esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations  
First, this is a single-hospital study and the generalizability of the study findings should therefore be made with caution. Second, about 39% (2159/5581) of patients were excluded from the analysis because of declining informed consent or missing 3-month data. However, the possibility of selection bias might not be a concern because the study population did not differ from excluded patients in age, sex, and stroke severity. Third, the dependent variable of interest, i.e., readmission or mortality, was obtained through interviews with patients or their proxies. Therefore, recall bias might cause underestimation of the dependent variable. Fourth, this study did not use variables related to the process of acute care or post-acute care hospitalizations to develop prediction models, thus possibly undermining the prediction performance. On the other hand, prediction models using only information available upon admission might be advantageous in delivering the early targeted intervention to patients at high risk of readmission or mortality.
## 6 Conclusions  
In the near future, MSP of the GAL E1B/C with an E1D signal could lead to significant performance gains for civil GNSS receivers in the upper L-band. The increase in ranging performance will depend on the exact shape and placement of the GAL E1D signal. However, the impact of payload and receiver distortions has to be closely monitored and expected theoretical performance gains will be reduced significantly for realistic levels of distortion. NELP TOA estimation with a single signal (e.g., E1C only) is more robust with respect to distortions and can even perform better than a solution based on MSP of signals with an insufficient frequency offset, if the distortions are too severe.  
Figure 6: Unsmoothed TOA estimation error stdev. for different noise levels for the two extreme distortion cases for NELP.  
Figure 7: Evolution of first and second statistical moments for a fixed noise level of 45 dB-Hz. (a) NELP TOA estimation error sample stdev. (b) TOA estimation error sample mean.  
**Author Contributions:** Conceptualization, F.C.B. and M.M.; methodology, F.C.B. and C.E.; software, F.C.B.; validation, F.C.B., C.E., and M.M.; formal analysis, F.C.B. and C.E.; investigation, F.C.B.; resources, F.C.B.; data curation, F.C.B. and S.T.; writing--original draft preparation, F.C.B.; writing--review and editing, F.C.B. and C.E.; visualization, F.C.B.; supervision, M.M.; project administration, S.T.; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Processing and Analysis
The statistical significance of the differences in the data (\(p<0.01\)), associated with the time of observation (i.e., am/noon/pm) and growth stage (i.e., Yn/M/S1/S2), were determined based on an ANOVA general linear model (GLM) analysis (SYSTAT 12.3, SYSTAT Software Inc., 2017), suggested as optimal for use with data with unbalanced design and uneven number of samples, which occurred due to outliers and missing values. The coefficients of determination (R\({}^{2}\)) are used to describe the level of variation in the measurements attributed to the ANOVA independent variables: (1) observation time (i.e., morning, midday, afternoon); (2) growth stage (i.e., young, mature, senescent); and (3) the interactions of the first two (Table 3). The ANOVA GLM least square means of the dependent variables (e.g., GPP, VIs, F, and SIF metrics, etc.) are reported, indicating the significance of the differences among the means with a different letter (Table 3). We anticipated that SIF observations at high PAR (i.e., saturating light level for photosynthesis) were likely to have stronger correlations with the leaf level F metrics and canopy GPP. To depict the relationship of fluorescence metrics (i.e., F and SIF) with CO\({}_{2}\) assimilation (GPP) not limited / controlled by PAR, we separately analyzed the measurements collected in periods of the day where PAR is known to be below saturation (am and pm) from the period where PAR commonly is at levels saturating for photosynthesis and electron transport (i.e., 12:30 noon \(\pm\) 1 h, Table 4), therefore providing a more consistent comparison of the relationships between photosynthetic performance and F metrics across the growth stages. To provide context for interpretation and relevance of the results to the satellite observations of SIF, the measurements collected under varying lower light levels were included in the analysis (Tables 3 and 4), and are identified in Figures 4 and 5 (i.e., am, noon and pm). The overpass time of the satellites measuring SIF over Greenbelt, MD is for MetOp GOME-2 9:30 equatorial local time (ELT), Envisat SCIAMACHY and FLEX 10:00 ELT ([[http://www.ceos-cove.org/en/](http://www.ceos-cove.org/en/)]([http://www.ceos-cove.org/en/](http://www.ceos-cove.org/en/))), ranging during the growing season by more than an hour, and is approximated by the morning (am) observations.
