to 0.99999 \\ Category III & 6.2 m/2 m & 1–\(10^{-9}\)/30 s (H) & 15.5 m/5.3 m & 1 s & 1–\(2\times 10^{-6}\)/30 s (H) & 0.99 to 0.99999 \\  & & 1–\(10^{-9}\)/15 s (V) & 15.5 m/5.3 m & 1 s & 1–\(2\times 10^{-6}\)/15 s (V) & 0.99 to 0.99999 \\ \hline \hline \end{tabular}  
* HAL, horizontal alert limit; VAL, vertical alert limit; APV, approach procedures with vertical guidance; H, horizontal; V, vertical.  
\end{table} 
Table 1: Aviation GNSS signal-in-space performance requirements [13].
## 4 Results
### NMR Experiments and Data Analysis  
#### 4.4.1 NMR T\({}_{2}\) Spectral Analysis  
The pore size distribution is closely related to the characteristics of the NMR \(T_{2}\) spectra. For example, the \(T_{2}\) relaxation times with longer values are usually associated with larger pores [48; 56]. Hence, NMR \(T_{2}\) spectra can provide crucial information for pore size evaluation. Different from the pore throat size distribution derived from MICP analysis, NMR experiments can provide information of the full-range pore structures [9]. According to the NMR experiments performed on 21 sandstone samples from the E\({}_{\text{std}}\) reservoir, the NMR \(T_{2}\) spectra are characterized as both unimodal and bimodal (Figure 8), indicating great differences among various sandstone samples [56]. Some typical NMR \(T_{2}\) spectral distributions are shown in Figure 8A-F.  
Samples with unimodal distribution (e.g., samples C276-2 and L218-3) display a continuous pore size distribution, while the \(T_{2}\) relaxation time is mainly between 0.1 ms and 1000 ms (Figure 8A,B). The pore systems are dominated by dissolution pores and residual intergranular pores (Figure 4A,G,H). Samples with bimodal behaviors are shown to have both long and short \(T_{2}\) components (Figure 8C-F), implying a wide range of pore size distribution and strong reservoir heterogeneities. The bimodal behaviors can be further separated into bimodal large pore and bimodal small pore behaviors, considering the morphological features on the NMR \(T_{2}\) spectra. The bimodal large pore samples (e.g., samples C141-1 and F119-1) usually have a wider range of \(T_{2}\) values (mainly in the range of 0.1-3000 ms) compared with these samples with unimodal distributions (Figure 8C,D). The pore systems are dominated by residual intergranular pores and dissolution pores (Figure 4I,J). Very large \(T_{2}\) values (>100 ms) are rare in the sandstone samples with bimodal small pore distributions due to the lack of the large pores (Figure 4K,L). The bimodal small pore distributions are usually characterized by a higher left peak and a lower right  
Figure 8: Typical NMR \(T_{2}\) spectral distributions (**A,B**) showing unimodal behaviors in samples C276-2 and L218-3; (**C,D**) showing bimodal large pores in samples C141-1 and F119-1; (**E,F**) showing bimodal small pores in samples G351-2 and G890-2.  
peak. The \(T_{2}\) values of the left peak are commonly less than 10 ms, and the amplitude is significantly higher than that of the right peak (Figure 8E,F).
# DDPG-Based UAV-RIS Framework for Optimizing Mobility in Future Wireless Communication Networks
## 1 Introduction  
The use of higher frequency bands such as millimeter wave (mmWave) and Terahertz (THz) in beyond 5G (B5G) networks is expected to greatly increase network capacity, support a large number of devices, and enable applications such as virtual reality (VR)and augmented reality (AR) [1; 2; 3; 4; 5]. To meet these requirements, UAVs have emerged as a promising solution. Unlike traditional ground base stations (GBSs), UAVs can be deployed flexibly, adjust their positions as needed, and provide cost-effective wireless coverage [6; 7; 8]. With their ability to establish line-of-sight (LoS) links with user equipment (UEs), UAVs improve data rates, connectivity, and service reliability [9; 10; 11].  
