esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Area and Research Method
### Research Materials  
The sampling was performed in the summer of 2021, the working scale was 1:50,000 and the sampling resolution was 2 samples/km\({}^{2}\). A total of 69 samples were collected, including 41 soil (25 inland soil samples and 16 sand bar soil samples) and 28 sediment samples (19 lagoon deposits and 9 offshore deposits). The distribution of the sample points and the elevation of the study area are shown in Figures 2 and 3. The soil samples were collected at a depth of 20-40 cm and passed through a 2 mm sieve on site, and the sample weight was not less than 0.5 kg. A grab sampler was used to collect sediment samples, visible foreign objects were removed and the sediment was passed through a 2 mm nylon sieve on location to ensure that the sieved sediment was no less than 1.5 kg. The wet soil that had been sieved was wrapped with 60-mesh nylon mesh, and the water was drained to the extent possible.  
Figure 1: (**a**) Satellite map of the study area; (**b**) sampling point distribution map.  
Figure 2: **(a)** Total nitrogen distribution, (**b**) organic nitrogen distribution, (**c**) inorganic nitrogen distribution, (**d**) ammonium distribution, (**e**) nitrate distribution, (**f**) nitrite distribution, (**g**) inorganic nitrogen/organic nitrogen distribution and (**h**) ammonium/nitrate distribution.
# Image Mosaicing Applied on UAVs Survey
## 3 Stitching Methods
### Feature-Based: Global Single Transformation
#### 3.1.2 Sift  
One of the feature methodologies most widely used for its performance is SIFT (Scale Invariant Feature Transform) [67]. This low-level feature methodology has the advantage of being robust to occlusion, clutter, and noise with a good quantity of key points generated for even small objects [69]. SIFT uses a sequence of four stages. An image pyramid is constructed by repeatedly convolving input images with Gaussians, including a set of scale-space images, shown on the left, and subtracting the adjacent Gaussian images to produce a difference-of-Gaussian (DoG) pyramid. The scale space is constructed by convolving an image repeatedly using a Gaussian filter, which changes the scales and groups the outputs into octaves [67; 68]. After the scale-space construction is complete, DoG images are computed from adjacent Gaussian-blurred images in each octave [21].
## 4 Discussion
The DR of a plate with VT is more complex than that of a UT because the propagation of waves, such as stress waves or vibration waves, is influenced by the changing thickness, leading to variations in wave speed, attenuation, and mode conversion. These complexities make it challenging to interpret the DR signals and accurately identify the location and severity of damage. Additionally, the DR of a plate including its natural frequencies and mode shapes is significantly affected by thickness variations. This can lead to ambiguities in interpreting the DR, making it difficult to distinguish between damage-induced changes and inherent variations due to thickness variations. Moreover, the material properties of the plate of VT, such as its density, stiffness, and damping, along with its geometric features, such as curvature, holes, and other irregularities, can further complicate the DR and make damage detection more challenging. Furthermore, in some cases, damage in plates can lead to nonlinear behavior in the DR, making it difficult to model and analyze the system using conventional linear methods. In addition, environmental factors, such as temperature fluctuations and external noise, can also influence the DR of the plate, adding additional challenges to damage detection. Similarly, the DR signals obtained from damaged plates may be contaminated by noise from various sources such as background vibrations or measurement errors. Effective noise reduction techniques are crucial for accurate damage detection. Finally, analyzing the DR of plates with varying thicknesses can be computationally demanding, especially for complex geometries or high-frequency excitations. This can limit the real-time applicability of damage detection methods based on the DR. Despite these challenges, DR-based damage detection methods have been actively researched and developed, and they continue to show promise in various applications. However, research must aim to improve the accuracy, robustness, and applicability of these methods by addressing the challenges above-mentioned.
# A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks
## 3 System Model
\[\Pr\left\{\sum_{i=1}^{T_{\text{max}}}C_{k}[t]\geq D/\Delta T\right\} \tag{7}\]  
where \(P_{r}\) denotes the probability, \(C_{k}[t]\) represents the achievable data rate of the V2V user \(K\) at time slot \(t\), \(D\) is the packet size, \(\Delta T\) is the transmission time of one packet, and \(T_{\text{max}}\) is the maximum allowed transmission time.
# Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development
## 3 Multi-Relational GCN Based Service Recommendation Model
### Overall Framework
#### 3.2.2 Graph Convolution
The  
module in the graph represents the feature transformation, aggregation, and nonlinear activation process of layer \(l\), and they share the same parameters \(W^{(l)}\) for each layer. Thus far, we have described the GCN framework on a single relation graph. As there exist three kinds of relations, we propose to fuse the multiple relations into a single relation with three fusion strategies. Inspired by [32], we take the symmetric normalized Laplacian matrices of the graph for the fusion of multiple relations, which can enrich the structural information propagated through nodes. Specifically, the symmetric normalized Laplacian matrix of the composition relation \(A^{C}\) is defined as \(L^{C}=D_{C}^{-\frac{1}{2}}(A^{C}+I)D_{C}^{-\frac{1}{2}}\), where \(D_{C}\) is the degree matrix of \(A^{C}+I\). \(L^{D}\) and \(L^{T}\) are defined in a similar manner that corresponds to the matrices \(A^{D}\) and \(A^{T}\). We propose three fusion strategies: (1) The _add_ strategy. It integrates the graph structure of three relations through adding the Laplacian matrices, i.e., \(L_{add}=L^{C}+L^{D}+L^{T}\); (2) The _max-pooling_ strategy. It takes the maximum value of the three Laplacian matrices in each entry--for example, if \(L^{C}(i,j)=0.9\), \(L^{D}(i,j)=0.8\), \(L^{T}(i,j)=0.5\), and \(L_{max-pool}(i,j)=0.9\); (3) The _weight-sum_ strategy. It adds three Laplacian matrices with different weights, which can integrate relations with different levels of importance. In particular, \(L_{weight-sum}=w_{c}\cdot L^{C}+w_{d}\cdot L^{D}+w_{t}\cdot L^{T}\), where \(w_{c}\), \(w_{d}\), and \(w_{t}\) are scalar values that multiply with each entry in the corresponding matrix, and we treat them as model parameters. In this way, the three relations are jointly considered and directly incorporated into a comprehensive relation by fusing the Laplacian matrices with different strategies. Alternatively, we can perform GCN directly on the three graphs with one single type of relation, and concatenate the final layer of the learned features of the three graphs. However, it adds the computation complexity of GCN training, and it is hard to tune the feature dimensions for each relation. We will evaluate different fusion strategies in the experiments.
# On Analyzing Routing Selection for Aerial Autonomous Vehicles Connected to Mobile Network
###### Abstract  
This paper proposes a two-phase algorithm for multi-criteria selection of packet forwarding in unmanned aerial vehicles (UAV), which communicate with the control station through commercial mobile network. The selection of proper data forwarding in the two radio link: From UAV to the antenna and from the antenna to the control station, are independent but subject to constrains. The proposed approach is independent of the intra-domain forwarding, so it may be useful for a number of different scenarios of Unmanned Aerial Vehicles connectivity (e.g., a swarm of drones). In the implementation developed in this paper, the connection is served by three different mobile network operators in order to ensure reliable connectivity. The proposed algorithm makes use of Machine Learning tools that are properly trained for predicting the behavior of the link connectivity during the flight duration. The results presented in the last section validate the algorithm and the training process of the machines.  
machine learning; two-phase selection algorithms; UAV; routing and forwarding; optimization 2021  
21 399
# On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning
## Appendix A Detailed Calculations  
### TCP Cubic  
As noted earlier in Section 3, TCP CUBIC dynamics can be mathematically modeled by using the congestion window (\(W\)) as follows. \(W\) is time-varying; therefore, \(W(t)\) evolves as  
\[W(t)=\left(s(t)-\sqrt[3]{\frac{W_{\max}(t)\beta}{C}}\right)^{3}+W_{\max}(t) \tag{1}\]  
where \(s(t)\) is the elapsed time since the last window reduction, \(W(t)\) is the current congestion window at time \(t\), and \(C\) refers to scaling constant and \(\beta\) is multiplicative decrease factor.  
Using Equation (1) with the fixed-point approach along the lines of [58], we obtain the equilibrium congestion window, \(W^{\star}\), as  
\[W^{\star}=\sqrt[4]{\frac{\tau^{3}C}{(p^{\star})^{3}\beta}} \tag{2}\]  
We consider to cases to explain the causes of unfairness and starvation.  
