esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving the Maritime Traffic Evaluation with the Course and Speed Model
## 4 Evaluation of Maritime Traffic Flow with the CS Model
### Evaluation with CS Models
This implied that smaller ships often deviated from the general navigation pattern, which was also considered a part of the traffic flow. Figure 13 presents the results of the Dk(\(\theta\)) and Dk(r) analyses for spatiality. Dk(\(\theta\)) was primarily distributed from around \(-5\) to \(+45\) degrees during departure and \(-100\) to \(+5\) degrees during port entry in relation to the standard plan. Dk(r) was mainly distributed from \(-100\) to \(+5\) degrees. As Dk(\(\theta\)) and Dk(r) depend on the COG and ROT of ships, their values, akin to Dk(v) and Dk(a), might vary based on the chosen location and time for the target assessment area. Any deviation from the standard plan in these metrics indicates the spatial characteristics of maritime traffic. The outcomes of the continuity index, derived from the analyses of Dk(v) and Dk(a) for each ship size, are presented in Figure 3:1 Similarly, the results of the spatiality index, obtained from the analyses of Dk(\(\theta\)) and Dk(r) for each ship size, can be found in Figure 3:2 Notably, a higher index value indicates a more substantial impact on the fluidity of maritime traffic flow, signifying a deviation from the standard navigation plan. The data in Table 2 reveal that the overall outbound continuity index outperforms the inbound index, and the data in Table 3 reveal that the inbound spatiality index outperforms the outbound index. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{**Dk(v)**} & \multicolumn{2}{c}{**Dk(a)**} & \multicolumn{2}{c}{**Continuity Index**} \\  & **Outbound** & **Inbound** & **Outbound** & **Inbound** & **Outbound** & **Inbound** \\ \hline S1 & 0.8548 & 0.8012 & 0.7854 & 0.5781 & 0.8201 & 0.6897 \\ S2 & 0.7712 & 0.7929 & 0.8045 & 0.7583 & 0.7879 & 0.7756 \\ S3 & 0.8426 & 0.8394 & 0.7826 & 0.7107 & 0.8126 & 0.7751 \\ S4 & 0.8849 & 0.8147 & 0.74 The CS index results, derived from the analyses of the continuity and spatiality indices, are listed in Figure 3:3 The outbound and inbound CS indices registered values of 0.7614 and 0.7501, respectively. This means that, in the target sea area, the outbound ship flow demonstrated a slightly weaker adherence to the standard plan than the inbound flow, impacting the fluidity of maritime traffic. Various elements contribute to the CS index. These encompass ship-associated factors indicating the vessel's dynamic performance, traffic conditions evaluating the ambient traffic environment, distinctions between the open sea and harbor approaches, overall traffic volume, the efficiency of pilot services, and temporal factors. Based on the findings of this study, the characteristics of the sea route leading to the port are aptly captured by the CS model.
# A Learning Game-Based Approach to Task-Dependent Edge Resource Allocation
## 3 System Model
### Local Computation  
Although our study's task offloading strategy involves completely offloading the dependent task to the ES, the time it takes for the dependent task to execute locally still serves as a baseline for comparison, thus necessitating their modeling. In terms of the task model, we emulated most of the existing literature [18; 19; 20; 21] by modeling a dependent task as a directed acyclic graph (DAG) \(\mathcal{G}=\{\mathbf{V},\mathbf{E}\}\), where \(\mathbf{V}=\{\mathbf{v}_{n}|n=1,2,...,N\}\) denotes the set of sub-tasks of the current dependent task. Each sub-task \(\mathbf{v}_{n}\) is represented by a binary group, i.e., \(\mathbf{v}_{n}=\{d_{n},r_{n}\}\), where \(d_{n}\) denotes the size of the current task and \(r_{n}\) denotes the size of the computation result. \(\mathbf{E}=\{e(\mathbf{v}_{i},\mathbf{v}_{n})|i,n\in 1,2,...,N\}\) denotes the dependency relationship between the sub-tasks, and when \(\mathbf{v}_{i}\) depends on \(\mathbf{v}_{n}\), \(\mathbf{v}_{i}\) can start the computation only after \(\mathbf{v}_{n}\) finishes and the result of the computation is transmitted to \(\mathbf{v}_{i}\).  
