esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Feature Selection with Metaheuristic Optimization Algorithms  
Although feature fusion is a robust method that improves classification performance, some features can cause noise and negatively affect the success of the model [38]. Therefore, determining the optimal feature subset and selecting the variables that best represent the dataset is a critical step. Metaheuristic optimization algorithms are widely used to improve the accuracy of the model by identifying the most appropriate features in the large search space and reduce the computational cost by eliminating redundant features.  
These algorithms try to maximize a fitness function while identifying the best subset of features, and at the same time preserving the simplicity of the model, avoiding over-learning. Their flexibility allows them to handle non-linear relationships, interactions between features and problem-specific constraints. Feature selection is usually performed using a binary optimization approach, where each feature is expressed as a binary vector. For a dataset with N features, a binary vector of size N is created, where a value of 0 indicates that the feature is excluded and a value of 1 indicates that the feature is included. The optimization process starts with an initial population or initial solution and is iteratively updated to determine the best feature subset. The binary vector-based feature selection process is shown in Figure 6. In all feature selection processes that included metaheuristic optimization algorithms (GA, ABC, PSO and HHO), the F1 score was used as the fitness function. This decision was made to provide a balanced assessment between precision and recall in multi-class classification, especially due to the clinical importance of minimizing false negatives in the diagnosis of hypertensive retinopathy.  
In this study, different approaches based on metaheuristic optimization were used to select the most suitable features. GA from the evolutionary algorithms group, ABC and  
Figure 5: Feature fusion workflow combining deep feature vectors extracted from three distinct CNN architectures to create a comprehensive representation for HR classification.
## 2 System Model and Problem Formulation
Since both objectives are normalized and bounded between 0 and 1, we can make the joint minimization objective as:  
\[F=w_{1}\left(1-F_{1}\right)+w_{2}F_{2} \tag{7}\]  
Using Equation (7), we can write the multi-objective optimization for GC-CRSN as:  
\[ \begin{split} OP1:\\ \underset{\boldsymbol{\varepsilon},\boldsymbol{p_{l}},\boldsymbol{p_{s }}}{\text{min}}& F\\ \text{subject to}&\\ C1:&\sum_{k}^{K}\varepsilon_{l}^{k}\leq 1,\forall l\\ C2:&\sum_{k=1}^{K}p_{k}^{s}\leq P_{s}\\ C3:& 0\leq p_{l}\leq\sum_{k=1}^{K}\varepsilon_{l}^{k}p_{l} ^{max},\forall l\\ C4:& p_{k}^{s}|g_{m,k}^{s}|^{2}\leq I_{m,k}^{max}, \forall(m,k)\\ C5:&\sum_{l=1}^{L}\varepsilon_{l}^{k}p_{l}|g_{m,k}^{ l}|^{2}\leq I_{m,k}^{max},\forall(m,k)\\ C6:&\varepsilon_{l}^{k}\in\{0,1\}\end{split}  \tag{8}\]  
In \(OP1\), the constraint, \(C1\), assures that a relay can only be assigned to one secondary user; \(C2\) and \(C3\) are the power constraints. The constraints, \(C3\), ensures that if the \(l\)th relay is not assigned to any secondary user, then the transmission power of the \(l\)th relay should be zero. Constraints, \(C4\) and \(C5\), are the interference constraints. The objective function in \(OP1\) is bounded by zero and one. The formulation in \(OP1\) is a multi-objective non-convex mixed-integer non-linear programming problem, which is generally NP-hard. In the next section, we will present a low-complexity hybrid estimation-of-distribution algorithm (EDA) for the GC-CRSN multi-objective optimization problem. ```
1:Initialize the population with uniform probability distribution
2:while\((\)true\()\)do
3:Evaluate the population
4:Rank the population according to fitness
5:Select the best individuals
6:Estimate the probability distribution from best selected individuals
7:Sample the probability model to generate new population
8:if Termination Criterion Satisfied then
9:\(break\)