Despite these features, UAV-based communication faces several challenges, especially in dense urban environments. High-rise buildings and other obstacles like trees block LoS paths, limiting coverage and causing user disconnections. Additionally, UAVs are highly mobile, which can cause frequent handovers, and maintaining stable connections over long distances can be difficult. These issues make UAV-only communication systems less reliable in dense urban environments.  
To address these issues, reconfigurable intelligent surfaces (RIS) technology has been proposed as a promising solution. RIS consists of an extensive array of passive reflecting elements that can intelligently adjust the phase of incoming signals to improve wireless transmission [12; 13; 14; 15]. Using adjusting and redirecting signal phenomena around obstacles, RIS can enhance network coverage and data rates in dense areas [16]. Since RIS elements are passive and do not require active power sources, they offer an energy-efficient alternative to traditional relay systems. However, RIS alone is not enough to fully solve the challenges of UAV-based communication. While RIS can improve signal transmission, it cannot handle the frequent link disruptions caused by UAV mobility and user movement. Therefore, integrating UAVs with RIS can provide a more robust solution using both technologies. The UAV ensures flexible coverage, while the RIS enhances signal quality by reflecting signals toward intended users.  
This work explores UAV-integrated RIS (UAV-RIS) to improve mobility management and HO performance in dense urban heterogeneous network (HetNet) environments. The UAV provides LoS communication, while the RIS dynamically adjusts signal reflections to mitigate blockages. To maximize throughput, improve energy efficiency (EE), and ensure seamless connectivity, we jointly optimize UAV trajectories and RIS phase shifts using a deep deterministic policy gradient (DDPG) approach. Simulation results demonstrate that the proposed UAV-RIS framework significantly reduces HO failures (HOF), enhances network reliability, and improves overall communication performance, highlighting its potential for B5G wireless networks.
###### Abstract  
This study analyzed the performance, antioxidant status, hepatopancreatic lipoperoxidation, and proximate composition of _Macrobrachium rosenbergii_ juveniles fed diets supplemented with clove basil (_Ocinum gratissimum_) essential oil (EO-OG). A total of 360 _M. rosenbergii_ (initial weight 0.028 g \(\pm\) 0.004) were randomly divided into four experimental groups with six replications each (\(n\) = 6). The pawns were fed diets with different EO-OG inclusion levels: 0.0, 1.0, 2.0, and 3.0 g kg\({}^{-1}\) EO-OG. After a 42-day feeding trial, dietary EO-OG showed no significant effect on prawn performance or carcass proximate composition, except on final antenna length. Prawns fed 3.0 g kg\({}^{-1}\) EO-OG displayed a 1.2- to 1.3-fold longer final antenna length than pawns from all other experimental groups. Likewise, pawns fed 3.0 g kg\({}^{-1}\) EO-OG presented a 2.6- to 3.2-fold higher catalase activity than pawns from all other experimental groups. Prawns fed EO-OG, regardless of the inclusion level, showed a 1.6- to 1.7-fold decreased hepatopancreatic lipoperoxidation compared to the control group. Therefore, EO-OG has been demonstrated to be a potential management tool as a non-nutritional dietary immunostimulant and animal welfare promoter for freshwater prawn farming, without affecting animal performance. This study recommends the dietary inclusion level of 3.0 g kg\({}^{-1}\) EO-OG for _M. rosenbergii_ juveniles.  
clove basil; crustacean welfare; enzymatic defense; giant river prawn +
Footnote †: journal: Applied sciences
# Developing and Field Testing Path Planning for Robotic Aquaculture Water Quality Monitoring
## 2 Overview of the Three Path Planning Algorithms
### Wind Considerations  
Whenever planning flights for any aircraft, it is important to take weather conditions into consideration. Significant wind can dramatically change the optimal flight path by increasing the cost of flying upwind. Some research has been done to investigate the effect of wind on path planning for aerial drones, but this research is divided into fixed wing [28; 29] and multicopter [38; 39] configurations. The so-called Windy Routing Problem (WRP) [40; 41; 42] addresses multicopter flight planning in windy conditions, as it is not constrained by the turn radius of a fixed wing aircraft, which requires additional consideration [28]. WRP assumes steady wind in a single direction and no complex wind phenomena such as turbulence and pressure or density changes.  