If \(f_{l}\) is used to represent the computing capacity of the local device, and \(\eta_{u}\) is the computing capacity required to process unit bit data, then the number of CPU cycles required to complete the task calculations locally is expressed as \(c_{n,loc}=d_{n}\eta_{u}\). Therefore, the time \(T_{loc}^{n}\) required to execute \(\mathbf{v}_{n}\) locally can be written as follows:  
\[T_{loc}^{n}=\frac{c_{n,loc}}{f_{l}}. \tag{1}\]  
Then, the local completion time of \(\mathbf{v}_{n}\) is the following:  
\[FT_{loc}^{n}=max\{AT_{loc}^{n},\underset{i\in pre(\mathbf{v}_{n})}{\max}\{FT_{loc}^ {i}\}\}+T_{loc}^{n}, \tag{2}\]  
where \(AT_{loc}^{n}=max\{AT_{loc}^{n-1},FT_{loc}^{n-1}\}\) is the earliest available time of the local processor and \(pre(\mathbf{v}_{n})\) is the set of direct predecessor tasks of \(\mathbf{v}_{n}\).
# Graphs from Features: Tree-Based Graph Layout for Feature Analysis
## 4 Methodology
### Feature Relevance  
From \(X\) a transpose \(X^{T}\) is obtained and a relevance value is evaluated between each feature and the target attribute \(t\), as a means to reflect similarity. The relevance values are mapped to visual features of the layout constructed downstream in the pipeline. Relevance values may also be used by the user as a means to select features. Two measures of relevance are currently available in GFF: the Pearson correlation and Extra Trees Classifier (ETC) [44].  
Pearson coefficient is a standard measure of correlation. Taking the absolute value turns Pearson correlation into a measure of relevance expressed as similarity and evaluated as  
\[R(t,y)=\left|\frac{\sum_{i=1}^{n}(t_{i}-\overline{t})(y_{i}-\overline{y})}{ \sqrt{\sum_{i=1}^{n}(t_{i}-\overline{t})^{2}(y_{i}-\overline{y})^{2}}}\right|\]  
where \(t\) is the target attribute, \(t_{i}\) denotes its \(i\)-th component, \(y\) is any feature of the dataset, and \(\overline{t}\) and \(\overline{y}\) are the mean for \(t\) and \(y\) respectively.  
Extra Trees Classifier [44] is an ensemble learning algorithm whose forest produces classification results. In ETC trees, internal nodes represent the features and the leaves determine the result of a classification. During the construction of the trees, a mathematical criterion (such as the Gini Index) is used to partition the values of features where the partitioning results are evaluated with relation to the results of the classification in a training stage. Used for feature selection, the ETC is regarded as an embedded method where the Gini Index values are used to attribute importance to the features. The output of the algorithm is a ranking of features with values in the range \([0,1]\), where \(1\) means higher importance and \(0\) means lower importance.  
To obtain the ranking of features, we consider the relevance values of each feature generated by Pearson or by ETC (user choice), against a categorical attribute or label of the data set. That generates a ranking of importance of features, presented by a colored bar from low to high rank. In this way,  
Figure 1: Analysis pipeline of Graphs from Features (GFF). From a dataset \(X\) with \(n\) data instances having \(d\) features each, (**a**) a proximity measure between pairs of features is evaluated, and (**b**) a complete graph is created where vertices represent features and edges represent dissimilarity between features. (**c**) A tree is constructed to summarize the information in the complete graph. (**d**) Visualization and interaction tools that combine information from the tree and from the underlying graph may be used by the user to search for patterns among the features and to select the most discriminating features. (**e**) Data instances may be projected based on the features selected by the user during the interaction.  
the features will form part of the interaction with the user where he or she can select features on the left bar in the GFF prototype tool (see Figure 2).
