esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In the ED_Foraging approach, a swarm of robots searches an unknown environment to collect and transport resources with dynamic deadlines to a known nest location. The main contribution of this approach is to address the modeling and DTA problems highlighted in the literature on swarm foraging that have dynamic deadlines. The dynamic deadlines of resources and their states were modeled using the DD_Model, which was developed to predict the state of each resource, although the robots searched blindly and had no prior knowledge about the resources' locations and states. The robots collected resource information and updated the DD_Model when reporting to the closest zone. Then, this shared information was used by nearby robots to continuously update their models and, hence, give priority to denser and more intense regions. Thus, it was possible to deal with dynamic and online deadlines while searching.  
In regard to DTA, the proposed hotspot DTA method was able to prioritize critical regions. The intensities of the hotspot regions were continuously updated, and thus, the allocation was dynamic, depending on the current state of the environment and the DD_Model. The main objective was to reduce the number of expired deadlines by observing the environment and updating its density and intensity. From the experimental results, it was possible to test the proposed approach with different DTA methods, and it was found that using multiple collection zones was more suitable for the proposed approach in terms of both the foraging and expiry rates. Although communication was limited, the use of the DD_Model and hotspot intensity gave higher priority to infected resources; the results showed that hotspot DTA performed better when comparing different map setups. In regards to scalability and robustness, it was found that the proposed method produced consistent results, as it tried to maintain a similar performance across changing environments. Thus, unlike the other methods in the literature, it produces flexible, scalable, and robust results.  
One important observation that needs to be addressed in the future is that the expiry rate can be relatively high in the experiments. This is because the search is blind and the communication among robots is limited. Thus, a future research direction will focus on the method of selecting the model parameters adaptively, in which the parameters of the DD_Model are automatically updated based on a real environment setup. In addition, ED_Foraging has many potential applications due to its inherent flexibility, scalability, and robustness. Therefore, future work will also involve modeling real-life problems, such as rescuing COVID-19 patients or operating a recycling factory, and testing them with real swarms of robots.  
**Funding:** This research received no external funding.  
This work was supported by the Deanship of Scientific Research (DSR) with King Saud University, Riyadh, Saudi Arabia, through a research group program under Grant RG-1441-503. The author also thanks the Ibn Khaldun Fellowship program for Saudi Women at MIT.  
The authors declare no conflict of interest.
## 3 Wind Turbine Model Validation and Disturbances
### Simplified Model  
The dynamic model of the floating turbine system is derived using the Euler-Lagrange approach, following the method outlined by [PERSON] et al. [22]. The simplified model is obtained by applying a small-angle approximation, resulting in a linear model consisting of three linear differential equations. Each state variable represents the dynamics of a specific element of the system: TMD, tower, and barge. The TMD is installed in the nacelle (Figure 1).  
The system of linearized equations that describe the model are as follows:  
\[l_{b}\ddot{\theta}_{b}=-d_{b}\dot{\theta}_{b}-k_{b}\theta_{b}-m_{b}gR_{b} \theta_{b}+k_{t}(\theta_{t}+\theta_{b})+d_{t}\Big{(}\dot{\theta}_{t}+\dot{ \theta}_{b}\Big{)} \tag{1}\]  
\[l_{t}\ddot{\theta}_{t}=m_{t}gR_{t}\theta_{t}-k_{t}(\theta_{t}-\dot{\theta}_{ b})-d_{t}\Big{(}\dot{\theta}_{t}-\dot{\theta}_{b}\Big{)}-k_{TMD}R_{TMD}(R_{TMD} \theta_{t}-x_{TMD})- \tag{2}\]  
\[d_{TMD}R_{TMD}\Big{(}R_{TMD}\dot{\theta}_{t}-\dot{x}_{TMD}\Big{)}-m_{TMD}g(R_{ TMD}\theta_{t}-x_{TMD})\]  
\[m_{TMD}\ddot{x}_{TMD}=k_{TMD}(R_{TMD}\theta_{t}-x_{TMD})+d_{TMD}\Big{(}R_{TMD} \dot{\theta}_{t}-\dot{x}_{TMD}\Big{)}+m_{TMD}g\theta_{t} \tag{3}\]  
The platform, tower, and tuned mass damper (TMD) are denoted by the subscripts \(b\), \(t\), and \(TMD\), respectively. The TMD has a single degree of freedom (DoF), corresponding to the translational motion of its mass, \(m_{TMD}\), while the platform and tower each have a single rotational DoF. In this model, the hydrostatic restoring forces and water friction are represented by a torsional spring and a damper attached to the barge, as shown in Figure 1. Additionally, a rotational spring and damper are included to represent the stiffness and damping characteristics of the tower. In the equations, \(d\) denotes the rotational or linear  
Figure 1: Diagram of 3 DoF model of the FOWT [6].  
damping constants, \(k\) represents rotational or linear stiffness, and \(I\) indicates the rotational inertia. The \(R\) terms refer to various distances: \(Rt\) is the distance from the tower's pivot point to its center of mass, \(Rb\) is the distance from the platform's center of mass to the pivot point, and \(R_{TMD}\) is the distance from the TMD's center of mass to the pivot point.  