10:endif
11:endwhile
```  
**Algorithm 1** Pseudo code for a typical EDA.
# Smart Black Box 2.0: Efficient High-Bandwidth Driving Data Collection Based on Video Anomalies
## 2 Related Work
### Traffic Video Anomaly Detection and Classification  
Several methods exist for identifying EOIs in autonomous vehicles. Pre-defined rules may be applied based on vehicle odometry [19; 20; 21] to identify certain EOIs. Other approaches use physiological signals from the driver [22]. In recent years, deep-learning computer vision techniques have been applied to anomaly detection in first-person driving videos [14; 15; 23; 24]. Other works further attempt to classify the type of anomaly occurring in the video, either offline after the video is fully observed [25; 26; 27] or in real time [16]. These methods present a way for generalized EOI detection based only on dash camera video. As such, we use methods from [14; 16] in the SBB to assign the data value in accordance with our focus on video data. Table 1 below presents a summary of the reviewed anomaly detection methods.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Aim** & **Description** \\ \hline TAD [14] & Anomaly detection (unsupervised) & Predicts future bounding boxes using RNN encoder-decoders, then takes the standard deviation of the predictions as the anomaly score. \\ DSA-RNN [23] & Anomaly detection (supervised) & Uses a dynamic-spatial-attention (DSA)-RNN, which learns to distribute soft attention to objects and model the temporal dependencies of detected cues. \\ STAG [24] & Anomaly detection (supervised) & Uses a spatio-temporal action graph (STAG) network to model the spatial and temporal relations among objects. \\ \hline TSN [25] & Action recognition (offline) & Sparsely samples video snippets and predicts action using RGB and optical flow data. \\ R(2+1)D [26] & Action recognition (offline) & Uses a 3D convolutional neural network with separate 2D and 1D convolutional blocks. \\ SlowFast [27] & Action recognition (offline) & Extracts frames from a low frame rate stream to capture spatial information and a high frame rate stream to capture motion. \\ TRN [16] & Action recognition (online) & Simultaneously detects the current action and predicts the action of the following frame. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Video-based anomaly detection and action recognition methods. TRN, temporal recurrent network.
# Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings
## 1 Dynamic Modeling of Integrated Bearing Systems
### Coordinate System of Integrated Bearings  
To facilitate the description of the motion of the various components of integrated bearings, a coordinate system for the integrated bearing is established, as shown in Figure 1.  
(1) Inertial Coordinate System \(\{O,X,Y,Z\}\): This system is fixed in space, with its origin coinciding with the center of the inner raceway of the integrated bearing. The \(X\)-axis aligns with the bearing's axis, and the \(YOZ\) plane passes through the center of the inner raceway, remaining parallel to the bearing's radial plane.  
(2) Spindle Coordinate System \(\{o_{i},x_{i},y_{i},z_{i}\}\): The origin of this system coincides with the centroid of the spindle. The \(x_{i}-\)axis aligns with the spindle's rotational axis, and the \(y_{i}o_{i}z_{i}\) plane passes through the spindle's centroid, coinciding with the spindle's radial plane.  
Figure 1: Integrated bearing coordinate system.  
(3) Cage Coordinate System \(\{o_{c},x_{c},y_{c},z_{c}\}\): The origin is located at the center of the pocket in the cage. The \(x_{c}-\)axis aligns with the cage's rotational axis, and the \(y_{c}o_{c}z_{c}\) plane passes through the center of the pocket, coinciding with the cage's radial plane.  
(4) Center Coordinate System of the (\(j\))-th Ball \(\left\{o_{bj},x_{bj},y_{bj},z_{bj}\right\}\): The origin coincides with the center of the (\(j\))-th ball. The \(y_{bj}-\)axis is directed radially with respect to the bearing and is perpendicular to the surface of the ball. The \(z_{bj}-\)axis aligns with the circumferential direction of the ball, and the orientation of \(x_{bj}-\)axes is determined using the right-hand rule. This coordinate system is fixed to the center of the (\(j\))-th ball and moves with the ball throughout its motion. Each ball possesses its own local coordinate system.
