esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Integrity monitoring is an essential task for ensuring the safety of positioning services. Under a selected probability of hazardous misleading information, the protection levels (PLs) are computed according to a considered threat model to bound the positioning errors. A warning message is sent to users when the PL exceeds a pre-set alert limit (AL). In the short-baseline real-time relative kinematic positioning, the spatially correlated errors, such as the the orbital errors and the atmospheric delays are significantly reduced. However, the remaining atmospheric residuals and the multipath that are not considered in the observation model could directly bias the positioning results. In this contribution, these biases are analysed with the focus put on the multipath effects in different measurement environments. A new observation weighting model considering both the elevation angle and the signal-to-noise ratios is developed and their impacts on the positional results are investigated. The coefficients of the proposed weighting model are determined for the open-sky and the suburban scenarios with the positional benefits maximised. Next, the overbounding excess-mass cumulative distribution functions (EMCs) are searched on the between-receiver level for the weighted phase and code observations in these two scenarios. Based on the mean and standard deviations of these EMCs, horizontal protection levels (HPLs) are computed for the ambiguity-fixed solutions of real experiments. The HPLs are compared with the horizontal positioning errors (HPEs) and the horizontal ALs (HALs). Using the sequential exclusion algorithm developed for the ambiguity resolution in this contribution, the full ambiguity resolution can be achieved in around 100% and 95% of the time for the open-sky and the suburban scenarios, respectively. The corresponding HPLs of the ambiguity-fixed solutions are at the sub-dm to dm-level for both scenarios, and all the valid ambiguity-fixed HPLs are below a HAL of 0.5 m. For the suburban scenario with more complicated multipath environments, the HPLs increase by considering extra biases to account for multipath under a certain elevation threshold. In complicated multipath environments, when this elevation threshold is set to 30 degrees, the availability of the ambiguity-fixed solutions could decrease to below 50% for applications requiring HAL as low as 0.1 m.  
RTK; GPS; integrity monitoring; overbounding CDF; weighting model +
Footnote †: journal: remote sensing  
Article
## Materials and Methods
### Notation and Preliminaries
Thus, a point in the cloud may be represented as a tuple:  
\[\mathrm{p}_{i}=(x_{i},y_{i},z_{i},R_{i},G_{i},B_{i},D_{i},n_{i,x},n_{i,y},n_{ i,z}) \tag{6}\]  
where \(x\), \(y\), and \(z\) describe the point position in the 3D space; \(R\), \(G\), and \(B\) describe the color components of the point; \(D\) is the depth value of the point; \(n_{x}\), \(n_{y}\), and \(n_{z}\) describe the normal vector at the position \(\mathrm{p}_{i}\). In the 3D modeling phase, the depth value of each pixel in the depth image of a RGB-D video can be used to calculate the corresponding 3D point using Equations (2) and (4). Many pixels in different depth frames may be mapped into the same points in the 3D scene because each image may contain content with multiple views. This implies that a point in the point cloud may be attributed with multiple color vectors \(\{c_{1}\), \(c_{2}\),..., \(c_{m}\)}, each obtained from different RGB images. To deal with the difficulty, we compute the color vector \(\mathrm{c}\) of the point \(\mathrm{p}_{i}\) with the median of these overlapped color vectors:  
\[\mathrm{c}=\underset{c_{i}}{\mathrm{argmin}}\sum_{c_{j}}d_{ij},i,j=1,\ldots,m \tag{7}\]  
where \(d_{ij}=\sqrt{\left(R_{i}-R_{j}\right)^{2}+\left(G_{i}-G_{j}\right)^{2}+\left(B_ {i}-B_{j}\right)^{2}}\) is the color distance between color vector \(c_{i}\) and \(c_{j}\). The normal vector at point \(\mathrm{p}_{i}\) implicitly describes the local shape of the target scene. An efficient and effective approach should be designed to deal with the difficulty of a large number of points comprising a point cloud. In this paper, we present an approach to compute reliable normal vectors of individual points in the 3D scene, which will be discussed later.
