esa-sceva/satcom-chunk-collection huggingface dataset:
# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles
## 3 Proposed Method
### Path-Based Clustering Model  
#### 3.2.1 Concept Definitions  
The proposed path-based clustering data dissemination protocol (PCDP) consists of two parts.  
The first part is a clustering method that forms clusters based on the expected path of the vehicle. When constructing a cluster, the survivability of the cluster is improved by constructing a cluster between nodes with the smallest difference by comparing the planned paths of the vehicles. In particular, in the city area where the route is frequently used, there is a certain probability that the node will depart due to the change in route, even if the connection rate is high, which accumulates every time the route distribution section includes the intersection passes.  
As can be seen in Figure 3, nearby vehicles can point in different directions at the next intersection. If the red vehicle in Figure 3 goes to the left, the yellow car goes straight, and the blue car goes to the right, there is a possibility that all of them belong to the same cluster according to the current communication intensity. However, they are clearly dispersed in other directions, and the cluster will collapse in an instant.  
If the survivability of the cluster is low, the clustering process occurs frequently and the overhead increases rapidly. Data dissemination is also delayed due to the clustering process.  
According to [PERSON], road conditions can be defined as a Markov chain in Bayesian terms [37]. This supports the fact that the better the future path, the better survivability of clusters.  
In this paper, we propose a method for constructing a cluster that can last for a long time using path information.  
The second part is a data dissemination method that can reduce the traffic redundancy and how much data can be delivered to the nodes on the network, that is, maximize the data dissemination rate. In this paper, we try to maximize the data dissemination rate by using the path-based cluster as a medium for message delivery. Each cluster has a high probability of heading to a different path, so there is a high probability that it will head to a path with a cluster that has not received the message. Each cluster moves to store the forwarded message and rebroadcasts the message each time it encounters a new neighbor cluster. This process maximizes the data dissemination rate of the entire network.  
Figure 3: Cluster Topology in PCDP.
## 3 Results and Discussion
### Geobia
#### 3.2.4 Accuracy of Classification Algorithms  
RTC proved to be the most reliable classification algorithm. RTC values (Table 2) produced the best results in three of the four accuracy measures used. The best results were achieved with _UA_, _OA_, and _KC_. The classification algorithm ML in all used measures shows slightly less accurate results compared to RTC. ML shows the least accurate results in _PA_, _UA_, and _KC_ values, while it is more reliable than SVM according to _OA_. The SVM classification algorithm shows the least reliable results according to the _OA_, while the best values are related to the _PA_. The results of the accuracy assessment using _UA_ and _KC_ are in the middle between the results of the two algorithms.  
Authors in [68] used SVM, MLC, and RTC algorithms to extract coastal coniferous forests, and by accuracy assessment measure KC, the RTC algorithm was selected as the most accurate one. In [69], RTC shows the best performance in object-based land-cover classification. In [63], the most accurate results for olive tree canopies extraction were shown by the SVM classification algorithm with 0.904 results in the AUC measure, while the second one was the MLC algorithm with 0.864. More accurate results in the mentioned study are explainable by greater study area and lower spatial resolution with a GSD of 18 cm for UAV\({}_{\text{MS}}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm/Measure** & _UA_ & _PA_ & _OA_ & _KC_ \\ \hline \multirow{2}{*}{RTC} & 0.8113 & 0.9195 & \multirow{2}{*}{0.7565} & \multirow{2}{*}{0.4615} \\  & 0.7378 & 0.5144 & & \\ \hline \multirow{2}{*}{MLC} & 0.7729 & 0.8997 & \multirow{2}{*}{0.7418} & \multirow{2}{*}{0.4311} \\  & 0.7306 & 0.5071 & & \\ \hline \multirow{2}{*}{SVM} & 0.7511 & 0.8818 & \multirow{2}{*}{0.7403} & \multirow{2}{*}{0.4328} \\  & 0.7361 & 0.5302 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: GEOBIA accuracy assessment measures results.  
Figure 11: Classified models: (**a**) SVM; (**b**) MLC; (**c**) RTC.
# Underwater Glider Propulsion Systems VBS Part 1: VBS Sizing and Glider Performance Analysis
## 5 Results and Discussion
### Limit Flight Angles  
To validate the equations, the results of [PERSON] et al. [27], who used the historical data of the hydrodynamic studies with the Slocum gliders, are presented in Table 2. The values obtained by Equations (12)-(15) match with the values obtained by [PERSON] et al. [27], therefore the validation process of the equations is correct and can be used to perform the glider performance analysis.  