The platform pitch angle, \(\theta_{b}\), is defined as the angle between the vertical \(z\)-axis and the vertical axis of the platform, while \(\theta_{t}\) is the angle between the vertical \(z\)-axis and the tower. The variable \(x_{TMD}\) represents the distance travelled by the TMD mass along the \(x\)-axis measured from the vertical \(z\)-axis. The state variables in this model are \(\theta_{t}\), \(\theta_{b}\), and \(x_{TMD}\) with their derivatives \(\dot{\theta}_{t}\), \(\dot{\theta}_{b}\), and \(\dot{x}_{TMD}\).
## 4 Classification of MAC Protocols Based on Channel
### Cooperative Multi-Channel MAC Protocols
#### 4.7.4 CT MAC  
The proposed cooperative TDMA (CT) MAC uses a relay strategy to improve communication efficiency hampered by interference or jammers at RSUs. It uses a slot sharing scheme; the status information of each node is present in the MAC header. The slots are shared and prioritized by analyzing the slot state. Every time slot is partitioned into two CWs; DSRC specifies the payload for safety application and a slot information (SI) header. The carrier signal sensed by vehicles at each time slot is used to detect the state of the slot. This slot is again divided into three states: first, check the packet is correctly received; second, when the packet is received; and third, when no packet is received and SINR is low (channel is idle). The slot is prioritized by a value accumulated and received during a frame, containing the data generated by the neighbors. The category of the channel sensed that is idle, busy, or noisy is defined. An overview of CTMAC is shown in Figure 14.  
Figure 14: Overview of CT MAC [90].  
The stability of the network is maintained by prioritizing those vehicles occupying the slot through a carrier sensing algorithm. The proposed work eliminates merger and access collision using the back-off method in the slots. Priority-based slot allocation avoids communication interference. However, the reliability and throughput metrics are not analyzed in dense and sparse scenarios.
## 4 Feature Representation of Mobile Video
### The Architecture of ELU-3 DCNN  
The proposed ELU-3 DCNN structure is shown in Figure 2, which includes eight convolution layers, five max-pooling layers, six ELU activation functions, and two fully connected layers followed by a softmax output layer. All 3D convolution kernels are \(3\times 3\times 3\) with stride 1 in both spatial and temporal dimensions. The number of filters are denoted in each box. The 3D pooling layers are denoted from Pool 1 to Pool 5. All pooling kernels are \(2\times 2\times 2\), except for Pool 1 (\(1\times 2\times 2\)). Each fully connected layer has 4096 output units. We used the existing YouTube-1M pre-trained ReLU-3 DCNN model as our ELU-3 DCNN initializations, then fine-tuned the ELU-3 DCNN on an UCF-101 (University of Central Florida) dataset with 20 epochs. The initial learning rate is set to 10-3, the batch size is 10, the momentum is 0.9, and the weight decay is 0.0005.