# Analysis of the Vibration Characteristics of Ballastless Track on Bridges Using an Energy Method
## 2 Methods
### Wheel/Rail Interaction Model
#### 2.3.2. Wheel/Rail Contact Force  
The solution of the wheel/rail contact force can be divided into two parts: **a.** the solution of the normal contact force; **b.** the solution of the tangent contact force.  
(1) The Normal Contact Force  
The solution of the normal contact force in this work is based on the theory presented by [PERSON] (1965), which is not appropriate for solving the contact condition in turnouts or sharp curves. The mathematical expression is shown in Equation (5):  
\[P(t)=(\frac{1}{G}\Delta Z(t))^{3/2}, \tag{5}\]  
where \(\Delta Z(t)\) is the elastic penetration at time \(t\). \(G\) refers to the wheel/rail contact constant, which can be obtained by Equation (6):  
\[G=3.86\text{R}^{-0.115}\times 10^{-8}, \tag{6}\]  
where \(R\) represents the radius of the wheel.  
The elastic penetration can be derived by the wheel/rail relative displacement, which is presented in Equation (7):  
\[\Delta Z(t)=Z_{wz}(t)-Z_{ri}(t)\ \ (i=1\sim 4), \tag{7}\]  
where \(Z_{wz}(t)\) denotes the vertical displacement of wheels and \(Z_{ri}(t)\) refers to the vertical displacement of rails at the corresponding contact point.  
(2) The Tangent Contact Force  
Considering the occurrence of friction between the wheel and rail, tangent force is generated at the contact patch, namely, creep force. The solution of the tangent contact force is found on the basis of [PERSON]'s linear theory [PERSON] (1965), in which the distribution of the creep force is assumed to be symmetric, and [PERSON]'s theory is employed to conduct nonlinear modification [PERSON] (1965). Based on the aforementioned two theories, the creep rates in the longitudinal, lateral, and spin directions are respectively defined in Equation (8):  
\[\left\{\begin{array}{l}\xi_{x}=\frac{V_{wz1}-V_{rl}}{V}\\ \xi_{y}=\frac{V_{wz}-V_{rl}}{V}\\ \xi_{spin}=\frac{\Omega_{wz1}-\Omega_{\lambda z}}{V}\end{array}\right., \tag{8}\]  
where \(V_{w1}\), \(V_{w2}\), and \(\Omega_{w3}\) denote the velocities of the wheel in the longitudinal, lateral, and spin directions, respectively; and \(V_{r1}\), \(V_{r2}\), and \(\Omega_{r3}\) represent denote the velocities of the rail in the longitudinal, lateral, and spin directions, respectively.  
Figure 3: The contact geometry of the wheel/rail.  
The tangent contact force can be obtained by Equation (9):  
\[\left[\begin{array}{c}\mathrm{T_{x}}\\ T_{y}\\ M_{z}\end{array}\right]=\mathrm{E}\cdot ab\left[\begin{array}{ccc}C_{11}&0&0\\ 0&C_{22}&(ab)^{1/2}\cdot C_{23}\\ 0&-(ab)^{1/2}\cdot C_{23}&C_{33}\end{array}\right]\left[\begin{array}{c} \xi_{x}\\ \xi_{y}\\ \xi_{spin}\end{array}\right], \tag{9}\]  
where \(T_{x}\) and \(T_{y}\) refer to the creep forces in the longitudinal and lateral directions, respectively; \(M_{z}\) is the spin creep moment of force; \(E\) denotes the elastic modulus; \(a\) and \(b\) are the lengths of the semi-axes of the elliptical contact patch; and \(C_{ij}\) represents [PERSON]'s coefficient, which can be obtained as a function of \(a/b\) and Poisson's ratio.
