esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Routing protocols based on trust mechanisms have been widely investigated for wireless sensor networks, and the works have achieved good results, while there are few works on trusted routing for underwater acoustic networks (UANs). However, trust-aware routing is the key to improving the packet delivery rate and the energy efficiency of UANs. Therefore, inspired by the theory of trust evaluation, a trust-aware and fuzzy logic-based reliable layering routing protocol (TAFLRLR) is proposed. In the TAFLRLR protocol, to avoid the problem of the void area and improve the transmission reliability, the candidate nodes of the next-hop forwarding nodes are determined according to the layers of neighbor nodes. Moreover, a fuzzy logic-based trust evaluation mechanism (FLTEM) is provided, which employs the fuzzy comprehensive evaluation decision model to calculate the comprehensive trust value for underwater sensor nodes. Further, the node density of a candidate node and its comprehensive trust value are taken as the input of a fuzzy control system and the forwarding probability (FP) of the node is taken as the output, and the candidate node with the highest FP is selected as the best forwarding node. Simulation results illustrate the superiority and effectiveness of the TAFLRR protocol in terms of energy efficiency, routing reliability, and transmission reliability.  
underwater acoustic networks; trust-aware; fuzzy logic; reliable routing
# Inter-UAV Routing Scheme Testbeds
###### Abstract  
With the development of more advanced and efficient control algorithms and communication architectures, UAVs and networks thereof (swarms) now find applications in nearly all possible environments and scenarios. There exist numerous schemes which accommodate routing for such networks, many of which are specifically designed for distinct use-cases. Validation and evaluation of routing schemes is implemented for the most part using simulation software. This approach is however incapable of considering real-life noise, radio propagation models, channel bit error rate and signal-to-noise ratio. Most importantly, existing frameworks or simulation software cannot sense physical-layer related information regarding power consumption which an increasing number of routing protocols utilize as a metric. The work presented in this paper contributes to the analysis of already existing routing scheme evaluation frameworks and testbeds and proposes an efficient, universal and standardized hardware testbed. Additionally, three interface modes aimed at evaluation under different scenarios are provided.  
FANETs; ad hoc networking drone swarms; efficient routing Article
## 3 Design of the Wide Passband SIW-SSPP Filters with Multi-Sub-Bands
### The Wide Passband SIW-SSPP Filter  
#### 3.1.1 Single Wide Bandpass SIW-SSPP Filter Structure  
To achieve bandpass characteristics within the target frequency range, a novel band-pass filter integrating the SIW with comb-shaped SSPPs is proposed, as illustrated in Figure 4a. The structure is made up of SIW formed by two rows of metallic vias connecting the top and bottom metal layers and seven SSPP unit cells embedded in the center. To enhance the roll-off rate in the high-frequency band, the parameters of the SSPP units are gradually tapered from the three central cells toward both ends. Owing to its cutoff characteristics, the effective impedance of the SIW increases sharply near the cutoff frequency. The spacings between adjacent comb-shaped SSPP transition structures are denoted as \(a_{2}\) = 0.4 mm and \(a_{3}\) = 0.2 mm, while the lengths of the comb-shaped SSPP transition structures are represented by \(b_{2}\) = 0.8 mm and \(b_{3}\) = 0.6 mm. The values are optimized.  
To improve impedance matching and form a filter structure, the microstrip-to-SIW transition is designed. Based on transmission line theory, the MSL (microstrip lines)-to-SIW  
Figure 4: Single-passband filter: (**a**) schematic diagram of the structure; (**b**) surface current; (**c**) S-parameters.  
SSPP structure transition segments achieve a smooth impedance transition through the gradual change in the line width in the tapered section of the MSL, thereby optimizing the impedance matching effect and reducing reflection loss. In addition, its gradual structure enables the progressive adjustment of electromagnetic field distribution, suppresses the generation of higher-order modes, reduces related losses, and ensures efficient energy transmission. The width (\(W_{ms}\)) of the microstrip line is 1.63 mm; the length (\(L_{ms}\)) of the microstrip line is 3.65 mm; the length (\(L_{t}\)) of the transition section is 2.4 mm; the width (\(W_{t}\)) of the microstrip-to-SIW coupling junction is 1 mm; the length (\(L_{t2}\)) of the microstrip-to-SIW coupling junction is 1.2 mm; and the length (\(L_{sinv}\)) of the SIW structure is 20.5 mm. The detailed parameters of the filter structure are summarized in Table 1.  