###### Abstract  
The purpose of this paper was to analyze the impact of extreme rainfall on the recurrence of slope instability using the Thulamela Municipality roads (R523) as a case study. To this end, the historical rainfall data of the area of study were analyzed between 1988 and 2018. The results show that a significant increase in rainfall is usually experienced in the summer months of December and January. Following this, the factor of safety (FoS) of slopes of silt clay, clay, and clay loam soils were estimated using the SLIDE simulator (Numerical software \"_Finite Element Method (FEM)_\") under sunny to rainy conditions of the area. A complementary model, FLACSlope (Numerical software \"_Finite Difference Method (FDM)_\"), was utilized to simulate FoS and pore water pressure in sunny and rainy conditions of the area. Simulation results show that extreme rainfall has the ability to reduce the shear strength and resistance of the soil slope material. This may explain the recurrent landslides noted in the area. Finally, the water pore pressure has been simulated to increase with the increased water table, which generally pushes the soil particles apart and reduces the stress between the particles resulting in soil slope failure. Extreme rainfall alters the phase of the material solid in a manner that may require further research for a better understanding.  
R Rainfall intensity; slope instability; numerical modeling; geotechnical; landslides; fluid-solid-structure interaction (FSSI) +
Footnote †: journal: Applied Sciences
# QoS-Aware Flexible Handover Management in Software-Defined Mobile Networks
## 4 Performance Evaluation
### Packet Loss
This means that, even though additional signaling is required, pre-updates for flow entries are required for delay-tolerant services. Figure 5a,b show the packet loss according to the rate of user mobility and average residence time, respectively. From both figures, we can see that Classes 2 and 3 have the lowest packet loss because they support buffering after L2 handover occurs. Based on the comparison between Class 1 and Class 4, Class 4 has lower packet loss because it has a lower handover disruption time due to the pre-updates for flow entries. Although the buffering support requires high signaling costs, as mentioned above in Figure 3, the mechanism is needed for the loss-tolerant services. Since the pre-updates for flow entries also have the effect of reducing the packet loss, finding the optimal method considering both signaling cost and packet loss could be the topic of one of our future works. Figure 4: Handover latency according to the wired (**a**) and wireless link delay (**b**).
# Multi-Feature-Enhanced Academic Paper Recommendation Model with Knowledge Graph
## 4 Experiments
### Experimental Results and Analysis
Therefore, L was set to 2. Finally, the effect of the number of neighbors M extracted per layer in the knowledge graph on the recommendation effect was explored, with experiments conducted using M = 8, M = 12, and M = 16. The experimental results indicated that as the value of M increased, the performance of the recommendation model also improved gradually. A smaller number of neighbors might restrict the model's perception range around a node, while a larger number of neighbors allows for a more comprehensive consideration of the node's neighboring information. Based on the experimental results and time considerations, M was set to 16 in this paper. To verify the performance of the user vector representation and paper vector representation modules proposed in this paper within the model, comparisons were made between this paper's model and its variants. In the experiments, MFPRKG-P represents the model that includes only the paper vector representation module, while the user vector is obtained through random processing. MFPRKG-U, on the other hand, represents the model that includes the user vector representation module, but the paper vector is obtained using the Word2 Vec model. The experimental results of the MFPRKG model and its variants on the dataset are presented in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Metrics** & **HR** & **NDCG** & **Prec@10** & **Rec@10** \\ \hline MFPRKG-U & 0.739 & 0.440 & 0.492 & 0.523 \\ MFPRKG-P & 0.716 & 0.427 & 0.478 & 0.507 \\ MFPRKG & 0.812 & 0.497 & 0.553 & 0.589 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of ablation experiment. Figure 4: The impact of different parameters on NDCG. From the ablation study results, it can be observed that the MFPRKG-U model performs better than the MFPRKG-P model in terms of evaluation metrics. This may be attributed to the fact that while the MFPRKG-U model only includes the user vector representation module, the paper vector representation obtained through the Word2 Vec model still provides a certain level of recommendation accuracy. In contrast, the MFPRKG model lacks a deeper level of processing for user features and offers little analysis of user interests and characteristics, which results in slightly lower performance. Meanwhile, in the FEPRKG model, the features of both users and papers are enhanced through a multi-feature approach; hence, it outperforms both the FEPRKG-P and FEPRKG-U models in these two metrics.