# Radiometric Method for Determining Canopy Stomatal Conductance in Controlled Environments
## 3 Results
### Diurnal Changes in \(G_{\rm S}\)  
Diurnal changes in \(\Omega\) reflect changes in \(G_{\rm S}\) because chamber \(g_{\rm A}\) and \(D_{\rm Bulk}\) are constant (Equation (10)). The effect of CO\({}_{2}\) concentration on the diurnal course of \(G_{\rm S}\) was examined in wheat (Figure 9A; (18-day-old; PPF = 1600 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\); T\({}_{\rm air}\) = 21 \({}^{\circ}\)C; RH = 68%)) and soybean (Figure 9B; (27-day-old; PPF = 750 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\); T\({}_{\rm air}\) = 21 \({}^{\circ}\)C; RH = 64%)) canopies.  
In the dark, \(\Omega\) of both canopies was below 0.2, Ds was coupled to \(D_{\rm Bulk}\), and canopy transpiration was small. Indeed, the nighttime VPDs within the wheat canopy (\(D_{\rm Aero}\) 1.12 kPa, and \(D_{\rm S}\) 1.15 kPa) were near the VPD (\(D_{\rm Bulk}\) 1.27 kPa) of the chamber. When the lights came on, the stomata opened, the chamber humidity and Gs increased, the boundary layer within the canopy became humidified by transpiration, and \(D_{\rm Bulk}\) decreased during the photoperiod. In wheat, VPDs within the canopy (\(D_{\rm Aero}\) 0.57 kPa and \(D_{\rm S}\) 0.36 kPa) became decoupled from \(D_{\rm Bulk}\) (0.74 kPa). At 400 \(\upmu\)mol mol\({}^{-1}\) CO\({}_{2}\), the mean daily \(\Omega\) of wheat was 0.67 and 0.55 in soybean (Equation (10); Figure 9) due to the differences in their corresponding \(G_{\rm S}\) and \(g_{\rm A}\). \(\Omega\) during the early part of the day rose to \(\sim\)0.8 in wheat and to \(\sim\)0.75 in soybean. As the photoperiod progressed, \(\Omega\) gradually declined to \(\sim\)0.6 in wheat and to \(\sim\)0.6 in soybean as a consequence of a diurnal decrease in \(G_{\rm S}\). At 1200 \(\upmu\)mol mol\({}^{-1}\) CO\({}_{2}\), \(\Omega\) of wheat and soybean reached a maximum of 0.4 due to reduced stomatal conductance and declined to near 0.2 at the end of the photoperiod (Figure 9).
## 3 Results and Discussion
### Wind Directionality Effects of Extreme Wind Loads
#### 3.3.2 Wind Directionality with Unknown Building Orientation  
To consider the influence of the building orientation, [PERSON] et al. [24] proposed a method to consider the influence of the building direction on the extreme wind pressure directionality factor. Based on the known building orientation, this study considers the wind speed-wind direction relationship of Shanghai Longhua station, determining the directionality factors in Section 3.3.1. This process is repeated 16 times, and the extreme wind directionality factors based on an unknown building orientation are obtained by using Formula (13):  
\[K_{\text{d}}=\sum_{\text{g}=1}^{16}P_{\text{r}}(\text{g})K_{\text{d}}(\text{g}) \tag{13}\]  
where \(P_{\text{r}}\) (g) denotes the probability of occurrence in all orientations, and g represents the building orientation.  
Equations (10)-(12) are used to calculate the \(K_{\text{d}}\) (g) value of each tap for each orientation, and then the \(K_{\text{d}}\) value of each tap considering all the building orientations is obtained by using Equation (13). Table 4 shows the \(K_{\text{d}}\) (g) values of Tap.96 for 16 orientations. It can be found that in the table, that the wind directionality factors of the same tap in different orientations are different, and the \(K_{\text{d}}\) value range is 0.71-0.97. Using Equation (13), the wind directionality factors are found to be 0.87 for an unknown building orientation.  