The simulated performance of the designed single wide bandpass filter structure is shown in Figure 4b. Within the \(-3\) dB passband of 24.25-29.5 GHz, the insertion loss is 1 dB with S\({}_{11}<-7\) dB. To further validate the filtering mechanism, surface current at different frequency points were analyzed, as illustrated in Figure 4b. At 23 GHz, the current flow is interrupted by the SIW structure's cutoff properties, while at 31 GHz, the SSPP structure effectively suppresses current propagation. In contrast, within the passband (24.25-29.5 GHz), the current successfully propagates across the entire structure from Port 1 to Port 2. These observations conclusively demonstrate the single wide bandpass filtering effect. It exhibits a manifest improvement with the concept structure in Figure 3, and Figure 4c shows the corresponding S-parameters, demonstrating the discrepancy.
# Two-Way Transmission for Low-Latency and High-Reliability 5G Cellular V2X Communications
## Appendix B Derivation of (15)
\begin{split}\varsigma_{3,1}&=\int_{0}^{\infty}\int_{ \tilde{\delta}\left(x\varpi\rho+1\right)}^{\infty}f_{\left|f_{1}\right|^{2}} \left(x\right)f_{\left|h_{k^{*},2}\right|^{2}}\left(y\right)dxdy\\ &=\sum_{j=0}^{K-1}\bigcup_{j^{\prime}}^{\left(K-1\right)}\frac{K \mu_{h_{2}}^{m_{h_{2}}}\mu_{f_{1}}^{m_{f_{1}}}(-1)^{j}\mathcal{A}_{j^{\prime}} B_{j^{\prime}}}{r\Gamma\left(m_{h_{2}}\right)\Gamma\left(m_{f_{1}}\right)} \int_{0}^{\infty}x^{m_{f_{1}}-1}e^{-\mu_{f_{1}}x}\int_{\tilde{\delta}\left(x \varpi\rho+1\right)}^{\infty}y^{m_{h_{2}}+\tilde{\gamma}-1}e^{-y\mu_{h_{2}}(1 +\tilde{\gamma})}dxdy\\ &=\sum_{j=0}^{K-1}\bigcup_{j^{\prime}}^{m_{h_{2}}+\tilde{\gamma} -1}\binom{K-1}{j}\int_{0}^{\infty}x^{m_{f_{1}}-1}e^{-x\left[\mu_{f_{1}}+ \tilde{\delta}\varpi\rho\mu_{h_{2}}(1+\tilde{\gamma})\right]}(x\varpi\rho+1)^ {\tau}dx\\ &\quad\times\frac{\Gamma\left(m_{h_{2}}+\tilde{\gamma}\right) \tilde{\delta}^{\tau}K\mu_{h_{2}}^{m_{h_{2}}}\mu_{f_{1}}^{m_{f_{1}}}(-1)^{j} \mathcal{A}_{j^{\prime}}\mathcal{B}_{j^{\prime}}e^{-\tilde{\delta}\mu_{h_{2}}( 1+\tilde{\gamma})}}{r!