## 4 Discussion  
### Response Law of AFT Hydraulic Performance to Structural Parameters  
In order to study the variation law of \(P\) within the range of \(n\) = 0\(-\)3000 rpm, this paper conducts simulation research by using Fluent 2022 R1 and adopts the grid motion simulation method with a fixed n value. Due to certain limitations of CFD simulation, factors such as friction loss and the mass of the shaft are not considered. The \(nRMSE\) between the simulated values and the measured values is 5.1%. The simulation precision is relatively high, but there are still certain deviations.  
AFTs can be utilized in hydroelectric power generation to convert water energy to electrical energy. At small- and medium-sized river power stations, for AFTs with M\({}_{\text{1}}\) = 1 and an impeller diameter of 76 mm, \(P\) increases and then decreases with increasing \(n\) (0\(-\)3000 rpm), following a quadratic relationship [27,44]. This trend is consistent with the relationship observed in this study for AFTs with M\({}_{\text{1}}\) = 2 to 4 and an impeller diameter of 63 mm. When the impeller diameter or operating conditions of AFTs change, \(P_{\text{max}}\) varies with the impeller structure. For example, in hydraulic turbines for residual pressure recovery during oil drilling, where \(H\) is 10 MPa (53.3 times greater than that in this study), \(P_{\text{max}}\) increases with M\({}_{\text{2}}\) when \(n\) = 735 rpm, M\({}_{\text{1}}\) = 1, and M\({}_{\text{2}}\) = 3 to 5 [45]. However, in this study, \(P_{\text{max}}\) decreases as M\({}_{\text{2}}\) increases. Similarly, tidal energy generators, which use AFTs with diameters of 3500 to 4500 mm (63.5 times the size of AFTs in this study) to convert flow kinetic energy to mechanical and electrical energy, operate with a flow rate (\(Q_{\text{S}}\)) of 25,000 to 40,000 m\({}^{3}\)/h (101.6 times that in this study). For these systems, \(P_{\text{max}}\) increases with M\({}_{\text{1}}\); when M\({}_{\text{1}}\) increases from 1 to 3, \(P_{\text{max}}\) increases by an average of 14.0% [46,47]. However, in this study, when M\({}_{\text{1}}\) increases from 2 to 4, \(P_{\text{max}}\) decreases by an average of 10.2%. A significance analysis of \(P_{\text{max}}\) in the \(P\)-\(n\) curve shows that AFTs with M\({}_{\text{1}}\) = 2 achieve  
Figure 15: Variation curves of the optimized axial-flow hydrodynamic fertilizer injectors (D33, D44) and the fertilizer injection flow (\(Q_{\text{inj}}\)) of the domestic and foreign venturi fertilizer injectors with the inlet pressure (\(H\)) [12,15,16,43].  
average increases of 8.7% and 22.7% in \(P_{\rm max}\) compared with those with M\({}_{1}\) = 3 and M\({}_{1}\) = 4, respectively. Among the impellers analyzed, the \(P_{\rm max}\) of D33 is the highest, with an average increase of 6.7% to 33.5% compared with those of the other AFTs across all \(H\) conditions.
# A Comprehensive Evaluation Algorithm of Multi-Point Relay Based on Link-State Awareness for UANETs
## 2 Related Works
### MPR Selection Model  
In OLSR, nodes are categorized into MPR nodes and general nodes based on their ability to forward control messages. General nodes are limited to receiving and processing messages, while only nodes designated as MPRs have the capability to forward control messages [27]. Therefore, selecting appropriate MPR nodes is pivotal for enhancing network performance. The MPR mechanism effectively limits the widespread dissemination of control messages, thereby reducing control overhead in the network, preventing resource wastage, and mitigating network congestion [28]. As illustrated in Figure 1, employing the MPR mechanism for flooding substantially decreases the transmission of control packets while covering all nodes. Furthermore, as the network expands, the benefits of this mechanism become even more pronounced.  