Case i: Considering two CUBIC flows with different RTTs, \(\tau_{1}\) and \(\tau_{2}\), and experiencing the same loss, \(p\), we can evaluate how the congestion windows \(W^{\star}_{1}\) and \(W^{\star}_{2}\) evolve as  
\[W^{\star}_{1}=\sqrt[4]{\frac{\tau^{3}_{1}C}{(p^{\star})^{3}\beta}};\ W^{\star}_{2 }=\sqrt[4]{\frac{\tau^{3}_{2}C}{(p^{\star})^{3}\beta}}\]  
\[\frac{W^{\star}_{1}}{W^{\star}_{2}}=\sqrt[4]{\frac{\tau^{3}_{1}}{\tau^{3}_{2}}} \tag{3}\]  
Using (3), if \(\tau_{1}=2\tau_{2}\), then \(\frac{W^{\star}_{1}}{W^{\star}_{2}}=\sqrt[4]{8}\), which explains the key reason for the observed unfairness.  
Case ii: Considering two CUBIC flows with the same RTTs, \(\tau\), and experiencing different losses, \(p_{1}\) and \(p_{2}\), we can evaluate how the congestion windows \(W^{\star}_{1}\) and \(W^{\star}_{2}\) evolve:\[W_{1}^{\star}=\sqrt[4]{\frac{r^{3}C}{(p_{1}^{\star})^{3}\beta}};\ W_{2}^{\star}= \sqrt[4]{\frac{r^{3}C}{(p_{2}^{\star})^{3}\beta}}\]  
\[\frac{W_{1}^{\star}}{W_{2}^{\star}}=\sqrt[4]{\frac{p_{2}^{3}}{p_{1}^{3}}} \tag{10}\]  
If \(p_{1}=2p_{2}\), then \(\frac{W_{1}^{\star}}{W_{2}^{\star}}=\sqrt[4]{\frac{1}{8}}\).  
As we know, **Starvation** is an extreme case of unfairness,and the two cases discussed above will have the following consequences:  
Case i: If \(p_{1}=np_{2}\), where \(n=1,\ldots,20\ \frac{W_{1}^{\star}}{W_{2}^{\star}}=\sqrt[4]{\frac{1}{n^{3}}}\);  
Case ii: If \(\tau_{1}=n\tau_{2}\), where \(n=1,\ldots,20\ \frac{W_{1}^{\star}}{W_{2}^{\star}}=\sqrt[4]{n^{3}}\).
## 2 Methodology
### The Proposed Method of SRS Images Generation
#### 2.1.3 SRS Image Generation
Image Superimposition of Target Ship Extracted from the 2D Ship Image and Selected Maritime Scene Background  
In the above, the pinhole imaging model was used to transform the 3D ship data into a 2D image, and then we calculated the position and size of the simulation ship and confirmed the posture of the simulation ship. The nest task is to extract the target ship from the 2D ship image with a monochromatic background, and overlay it onto the selected maritime background scene image to form SRS images. The flow chart of image superimposition is shown in Figure 6. The SRS images point set is as follows:  
\[f(x_{i},y_{i})=(1-a)\cdot f(p_{i},t_{i})+a\cdot f(u_{i},v_{i})\ \ \ \ \ a\in(0,1)\ \ \ \ (i=1,2,\cdots n) \tag{9}\]  
where \(f(u_{i},v_{i})\) is the simulation ship image point set, \(f(p_{i},t_{i})\) is the real scene image point set, and \(f(x_{i},y_{i})\) is the SRS images point set. Simulation ships in SRS images have three elements: trajectory, size, and posture. The experiments in the experimental scenario are shown in Figure 2; the observation perspective does not change in this scene, and the navigation posture of the ship is assumed to be unchanged or slightly changed. We needed to select the sailing posture before the experiment. The size L of the ship is related to the position of the simulated ship in the background image. In the experiment, we used the YOLO algorithm [12] for modeling, as shown in Figure 7. Coordinates (x1, y1) and L are used to form three-dimensional coordinates (x1, y1, L1) which are the position of the ship in the SRS image. Then, we used the YOLO algorithm to count the data (x1, y1, L1) of 100 ships (cargo ships, passenger ships, sand ships, and small ships.) in the experimental scene, and performed modeling. We determined L by simulating the position of the ship (xi, yi). Finally, the ships' trajectories were determined based on the voyage data of 100 ships in the experimental scene. The overall flow chart of the SRS image generation method is shown in Figure 7.  