\Gamma\left(m_{h_{2}}\right)\Gamma\left(m_{f_{1}} \right)\mu_{h_{2}}^{m_{h_{2}}+\tilde{\gamma}-r}(1+j)^{m_{h_{2}}+\tilde{\gamma} -r}}\\ &=\quad\sum_{j=0}^{K-1}\bigcup_{j^{\prime}}^{m_{h_{2}}+\tilde{ \gamma}-1}\sum_{t=0}^{r}\binom{r}{t}\binom{K-1}{j}\int_{0}^{\infty}x^{m_{f_{1} }+r-t-1}e^{-x\left[\mu_{f_{1}}+\tilde{\delta}\varpi\rho\mu_{h_{2}}(1+\tilde{ \gamma})\right]}dx\\ &\quad\times\frac{\Gamma\left(m_{h_{2}}+\tilde{\gamma}\right) \tilde{\delta}^{\tau}K\mu_{h_{2}}^{m_{h_{2}}}\mu_{f_{1}}^{m_{f_{1}}}(-1)^{j} \mathcal{A}_{j^{\prime}}\mathcal{B}_{j^{\prime}}e^{-\tilde{\delta}\mu_{h_{2}}( 1+\tilde{\gamma})}}{r!\Gamma\left(m_{h_{2}}\right)\Gamma\left(m_{f_{1}}\right) \mu_{h_{2}}^{m_{h_{2}}+\tilde{\gamma}-r}(1+j)^{m_{h_{2}}+\tilde{\gamma}-r}} \\ &=\quad\sum_{j=0}^{K-1}\bigcup_{j^{\prime}}^{m_{h_{2}}+\tilde{ \gamma}-1}\sum_{t=0}^{r}\binom{r}{t}\binom{K-1}{j}\\ &\quad\times\frac{\Gamma\left(m_{h_{2}}+\tilde{\gamma}\right) \Gamma\left(m_{f_{1}}+r-t\right)\tilde{\delta}^{\tau}K\mu_{h_{2}}^{m_{h_{2}}} \mu_{f_{1}}^{m_{f_{1}}}(-1)^{j}\mathcal{A}_{j^{\prime}}\mathcal{B}_{j^{\prime}} e^{-\tilde{\delta}\mu_{h_{2}}(1+\tilde{\gamma})}}{r!\Gamma\left(m_{h_{2}} \right)\Gamma\left(m_{f_{1}}\right)\mu_{h_{2}}^{m_{h_{2}}+\tilde{\gamma}-r}(1+ j)^{m_{h_{2}}+\tilde{\gamma}-r}\left[\mu_{f_{1}}+\tilde{\delta}\varpi\rho\mu_{h_{2}} \left(1+\tilde{\gamma}\right)\right]^{m_{f_{1}}+r-t}}\\ \end{split}
# Mini-Crack Detection of Conveyor Belt Based on Laser Excited Thermography
## 3 Principle of the Proposed Method  
In this section, a new assessment method of conveyor belt mini-crack is proposed. The method consists of mini-crack feature extraction, image enhancement, threshold segmentation, and mini-crack determination.
## 5 Result and Analysis  
### Accuracy and Loss of the 10-Fold Cross Validation on Both Training and Test Sets  
The Euler angle data collected by sensors and calculated using Algorithm 1 is used as input for the information fusion experiment after separating the training and test sets. Figure 11 shows that the upgraded GAM-MLP method's Train acc and Test acc curves exhibit a better match.  
Additionally, the accuracy of the test set and training set stabilize as the number of iteration rounds approaches 120, demonstrating outstanding robustness and generalization capabilities.  
From the accuracy curves of the training and testing sets in Figure 11, it can be seen that with the increase of iteration rounds, the curves representing the accuracy of the training and testing sets steadily increase and maintain alignment. This indicates that GAM-MLP performs as well as the training data when facing new data, demonstrating good generalization ability.