# Loop Closure Detection Based on Multi-Scale Deep Feature Fusion
## 5 Experiment
### Different layers of SPP  
SPP is used in the feature fusion layer to fuse the extracted depth features of different scales. In order to verify how different layers of SPP influence the algorithm results, an SPP with 1 \(\times\) 1, a two-layer SPP with 1 \(\times\) 1, 2 \(\times\) 2, and a three-layer SPP with 1 \(\times\) 1, 2 \(\times\) 2, 4 \(\times\) 4 were used for experiments. The loop closure detection algorithm with these three different SPP layers, noted as spp1, spp12 and spp124, respectively, was compared with FabMap [10].  
We selected 689 key frames from the Fr2/rpy, Fr2/large_with_loop and Fr3/long_office_household and labeled 45 loop closure places, which were noted as data_1. The experimental results of the data_1 are shown in Figure 7. It can be seen from Figure 7 that the P-R curves of spp1, spp12 and spp124 are basically on the upper right of the coordinate system, which means they have higher precision and recall rates. Our method can reach 100% precision at 50% recall. In addition, spp124 is better than spp12 and spp1, and spp12 is better than spp1. When the recall rate is 100%, spp124 reaches as high as 78% precision; spp12 is about 62% precision, and spp1 is less than 50% precision. It shows that the greater the layers of SPP, the higher the precision and recall rate of the algorithm.  
The above algorithm was also tested in the Fr2/pioneer_slam and Fr2/pioneer_slam2 image sequences which are sampled in a very empty indoor environment and have many similar scenes, such as walls, boards and ground. We selected 435 key frames from the two sequences and labelled 20 loop closure places, which were noted as data_2. The test results on data_2 are shown in Figure 8.  
Figure 8: P-R curves on data_2.  
Figure 7: P-R curves on data_1.  
As can be seen from Figure 8, spp124 has the highest precision-recall rate, followed by spp12. At the 50% recall rate, both spp124 and spp12 can reach 100% precision. For spp1, when the recall rate is less than 50%, the accuracy is higher than the accuracy of FabMap, but when the recall rate is greater than 50%, the accuracy of spp1 is lower than that of FabMap. It is worth mentioning that the performance of all algorithms on data_2 is worse than on data_1. Because there are many similar scenarios in data_2, the algorithms got some false positive results. Overall, the depth features extracted by CNN are more suitable for loop closure detection compared with the traditional artificial design visual features, and increasing the number of SPP layers in the feature fusion layer can improve the accuracy and recall rate.
## 1 Introduction
Additionally, the method employs both a top-down and bottom-up strategy for group key generation, making it collusion-resistant with lower communication costs than the original OFT scheme. The suggested approach's performance analysis demonstrates that it has less communication overhead and better computation overhead than collusion-resistant OFTs. The existence of forward and backward secrecy in the MOFT approach is verified via security analysis. The existence of collusion resistance in MOFT is verified by a new proposition. Our main contributions are as follows:  
* A novel technique, MOFT, for centralized group key management. * A new proposition that proves the collusion resistance property of MOFT. * The evaluation of MOFT proves MOFT reduces network traffic, with limited storage cost and optimal computation cost, proving it is scalable. The remainder of the manuscript is organized as follows: Section 2 depicts the works that are related to SDN-based security for IoT and group key management techniques; Section 3 describes the proposed framework for group management using SDN, provides a brief on the original OFT scheme, and finally, elucidates on the proposed MOFT scheme;Section 4 verifies the security strength of the proposed approach; Section 5 evaluates the proposed approach in terms of computation, storage, and communication cost; finally, Section 6 depicts the IoT tailored version of MOFT; and Section 7 gives the conclusion.
