esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Drones: Routing Algorithms, Techniques and Challenges
## 5 Future Research Directions
### Routing Challenges  
Routing in a IoD is prone to challenges like any other communication/localisation system. It is evident that IoD routing protocols are still in their developmental stage [9]. Changes in the network environment demonstrate a great need to provide new equipment that can overcome network challenges. New paths in IoDs bring dynamic changes to the network. Therefore, enabling effective routing to these paths is the job of the network administrator. Table 3 lists the routing techniques of IoD and their challenges.  
In general, IoD routing is broadly classified into structure-based routing and protocol-based routing, as shown in Figure 9. Structure based routing consists of changes in the network structure while deploying an efficient routing mechanism. Protocol-based routing, in contrast, encompasses changes in the protocols being used in the routing mechanism. Structure-based routing is further classified into flat routing, linear sensor routing, cluster-based routing, location-based routing, and tree-based routing. Protocol-based routing is further classified into swarm intelligence routing, multi-path routing, and shortest path routing.  
\begin{table}
\begin{tabular}{c l l} \hline \hline  & **Characteristics** & **Improvement Area** \\ \hline
1 & Routing & New Path Formation \\ \hline
2 & Packet Mobility & Seamless Data Transfer \\ \hline
3 & Packet Delivery Ratio & Time Constraint \\ \hline
4 & Quality of Service & Latest Equipment \\ \hline
5 & Energy Consumption & Li-ion, H-fuel batteries \\ \hline
6 & Reliability & Lossless Transfer, Improved PDR \\ \hline
7 & Throughput and Delay & Efficiency \\ \hline
8 & Failure detection & Fault replacement \\ \hline
9 & Performance Evaluation & On-time Delivery \\ \hline
10 & Security & Encryption \\ \hline \hline \end{tabular}
\end{table}
Table 2: Improvement areas for IoDs.
## 5 Discussion
### Hypothesis Validation  
Based on the results of our analysis, we thus cannot validate H1. Indeed, the fade radius did not appear to have an impact on perceived presence: even when viewers were constrained to stay within a small viewing volume, they reported feeling a sense of \"being there\", with levels similar to those reported in larger viewing volumes. In this way, it seems that viewers' mental construct of being in a place does not require the place to be visible at all times. Perhaps this result can be interpreted by analogy with the fact that the real world around us \"disappears\" regularly when we close our eyes, yet does not feel less real because of it, because we know by experience that opening our eyes again will make the world appear once more. In the same way, perhaps viewers feel that the virtual world still exists around them even though it disappears beyond a given range of motion, simply because the place consistently re-appears when they move back to the central viewpoint: they can regularly check that the world exists by moving back to this viewpoint, confirming that it is not an intangible, fleeting world that stops existing beyond a given range, but rather a world upon which a dark veil is placed beyond this range, the world itself still existing in spite of it.  
On the other hand, our analysis is quite consistent with H2, and the results of our experiment in the \"Museum\" scene confirm that rendering view-dependent highlights can significantly enhance viewers' perception of visual realism. However, our results on the \"Pyrite\" scene also lead us to exert caution: the added visual accuracy does not appear to always be perceived as enhancing the scene's realism, as some reflections may be perceived as being too exaggerated or visually uncomfortable to seem realistic. Consequently, additional research should lead to a better understanding of the conditions under which view-dependent effects are perceived as realistic when rendered with image-based techniques, i.e., whether this depends on the type of effect, sampling density, or other aspects of the rendering method that still have to be investigated.
# Layer-Wise Query Selection to Eliminate Redundant Queries in DETR
## 1 Introduction
To address these challenges, we propose a novel approach called selected query (SQ), which directly reduces object queries in the decoder stage based on their importance. Our key hypothesis is that eliminating redundant object queries can improve both training efficiency and inference speed without compromising detection performance. However, simply reducing the number of queries risks degrading accuracy, particularly in complex scenes where objects vary in size or overlap. Thus, an effective query reduction strategy is required to balance efficiency and performance. We introduce a layer-wise query selection mechanism that evaluates the class probability of each query at each decoder layer. Queries with high confidence scores are retained, while low-confidence queries are gradually pruned. This adaptive selection process ensures that only the most relevant queries remain, thereby enhancing computational efficiency while maintaining detection accuracy. This paper introduces significant contributions to enhance the efficiency of DETR-based object detection models, detailed as follows. * **Proposal of selected query, a novel object query reduction methodology:** We propose selected query, a new method that systematically reduces the number of object queries in the decoder by selectively removing low-confidence queries. We provide a detailed analysis of the query selection mechanism and its layer-wise removal process, highlighting the simplicity and effectiveness of the methodology. * **Demonstration of selected query's efficiency and performance:** Through extensive experiments on major object detection benchmark datasets, we demonstrate that selected query significantly reduces computational cost and GPU memory usage while maintaining competitive detection performance. Key performance indicators, including FLOP reduction, inference time improvement, and average precision (AP) scores, are presented. * **Verification of accelerated model convergence:** Our findings indicate that the proposed method accelerates model convergence, particularly in settings with a small number of decoder layers, further validating its potential for lightweight DETR-based architectures.