## 3 Results and Discussion
### Influence of Genotype on Fruit Quality  
All the studied parameters were strongly influenced by the cultivar (Table 1), which is consistent with data reported by other authors [22; 49]. 'Rocio' was the cultivar with the highest SSC and TA. Although, sugars are known to play an important role in the sensory quality of fruit, berry fruits with lower TA values were perceived as taxeless. Thus, a balanced SSC/TA ratio improves the overall sensory quality of blueberries. Besides, this cultivar showed the lowest average values in the quality parameters evaluated, such as firmness, fruit size and weight. Regarding color parameters, blueberries from the 'Rocio' variety were more bluish and had a darker and duller blue. The V2 berries had the highest firmness values, whilst the values of SSC, TA and berry size were intermediate in comparison with the other two varieties. Regarding berry weight, they had the lowest values together with the 'Rocio' cultivar. Additionally, the berries from the V2 variety showed the lowest hue angle values and the highest values for chroma and lightness and therefore a bright and vivid blue fruit. The berries from the V3 variety had the lowest values for acidity and SSC. These data could be linked to taxeless feeling. Moreover, the fruits from this cultivar showed the highest size and weight per berry. Consumer preferences regarding berry size are not clear and depend on the traditions of each region and how they are to be consumed (fresh consumption, decoration in desserts, etc.) [50]. Besides, the V3 berries presented intermediate values for firmness and color. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & **SSC** & **TA** & **pH** & **Firmness** & **h\({}^{\circ}\)** & **C\({}^{\star}\)** & **L\({}^{\star}\)** & **Weight** & **Size** \\ \hline \multirow{2}{*}{**Open Field**} & 2011 & 12.42 \({}^{\text{c}}\) & 0.42 & 3.92 \({}^{\text{b}}\) & 1.66 & 269.98 & 4.69 & 32.32 & 2.32 & 15.39 \({}^{\text{c}}\) \\  & 2012 & 13.31 \({}^{\text{b}}\) & 0.62 & 3.32 \({}^{\text{d}}\) & 1.95 & 276.51 & 5.69 & 32.82 & 2.32 & 16.42 \({}^{\text{a}}\) \\ \hline \multirow{2}{*}{**Plastic Tunnels**} & 2011 & 14.89 \({}^{\text{a}}\) & 0.22 & 4.37 \({}^{\text{a}}\) & 1.64 & 270.96 & 4.67 & 32.14 & 2.59 & 15.80 \({}^{\text{b}}\) \\  & 2012 & 14.46 \({}^{\text{a}}\) & 0.45 & 3.56 \({}^{\text{c}}\) & 1.97 & 275.59 & 5.26 & 33.08 & 2.31 & 16.17 \({}^{\text{a}}\) \\ \hline \hline \end{tabular} Different letters within a column indicate significant differences by LSD test. SSC: soluble solids content (\({}^{\text{c}}\)Brix); TA: tittatable acidity (% citric acid); Firmness (N); L\({}^{\star}\): lightness; h\({}^{\circ}\): hue angle; C\({}^{\star}\): chroma; weight (g); size (mm).  
\end{table} 
Table 2: Effect of cultivation system and sampling year on the parameters evaluated for blueberry fruits.
# Safety Evaluation Method and Management Strategy for Aviation Flight Plans
## 3 Method
### Safety Evaluation of Flight Plans
#### 3.2.2 Airspace Environment Modelling  
Conflict probability calculation is the basis for quantitative evaluation of the safety of civil aviation flight plans. This quantitative evaluation can predict the time and location of potential conflicts, as well as the degree of conflict risk.  
Airspace environment modelling is the basis for a conflict probability estimation. Reasonable environment modelling can effectively reduce the consumption of time and space, and it can improve the calculation efficiency. The first step of airspace environment modelling is to discretize the free space of the environment where the aircraft is located.  
The raster method is a discretization method for modelling the airspace environment (Figure 6) by dividing the airspace into units. The area where the obstacle is located is marked as 1 in the map matrix, while the free passage area is marked as 0. The raster method has a high safety factor and outstanding description ability, and it is generally combined with other intelligent algorithms to achieve the environment recognition, path planning, and autonomous movement of robots [23]. In this paper, the proposed evaluation method and management strategy for aviation flight plans emphasizes the importance of the temporal dimension. Therefore, the raster method is selected for spatial environment modelling to provide a basis for the subsequent construction of the safety index.  