\(N_{1}(i)\) means 1-hop neighbor set of node \(i\), \(N_{2}(i)\) means 2-hop neighbor set of node \(i\), and \(M(i)\) means MPR set of node \(i\). The steps for selecting MPR nodes are as follows [29]:  
**Step 1.** Select nodes from \(N_{1}(i)\) through which node \(i\) can only reach certain 2-hop neighbors, and then add them to \(M(i)\).  
**Step 2.** Sort 1-hop neighbors from high to low based on the number of the coverage for 2-hop neighbors, and select the ones with the highest coverage to join \(M(i)\).  
**Step 3.** Update and remove the 1-hop neighbors from \(N_{1}(i)\) and 2-hop neighbors from \(N_{2}(i)\) for each addition operation.  
**Step 4.** Repeat the Step 2 and remove nodes through the Step 3, and finally end operation until the nodes of \(M(i)\) can completely cover all of the 2-hop neighbors from \(N_{2}(i)\).  
The ultimate objective of MPR selection within the 1-hop neighbor set of a node is to ensure the connectivity of data transmission links by achieving full coverage of the 2-hop neighbor set of the node in the network [30].  
As depicted in Figure 2, the MPRs for node S are chosen from its 1-hop neighbor set, where nodes A to E represent 1-hop neighbor nodes, and nodes F to N represent 2-hop neighbor nodes. Initially, node A, the sole neighbor capable of reaching 2-hop neighbor F, is selected to join the MPR set. Subsequently, nodes are arranged based on their coverage, with nodes C, B, and D sequentially added to the MPR set. Ultimately, the MPR set of node S comprises {A, C, B, D}. It is worth noting that some nodes might receive the same control message forwarded by different nodes, and traditional selection algorithms may not always yield the optimal and minimal MPR set. In the scenario described above, the set {A, B, D} could also achieve full coverage of the 2-hop neighbors.
# Security Architecture for Swarms of Autonomous Vehicles in Smart Farming
## 4 Discussion  
The work presented in this paper is based on the hypotheses that a swarms of heterogeneous aerial and terrestrial vehicles can collaborate together in agriculture scenarios by exchanging information and by combining their capabilities, advanced sensors and equipment to carry out complex missions in a more time- and cost-efficient manner.  
Such a system needs to manage secure communications to elude cyber security attacks or malicious use of vehicles; multi-node real-time communications, reliability and QoS management for low energy consumption; and interoperability among heterogeneous vehicles and data formats for data exchange comprehension.  
Therefore, the results and security architecture presented in this paper covering confidentiality, integrity, authentication, access control or authorisation and non-repudiation are of interest to any system where two or more entities or nodes communicate and transmit information across a network.  
If, in addition, these entities are large in number or part of a critical system, which means that the system must be highly reliable and must process the data in real-time with well-defined and fixed time constraints, then a distributed DDS-based communication system is recommended over socket-based solutions or other publish/subscribe mechanisms based on central brokers.  
Finally, there are several distributions of the DDS standard. However, commercial ones are too expensive for farmers and small companies. On the other hand, open-source licenses do not offer embedded security mechanisms or dynamic discovery over different networks. Hence, the security implementations and recommendations described in this paper make up for this lack, whereas the VPN approach adds an additional security layer and provides a shared network where dynamic discovery is possible with open-source solutions.
# Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle
## 5 Reflection: Practical Challenges and Solutions
In order to enhance the performance of LTE connectivity for drones, some technical challenges, such as down-tilted BS antennas and inter-cell interference, need to be addressed. Interference is one of the inherited issues of cellular networks which has been widely studied, and a rich set of advanced interference mitigation solutions have been developed especially for LTE networks overthe last decade [47]. Drones generate more uplink interference to neighbor cells while experiencing more downlink interference from those cells. To prevent performance degradation, the interference needs to be appropriately controlled and managed. Some of the prominent interference mitigation techniques are Coordinate Multi-Point (CoMP) [48], interference rejection combining, and network-assisted interference cancelation and suppression [49]. However, both techniques are based on adjacent cells' cooperation, which results in high overhead on the interface between adjacent cells (known as X2). Another strategy that can be used is radio resource partitioning, in which the assigned resources for aerial connectivity need to be orthogonal with utilized resources for terrestrial communications. However, in using such techniques efficiency of resource partitioning should be taken into account. The power control technique is another effective technique that can be used to mitigate interference, especially in the uplink. Beamforming is another technique that is supported in LTE. In this technique, the antenna beams are concentrated in a specific direction and thus increases the reach by separating users in space. This technique can be considered an effective solution since drones are in LoS of a large number of BSs, in which omni directional antennas will cause severe interference in the downlink to drones [50]. To address the challenge of a down-tilted BS antenna, one solution is the use of dedicated cells for UAV connectivity where the antenna is pointed towards the sky. Thus, the use of dedicated directional antennas and radio resources for providing drone connectivity can be extremely useful in enhancing aerial coverage and LTE connectivity performance and suppressing the interference in both uplink and downlink channels. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Conventional Drone** & **LTE-Powered Drone** \\ \hline \multirow{6}{*}{Data type} & & Telemetry data \\  & Telemetry data & Autonomous waypoints update \\  & Autonomous waypoints update & Real-time drone control \\  & Real-time drone control & Video streaming \\  & Video streaming & Image feed \\  & Image feed & Real time GPS tracking \\  & Image feed & Navigation database update \\  & Transmission for other sensor data \\ \hline \multirow{2}{*}{Frequency} & Telemetry: –2.4 GHz & \multirow{2}{*}{4G LTE–2.6 GHz} \\  & Video: –5.0 GHz & \\ \hline Flight distance & Line of sight & Beyond the line of sight \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison between the conventional and the developed LTE-powered drone.
## 5 Experimentation and Analysis
### Prediction Error Analysis  
An accurate load-forecasting process is the process of infinite approximation to the actual power load value, but no matter how accurate, the forecasting still cannot avoid the generation of errors, so the degree of approximation through the error evaluation indexes to measure the degree of scientific estimation can improve the accuracy of the model prediction and have guiding significance for the prediction ability of the model in order to evaluate the results of the proposed model prediction. The hybrid model proposed in this paper includes signal-processing aspects with commonly used performance metrics such as PRD, SNRout and SNRimp. However, in the field of prediction, most of the performance metrics used to evaluate the goodness of the model are generally used, such as the RMSE, MAE, \(R^{2}\), etc. Therefore, these three performance metrics are applied in this paper to evaluate the superiority of the proposed model [45]. This paper adopts the root mean square error (RMSE) and mean absolute error (MAE) to evaluate the results. These two indicators allow the characterization of the prediction accuracy, and the smaller the value, the higher the accuracy [46]. The coefficient of determination \(R^{2}\) measures the proportion of variance explained by the model to the total variance, and the closer its value is to 1, the stronger the explanatory power of the model. It is an important indicator for evaluating the overall goodness of fit of the model. These three metrics are defined below:  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}\abs{y_{ir}-y_{ip}} \tag{26}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\left(y_{ir}-y_{ip}\right)^{2}} \tag{27}\]  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}\left(y_{ir}-y_{ip}\right)^{2}}{\sum\limits _{i=1}^{n}\left(y_{ir}-\overline{y_{ir}}\right)^{2}} \tag{28}\]  
where \(y_{ir}\) is the true value, \(y_{ip}\) is the predicted value, and \(n\) is the number of samples. The closer the values of \(MAE\) and \(RMSE\) in the above indexes are to 0, the better, while the closer \(R^{2}\) is to 1, the better.