To model this for our aquaculture dataset, we increase the cost of travel in upwind directions and reduce the cost in downwind directions. A random direction and wind speed is chosen, with the maximum wind speed being just less than the top speed of the drone. This process is described in Figure 4. We repeat the aquaculture simulation path planning with this added factor and report the results in Tables 4-6. GAM did not perform as stably when wind conditions were factored in, and much of its increase is attributed to its total failure to plan efficient routes for a few samples while performing reasonably well for most samples. HPP and GLOP performed similarly as in non-windy conditions, with GLOP providing the optimal routes but being unable to produce results in a reasonable amount of time for real-time operations.  
Figure 4: Flowchart describing the process of generating the simulated data with and without wind considerations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ponds** & **HPP** & **GAM50** & **GAM100** & **GLOP** \\ \hline
50 & **0.013** & 1.1 & 1.2 & 735 \\ \hline
100 & **0.030** & 3.5 & 3.1 & 6581.5 \\ \hline
200 & **0.047** & 9.0 & 10.7 & 54,352.6 \\ \hline
300 & **0.095** & 21.5 & 23.9 & - \\ \hline
500 & **0.110** & 59.7 & 64.9 & - \\ \hline
700 & **0.115** & 179.7 & 185.1 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average Run Time (ms).
## 3 Results
### Optimization Results for the Linear Load Case with Infeasibility  
The third analysis compares the two algorithms for the same b-pillar with two more design variables and the infeasibility constraint. Figure 7 shows three different configurations of the b-pillar in its thinnest, the initial, and the thickest configuration. The used design and package-constraint surfaces are the same as in [19]. The stiffener can still vary between the upper and the lower shell. Furthermore, the design space, representing the external geometric constraint from package and design, is highlighted in red.  
Another problem is the ratio of infeasible designs by the intersecting hull surface, which reduces the number of calculated designs [18; 20]. Accordingly, the sampling size of actual calculated samples shrinks by a factor of 0.2 for the given sample. The sampling size is initially set to 1000 members to compensate for that effect. Hence, approximately 200 members are calculated during the optimization; still, the number of design variables is increased by two, which increases the size of the solution space. The number of iterative samples is set to 200. Finally, Figure 8. shows the resulting point diagram with the normalized mass and z-displacement.  
Figure 6: Optimization result of the non-linear load case without any infeasibility constraint for the NSGA-II and GOMORS. The figure shows the sampling of both algorithms shaded and the initial design in green. All values are relative to the initial sheet metal’s weight and the initial setup’s intrusion.  
The distribution of the samples differs due to the different model setups. Conversely to the previous optimization runs, the optimization algorithms perform very differently. The NSGA-II and the GOMORS act pretty similar for the shape optimization task. Both form an interval of potential candidates but do not converge into a region of ideal design. For equal mass, both algorithms find solutions, which decrease the displacement by 20%. For a similar displacement, the max decrease is about 1.5%. Notably, the NSGA-II shows a slightly better performance than the GOMORS, which the small sampling size could cause. Both algorithms mostly create feasible designs considering the infeasibility constraint. The GOMORS creates three infeasible members, and the NSGA-II creates four infeasible  
Figure 8: Optimization result of the linear load case with infeasibility constraint for the NSGA-II and GOMORS. The figure shows the sampling of both algorithms shaded and the initial design in green. All values are relative to the initial sheet metal’s weight and the initial setup’s intrusion.  
Figure 7: The b-pillar for the third scenario. The red line represents the geometric hull surface. The stiffener can be changed the same way as in the first scenario. Additionally, the thickness of the b-pillar is variable by the upper and lower shell.  
members. Still, both results seem to be insufficient in the improvement of performance. Hence, a different approach is needed, discussed in the following section.