## 3 System Model
### WBAN Blockchain System Performance Analysis
Figure 16, evaluated the impact of varying the number of peer nodes on the performance of the proposed blockchain platform. Figure 16a illustrated the average latency with different peer nodes over the transaction send rate ranges between 25 to 200 tps. It is observed from the graph that the network latency increased with the increase in the number of peers in the network. Moreover, the increase the peer's nodes also increased the network traffic volume, which ultimately decreased the network throughput as presented in Figure 16b. In Hyperledger Fabric, the ordering of transaction was performed by ordering node along with the other nodes to form ordering service. In Figure 17, we assessed three kinds of ordering service,  
Figure 14: Average transaction latency. i.e., solo, raft, and solo raft (the network with single node raft) with varying number of transaction send rate ranges from 25 to 200 transaction per second. It is observed from the graph that the latency of solo ordering was less than the solo raft and raft because solo raft and raft support had additional transport-layer security which provide authentication and security between peer nodes. Figure 17a shows the average latency of the orderer node with a varying number of transaction send rate. Furthermore, the throughput increased linearly as expected until it compressed at 75 tps. The throughput of raft and solo raft was significantly less as compared to solo. The solo ordering showed better application performance than solo raft and raft ordering services since it is a single node and does not require the process of TLS as shown in Figure 16b. Table 5 shows the resource utilization of the proposed blockchain-enabled WBAN platform in terms of CPU usage, memory utilization, and traffic. For the experimental environment, we used Hyperledger Caliper with five iterations. The peer node in the proposed blockchain network consumed an average memory of 94.32 MB, whereas the average CPU utilization was noted as 5.48%. In the case of the orderer node, the average CPU utilization and average memory consumption was 1.19% and 25.7 MB, respectively. Finally, the certificate authority (ca) node used an average 0% CPU utilization and consumed an average of 5.3 MB memory per processing. Evaluation results illustrate that the  
Figure 10:0 Impact of varying peer node with different transaction rate. (**a**) Average latency; (**b**) average throughput. Figure 10:1 Average read latency. proposed protocol performed significantly better in balancing of temperature (to avoid heat damaging effect of the body tissues) and energy consumption (to avoid replacement of battery and to increase the embedded sensor node life) with efficient data transmission achieving a high throughput value. Similarly blockchain-enabled WBAN platform outperformed in terms of resource utilization like memory consumption, machine utilization, and user experience.
# A Trajectory Optimisation-Based Incremental Learning Strategy for Learning from Demonstration
## 3 Research Methodology
### Extended Demonstration via G-PRM  
An extended demonstration will be created for the new target. The extended demonstration not only retains the features of the original demonstration but also reduces the computational complexity compared to traditional neural network algorithms. The process is detailed below.  
After segmentation, the original demonstration \(y_{d}\) is divided into several sub-demonstrations (specifically, sub-1 and sub-2 as illustrated in Figure 4b). Subsequently, the \(Eudis\) from the segmentation point and the target point to the new target point are calculated as \(\|\mathbf{d}_{1}\|\) and \(\|\mathbf{d}_{2}\|\), respectively, as shown in Figure 4b. Since \(\|\mathbf{d}_{2}\|<\|\mathbf{d}_{1}\|\), sub-2 is closer to the new target point. Consequently, sub-2 is selected as the trajectory to be imitated (\(Tra_{imi}\)) for generating the extended demonstration (\(Tra_{gen}\)), as shown in Figure 4b,c.  
For generating the \(Tra_{gen}\), the PRM-based method is a preferred choice [31]. Figure 5 illustrates the fundamental processes of PRM. Initially, PRM involves generating sampling points (represented by blue dots) while avoiding obstacles, as depicted in Figure 5a. Subsequently, each sampling point is connected to its adjacent sampling points to form a roadmap, as illustrated in Figure 5b. Finally, to determine the shortest trajectory from position 1 to position 2, the Dijkstra algorithm is employed to search results in the roadmap. Figure 6 displays the flowchart of the G-PRM, with two red blocks highlighting the improvements. To more accurately express the meaning of the following equations, specific letters that represent sets and matrices will be presented in bold.  
Figure 4: Demonstration segmentation and extended demonstration. (**a**) Original demonstration. (**b**) Demonstration segmentation. (**c**) Trajectory generation.  
Figure 3: A trajectory with curvature regions.
## 2 Vehicle Modeling for State Estimation
### System Discretization  
Discretization must be accomplished before the observer design. The forward Euler difference method is applied to discretize the continuous system described in Section 2.2. Then the nonlinear estimation system in the form of discretization is rewritten as  
\[\begin{split}&\mathbf{x}(k+1)=f(\mathbf{x}(k),\ \mathbf{u}(k))+\mathbf{w}(k)\\ &\mathbf{y}(k)=h(\mathbf{x}(k),\ \mathbf{u}(k))+\mathbf{v}(k),\end{split} \tag{22}\]  
where \(\mathbf{x}(k)\), \(\mathbf{y}(k)\), and \(\mathbf{u}(k)\) are the system state, measurement input and control input, respectively, at the \(k-1\) step. Besides, \(\mathbf{w}\) and \(\mathbf{v}\) are the process noise and measurement noise vectors.