The flight path of the aircraft is in the \(X\)-\(Y\) plane, while the time is the \(Z\)-axis (Figure 7). The cube ABCD-EFGH is constructed in the coordinate system O-XYZ, representing the planning space, where the plane ABCD is on the XOY plane, AB is parallel to the \(Y\)-axis, BC is parallel to the \(X\)-axis, and the origin O coincides with the point A. The planned space is divided into \(n\) equal parts along the \(Z\)-axis, and a plane is drawn parallel to the ABCD plane through each bisected point to obtain \(n-1\) horizontal planes \(\prod_{j}\left(j=0,1,2,\ldots,n-1\right)\). Each horizontal plane \(\prod_{j}\) is divided into m equal parts along the \(X\)-axis and the \(Y\)-axis and into \(m\times m\) grids. Thus, the planning space ABCD-EFGH is divided into \(m\times m\times n\) grids, each of them are denoted by \((m,m,j),j=1,2,\ldots,n\).  
Figure 5: Accessible area of aircraft A and B.
# Mobile Robot Path Planning Using a Laser Range Finder for Environments with Transparent Obstacles
## 3 Mobile Robot System
### Laser Range Finder (LRF)  
The goal of this paper was to create an environment map that allows mobile robots to conduct automatic driving using only LRF within an environment where transparent obstacles are included. As such, the LMS100-10000 laser range finder (Figure 6) from SICK (Sick AG company, Germany) [34] was used.  
Figure 4: System overview.  
Figure 5: Pioneer P3-Dx.  
The maximum measurable angle of the corresponding LRF is 270 degrees, the measurable distance varies from a minimum of 50 cm to a maximum of 20 m, the systemic error is \(+-30\)mm, and the stochastic error rate is \(+-12\) mm. Additionally, it has an angular resolution of 0.25 degrees or 0.5 degrees [17]. In this study, an angular resolution of 0.25 degrees was used, and 1081 units of information can be collected within the range when the robot stands between \(-135\) and \(135\) degrees using the 0.25 degree of angular resolution.  
After collecting data on the obstacles surrounding the robot, the coordinate system requires additional transformation to apply this data to the environment map [16; 26; 34; 35]. The data collected using LRF corresponds to the polar coordinate system and contains information of the distance away from the sensor and direction. This data must also be converted to a rectangular coordinate system that the robot uses.  
The following equation can be derived from Figure 7.  
\[x_{obstacle}=x_{robot}+d*\cos\Bigl{(}\theta_{trf}+\theta_{robot}\Bigr{)},\ y_{obstacle}=y_{robot}+d*\sin \Bigl{(}\theta_{trf}+\theta_{robot}\Bigr{)} \tag{1}\]  
\(x_{robot}\) and \(y_{robot}\) correspond to the current coordinates of the robot in the rectangular coordinate system. \(\theta_{robot}\) is the direction of the robot, and \(\theta_{trf}\) is the angle between the beam and the direction that the robot is moving towards. \(d\) indicates the distance to the obstacle from the robot's position. Using these methods, the coordinates of \(x_{obstacle}\) and \(y_{obstacle}\) can be found on the rectangular coordinate system.  
Figure 6: SICK (Sick AG company, Germany) laser range finder (LMS100-10000).  
Figure 7: The relationship between the coordinates of detected obstacles and the coordinates of the robot.
## 5 Conclusions  
In this paper, the vibration characteristics of an ABA at rail welds in the time-frequency domain have been analyzed at the speed of 300 km/h according to experimental data. A corresponding FE model was developed to simulate ABA and wheel/rail force under different rail welds, which established a quantitative relation between ABA and the wheel/rail force. Based on the results of this study, the following conclusions can be drawn:  
1. According to field tests, the acceleration of an axle box changed within the range of \(\pm\)50 m/s\({}^{2}\) under normal operation, which significantly increased at the welds to amplitudes ranging from 66 to 200 m/s\({}^{2}\). High-frequency vibrations at welds existed for a very short time, around 2 to 5 ms. Energy of the high-frequency vibration centralized mainly in two frequency bands: 350 to 500 Hz and 1000 to 1200 Hz.
2. ABA at rail welds exhibited drastic fluctuations both in measured data and in simulations. The first wave was caused by the impact of the wheel/rail force, while the continued vibration depended on the resonance of the vehicle-track system, for which the amplitude was greater than that generated by the wheel/rail force in certain conditions. Therefore, the maximum  
Figure 17: Schematic diagrams of axle box acceleration induced by (**a**) rail corrugation and (**b**) wheel flat.  
value of ABA in the time domain always overestimated the wheel/rail dynamic interaction. To deal with this problem, the position of the rail welds should be determined first by means of time-frequency analysis.