# A New GNSS-Derived Water Vapor Tomography Method Based on Optimized Voxel for Large GNSS Network
## 3 Experiment and Validation
### Validation of the Optimized Tomographic Method
In addition, owing to these interference constraints, the WV profile obtained by Method 1 is closer to the conventional WV distribution than that obtained by Method 2, which can be attributed to the low performance of Method 1 during the rainless days. To further evaluate the performances of the two tomographic methods, we calculate the mean bias, MAE, and RMSE between the RS and above methods; the numerical results are listed in Table 4. The RS data comparison shows that the mean RMSE of the proposed method (Method 2) is 1.3 g/m\({}^{3}\), while that of the conventional method (Method 1) is 1.8 g/m\({}^{3}\), which indicates that the accuracy of the tomographic method is increased by 27.8%. The MAE and bias of RS-Method 2 are 0.9 g/m\({}^{3}\) and \(-0.8\) g/m\({}^{3}\), while those of RS-Method 1 are 1.3 g/m\({}^{3}\) and \(-1.2\) g/m\({}^{3}\), respectively. These accuracy performance indicators show that Method 2 provides better tomographic results than those obtained by Method 1. Notably, the biases of RS-Method 1 and RS-Method 2 are negative, which reveals that the GNSS WV tomography usually overestimates the WV content of the troposphere. Accordingly, \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Root Mean Square Error (RMSE) (Rainless)** & **RMSE (Rainy)** \\ \hline RS–Method 1 & 2.2 & 1.3 \\ RS–Method 2 & 1.7 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of the tomography accuracy with respect to the RS method during a rainless period and a rainy period (g/m\({}^{3}\)). Figure 8: Statistics of the root mean square error (RMSE) differences for RS–Method 1 and RS–Method 2 at 00:00 and 12:00 UTC in the period of DOY 182–184, 2019, and DOY 244–246, 2019. Figure 9a shows that the WV density obtained by the GNSS tomographic technique (blue and red lines) is higher than that obtained by the RS method (black lines) at almost all altitudes. For a more intuitive comparison of the two methods in terms of altitude, the RMSE and relative error for different heights during the test period are shown in Figure 10. The RMSE and relative error of Method 2 are smaller than those of Method 1, particularly in the lower layer of the troposphere. Moreover, Figure 10a shows that the RMSEs of the two methods initially increased, and then they decreased with the altitude. The inflection point is observed at a height of approximately 1 km. This shows that the WV distributed at the height of approximately 1 km frequently varies, compared to other
# Optimal Integrated Single-Framework Algorithm for the Multi-Level School Bus Network Problem
## 5 Example  
In order to test the algorithm, a hypothetical school bus network consisting of one bus depot, two high schools, three middle schools, and four elementary schools was developed for the simplified school bus operation.  
It is assumed that there are 240 students for each level of schools, which means there are 240 students in two high schools (H1 and H2) with 120 students for each high school, three middle schools (M1, M2, and M3) with about 80 students for each middle school, and four elementary schools (E1, E2, E3, and E4) with 60 students for each elementary school; in total, there are 780 students in the network. It is assumed that a total of 12 buses are available, which means that 12 school buses can run every morning and afternoon. Also, it is assumed that each bus has a capacity of 30 seats and that the average speed of each bus is 30 km per hour. The bus operating cost is assumed to be USD 3 per kilometer, and the time value of each student is USD 10 per hour.  
The problem has two separate time periods: morning and afternoon. In the morning, there are three time windows for each time period, and in the first time window (6 a.m. to 7 a.m.), the school buses only serve high school students by picking them up at their location and dropping them at their designated high schools. Then, in the second time window (7 a.m. to 8 a.m.), buses start their trips from high schools and go to middle schools while picking up only middle school students. In the last time window of the morning (8 a.m. to 9 a.m.), the buses start their trips from middle schools to elementary schools to serve elementary students. Finally, after serving all three levels of schools, the buses make deadhead trips to the depot.  