## 2 Optimization Model for Network Reconfiguration Problem of Ship Power System
### Load Branch Correlation Matrix Method
#### 2.4.2 Load Branch Matrix Role  
1. Analyzing grid topology institutions  
The load branch correlation matrix clearly represents the relationship between loads and branches, allowing researchers to understand load supply paths comprehensively. After a fault occurs in the ship's grid, the fault node and its number can be identified using the matrix. By determining the coordinates of the fault point within the load branch matrix, researchers can take the fault coordinates as a starting point and search for non-zero elements to the right. The load number in the penultimate row of the column containing the identified non-zero element is then set to zero. This approach allows the load supply configuration and status to be quickly determined, clarifies the reconfiguration path, and reduces both computational complexity and processing time. Taking the example of the ship's ring grid used in this paper, assuming that one of branches 7 and 41 fails, some of the loads reconfigure the previous supply path matrix:  
\[[X\ 0\ 2\ X\ X\ X\ X\ 0\ 2\ X\ X\ 1\ X\ X\ X\ X\ 0\ 2], \tag{18}\]  
\(X\) indicates that the load power supply line is uncertain; 0 means that the load is unloaded; 1 means that the load is supplied normally; 2 means that the load is supplied with standby power. The analysis reveals that among the 20 loads, loads 2 and 9 experience power loss due to the absence of backup power supply lines following the identification of a fault in the branch circuit. Loads 3 and 10 are classified as critical loads; they can receive power from the backup supply line indicated by the dotted line in the event of damage to the normal power supply path. In contrast, loads 13 and 20, which are also classified as important loads, can only be powered by the normal supply line when the backup power supply path is compromised. 2. Line capacity limitations  
According to the capacity matrix \(C\), the branch capacity analysis can be carried out and specific capacity limitation values can be calculated. After the fault reconfiguration of the distribution network of the ship power system, the constraints that the capacity of each branch is within the required range need to be met. The method of capacity limitation in the load branch matrix is to search the right neighboring elements of the matrix elements from the second row of the matrix to the right, and if the right neighboring element is zero, record this node until a non-zero element is found. As shown in the matrix, 1, 34, 66, and 93 are directly connected to the root branch generator; 7, 16, 35, 41, 16, 6, 91, 92 and the root branch for the line meet the capacity limit of the main focus of the object of determination.
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 3 Methodology
### Encoder with DeMBFF  
The encoder design considers two aspects: First, the encoder effectively integrates features from various scales. Second, they need to filter out noise and retain useful information for the restoration. Therefore, we combined the MBFF mechanism (Section 3.3) and the denoising block (Section 3.4) to construct the encoder and decoder, namely, the encoder/decoder with denoising multiscale back-projection feature fusion (DeMBFF). The pseudo-code of the feature fusion process for an encoder with DeMBFF is shown in Algorithm 2.  
Figure 4: Details of the denoising block for the encoder and decoder. The top part illustrates the denoising block in each encoder. The figure below is the denoising block used during decoding.  
For the \(K\)-th encoder \(EB^{K}\), we denote \(E^{K}\) as the input of the current layer, \(E^{K-t}\) as the output feature from the \((K-t)\)-th encoder, \(FS=\{E^{1},\dots,E^{K-1}\}\) as the multiscale features to be fused in the \(K\)-th encoder layer, and \(E^{K}\) represents the features after multiscale back-projection fusion. The feature set, \(FS\), consists of the outputs from the 1 st to the \(K-1\)th encoders. The feature fusion calculation for the \(K\)-th encoder layer (as shown in Figure 5) is represented as follows:  
\[E^{K}_{t}=\left\{\begin{array}{ll}DB_{en}(E^{K}))&if\ t=0,\\ MBFF_{en}(E^{K}_{t-1},E^{K-t})&if\ t>0,\end{array}\right. \tag{9}\]  
where \(t\) indicates the index number of the feature fusion, which ranges from \(0\) to \(K-1\). \(DB_{en}\) represents the denoising block, and \(MBFF_{en}\) represents the back-projection feature fusion of the encoder. The multiscale feature fusion mechanism of the encoder is formulated as per Equation (5).  