## 2 CKF Algorithm  
### Bayesian Estimation Rule  
In this paper, we consider the nonlinear dynamic system, whose state space model is defined by the difference equation in discrete time [27]:  
\[\begin{array}{rcl}\mathbf{x}_{k}&=&f(\mathbf{x}_{k-1},\mathbf{u}_{k-1})+\mathbf{v}_{k-1}\\ &z_{k}&=&h(\mathbf{x}_{k},\mathbf{u}_{k})+\mathbf{w}_{k}\end{array} \tag{1}\]  
where \(\mathbf{x}_{k}\) and \(\mathbf{z}_{k}\) are the n-dimensional state and the m-dimensional measurement of the dynamic system, respectively; \(\mathbf{u}_{k}\) is the n-dimensional control input; \(\{\mathbf{v}_{k-1}\}\) is a n-dimensional system noise sequence with zero mean and covariance \(\mathbf{Q}_{k-1}\); \(\{\mathbf{w}_{k}\}\) is an m-dimensional measurement noise sequence with zero mean and covariance \(R_{k}\); \(f(\bullet)\) and \(h(\bullet)\) are the nonlinear state function and measurement function of the system, respectively.  
When \(\mathbf{v}_{k-1}\) and \(\mathbf{w}_{k}\) are independent, the posterior density of the state \(\mathbf{x}_{k}\) of the nonlinear dynamic model (1) can be obtained based on the Bayesian estimation rule as follows:  
* _Time update_, calculating the predictive density: \[p(\mathbf{x}_{k}|\mathbf{D}_{k-1})\ =\ \int p(\mathbf{x}_{k},\mathbf{x}_{k-1}|\mathbf{D}_{k-1}) \textit{dx}_{k-1}\ =\ \int p(\mathbf{x}_{k-1}|\mathbf{D}_{k-1})\times p(\mathbf{x}_{k}|\mathbf{x}_{k-1},\mathbf{u}_{k- 1}),\textit{dx}_{k-1}\] (2) where \(\mathbf{D}_{k-1}\ =\ \{u_{i},z_{i}\}_{i\ =\ 1}^{(k-1)}\) describes the history data up to time \((k-1)\); \(p(\mathbf{x}_{k-1}|\mathbf{D}_{k-1})\) is the old posterior density at time \((k-1)\); the state transition density \(p(\mathbf{x}_{k}|\mathbf{x}_{k-1},\mathbf{u}_{k-1})\) is calculated by Equation (1).
* _Measurement update_ The posterior density of the current state can be obtained by Bayesian rule: \[p(\mathbf{x}_{k}|\mathbf{D}_{k})\ =\ \frac{1}{c_{k}}p(\mathbf{x}_{k}|\mathbf{D}_{k-1},u_{k-1})p( \mathbf{z}_{k}|\mathbf{x}_{k},\mathbf{u}_{k})\] (3) \[c_{k}\ =\ \int p(\mathbf{x}_{k}|\mathbf{D}_{k-1},\mathbf{u}_{k})p(\mathbf{z}_{k}|\mathbf{x }_{k},\mathbf{u}_{k})\textit{dx}_{k}\] where \(c_{k}\) is a constant. However, for nonlinear/non-Gaussian cases, the computational complexity of \(c_{k}\) is typically prohibitive. A computationally feasible approximation is provided by PF [28]. Since the denominator is a normalizing constant, Equation (3) can be regarded as an unnormalized density, the fact does not affect the following derivation. If the \(p(\mathbf{x}_{k}|\mathbf{D}_{k})\) can be calculated exactly, we obtain the state estimation and covariance matrix of \(x_{k}\) as follows: \[\hat{\mathbf{x}}_{k}\ =\ \int\mathbf{x}_{k}p(\mathbf{x}_{k}|\mathbf{D}_{k})\textit{dx}_{k}\] (4) \[\mathbf{P}_{k+1}\ =\ \int\big{(}\mathbf{x}_{k}-\hat{\mathbf{x}}_{k}\big{)}(\mathbf{x}_{k}- \hat{\mathbf{x}}_{k}\big{)}^{\mathrm{T}}p(\mathbf{x}_{k}|\mathbf{D}_{k})\textit{dx}_{k}\] (5)  
In fact, the analytic solutions for \(p(\mathbf{x}_{k}|\mathbf{D}_{k})\) is difficult to be obtained in the nonlinear system. Therefore, we need to approximate the state estimation and covariance matrix.