The hydrodynamic coefficients of the vehicle Kay Jull 2 [25] are included in the Table 2; this vehicle is a winged body of revolution with an ellipsoidal external shape, a fineness ratio of 6.6, and a body length of 2 m. The polynomial coefficients of [25] have been updated to have regressions of between \(-10^{\circ}\) to \(10^{\circ}\) of the angle of attack to improve the linear function, this was because the lift state presents  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & & & **Coefficients** & & \multicolumn{3}{c}{**[PERSON] et al. [27]**} & \multicolumn{3}{c}{**Present Work**} \\  & & & & & \(\alpha_{1,2}\) & \(\frac{1}{E_{c}(\alpha_{1,2})}\) & \(\frac{1}{E_{c}(\alpha_{1,2})}\) & \(\gamma_{1,2}\) & \(\frac{1}{E_{c}(\alpha_{1,2})}\) & \(\frac{1}{E_{c}(\alpha_{1,2})}\) & \(\gamma_{1,2}\) \\ \hline
2003 & VCT [18] & 219.93 & 2.50 & 93.24 & 9.38 & 7.21 & 7.90 & 9.38 & 7.20 & 7.90 \\ \hline
2005 & [PERSON] [21] & 219.43 & 3.80 & 573.32 & 4.66 & 2.35 & 23.04 & 4.66 & 2.35 & 23.05 \\ \hline
2006 & [PERSON] [35] & 135.00 & 2.00 & 45.00 & 12.08 & 7.12 & 8.00 & 12.08 & 7.12 & 8.00 \\ \hline
2007 & Williams Dive [36] & 82.98 & 4.37 & 109.82 & 11.43 & 1.89 & 27.84 & 11.43 & 1.89 & 27.83 \\ \hline
2007 & Williams Climb [36] & 70.37 & 3.36 & 116.21 & 9.74 & 1.78 & 29.31 & 9.74 & 1.78 & 29.32 \\ \hline
2010 & [PERSON] (2010) [37] & 305.00 & 5.00 & 144.00 & 10.68 & 5.68 & 9.98 & 10.68 & 5.68 & 9.98 \\ \hline
2010 & [PERSON] (2010) [38] & 135.52 & 1.99 & 44.23 & 12.16 & 7.22 & 7.89 & 12.15 & 7.22 & 7.88 \\ \hline
2011 & [PERSON] V = 0.3 m/s [39] & 212.28 & 6.06 & 154.39 & 11.35 & 3.47 & 16.08 & 11.35 & 3.47 & 16.08 \\ \hline
2011 & [PERSON] V = 0.7 m/s [39] & 212.28 & 5.00 & 154.39 & 10.31 & 3.82 & 14.67 & 10.31 & 3.82 & 14.67 \\ \hline
2011 & [PERSON] 2 [24,25] & 329.4 & 9.43 & 330.9 & - & - & - & 9.67 & 2.95 & 18.73 \\ \hline
2011 & [PERSON] 2 (updated) & 443.48 & 7.75 & 413.99 & - & - & - & 7.84 & 3.91 & 14.33 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Validation of energetic and geometric equations.  
a small effect close to the angle of attack of 10\({}^{\circ}\) and the previous regression considered a range between \(-\)15\({}^{\circ}\) to 15\({}^{\circ}\). Both models have been considered in the discussion of the results.
### Motivations  
Channel models are essential for the development and performance analysis of UAV communication systems. However, the majority of existing channel models concentrate on terrestrial or A2G scenarios, and studies focusing on A2A scenarios are scarce. In traditional terrestrial communications, the heights of the transmitter (Tx) and receiver (Rx) are typically small compared to the distance between them. The small grazing angles lead to a significant amount of energy being reflected by the ground. Hence, the amplitude of the reflection coefficient is approximated to one, and the phase shift between the direct and ground reflection paths is close to 180\({}^{\circ}\), known as perfect ground reflection [32]. However, in A2A scenarios, UAVs have the ability to hover at high altitudes. The large grazing angles lead to a significant portion of the signal energy being scattered rather than reflected. Hence, it is essential to accurately model the diffuse scattering components in the A2A channel. Additionally, in A2A scenarios, both the Tx and Rx are relatively high, resulting in a different distribution of the scatterers compared to A2G scenarios [33]. Hence, existingA2G models are not applicable to A2A scenarios. Moreover, although some existing models primarily focus on the vibration effects on UAVs, in these scenarios, UAVs undergo turning or rotational motions, which are caused by wind gusts or atmospheric turbulence [10; 11; 12; 13]. However, when considering the operation of propellers and engines, the UAV can undergo frequent repetitive movements, involving oscillating or back-and-forth motions around an equilibrium position [14; 34]. Unlike A2G channels, A2A channels undergo dual vibrations at both ends of the link. The statistical features of A2A channels, incorporating vibrations from UAVs at both ends of the link and considering LoS, specular, and diffuse components, have not been adequately investigated.