In the afternoon time window, the buses are running in a reverse process from their morning trips. The buses start with deadhead trips from the depot to high schools to pick up high school students at 2 p.m.; then, after delivering students to their stops or homes, the buses go directly to middle schools to pick up and deliver middle school students. After delivering the last student, the buses head to elementary schools to pick up and deliver elementary students. After delivering all elementary students, the buses make deadhead trips to the depot. Figures 1 and 2 show the simplified conceptual operations of school buses during the morning and afternoon time windows. In Figure 2, the sequence of dropping off students may not be the exact opposite of the morning's picking up sequence, and the routes with different sequences are indicated with dashed lines to show the difference. Also, Figure 3 shows the locations of schools in the example.  
Figure 1: Simplified conceptual operation of school buses in morning trips.
# Visual Tracking with FPN Based on Transformer and Response Map Enhancement
## 4 Results
### Algorithm Comparison  
#### 4.2.1 UAV123 Benchmark  
UAV123 contains 123 image sequences, which were collected by a low altitude unmanned aerial vehicle. This benchmark had obvious characteristics, including tiny targets, similar objects, occlusion and so on. Success and Precision plots were used to evaluate the performance of the trackers. Precision plots refer to the percentage of the number of frames whose CLE is less than 20 pixels in the total frames. The formulas are as follows.  
\[\begin{split} CLE=\sqrt{\left(x_{pr}-x_{gt}\right)^{2}+\left(y_{ pr}-y_{gt}\right)^{2}}\\ f=\begin{cases}1,&CLE<20\\ 0,&CLE\geq 20\end{cases}\\ Precision=\frac{\sum_{i=1}^{N}f}{N}\end{split} \tag{3}\]  
\((x_{pr},y_{pr})\) and \((x_{gt},y_{gt})\) refers to the center position coordinates of the prediction box and ground truth. Success rate means the percentage of the number of frames whose overlap S is larger than 0.5 in the total frames. Overlap shows the IOU of the prediction box and ground truth of each frame. The result takes the average value of the whole frames. The formulas are as follows.  
\[\begin{split} S=IOU\frac{\left|R_{pr}\cap R_{gt}\right|}{\left|R _{pr}\cup R_{gt}\right|}\\ f=\begin{cases}1,&IOU\geq 0.5\\ 0,&IOU<0.5\end{cases}\\ \text{Success rate}=\frac{\sum_{i=1}^{N}f}{N}\end{split} \tag{4}\]  
The Success plots and Precision plots of the one-pass evaluation are shown in Figure 5. TR-Siam is observed to achieve a success rate of 0.635 and precision plot of 0.832. Addition  
Figure 6: Experimental results corresponding to 5 typical scenarios, including (**a**) Similar Object; (**b**) Partial Occlusion; (**c**) Full Occlusion; (**d**) Aspect Ratio Change; (**e**) Viewpoint Change.
## 5 Data Evaluation
### Accuracy Results
#### 5.3.2 Estimating Three-Node Motif Frequencies  
Table 4 reveals our estimated NRMSEs of three-node undirected motif frequencies at \(p=0.01\) and \(p=0.05\), respectively, using graphs fpr Flickr, Pokec, LiveJournal, Wiki-Talk and Web-Google. The triangular motif structure with \(id=2\) in the undirected motif in Table 4 is more rare, thus, the result with \(id=2\) is better compared with [16]. We can see that the NRMSE for \(p=0.05\) is about ten times less than the NRMSE for \(p=0.01\). When \(p=0.01\) for all these five graphs, the NRMSEs are less than 0.05. Figure 7 reveals our estimated NRMSEs for three-node directed motif frequencies at \(p=0.01\) and \(p=0.05\). Likewise, we observe that NRMSE at \(p=0.05\) is almost ten times less than NRMSE at \(p=0.01\). The NRMSE of our estimates of \(n_{7}^{(3)}\) (i.e., the seventh three-node directed motif frequency) exhibits the largest error. Except \(n_{7}^{(3)}\), the NRMSEs of the other motif frequency estimates are smaller than 0.01 when \(p=0.05\). Figure 8 reveals our estimated NRMSEs for three-node signed and undirected motif frequencies for \(p=0.01\), \(p=0.05\), and \(p=0.1\) using the graphs Sign-Epinions, sign-Slashdot08, and sign-Slashdot09. For all three signed graphs, the NRMSEs are less than 0.5, 0.1, and 0.06 for \(p=0.01\), \(p=0.05\), and \(p=0.1\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(i\) & **Flickr** & **Pokec** & **Live-** & **Wiki-** & **Wiki-** & **Web-** \\  & & & **Journal** & **Talk** & **Google** \\ \hline \multicolumn{6}{c}{undirected three-node motifs} \\ \hline
1 & \(1.30\times 10^{10}\) & \(1.99\times 10^{9}\) & \(6.59\times 10^{9}\) & \(1.26\times 10^{10}\) & \(6.87\times 10^{8}\) \\
2 & \(5.49\times 10^{8}\) & \(3.26\times 10^{7}\) & \(3.11\times 10^{8}\) & \(9.20\times 10^{6}\) & \(1.34\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Real values of three-node undirected motif frequencies (\(i\) is the motif ID).  