## 4 Evaluating the Performance of WSN Routing Strategies with Respect to DSA
### Reactive Strategy
#### 4.3.1 Real-Time Load Distribution Routing Protocol (RTLD)  
In RTLD, nodes always maintain an updated neighborhood table that records the values of the velocity to route packets to all neighbors, the packet reception rate and the remaining power. This information is gathered by means of a periodic broadcast of \"hello\" packets to help refresh the neighborhood table. Routing is then performed based on the geographical location of the nodes in a quadrant. Nodes that have data packets to send select only eligible recipients that reside in its quadrant until the packet is routed to the sink. Hence, the best path to the sink is explored based on the three gathered metrics from the \"hello\" broadcast.
## 5 Conclusions  
In this paper, the electric field characteristics of composite insulators under complex working conditions were explored by simulation, and optimization strategies were proposed for detection methods. A pre-detection method based on sensitive insulator criterion was proposed by combining theoretical derivation and experimentation, and the following three conclusions were obtained.  
1. The contactless detection means of insulators are easily affected by complex site operating conditions. The influence of the surface state of an insulator on its space electric field is less than 4%; the attitude disturbance of UAV can easily affect the detection results of the electric field of insulators at the high-voltage end, 8.6% at most; the natural falling of insulators can also cause a deviation of about 4.8% for the insulator electric field. The optimization strategy proposed in this paper can effectively reduce such errors.
2. It was found by the degraded insulator detection that: If the zero-value insulator is set at any position, the changes of electric fields of the last insulators close to the low-voltage side are the most sensitive, and the rate of change of space electric field is basically more than 5.0%. Therefore, the sensitive insulator set under a voltage level of 220 kV is generally at the low-voltage end.
3. Based on the criterion for sensitive insulators, and combined with the contactless electric field detection method, the insulation performance of the insulator string can be accurately judged by measuring the space electric field of the sensitive insulator set. This method combines the advantages of the electric field distribution measurement method and sensitive insulator method, which can improve the detection efficiency and safety and be of great significance for the promotion of the contactless detection method of insulator electric fields in practical applications.
4. The detection method proposed in this paper is mainly for insulator strings at the 220 kV voltage level. The next step is to determine the sensitive insulator groups at different voltage levels and then propose corresponding detection strategies.  
**Author Contributions:** Conceptualization, S.G. and Y.Z.; data curation, S.G.; formal analysis, S.G., Y.L. and J.G.; funding acquisition, J.J.; investigation, J.J.; methodology, J.J., Y.L. and Y.Z.; project administration, X.H. and J.G.; resources, X.H. and Y.L.; software, X.H. and B.Z.; supervision, B.Z.; validation, B.Z.; visualization, J.G.; writing--original draft, Y.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Science and Technology Project of the State Grid Jiangsu Electric Power Company (J2021060).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Modified Boost Converter (MBC)  
Figure 1a-c shows the conventional, cascaded, and proposed boost converters, respectively. As can be seen in Figure 1c, a boosting structure is located between the entrance inductor and the power switch. Based on switch ON or OFF working modes, only one of the \(D_{1}\) and \(D_{2}\) diodes will be active in the structure and in this way, it makes a preamplifier layer for boosting purposes. All working principles and mathematical analyses have been stipulated for both ON and OFF states of the power switch.  
Figure 2 presents the Pulse Width Modulation (PWM) wave applied to the power switch in the boost converter. In this figure for the [0, t\({}_{1}\)] and [t\({}_{1}\), T\({}_{S}\)] time intervals switch S\({}_{1}\) is in ON and OFF modes, respectively. So, if we consider [0, t\({}_{1}\)] interval as DT\({}_{S}\), the [t\({}_{1}\), T\({}_{S}\)] interval will be (1-D) T\({}_{S}\). In other words, D is the duty cycle of power switch S\({}_{1}\).