Figure 6: Flow chart of image superimposition. Figure 7: Flow chart of SRS images generation method.
## 2 Materials and Methods
### Methods
#### 2.2.1 Data Sampling  
First of all, since the data used in this work are characterized by high heterogeneity, the features have wide and poorly comparable range of values; accordingly, a data standardization was performed as a preliminary in which we rescaled data to have a mean of 0 and a standard deviation of 1. For the modelling phase, the data presented a typical issues of a learning analysis--data imbalance. A dataset is balanced if the classes are approximately equally represented. Learning algorithms require a balanced dataset in order to yield accurate predictions, which, otherwise, would be severely biased towards the majority class. The data imbalance can be solved using two opposite strategies: on the one hand, it is possible to over-sample the classes less populated; on the other hand it is possible to under-sample the majority class. Every strategy has its own advantages and drawbacks, for the present case we chose an oversampling technique because of the limited available sample size.  
In particular, in this work we used the Synthetic Minority Over-sampling Technique (SMOTE) sampling method. This technique creates new instances of the minority classes from combinations of preexisting instances. A particular advantage of such an approach is to create novel synthetic examples rather than using duplicates, which is the case of other more standard approaches such as bootstrap. A peculiar aspect of SMOTE is that it generates synthetic examples by operating in feature space rather than data space [37]. To over-sample the minority class, each minority class observation is taken into account and its \(n\) minority-class neighbors are also considered; then, new synthetic examples are introduced along the directions connecting the considered example and its neighbors, thus resulting in \(n\) novel synthetic observations.  
Figure 1: Flowchart of the proposed methodology. After sampling we selected the most performing learning algorithm and implemented a feature reduction procedure. Finally we used the selected learning model to classify the patients in the three CVD risk classes.
# A Terminal-Oriented Distributed Traffic Flow Splitting Strategy for Multi-Service of V2X Networks
## 5 Simulation Results
### Service Shunting Scheme Based on Queuing Theory
It is also apparent that the system delay of the delay-sensitive service is always lower than the cost-sensitive service, which also reflects that the adaptive shunting algorithm can effectively implement service offload when it is oriented to specific V2X service requirements. Moreover, when the terminal service rate requirement is greater than 4 Mbps, the system delay starts to increase rapidly. This is primarily because when traffic shunting occurs in a heterogeneous network, system congestion will also occur, which affects the service delay. Figure 4: Utility value comparison results. Figure 6 shows the comparison of system cost consumption of terminal services in different rate demand scenarios. The system cost consumption of the adaptive shunting algorithm is significantly lower than that of the load balancing algorithm, and as the terminal traffic increases, the trend effect becomes increasingly obvious. Moreover, the cost of the adaptive shunting algorithm for cost-sensitive services is lower than that of delay-sensitive services. This indicates that the adaptive shunting algorithm has certain advantages in dealing with network tariff consumption in the distribution of traffic through different links. Figure 5: System delay comparison results. Figure 6: Total system cost comparison results.
## 2 Related Work  
### CNN-Based Object Detectors  
CNN-based object detection methods can be organized into two main categories, two-stage detection framework with region proposal and one-stage detection framework, i.e., region proposal free framework. In the two-stage framework, category-independent region proposals are generated from an image, CNN features are extracted from these regions, and then category-specific classifiers are used to determine the category labels of the proposals. RCNN series of methods is the representative of this category. [PERSON] proposed R-CNN in 2014 [12], which generates candidate regions by selective search algorithm [13], uses convolutional neural network for feature extraction, and uses SVM for object classification. Inspired by SPP-net [14], for the problem of feature extraction for candidate regions of R-CNN [12] which resulted in a large number of repeated redundant computations and a large amount of caches, [PERSON] improved R-CNN and proposed the Fast-RCNN [15], which input the entire image into the convolutional neural network to extract features, and then directly obtain the features of the candidate region from the feature map of the entire image according to the positional relationship, which greatly improves the detection speed. Subsequently, [PERSON] proposed the faster Faster R-CNN [16] by replacing the time-consuming selective search [13] with a CNN-based Region Proposal Network (RPN).  