3. A linear relationship was found between the ABAF and \(F_{\text{dyn,max}}\), and corresponding expressions were established. A value of 220 m/s2 after 1500 Hz LPF can be used as a safety value for real-time onsite monitoring in judging the necessity of maintaining the rail welds.  
For a further study, a more detailed database of rail weld geometries and axle box accelerations based on measurements should be established. In addition, simulations should be performed, taking into account the contribution of track stiffness and its variation on ABA, which can be used to detect deterioration of track structure.  
The present work has been supported by the National Key R & D Plan of China (2016 YFC080203-4), National Natural Science Foundation of China (51425804, 51608459, 51605394 and 51778542) and Doctoral Innovation Fund Program of Southwest Jiaotong University.  
[PERSON] and [PERSON] conceived and designed the idea and numerical study; [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data and contributed in writing the paper; [PERSON], [PERSON], [PERSON] and [PERSON] contributed in obtaining the funding for performing the research.  
The authors declare no conflict of interest.
# Artificial Intelligence Enabled Routing in Software Defined Networking
## 3 Proposed AIER Mechanism  
### System Architecture  
The SDN architecture we consider is illustrated in Figure 6. The routing module in the application plane is used to find the route between any source-to-destination pairs at the beginning. In the control plane, the well-established Ryu controller [39] is adopted. There are three modules in the controller,  
Figure 5: Example of the system load vs. time.  
topology discovery, period monitor, and an ANN model, which are used to explore the link states of the OpenFlow switches, periodically receive the exchange information from the data plane, and implement the proposed AIER mechanism to select an intelligent path with congestion avoidance, respectively. The data plane consists of \(n\) source nodes (denoted as \(S_{1}\), \(S_{2}\),..., \(S_{n}\)), \(m\) destination nodes (denoted as \(D_{1}\), \(D_{2}\),..., \(D_{m}\)), and a number of OpenFlow switches. Therefore, we assume that a maximum of \(m\cdot n\) (denoted as \(d\)) data flows are generated and a total of \(R\) paths between any source-to-destination pairs are available.
# Research on Leaf Area Density Detection in Orchard Canopy Using LiDAR Technology
## 2 Materials and Methods
### Leaf Area Density Detection Algorithm Based on Leaf Fitting
First, the canopy point cloud data is downsampled using a voxel grid size of 0.02 m \(\times\) 0.02 m \(\times\) 0.02 m. This step can reduce the interference of detection distance on the point cloud spacing and improve computational efficiency. Before conducting the leaf area density detection experiment, the length (L) and width (H) of the target leaves are measured and statistically analyzed. Using Equation (1), the number of point clouds (\(n_{S}\)) scanned from the front side of a single leaf after downsampling is calculated. Based on the result of Equation (2), the initial value of k for K-means clustering is determined, where \(N_{f}\) represents the total number of valid point clouds in the canopy. Subsequently, the number of valid clusters obtained after the initial clustering is counted and the value of k are updated accordingly. Then, the K-means clustering continues and iterates three times until the value of k stabilizes. \[n_{S}=\frac{L\times H}{0.0004} \tag{1}\]  
\[k=\frac{N_{f}}{n_{S}} \tag{2}\]  
The fourth step is the calculation of leaf area density. Using a method based on plane fitting of point cloud data, all point clouds in each cluster are projected onto the fitted plane. The coordinates of the edge point clouds of the entire cluster are statistically analyzed to form the outer contour of the cluster. The mark in Figure 2 represents an outer contour of the cluster. The enclosed area is then calculated using the polygon area calculation formula. By summing the areas of all clusters, the total leaf area of the fitted leaves from the canopy point cloud of the orchard is obtained. This fitted leaf area is then substituted into the established mathematical model for calculating canopy leaf area density of the orchard to calculate the density.