Figure 5: Details of the encoder and decoder with DeMBFF. The above part illustrates the structure of the encoder. The figure below is the decoder. The star symbols for ’Decon’ and ’Con’ indicate that these operations were repeated multiple times, with the exact number of repetitions depending on the size of the current feature and the fusion feature. For example, given a current feature of size \(256\times 256\times 16\) and a fusion feature of size \(64\times 64\times 64\), the current feature undergoes the convolution operation twice to match the size of the fusion feature.
## 4 Methodology
### Spectral Indices
#### 4.4.2 Simple Ratio (SR)  
Simple ratio (SR) is a spectral ratio index that is calculated with the ratio between reflectance recorded in the NIR and red bands. It is used for distinguishing objects that have green leaves from other objects in the images. Green leaf objects have low reflectance values in the red region but higher reflectance in the NIR region of the sensors. It is expected that soil regions and bare lands would have SR values of almost 1, but green leaf objects would have SR values much greater than 1. In addition, the SR index can be used to estimate the relative biomass in images.
## 4 Results and Discussion
### Assessment of BDS-Only PPP with Multipath Correction
Panel (**a**) presents the BDS PPP solution for which all BDS satellites except for the GEO satellites are sidereally filtered, while panel (**b**) presents solutions for which all the satellites are filtered. The lines have been shifted vertically to avoid overlap.
## 2 System and Signal Model
### Mode of Relay Selection
#### 2.4.4 Case 4 (Dual-Distributed-Select)  
In this mode, the relay nodes are divided into two parts \(N_{a}\) and \(N_{b}\), and \(N_{a}+N_{b}=N\). The source node \(S\) and relay nodes \(N_{a}\) are equipped with CSI feedbacks, as well as the destination node \(D\) and relay nodes \(N_{b}\). Based on the received CSI, the source node selects the highest instantaneous SNR relay \(I_{a}\) between \(S\) and \(R_{i}\). Similarly, \(D\) selects the highest instantaneous SNR relay \(I_{b}\) between \(R_{i}\) and \(D\). The relay selection mode is given as  
\[I_{a}=\arg\max_{i=1,2\cdots,N_{a}}\bigl{\{}\gamma_{SR_{i}}\bigr{\}},\;\;I_{b}= \arg\max_{i=1,2\cdots,N_{b}}\bigl{\{}\gamma_{R_{i}D}\bigr{\}}, \tag{10}\]  
where \(I_{a}\in\{1,2\cdots,N_{a}\}\) and \(I_{b}\in\{1,2\cdots,N_{b}\}\) are the indexes of relay. The selected two relays are simultaneously activated and cooperate to transmit information during signal transmission, and the selection combining (SC) technique is adopted in the destination node. The addition of a relay in case 4 significantly reduces the system's CSI requirements compared to case 1.
# Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols
### Ad Hoc On-Demand Distance Vector Routing (AODV) Protocol  
A reactive traffic routing protocol to support AODV, existing paths merely need to have their routing changed [9]. Information about routing is kept in the routing tables of nodes. Each mobile node has its own \"next-hop routing database,\" which stores details about the nodes it can connect to in the next hop. If a routing table entry is inactive for a certain amount of time without being utilized or activated again, it may be destroyed. The AODV uses the same on-demand utilization of the destination sequence number method as the DSDV. When there is no known route to the destination, AODV will start the route discovery process using the transmitting node. The source will broadcast route request  
Figure 1: Typical underwater wireless communication structure.  
(RREQ) packets to the network in an effort to hasten the process of routing discovery. The addresses of the transmitting and receiving nodes, the broadcast ID used to identify the packet, the sequence number last seen at the destination node, and the sequence number last seen at the sending node are all information that is provided by RREQs. In order to create routes that are both current and error-free, sequence numbers are a need. A node may use the expanding ring search strategy to ignore previously received RREQs and so reduce the amount of flooding overhead during the route-finding process. The RREQs Time-To-Live will be significantly compressed in its early stages (TTL). In the case that more RREQ attempts to the same destination fail, the TTL will be prolonged.