# A Sentence Prediction Approach Incorporating Trial Logic Based on Abductive Learning
## 4 Experiments and Results
### Result Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Training Ratio** & **MAE** & **MSE** & **RMSE** & **R2 Score** \\ \hline TFIDF-SVM & 10\% & 8.50 & 89.06 & 9.44 & 0.3772 \\ CNN & 10\% & 8.04 & 80.47 & 8.97 & 0.4286 \\ LSTM-FC & 10\% & 7.95 & 80.28 & 8.97 & 0.4173 \\ Text CNN & 10\% & 8.34 & 82.74 & 9.10 & 0.3858 \\ BERT-FC & 10\% & 7.12 & 80.39 & 8.96 & 0.4592 \\ SS-ABL & 10\% & **3.46** & 56.48 & 7.52 & 0.5934 \\ SPITL & 10\% & 3.49 & **41.55** & **6.45** & **0.6684** \\ \hline TFIDF-SVM & 50\% & 7.50 & 78.35 & 8.85 & 0.4592 \\ CNN & 50\% & 7.04 & 80.47 & 8.97 & 0.4257 \\ LSTM-FC & 50\% & 6.34 & 82.74 & 9.10 & 0.3876 \\ Text CNN & 50\% & 7.34 & 82.74 & 9.10 & 0.3928 \\ BERT-FC & 50\% & 5.68 & 67.20 & 8.20 & 0.6735 \\ SS-ABL & 50\% & 3.53 & 45.36 & 6.73 & 0.6472 \\ SPITL & 50\% & **3.25** & **34.94** & **5.91** & **0.7895** \\ \hline TFIDF-SVM & 90\% & 7.02 & 70.33 & 8.39 & 0.6782 \\ CNN & 90\% & 5.86 & 64.36 & 8.02 & 0.6895 \\ LSTM-FC & 90\% & 5.32 & 52.71 & 7.26 & 0.6361 \\ Text CNN & 90\% & 5.51 & 49.56 & 7.03 & 0.6530 \\ BERT-FC & 90\% & 4.25 & 40.42 & 6.36 & 0.6982 \\ SS-ABL & 90\% & 3.28 & 38.05 & 6.17 & 0.7468 \\ SPITL & 90\% & **2.62** & **22.83** & **4.77** & **0.8734** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance results for different methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Training Ratio** & **MAE** & **MSE** & **RMSE** & **R2 Score** \\ \hline SVM & 50\% & 3.43 & 59.96 & 7.74 & 0.5853 \\ KNN & 50\% & 3.23 & 38.44 & 6.20 & 0.6893 \\ Bayes & 50\% & 7.32 & 36.46 & 6.03 & 0.7239 \\ Decision Tree & 50\% & 3.58 & 41.93 & 6.48 & 0.6702 \\ Linear Regression & 50\% & 5.02 & 68.15 & 8.26 & 0.4570 \\ Our Model & 50\% & **3.01** & **32.72** & **5.72** & **0.7864** \\ \hline SVM & 90\% & 3.04 & 26.55 & 5.15 & 0.8075 \\ KNN & 90\% & 3.04 & 23.69 & 4.87 & 0.8210 \\ Bayes & 90\% & 7.11 & 30.03 & 5.48 & 0.7926 \\ Decision Tree & 90\% & 3.24 & 29.41 & 5.42 & 0.8083 \\ Linear Regression & 90\% & 3.53 & 29.11 & 5.40 & 0.8095 \\ Our Model & 90\% & **2.60** & **20.76** & **4.56** & **0.8962** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Calculation of sentences for different methods. the MAE, MSE, and RMSE values are smallest, and obviously, our model is more accurate and stable. Furthermore, at the same training ratio, our model has a higher R2 Score value, closer to 1. From this perspective, the sentence calculation method of our model is better than the others. With the increase in the amount of data, the accuracy of traditional identification improved, but in the calculation of sentences, it cannot absolutely rely on the key circumstance elements, but needs to refer to the whole process of the case, such as the motive for the crime, the means of the crime, etc. Therefore, the new sentence calculation model we used will be more accurate with less error.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 4 Edge Computing: Opportunities in ITS Sensing Challenges
### Objectives
#### 4.1.3 Real-Time Sensing  
Sensing in real-time is critical for many future ITS applications. Connected infrastructure, autonomous vehicles, smart traffic surveillance, short-term traffic prediction, and so on, all expect real-time capability, and they cannot tolerate even milliseconds of delay in processing due to effectiveness and safety. These tasks that require fast response time, low latency, and high efficiency, especially when on a large scale, cannot be achieved without edge computing architecture. However, there is always a tradeoff between real-time sensing and high intelligence: as intelligence increases, efficiency commonly decreases. This conflict stands out in edge computing, given the limited resources at the edge. Sometimes, the input data itself is intense, such as video data and sensor fusion data, which puts additional burdens on the edge computing devices. Careful design in system architectures that balance the computation load between edge and cloud is expected to move towards this goal. Algorithm design that targets innovation in light-weight neural network structures and other models has shown effectiveness in reducing computation load at the edge while maintaining a good sensing performance. In summary, concerns are a tradeoff between real-time sensing and high intelligence, the network and computation resource constraint, and intense data input at the edge.
# Edge-Nodes Representation Neural Machine for Link Prediction
## 3 Edge-Nodes Representation Neural Machine (ENRNN)  
In this section, we introduce the ENRNM in detail as shown in Figure 3. It includes four stages: edge pattern encoding, node pattern encoding, link pattern encoding and neural network learning.  
ENRNM includes the following four main steps:  
* Edge pattern encoding, which learns edge's representation by WLNM.
* Node pattern encoding, which learns node's representation by network embedding model.
* Link pattern encoding, which joints the edge's representation and two nodes' on the two sides of the edge as link's representation for each link.