# Effect Mechanism of Connection Joints in Fabricated Station Structures
## 4 Numerical Results
### Different Ground Motion Records
#### 4.1.1 Plastic Strain  
Compared with the cast-in-place station structure, the fabricated station structure is connected by dry-type joints and has less tensile damage. The compression damage of concrete can be used as the evaluation index of the seismic response of the fabricated station structure. The CDP model defines the compression damage to measure the structure's compression failure state with a variation range of 0-1, corresponding to the compression plastic strain in ABAQUS. When the compressive plastic strain value is 0, the structure has no compressive damage. A value of 1 means the structure is completely destroyed. Figure 11 shows the plastic strain cloud of the fabricated subway station structure with different joint locations. Table 4 shows the maximum plastic strain of fabricated station structures with the different joint distributions. The plastic strain mainly occurred at the B-C joint and C-D/E joint of the sidewall, and block B under different ground motion excitations. The maximum plastic strains of the CC model, QD model, and SZ model were 0.0143, 0.0141, and 0.0145, respectively. The maximum impact rate of the vault or bottom plate joints on the structural failure was 3.2%. Under the action of an earthquake, the underground structure deforms under the constraint of the surrounding soil. The most serious damage in the prefabricated structures lay in the B-C joints. The joints of the vault and the bottom plate were always in the extrusion state, as in the cast-in-place structure. Therefore, the joint changes of the vault and bottom plate had little influence on the plastic strain of the structure.  
Figure 11: The cloud diagrams of the plastic strains.
## 5 Discussion
### Ethical Considerations
#### 5.4.2 Privacy and Consent  
Since our study utilized synthetic datasets generated by a large language model, no personally identifiable information (PII) from real individuals was involved. However, we ensured that the synthetic data generation process did not inadvertently reproduce sensitive or private information by employing strict filtering mechanisms and auditing generated text for anomalies. Additionally, our methodology adheres to ethical AI principles by ensuring transparency in data sources and generation processes, preventing potential privacy violations.
# An Attention-Based Method for the Minimum Vertex Cover Problem on Complex Networks
## 2 Basic Concepts and Problem Definition
### Memory Networks  
Despite feed-forward neural networks, recurrent neural networks (RNNs) [46] are used to model sequential data and to tackle time dimension. RNNs process one element of the input sequence at a time, and they model short-term dependencies by building their output, taking into account both the input at a given timestamp and the network output at the previous timestamp. Thus, RNNs model short-term dependencies using backward connections from a hidden layer of the network to the previous one, i.e., the state of a hidden layer \(h_{t}\) (with \(h\in\mathbb{R}^{D}\)) depends both on the input \(x_{t}\) (with \(x\in\mathbb{R}^{N}\)) at a given timestamp \(t\) and on the previous hidden state of the network \(h_{t-1}\). A non-linear transformation of this contributes to defining the final output of an RNN as described in Equation (4):  
\[h_{t} =g(Wh_{t-1}+Ux_{t}+b) \tag{7}\] \[o_{t} =f(Vh_{t}+c)\]  
where \(W\in\mathbb{R}^{D\times D}\), \(U\in\mathbb{R}^{N\times D}\) and \(V\in\mathbb{R}^{D\times M}\) are learnable weight matrices for hidden-to-hidden, input-to-hidden and hidden-to-output connections, respectively, \(b\in\mathbb{R}^{D}\) and \(c\in\mathbb{R}^{M}\) are learnable bias parameters, \(g\) and \(f\) functions as in the feed-forward neural network.  