The one-stage object detection method abandons the time-consuming candidate region generation process and directly generates the position coordinates and category probability of the object to be detected from the original image, which greatly improves the real-time performance of object detection. Typical representative methods of this branch include YOLO (You Only Look Once) [17] proposed by [PERSON], CornerNet [18] proposed by [PERSON]  
Figure 1: Hardware architecture of edge–cloud cooperation airport apron surveillance video intelligent analysis.  
and SSD [19] proposed by [PERSON]. One-stage object detection methods are widely used in object detection tasks with high real-time requirements due to good real-time performance.
# CNT-PUFs: Highly Robust and Heat-Tolerant Carbon-Nanotube-Based Physical Unclonable Functions +
## 5 Evaluation of the Fabricated CNT-PUFs
### Evaluation of CNT-PUFs under Normal Conditions
#### 5.2.2 Uniqueness  
To evaluate the uniqueness of our CNT-PUFs, the 144-bit binary responses returned from each of the 30 CNT-PUFs were compared. We used the inter-device Hamming distance introduced in Section 3 to perform this comparison. Figure 9a shows the Hamming distances of all 30 CNT-PUFs to each other (inter-device Hamming distances), excluding the Hamming distance of each PUF to itself (intra-device Hamming distances). The results showed that almost all values were centred on the optimal value of 0.5. As we can see from the box plot in Figure 9b, the exact average inter-device Hamming distance was 0.48, with no inter-device Hamming distance above 0.62 or below 0.37. These results demonstrated a high level of uniqueness.
# Point Cloud Measurement of Rubber Tread Dimension Based on RGB-Depth Camera
## 3 Methodology and Design
### Three-Dimensional Dimensional Inspection of Tread Based on Point Cloud Data  
In this study, the calibrated RGB-D camera is utilized to combine the point cloud data generated from tread depth information to perform tread quality inspection experiments and result analysis. Based on the production line requirements for tread size, the object distance is set to 0.7 m. Tread depth information is acquired using an Astra Pro. After collecting the undistorted RGB color image and the corresponding depth map of the tread, the RGB color map is aligned to the depth camera coordinate system to establish a one-to-one correspondence between the depth point cloud and the color pixel points. The library function of Open3D is then used to transform the data into a PCD object supported by the Open3D library, resulting in high-density point cloud data aligned with the color and depth maps, as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Corner Point Retention (\%)**} & \multicolumn{4}{c}{**Reprojection Error**} \\ \cline{2-5}  & **RGB Camera** & **IR Camera** & **Binocular Camera** \\ \hline Pre-optimization & 100 & 0.213 & 1.372 & 0.964 \\ Post optimization & 78 & 0.101 & 0.737 & 0.379 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of optimization results.  
Figure 8: Alignment diagram of color depth information collection. (**a**) Acquisition of tread depth information and image color using RGB\(-\)D. (**b**) Correspondence between the depth point cloud and colored pixel points in the coordinate system of the depth camera.  
Figure 7: Experimental Pareto solution set.
## 3 Results and Discussion
### Powder Characterization  
The morphology of the powder particles obtained by FESEM is shown in Figure 7a. Near-spherical large-size powder particles can be seen with a high number of small-size satellite powder particles on their surface. Agglomerated particles can also be observed. These effects are well-known features of gas-atomized powders. The smaller particles are lifted by gas circulating in the cooling chamber, and they collide and stick to larger particles that are still partly molten. The measured average circularity is quite high, 0.77. Figure 7b shows the wide particle size distribution of the deposited powder, which has a size range of 35-105 \(\upmu\)m, where \(D_{50}\) is 71.3 \(\upmu\)m. The EDX analysis shown in Figure 8 allowed us to determine that the main alloying elements (Ni, Cr, Fe, Mo, Nb, and Ti) were uniformly distributed in the powder particles without any significant segregation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Type** & **Name** & **Ni** & **Cr** & **Mo** & **Fe** & **Nb** & **Ti** & **Mn** & **C** & **Si** & **Al** & **Others** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} Wrought base \\ Powder \\ DED-LB deposited \\ \end{tabular} } & Inconel 718 & Bal. & 18.1 & 2.79 & 18.4 & 5.07 & 0.95 & 0.067 & 0.043 & 0.056 & 0.60 & \textless{}0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical compositions in wt. % of the employed wrought base material, filler Inconel 718 powder, and optimum deposited overlay.  
Figure 6: (a) Cross-section of test specimen 6 and (b) cross-section of the deposited coupon.