Figure 7: NRMSEs of \(\hat{n}_{i}^{(3)}\), the estimates of three-node directed motif frequencies for \(p=0.01\) and \(p=0.05\), respectively. Flickr, Pokec, LiveJournal,Wiki-Talk, and Web-Google have \(1.35\times 10^{10}\), \(2.02\times 10^{9}\), \(6.90\times 10^{9}\), \(1.2\times 10^{10}\), and \(7.00\times 10^{8}\) three-node CIS, respectively: (**a**) \(p=0.01\); (**b**) \(p=0.05\).
# Gear Wear Process Monitoring Using a Sideband Estimator Based on Modulation Signal Bispectrum
###### Abstract  
As one of the most common gear failure modes, tooth wear can produce nonlinear modulation sidebands in the vibration frequency spectrum. However, limited research has been reported in monitoring the gear wear based on vibration due to the lack of tools which can effectively extract the small sidebands. In order to accurately monitor gear wear progression in a timely fashion, this paper presents a gear wear condition monitoring approach based on vibration signal analysis using the modulation signal bispectrum-based sideband estimator (MSB-SE) method. The vibration signals are collected using a run-to-failure test of gearbox under an accelerated test process. MSB analysis was performed on the vibration signals to extract the sideband information. Using a combination of the peak value of MSB-SE and the coherence of MSB-SE, the overall information of gear transmission system can be obtained. Based on the amplitude of MSB-SE peaks, a dimensionless indicator is proposed to assess the effects of gear tooth wear. The results demonstrated that the proposed indicator can be used to accurately and reliably monitor gear tooth wear and evaluate the wear severity.  
m _Applied_  
_sciences_
# Application and Evaluation of the Gaofen-3 Satellite on a Terrain Survey with InSAR Technology
## 5 Conclusions  
The Gaofen-3 satellite is China's first SAR satellite to have interferometric abilities. In this study, interferometric data processing and height measurement experiments were carried out. Data from the Dengfeng area was used to evaluate the interferometric imaging and measurement capabilities of the domestic SAR satellite. In order to reduce the errors introduced by the processing as much as possible, a high-accuracy InSAR image co-registration strategy was adopted. The co-registration accuracy reached the 0.01-pixel level. Moreover, a multi-scale phase filtering method in the wavelet domain was employed to improve the SNR of the interferometric phase. The interferometry baselines were subsequently calculated with and without ground control point schemes, respectively. The elevation inversion was performed to evaluate the accuracy of the interferometric height measurement under  
Figure 16: The effective elevation distributions obtained by Yaogan-29 (**a**) and Gaofen-3 (**b**).  
Figure 15: DEM acquired by Yaogan-29 sensor with GCPs (**a**) and the distribution map of the evaluation difference between the 1:2000-scale DEM and the obtained DEM (**b**).  
these conditions. Initially, our experimental results showed that the height measurement accuracy of Gaofen-3 repeated-orbit interferometry under control conditions was higher than 4 m and higher than 7 m under non-control conditions.  
Our experiments proved that China's SAR satellite Gaofen-3 has a certain degree of interferometric imaging and measurement capabilities. It has a high coherence and SNR in the presence of certain time and geometrical decorrelation, which indicates its mapping abilities and constitutes a major breakthrough in the field of Chinese SAR technology.  