## 3 Numerical Simulations
### Discussion  
It can be observed from Figure 3 that under harmonic excitation, active control has a better control effect on the system under low-frequency excitation. This is in accordance with the theoretical formulations since the excitation vibration period on this occasion is larger or similar to the response time of the system under active control. However, as the excitation frequency increases, and since the response time does not change under linear active control law, the system would not respond in time, thus resulting in the deterioration of the control effect. The control effect of dry friction in a low-frequency band is not as good as that in a high-frequency band. Therefore, the combination of these two control methods would better suppress the vibration and, in the meanwhile, reduce the input energy of the active control force at high frequencies, as shown in Figures 4 and 8.  
It should be noted that the control efficiency of this proposed hybrid control law could still be improved under low-frequency excitation.
## 3 Results and Analysis
### Evaluation of Soil Nutrient Status by LightGBM Model
#### 3.3.2 Eigenvalue Analysis  
Eigenvalue analysis is an important part of the feature engineering operation in the LightGBM model, and it can directly reflect the own features of the independent variables in classifying the samples. Through its own weight calculation, it can obtain its own importance to the classification results, which is called the importance of the independent variables [32]. In the process of the LightGBM model operation, the eigenvalue is the independent variable, that is, the nine nutrient factors in the nutrient status evaluation. The importance ranking is shown in Figure 5. The larger the value, the higher the importance.  
In Figure 5, the eigenvalue score of available K is more than 1000, which is much higher than the other eight nutrient factors, indicating that it has the strongest importance and the greatest contribution to the evaluation and grading of the nutrient status of tobacco planting soil. The contribution degree from high to low is available K, available P, organic matter, total N, available B, pH value, available Zn, exchangeable Ca and exchangeable Mg, which indicates that these nutrient factors have certain differences in the evaluation of the nutrient status of tobacco planting soil and that it is easy to identify and classify the models. The top six characteristic values, available K, available P, organic matter, total N, available B and pH value, are also important nutrient factors for the comprehensive evaluation of nutrient status of tobacco planting soil. For example, when [PERSON] [37] studied the nutrient status of tobacco planting soil in the Erhai Lake Basin, the nutrient factors selected were available K, available P, organic matter, total N and pH value. [PERSON] [38] showed that available B is one of the trace elements necessary for crop growth, and its abundance affects the development and quality of tobacco growth. Therefore, according to the above, the LightGBM model is consistent with the actual research results.  
In addition, the contribution rate of available Zn, exchangeable Ca and exchangeable Mg is low, and the characteristic score is lower than 200. The quality of soil nutrient status is a comprehensive reflection of various nutrient factors in the soil. The LightGBM model is reasonable because of the common existence and mutual influence of nine soil nutrient factors and can obtain more accurate and objective evaluation results of nutrient status.
## 3 Cross-Layer Adaptive Feedback Scheduling
### Adaptive Feedback Scheduling Algorithm
#### 3.3.2 Deadline Miss Ratio Setpoint  
For a given control system, the effects of sampling period and DMR on QoC are deterministic, while the DMR is related to the sampling period. Therefore, for a given system setup, there exists an optimal operating point, say (\(h_{r},\rho_{r}\)), at which the system will in principle achieve the best QoC. Ideally, the best feedback scheduling performance could be achieved by setting the desired level of DMR to this optimal point. In practice, the relationships between the QoC, the DMR, and the sampling period are complicated, and therefore cannot be explicitly described. Most often a DMR setpoint close to the optimal one could be chosen through simulation and/or experimental studies.  