Long short-term memory (LSTM [47]) networks have been proposed to extend to longer time periods, the possibility to take into account time dependencies. In a current LSTM, \"past relevant inputs\" are stored in a cell memory vector, which is determined by  
\[c_{t} =f_{t}*c_{t-1}+i_{t}*\widetilde{c_{t}} \tag{8}\] \[\widetilde{c_{t}} =g(Wh_{t-1}+Ux_{t}+b)\] \[h_{t} =o_{t}*g(c_{t})\]  
where _input gate_\(i_{t}\), _forget gate_\(f_{t}\) and _output gate_\(o_{t}\) are functions with values in range \([0,1]\), that, respectively, determine the quantity of input, past information and current hidden state to be stored into the cell memory, and thus to be used to produce the output.  
\(*\) denotes element-wise multiplication, \(c_{t-1}\) is the previous cell value, while \(\widetilde{c_{t}}\) is the candidate value for current cell memory, and \(h_{t}\) is the final hidden state.  
In LSTM, the forget gate \(f_{t}\) plays a crucial role in enabling the network to capture long-term dependencies. If \(f_{t}\to 1\), previous memory values will be preserved; thus, even distant inputs can take part in output computation. On the other hand, if \(f_{t}\to 0\), the connection with previous cell values is lost. Thus, the model tends to forget long-term events and to remember only short-term ones.  
Given an input sequence \(x=\{x_{1},x_{2},\ldots,x_{T}\}\), it has been shown [48] that LSTM can be used to model the conditional probability \(p(x_{t}|x_{0:t-1})\) as a function of the network output \(h_{t}\) as:  
\[p(x_{t}|x_{1:t-1})\approx\prod_{t=1}^{T}p(x_{t}|h_{t}) \tag{9}\]
## 2 Literature Review
### IoT Adoption in the MS  
The IoT has the potential to perform processes in an easier-than-ever way and achieve numerous MS goals through increasing productivity, enhancing services efficiency, and improving products quality [5, 16, 17, 19]. From the literature review, it can be observed that many studies have started to explore IoT adoption aspects in the MS from different points, such as enabling technologies for the IoT, potential application areas in the MS, and the key challenges [2, 3, 4, 37, 38, 39]. [PERSON] et al. [10] investigated IoT benefits and challenges in the MS in terms of ecological, economic, and social sustainability and how IoT implementation can aid the achievement of sustainability goals. Similarly, [PERSON] et al. [19] discussed IoT deployment benefits, challenges, and practical concerns for smart manufacturing.  
[PERSON] and [PERSON] [64] studied barriers to IoT adoption in the MS by evaluating organizational priorities using maximum mean de-entropy (MMDE), interpretive structural modeling (ISM), and decision-making trial and evaluation laboratory (DEMATEL). [PERSON] and [PERSON] [1] examined the prioritization of the factors that affect decision-makers' IoT application selections with a multiple-criteria decision-making (MCDM) method. [PERSON] [17] proposed an evaluation framework for the degree of IoT adoption readiness using interval-valued Pythagorean fuzzy sets (IVPFS) and analytic hierarchy process (AHP). [PERSON] et al. [18] developed a conceptual model to identify the determinant factors for IoT adoption in the MS based on the technology-organization-environment (TOE) framework.  
Nevertheless, the IoT adoption models in the MS were examined in only a few studies [7, 8, 12, 16, 27, 32, 36, 44]. Most of these studies used the TOE framework and the diffusion of innovation theory (DOI) to identify the elements that enable or inhibit the adoption of the IoT [7, 8, 12, 16, 27, 32]. The TOE and DOI are not sufficient holistically to capture IoT adoption factors and might overlook some factors.  
In IT literature, the perceived value was used as a critical indicator in technology-adoption decisions [21, 65, 66]; however, none of the previous studies explored the effect of perceived value on IoT adoption in the MS. The IoT is a new technology with unique capabilities and is expected to yield substantial value to organizations [23]. [PERSON] and [PERSON] [1] asserted that the value provided by IoT applications is an important  
Figure 1: Perceived Value of IoT Technology.  
factor for decision-makers when selecting MS applications. [PERSON] et al. [67] stated that the prospect for the MS to leverage the IoT would become more evident by providing higher value, and a more complex model is required to enrich IoT adoption by examining the antecedents and provided value; thus, there is a need for a new model that clarifies the antecedents and the effect of perceived value in enhancing organizational IoT adoption.