Figure 18 shows the histogram and probability distribution fitting of the \(K_{\text{d}}\) for the 308 taps with an unknown building orientation. For the normal distribution, the value of 0.88 meets the 50% guaranteed rate, and the guaranteed rate of 0.90 in Chinese code is 75%.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Direction (g)** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline \(K_{\text{d}}\) (g) & 0.90 & 0.92 & 0.95 & 0.81 & 0.88 & 0.87 & 0.85 & 0.90 \\ \hline
**Direction (g)** & **9** & **10** & **11** & **12** & **13** & **14** & **15** & **16** \\ \hline \(K_{\text{d}}\) (g) & 0.90 & 0.83 & 0.71 & 0.89 & 0.80 & 0.80 & 0.90 & 0.97 \\ \hline \end{tabular}
\end{table}
Table 4: Directionality factors \(K_{\text{d}}\) (g) of typical taps (Tap.96) in unknown building orientations.  
Figure 17: Fitting results of the probability distribution of \(K_{\text{d}}\) value. (**a**) Gumbel probability distribution fitting, (**b**) GEV probability distribution fitting.  
Similarly, Table 5 shows the wind directionality factors of 10 typical taps based on the analysis with the unknown building orientation. Compared with the cases in which the building orientation is given, the \(K_{\rm d}\) value with the unknown building orientation is safe.
## 2 Previous Work
### Graph Neural Networks  
From a different perspective, the points in a point cloud can be seen as nodes in a graph, and the edges can be defined as relationships between the points. Graph neural network techniques attempt to apply a filter on a graph through the nodes' properties and edges, similarly to applying a CNN on a graph. [PERSON] and [PERSON] [27] were among the first to develop a framework that treats each point as a vertex of the graph and applies filters around the neighbors of a point. The information from the neighbors is aggregated to create a coarse graph from the original one. A dynamic graph CNN (DGCNN) [28] uses an MLP to implement edge convolution from the edges. The framework employs channel-wise symmetric aggregation, EdgeConv, to dynamically update the graph after each network layer. Inspired by the DGCNN's approach, [PERSON] and [PERSON] [29] developed a multi-task method to learn shape features using an encoder from multi-scale graphs and a decoder to process three unsupervised tasks. ClusterNet [30] employs a proven rotation-invariant module to generate rotation-invariant features from each point by processing its neighbor and constructing a hierarchical structure of a point cloud, which is similar to the approaches of the DGCNN and PointNet++. The approach uses edge labels to achieve a convolution-like operation that is more suitable for point clouds. Unlike images, the 3D point cloud can be rotated, and the classification model should classify the same object, making the structure rotationally invariant.  
So far, the existing methods rely on prior information but not the geometric properties of the point cloud. Even if some of the previous models try to obtain the local features of the 3D data, they still try to do so by applying a model to the smaller portion of the data without understanding the information. In applying model-distilled data to the local 3D data, the model tries to find a pattern without understanding the data. However, for some particular kinds of 3D data, like mechanical components, detailed information in each category of such data is only sometimes present in other categories. For example, some kinds of mechanical components would display special geometric features.  
We deem such information necessary for identifying objects like mechanical components. Therefore, inspired by other papers emphasizing the importance of treating the geometry of the input [31], we propose the following model, MC Classifier, which tries to take in geometric priors and achieves a better classification accuracy. From a more abstract point of view, our model displays the importance of including priors relevant to the dataset to improve efficiency.