* Train a fully connected neural network, which learns nonlinear graph topological structures and link's formation mechanism for link prediction.  
Our proposed method ENRNM is explained in the Algorithm 1.  
The paper mainly focuses on the undirected and unweighted network. A network can be represented as an undirected and unweighted graph \(G(V,E)\) where \(V=\{v_{1},v_{2},v_{3}...v_{n}\}\) indicates the set of nodes and \(E\subseteq V\times V\) indicates the set of edges observed. Nodes \(v_{1},v_{2},v_{3}...v_{n}\) are integer numbers set manually and edges are presented in the form of \((v_{i},v_{j})\). In the graph, there is no self-loop and there is at most an edge between two nodes. A graph can be represented as an adjacency matrix \(A\), \(A_{ij}=1\) if there is a link between node \(v_{i}\) and node \(v_{j}\) and \(A_{ij}=0\) otherwise. Adjacency matrix A is symmetric since the graph is undirected. We use \(\Gamma(x)\) or \(\Gamma^{1}(x)\) to indicate the set of all the neighbors of node \(x\), in other words, \(\Gamma^{1}(x)\) is the set of the nodes whose distances to node \(x\) are equal to 1. We use \(\Gamma^{n}(x)\) to indicate the set of the nodes whose distances to node \(x\) are less than or equal to \(n\).  
Figure 3: Illustration of the method ENRNM including edge pattern encoding, node pattern encoding, link pattern encoding and neural network learning. Due to the space limitation of matrix in the figure, I expound the 45-dimensional vector here. For instance, the vector representation of the node 1 is \(\{0.28701,-0.1449,-0.26971.\ldots\ldots\ -0.61898,0.031769,-041929\}\) for dataset USAir.  
```
0: network \(G\)
0: AUC
1: devide the observed and unobserved links of network G into two parts: training and testing links
2:for each link \((x,y)\)do
3: learn the edge's representation \(edgevec(x,y)\)
4: learn the nodes' representations \(nodevec(x)\) and \(nodevec(y)\)
5:\(linkvec(x,y)=joint(edgevec(x,y),nodevec(x),nodevec(y))\)
6:endfor
7: feed \(linkvec(x,y)\) and label \((1:(x,y)\in E,0:(x,y)\
otin E)\) to neural network for the training data
8: feed \(linkvec(x,y)\) of the testing data to the pre-trained neural network
9: compare the predicted labels with real labels
10:return AUC value
```  
**Algorithm 1** ENRNM
## 3 Results and Discussions
### Validation  
To validate the model's accuracy, data that were not utilized during training were used. Specifically, the unused data used for sample validation were located at 14.683126, 120.942226 (Latitude, Longitude) in Dampalit, Malabon City. The ground elevation was estimated to be 3 m as the initial estimated parameter for site-specific properties. With an accuracy rate of 83%, this estimate was found to be close to the USGS-obtained value of 2.5 m.  
Estimating the depth of groundwater was also essential for geotechnical engineering, particularly in Metro Manila's coastal regions, where the depth of groundwater is typically shallow. Both the estimated and collected data yielded a value of 1m, resulting in a 100 percent accuracy rate. The model framework permitted the estimation of several site-specific parameters, including the SPT N-value, USCS (soil type), liquid limit, and plastic limit of the target site. The accuracy of these estimates ranged between 75% and 86%, indicating that the estimated parameters and the collected data are in excellent agreement.  
However, it was determined that the average accuracy of the N-value estimates was 84%, with some layers exhibiting low accuracy. This can be attributed to the tree model's inability to account for erratic soil layer trends. This is applicable to USCS and Atterberg limits. In contrast, the accuracy of unit weight estimates varied between 87% and 95%.  
With regard to the seismic behavior of the target location, the estimated peak ground acceleration was compared to the PHIVOLCS map of Metro Manila's 1000-year return period for peak ground acceleration. This comparison was found to be 96% accurate, indicating a reliable estimate. In addition, both the estimated classification and the classification from the Metro Manila Earthquake Impact Reduction Study (MMEIRS) concur that the liquefaction classification for the target area is Very High. This is likely due to the target area's proximity to the coast and the prevalence of sand layers.
# Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data
## 2 Study Area and Data
### Dataset and Processing  
One image of Landsat 8 OLI data (LC81340332014244 LGN00) with few clouds provided by the USGS EROS data center (Sioux Falls, SD, USA) [46] was used in this study, and is detailed in Table 1. The data were collected on 1 September 2014, close to the summer season (from June to August) to minimize snow and ice cover and maximize the exposure of rocks and minerals. The images acquired are Level 1 digital number data products, with radiation calibration and geometric correction methods  
Figure 2: (**a**) Generalized lithological map of the study area, showing the major lithology boundaries, modified after [PERSON] et al. [49], Copyright 2014, Elsevier; and (**b**) false color composition with R = _band_ 5 (near-infrared (NIR), 0.845–0.885), G = _band_ 4 (Red, 0.630–0.680), and B = _band_ 3 (Green, 0.525–0.600) in the study area with the lithological boundaries of gabbro, serpentinite, granite and Lower Ordovician Sandstone–Slate Group derived from Figure 2 (**a**) to highlight the distribution of the discussed rock types in this paper.  
previously applied. The data were processed as follows: (i) VNIR-SWIR images (_band_ 1 to _band_ 7, 30 m resolution) were radiometrically calibrated to obtain the top of atmosphere reflectance data; (ii) the Fast Line-of-Sight atmospheric analysis of spectral hypercubes (FLAASH) models were conducted for atmospheric correction to obtain surface reflectance data; (iii) the images were fused with the panchromatic band (_band_ 8, 15 m resolution) to a spatial resolution of 15 m. The final reflectance images from VNIR to SWIR wavelengths, with a spatial resolution of 15 m, were used to derive texture images for lithological classification. All the procedures of data processing in this section were conducted in ENVI (ENVI5.2 SP 1, ITT Visual Information Solutions, Boulder, CO, USA, 2015).  
Four rock types are discussed in this paper: gabbro, serpentinite, granite, and quartzose rocks, which are widely distributed within the earth's crust and are well exposed in the study area. Rocks or minerals that are much covered by vegetation are not addressed in this paper, as the texture and spectral information would be concealed or impaired by vegetation. In addition, some samples of these rocks were selected based mainly on the geological map (Figure 2a). In addition, some remote sensing indices derived from the Advanced SpaceborneThermal Emission and Reflectance Radiometer (ASTER, National Aeronautics and Space Administration, Washington, DC, USA) imagery (i.e., \(\left(\text{b12}\times\text{b14}^{3}\right)/\text{b13}^{4}\), \(\text{b13}/\text{b12}\)) were also used as supplementary information for selecting samples of gabbro and quartzose rock. In fact, the method of choosing rock samples in this study is the same as that used in previous studies [11; 49], and is detailed in [PERSON] et al. [48].
# An Integrated Cognitive Radio Network for Coastal Smart Cities
## 4 Conclusions  
This paper proposed a new integrated cognitive coastal city for both hybrid and pure communications between vehicular and maritime networks. The idea of integrating software-defined networking, network function virtualization, and fog computing in a cognitive routing scheme for different networks makes this integrated framework unique. Vehicle-to-ship communications was introduced to provide efficient and reliable services to users on different network systems. Two virtual networks for each vehicular and maritime application were launched by NFV based on the user demands, and the SDN controller was responsible for controlling and managing these VNs. Routing is the service provided by the VNs, where the SDN controller provides a stable path between each source-destination pair for each querying node. These pairs are vehicle-to-ship, vehicle-to-vehicle, and ship-to-ship. The border gateway is used as a communicating device between the two networks. The paths were established for communication between different cognitive networks using this integration. The idea was to facilitate citizens' demands for services from different applications under the same infrastructure.  
Our results show better performance in hybrid communications for packet delivery ratio, end-to-end delay, and routing overhead ratio. We considered both pure and hybrid communications in this scheme, and concluded that the marine environment has more fragile links than the terrestrial environment. However, for hybrid communications, we achieved better performance in terms of delivery ratio, delay, and overhead, since the path was divided into two types of networks and border gateways, and local controllers reduce the chances of link fragility. Hence, the demand of each application user in a hybrid environment can be fulfilled with a quicker response time via the proposed scheme. In comparison with the existing schemes in literature, our scheme performs better, due to the following reasons: Firstly, it is because of our selection criterion that depends on the transmission delay, and secondly, because of the stability that our scheme shows due to the integration of software-defined networking, network function virtualization, and fog computing. We will extend the applications for both vehicular and maritime networks along with different services in the near future.  
Conceptualization, H.G. and I.K.; formal analysis, H.G.; methodology, H.G. and I.K.; Writing--Original draft preparation, H.G.; Writing--Review and editing, I.K.; supervision, I.K. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2018 RIA2B6001714).  
The authors declare no conflict of interest.  
Figure 8: Performance comparison between the proposed S2S and the reference scheme for S2S in terms of (**a**) packet delivery ratio, (**b**) end-to-end delay, and (**c**) routing overhead ratio.