## 2 Materials and Methods
### Linear Regression Model with Noise
The adjusted R-squared is formulated as in Equation (3):  
\[R_{a}^{2}=1-\left(\frac{(1-R^{2})\times(n-1)}{n-p-1}\right) \tag{3}\]  
where  
R\({}^{2}\)a = adjusted R-squared  
R\({}^{2}\) = conventional R - squared value  
n = number of samples  
p = number of predictors in the model  
Unlike R\({}^{2}\), which always increases or remains constant with additional predictors, R\({}^{2}\)a can decrease if the added variables do not sufficiently improve the model's fit relative to their contribution to complexity [12]. The term (n \(-\) 1)/(n \(-\) p \(-\) 1) in Equation (3) serves as the adjustment factor. As p increases, this factor grows, effectively penalizing each added predictor. This mechanism ensures that R\({}^{2}\)a only increases if a new variable's contribution to explained variance outweighs the penalty for increased complexity [43]. The adjusted R-squared is particularly valuable in geotechnical modeling, such as predicting soil expansion rates downstream of earth dams. Soil behavior is influenced by numerous factors--rainfall, clay content, plasticity index, organic matter, and more--tempting researchers to include many predictors. The adjusted R-squared thus guides parsimonious model selection in geotechnical engineering. It encourages retaining only those soil properties that substantially enhance predictive power, leading to models that are both accurate and interpretable. This approach not only improves statistical rig geotechnical principles, focusing on the most geologically relevant factors affecting soil behavior. Figure 4 shows the flowchart that summarizes the methodology in this research.
# Study on Drone Handover Methods Suitable for Multipath Interference Due to Obstacles
## 2 Related Work
### Introduction of Relay Technology by Drone
More specifically, because the authentication procedure is not the subject of discussion, this study assumes the use of over-the-DS FT authentication, which is largely independent of the authentication time of the wireless segment and focuses on the seamlessness of the part where the wireless connection is physically switched. In addition, other work related to this study is presented. In relay methods that use drones, drones have been emphasized to improve the transmission rate and reduce the error rate [28]. The proposed method uses an uplink multi-user multiple input multiple output (MU-MIMO) cooperative retransmission control scheme. When the drones in flight communicate directly with each other, another drone, which cooperates and assists in communication based on the transmission distance, performs a relay transmission to guarantee transmission errors. The method guarantees transmission errors and achieves a transmission rate of 1.5 times even when there is considerable interference from the ground, and it is also expected to improve blind zones because it establishes relays over a wide area. In addition, in [29], the authors improved the communication capacity characteristics of MIMO systems using drone relay stations by introducing a propagation environment control method (PECM) that selects the best arrangement of drones from among multiple drones in a drone relay network. However, neither of these methods discusses how  
Figure 4: Over-the-air FT roaming. Figure 5: Over-the-DS FT roaming. drones move to the relay, and handovers by movement require different and new capabilities. Ad hoc networks that use drones were studied by [30]. In this research, when the communication infrastructure on the ground fails, such as during a disaster, unmanned aerial vehicles (UAVs), such as drones, can be deployed to provide a rapid communication environment for victims and emergency workers, efficiently delivering information in the disaster area even when the communication infrastructure is unavailable. However, handovers to ground stations have not been discussed. In these related studies, handover is beyond the scope, including the effects of the drone's maneuverability, movement speed, and obstacles, such as buildings. In air-to-ground and air-to-air, communication channel characteristics are highly dynamic [31; 32]. These papers mention channel modeling for UAVs in various environments, which can be evaluated with high accuracy by analyzing them through rigorous channel modeling [33; 34; 35]. Research on channel modeling for operating UAVs is also in progress. The simulator developed in this study is a simplified channel modeling that only implements geometry such as buildings. In the future, when more detailed analysis becomes necessary, we plan to perform detailed modeling of the propagation environment, taking into account scenarios, parameters, shadows, etc.
## 5 Results and Discussion
### Effects of the Parameters on \(\overline{Nu}\)  
#### 5.2.1 The Effect of \(\phi\) on \(\overline{Nu}\)  
At fixed equivalence ratios of 0.9, 1.0, 1.1, and 1.2, respectively, the response surface plots of the average Nusselt number within the operating parameter range (\(Re=600\)-\(1200\), \(H/d=1.5\)-\(3.0\)) are shown in Figure 9. \(\overline{Nu}\) rises as the equivalence ratio increases because the flame temperature is higher at stoichiometric and slightly rich conditions than at lean conditions [19]. Additionally, although the maximum flame temperature at \(\phi=1.2\) is lower than that at \(\phi=1.0\) and 1.1, the global heat transfer performance is actually better due to the longer flame length and more released heat. Section 5.4 discusses this in detail.