# A Multi-Hop Clustering Mechanism for Scalable IoT Networks
## 3 Multi-Hop Clustering for IoT Networks  
It is assumed that the IoT network is composed of a set of IoT nodes, hereafter denoted as \(N=\left\{n_{1},\ n_{2},\ \ldots,n_{j},\ldots\right\}\), and all of the nodes are capable of taking the role of a coordinator. With the proposed mechanism, each IoT node \(n_{j}\) transfers its data to its selected coordinator instead of transmitting the data directly to an Internet server so that the number of Internet connections required for the IoT network, which is equivalent to the number of the coordinators can be saved. The objective of the proposed mechanism is to find the smallest set \(C\) of the selected coordinators \(c_{i},\ i=1,\ 2,\ \ldots,k\) for IoT network with \(N\), while satisfying the maximum hop count constraint \(H\) between a node \(n_{j}\) and its coordinator \(c_{i}\). Furthermore, we opt to minimize a sum of hop counts between a coordinator \(c_{i}\) and its member nodes, i.e., \(\forall n_{j}\in M_{i}\) while minimizing a total count of coordinators, i.e., \(\left|C\right|,\) where \(M_{i}\) is a set of the member nodes which belong to a coordinator \(c_{i}\). The objective of the proposed mechanism is then formulated as follows:  
\[\text{Minimize}\ \left|C\right|,\ C=\left\{c_{i}\ \middle|\ i=1,\ 2,\ \ldots,k \right\},\ C\subset N\ \text{subject to}\]  
\[\sum\
olimits_{\forall c_{i}\ \in\ C}\sum\
olimits_{\forall n_{j}\ \in\ M_{i}}dist(c_{i},n_{j})\text{ \emph{is minimized}},\]  
where  
\[M_{i}\ =\left\{\forall n_{j}\in N\middle|\ dist(c_{i},n_{j})\leq H,\ c_{i} \in C\right\},\ M_{i}\cap M_{j}=\varnothing\,\forall i\
eq j\,\ \bigcup_{i=1}^{k}M_{i}=N,\]  
and \(dist(a,\ b)=\) a hop count in the shortest path between \(a\) and \(b\).  
In Section 3.1, the proposed mechanism is explained in detail and its complexity is analyzed in Section 3.2.
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 6 Experimental Results
### Pose Estimation on Antennas
#### 6.3.2 Extrinsic Camera Calibration from Pattern and SfM

Table 5: Comparison of estimation errors from cameras extrinsically calibrated using an SfM technique and with a calibration pattern. The pattern-based results are duplicated from Table 3.
# Transaction Mechanism Based on Two-Dimensional Energy and Reliability Pricing for Energy Prosumers
## 4 Case Study
### Characteristics  
In the performance evaluation section, it is shown that the prosumer benefit and grid reliability with the proposed ERT is enhanced more than under the conventional pricing case. Furthermore, it is mentioned that the performance enhancement is related to the characteristics of the price and the resource. To explain that, it is shown how to change the band hit ratio under the conventional pricing case and the proposed ERT for various EES capacities. Band hit ratio means the normalized absolute error distribution at each band range as shown in Figure 6, after the EES operation to maximize the prosumer revenue under each price condition. The EES enhances the system's ability to achieve the operational objective, i.e., the prosumer revenue maximization in this work. Therefore, the results of increasing EES capacities show the direction of achieving the purpose of the system.  
In Figure 9(a), as the operational room increases, such as EES capacity, the second band hit ratio grows under the conventional price. This is because the average MAE without EES is about 18%, included in the second band of the conventional price. The results show that, under the conventional price, the prosumer works to maximize their benefit by reducing their average penalty. This is because the incentive-penalty is settled according to the result of operation under the conventional pricing case. However, under the ERT, the first band hit ratio and the out-of-band ratio are increased in relation to the EES capacity in Figure 9(b). This means that the prosumer in the proposed ERT achieves enhanced benefits by maximizing the incentive. In the proposed ERT, the incentive-penalty is determined according to the previously selected band and operation result. Therefore, the prosumer operates the system considering the reliability as well as the benefit, as shown in Figure 9(b). From these results, it can be said that the prosumer under the conventional pricing case operates the system in the form of an average maximization criterion, and a max-max-type operation is worked by the proposed ERT.  
In addition, the out-of-band ratio increases from 10% EES capacity (the sixth bar in Figure 9(b)) to 15% EES capacity (i.e., the seventh bar in Figure 9(b) is larger than the others). This is why the MAE improvement under the proposed ERT is slightly reduced as shown in Figure 9. These results show that the proposed ERT effectively works in relation to the resource characteristics and gives the prosumer more motivation to control their resources than under the conventional pricing case.  
Figure 9: Comparison of grid mean absolute error improvement under the conventional pricing mechanism and the proposed ERT.