Since Gaofen-3 is a single-antenna SAR satellite, with a repetitive orbit interferometry mode, it would be more useful when applied to the field of surface deformation monitoring. However, the capability of specific deformation monitoring needs further verification. In this study, the interferometry verification and evaluation of Gaofen-3 satellite data with a fine strip I model was carried out. Other imaging modes, including all-polarization and bunching modes, should be verified, and the applications and evaluations related to surface deformation monitoring should be studied in the near future.  
Data curation, Z.C.; formal analysis, Y.Z.; investigation, Y.Z.; methodology, Y.Z. and Z.C.; project administration, G.Z.; supervision, G.Z.; validation, Y.Z.; visualization, Z.C.; writing--original draft, Z.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Wuhan university (Wuhan, China) through National Key R&D Program of China (2018 YFC0825803), Natural Science Foundation of China projects (NSFC) (No: 41801397).  
The authors would like to thank the editors and the anonymous reviewers for their constructive suggestions.  
The authors declare no conflict of interest.
# Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery
## 2 Materials and Methods
### Data Analysis
#### 2.3.3 Classification  
In this work, three fully connected layers were designed for the classification tasks. The first fully connected layer (the sixth layer, denoted as F5 in Figure 5) takes the cascade of all feature maps of the fifth layer (denoted as \(h_{5}\)) as input. Let us denote that the weights and bias of this layer as \(W_{6}\) and \(b_{6}\), respectively. The output of this layer (denoted as \(h_{6}\)) can be denoted as (2):  
\[h_{6}=\varphi(W_{6}h_{5}+b_{6}) \tag{2}\]  
where the symbol \(\varphi(\cdot)\) denotes a non-linear activation function, \(W_{6}\) and \(b_{6}\) represent the connected weights and biases respectively. Similar with the convolutional operation, ReLu was employed as the activation function.  
The last fully connected layer is the output layer, which exports the probabilities of different HLB disease categories. This layer has four neurons corresponding to four HLB disease categories (normal, light, medium, and heavy). Let us denote the output of this layer is \(y=[y_{1}\;y_{2}\;y_{3}\;y_{4}]\), where \(y_{i}\) is the output probability for the \(i\)th category. Thus, the maximum value in \(y\) (\([y_{1}\;y_{2}\;y_{3}\;y_{4}]\)) corresponds to the final classified category.
# Improving Bundle Routing in a Space DTN by Approximating the Transmission Time of the Reliable LTP
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ARQ & Automatic repeat request \\ BER & Bit error rate \\ BP & Bundle protocol \\ CGR & Contact graph routing \\ CGR-ETO & CGR with earliest transmission opportunity \\ DTN & Delay- or disruption-tolerant network \\ GEO & Geosynchronous equatorial orbit \\ IPN & Interplanetary Internet \\ ION & Interplanetary Overlay Network \\ LTP & Licklider transmission protocol \\ NACK & Negative acknowledgment \\ NASA & National Aeronautics and Space Administration \\ SNR & Signal-to-noise ratio \\ TDRS & Tracking and data relay satellite \\ TLE & Two-line element \\ \end{tabular}
# Minimizing the Number of Distrustful Nodes on the Path of IP Packet Transmission
## 3 Results and Discussion
### Scenario D, with Two Added Subnets and Three Distrustful Nodes  
The following example (Figure 13) differs from the previous one, by adding one more subnet consisting of nodes Q, R, T, and S. Now, the total number of nodes in the network is 20, of which three are not trusted. The distrustful nodes are nodes E, G, and H.  
Figure 12: Fragments of routing tables for the network of Figure 9, according to the (**a**) classical Dijkstra algorithm and (**b**) modified Dijkstra algorithm.  
Figure 13: Network topology with 20 nodes and 3 distrustful nodes.  
Similarly, as in the previous example, consider the tree of the shortest paths for the classic Dijkstra algorithm (Figure 14a) and for the modified Dijkstra algorithm (Figure 14b). The root of the tree is, again, node A.  
The topology of the network in Figure 13 and the location of distrustful nodes are selected in such a way as to illustrate the case of the extremely high efficiency of the proposed approach. Note that such cases can also occur in real networks. Analysis of Figure 14b shows that, when using the proposed approach, the impact of distrustful nodes E, G, and H on the security of data transmission is absent, since they will not broadcast any packets. All flows bypass these nodes. In the classical approach (Figure 14a), distrustful nodes will participate in the transport of packets up to 10 nodes (P, O, J, K, M, L, N, I, R, T, and S). Practically, only a third of the paths will not have untrusted nodes in their paths.