Suppose the point A(\(h_{r1},\rho_{r1}\)) in the schematic diagram Fig. 4 is the setpoint for \(r=5.5\) Mbps, which is (very close to) the optimal operating point. When the transmission rate changes, e.g., from 5.5 to 11 Mbps, the operating point of the system will become B(\(h_{r2}\), \(\rho_{r1}\)) if a fixed DMR setpoint is used. Clearly, the sampling period at point B decreases relative to A. Since trade-offs should be made between DMR and sampling period so as to achieve the best QoC, it is still possible to improve the QoC relative to the operating point B by properly increasing the sampling period, which reduces the DMR. Therefore, if A is the optimal operating point for \(r=5.5\) Mbps, then the optimal sampling period for \(r=11\) Mbps will be some value, say \(h_{r3}\), that falls in the interval (\(h_{r2}\), \(h_{r1}\)). This suggests that when the transmission rate increases, the QoC could be improved through decreasing the value of \(\rho_{r}\), for example, using \(\rho_{r2}\) as the new setpoint.  
Based on this observation, we propose to adapt the DMR setpoint to different transmission rates. The setpoints used in this paper are simply set to:  
\[\rho_{r}=\begin{cases}10\%&\text{if}\quad\text{r=5.5}\\ 5\%&\text{if}\quad\text{r=11}\end{cases} \tag{5}\]  
Since only two cases, i.e. \(r=5.5\) and 11 Mbps, are considered in our simulation experiments, Equation (5) gives only the corresponding two values for \(\rho_{r}\). Since practical control systems are always designed capable of tolerating some level of DMRs, there is often a considerably large room for choosing the value of \(\rho_{r}\). Alternatively, compensation methods, e.g. [4], for packet losses can be adopted in control loops to alleviate the negative effect of deadline misses on QoC.  
Figure 4: Schematic diagram for adapting deadline miss ratio setpoint.  
Figure 5: Pseudo code for cross-layer adaptive feedback scheduling.  
Fig. 5 gives the pseudo code for the above-described feedback scheduling algorithm. It can be seen that this algorithm exhibits online adaptability in two aspects: 1) the adaptation of the proportional coefficient \(K\) to deal with the nonlinear relationship between the DMR and the sampling period; 2) the adaptation of the DMR setpoint to deal with the changes in the transmission rate.
###### Abstract  
Better methods are needed for mapping agricultural drainage pipe systems. Prior research on small test plots indicates that ground penetrating radar (GPR) is oftentimes capable of detecting buried drainage pipes; however, the feasibility of employing this geophysical technique in larger field areas has not been adequately evaluated. Ground penetrating radar integrated with a Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) may be an effective and efficient means of mapping drain lines within agricultural fields. Therefore, GPR-RTK/GNSS was tested in three agricultural settings; with Site 1 and Site 2 located in Beltsville, MD, USA and Site 3 near Columbus, OH, USA. Soils at the three sites ranged from silty clay loam to loamy sand. A GPR unit with 250 MHz antennas was used to detect drainage pipes, and at Sites 1 and 2, a physical GNSS base station was utilized, while a virtual base station was employed at Site 3. The GPR-RTK/GNSS configurations used in this study delineated a complex rectangular drainage pipe system at Site 1, with one set of drainage pipes oriented southwest-northeast and a second oriented southeast-northwest. At Site 2, a herringbone drain line pattern was outlined, and at Site 3, random drain lines were found. When integrated with RTK/GNSS, spiral or serpentine GPR transects (or spiral/serpentine segments of a GPR transects) were utilized to provide insight on drain line directional trends. Consequently, given suitable field conditions, GPR integrated with RTK/GNSS can be a valuable tool for farmers and drainage contractors needing to map subsurface drainage systems.  
Drainage Pipe Mapping; Ground Penetrating Radar (GPR); Real-Time Kinematic Global Navigation Satellite System (RTK/GNSS) +
Footnote †: journal: Agriculture  
0000-0001-5000-0000-0000]Berry Allred 0000-0001-5000-0000]DeBonne Wishart 0000-0001-5000-0000]Luis Martinez 0000-0001-5000-0000]Harry Schomberg 0000-0001-5000-0000]Steven Mirsky 0000-0001-5000-0000]George Meyers 000-0001-5000-0000]John Elliott 0000-0001-5000-0000]Christine Charyton 0000-0001-5000-0000]C.M.