## 3 Results and Discussion
### Effects of LFG Generation Rate  
The LFG generation rate alters the gas composition within the cover, thereby impacting MAMO [10]. Additionally, it modifies the flux of water vapor that enters the cover along with the LFG [26]. Figure 6a illustrates the variations in surface evaporation rates of the cover under different LFG generation rates. Under identical temperature gradients and initial moisture contents (see Table 2), the evaporation rate on the 30 th day exhibits a generally positive correlation with the LFG generation rate. As the LFG generation rate increases from 2.656 mol m\({}^{-2}\) d\({}^{-1}\) to 42.5 mol m\({}^{-2}\) d\({}^{-1}\), the surface evaporation rate increases from 1.6 mm/d to 1.8 mm/d. This difference may be related to the variations in the flux of water vapor entering the bottom of the cover [48]. Figure 6b indicates the influence of the LFG generation rate on the CH\({}_{4}\) oxidation rate and efficiency in the cover on day 30. With the increase in the LFG generation rate, the CH\({}_{4}\) oxidation rate initially rises rapidly and then gradually declines, while the CH\({}_{4}\) oxidation efficiency continuously decreases. At relatively low LFG generation rates, the primary factor limiting MAMO in the cover is likely the CH\({}_{4}\) supply [50]. As the CH\({}_{4}\) influx increases, the CH\({}_{4}\) oxidation rate also increases, and the CH\({}_{4}\) oxidation efficiency is relatively high. Correspondingly, at relatively high LFG generation rates, the limiting factor for MAMO may be the O\({}_{2}\) supply [14]. The CH\({}_{4}\) oxidation rate decreases with the further increase in CH\({}_{4}\) influx, resulting in a relatively low CH\({}_{4}\) oxidation efficiency.  
Figure 5: Effects of initial moisture content on (**a**) distribution of O\({}_{2}\) concentration and (**b**) CH\({}_{4}\) oxidation rate.  
Figure 6: Influence of LFG generation rate on (**a**) evaporation and (**b**) MAMO.
# A 3D Vision-Based Weld Seam Extraction Method for Arbitrarily Placed Spatial Planar Weldments
## 4 Experimental Verification  
### Experiment Setup  
To evaluate the proposed method with weldments, an experimental platform consisting of a turntable and a 3D structured light camera is built, depicted in Figure 12. The 3D camera (DM Series production of Hanchine) has a FOV of \(110\times 70\) mm (close end) to \(300\times 210\) mm (far end), with a resolution of 2.3 million and an average error of 0.15%. The turntable diameter is 250 mm, and the repeatability is \(\pm 0.05^{\circ}\).  
According to Formula (5), the accurate turntable axis vector \(n\) and center point \(c\) with respect to the camera coordination are required for 3D reconstruction. We employ the method proposed by [PERSON] et al. [24] to calibrate and verify the parameters in the following steps.  
1. A standard metal cube (of size \(80\times 100\times 120\) mm\({}^{3}\)) is used as the reference object for calibration. The setup is displayed in Figure 13, where 36 frames of point clouds of the cube are captured with the turntable rotating \(10^{\circ}\) for each frame (Figure 14a).  
Figure 12: Experiment setup for 3D reconstruction.  
Figure 13: Experiment setup for turntable calibration.  
2. All frames of point clouds are merged into one with point cloud registration, where \(n\) and \(c\) are the main parameters. The calculation of \(n\) and \(c\) is turned into the optimization of the point cloud registration that leads to the minimum registration error, which is further decomposed into axis calibration and center point calibration. The result is recorded in Table 1.  
(3) Point cloud registration is performed on all the point clouds obtained in step (1) with the parameters calculated in step (2), as depicted in Figure 14b.  
(4) Perform plane segmentation on the registered point cloud in step (3) (Figure 14c). The registration error in step (3) is measured as the plane segmentation error, as visualized in Figure 14d. Since the segmentation error is small, we reasonably infer that the calibration is accurate enough.