esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 3 System Model
### Channel Model
#### 3.2.3 Packet Error Rate  
Considering the interference packet, we divide each packet into M parts and then calculate the signal-to-interference plus noise ratio (SINR) of the \(m_{th}\) part [23; 41; 42] as  
\[SINR_{m}=\int_{B}\frac{p_{s}(f)/A(f,d)}{N(f)+I_{m}(f)}df, \tag{5}\]  
where \(p_{s}(f)\) is the power density of the sender. \(B\) is the total bandwidth in Hz. The \(I_{m}(f)\) denotes the superposition of the interference power density in the \(m_{th}\) segment, which is evaluated as [23]  
\[I_{m}(f)=\sum_{i=1}^{N_{m}}\frac{p_{i}(f)}{A(f,d_{i})} \tag{6}\]  
where \(N_{m}\) represents the amount of interferences in the \(m_{th}\) part of a packet, \(d_{i}\) is the distance between a receiver and its \(i_{th}\) interference node, and \(p_{i}(f)\) is the power density of the \(i_{th}\) interference in the \(m_{th}\) segment for carrier frequency \(f\). For simplicity, we assume \(p_{s}(f)\) as a constant function and \(p_{i}(f)=p_{s}(f)\) is the power density of the \(i_{th}\) interference within the \(m_{th}\) part of a packet. Then, the bit error rate (BER) of the \(m_{th}\) segment \(p_{b}(m)\) can be calculated by using the BPSK performance function in the AWGN channel of \(1/2 erfc(\sqrt{SINR_{m}})\)[37; 41]. We get the BER of the packet \(p_{b}\), which is given by:  
\[p_{b}=\frac{\sum_{m=1}^{M}p_{b}(m)l_{m}}{L} \tag{7}\]  
where \(l_{m}\) represents the length of the \(m_{th}\) segment in bytes, and \(L\) is the total length of the packet in bytes.  
Finally, we calculate PER of a packet by:  
\[PER=1-(1-p_{b})^{8L} \tag{8}\]
## 5 Analysis and Discussion
### Real-Time Deployment and Field Testing
#### 5.2.1 Real-Time Deployment  
To facilitate the deployment of the SER system in real educational settings, we have developed a user-friendly web interface using the Flask framework. This web interface allows users to upload audio files and obtain real-time emotion classification results. The backend of the system is powered by the proposed ensemble model, which processes the uploaded audio files and returns the predicted emotion labels.  
The deployment process involves the following steps:  
Model Serialization: The trained ensemble model is serialized and saved to a disk, enabling efficient loading and inference in the web application.  
Flask Application Development: A Flask application is created to handle the user interactions and manage the audio file uploads. The application provides a simple and intuitive user interface for uploading audio files and displaying the emotion classification results, as shown in Figure 19.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model/ACC** & **RAVDESS** & **EMO-DB** & **SAVEE** & **IEMOCAP** & **SHEIE** \\ \hline EM-T & 91.2\% & 96.5\% & 92\% & 82.7\% & 80.4\% \\ \hline EM-C & 92.3\% & 98.2\% & 94.3\% & 83.9\% & 81.6\% \\ \hline EM-L & 91.8\% & 98.6\% & 94.9\% & 83.7\% & 82.3\%  
Audio Preprocessing: Upon receiving an uploaded audio file, the Flask application applies the necessary preprocessing steps, such as resampling, normalization, and feature extraction, to prepare the audio data for input to the ensemble model.  
Emotion Classification: The preprocessed audio data are fed into the loaded ensemble model, which performs the emotion classification task and returns the predicted emotion label.  
Result Visualization: The predicted emotion label is then presented to the user through the web interface, providing real-time feedback on the emotional content of the uploaded audio file.  
This real-time deployment of the SER system using Flask and a web interface allows for its easy integration into existing distance learning platforms and enables educators and learners to benefit from the system's emotion recognition capabilities.
## 2 Proposed System Model
### Plc Channel Model  
From the source node, the power cables carry the information data to the FSO link with the assistance of a DF relay. Along the power lines, the channel gain of the PLC link is subjected to a log-normal distribution [6] with Bernoulli-Gaussian noise as the signal passes through reflections, impedance mismatches, and impulsive noise. More specifically, the PDF of these coefficients is given as  
\[f_{H_{1}}(h_{1})=\frac{1}{h_{1}\sqrt{2\pi\sigma_{1}^{2}}}\exp\left(-\frac{\left( \ln h_{1}-\mu_{1}\right)^{2}}{2\sigma_{1}^{2}}\right), \tag{1}\]  
where \(h_{1}>0\), \(\mu_{1}\) and \(\sigma_{1}^{2}\) are the mean and the variance of \(\ln h_{1}\), respectively. Moreover, the PLC link suffers from both impulsive noise and background noise due to various switching transients, low-power noise sources, and the cables [6]. The instantaneous SNR value on the PLC link is \(\gamma_{1}=\bar{\gamma}_{1,1}{\left|h_{1}\right|}^{2}\) when no impulsive noise occurs. The SNR value is \(\gamma_{1}=\bar{\gamma}_{1,2}{\left|h_{1}\right|}^{2}\) in the presence of both impulsive noise and background noise where the average SNR are \(\bar{\gamma}_{1,1}=\frac{E_{b}}{\sigma_{0}^{2}}\) and \(\bar{\gamma}_{1,2}=\frac{E_{b}}{\sigma_{1}^{2}+\sigma_{0}^{2}}\), respectively. Beside, \(E_{b}\) denotes the average energy of the signal. Since the channel coefficient of the PLC link is log-normally distributed, the corresponding PDF of \(\gamma_{1}\) is also log-normally distributed. Assuming that \(\rho\) is the arrival probability of the impulsive noise, the PDF of the instantaneous SNR \(\gamma_{1}\) at the PLC link can be represented as  
\[f_{\gamma_{1}}(\gamma)=(1-\rho)\left(\frac{1}{\sqrt{2\pi\sigma_{ 1}^{2}}\gamma}\exp\left(-\frac{\left(\ln\gamma-\mu_{u}\right)^{2}}{2\sigma_{u} ^{2}}\right)\right)\] \[+\rho\left(\frac{1}{\sqrt{2\pi\sigma_{2}^{2}}\gamma}\exp\left(- \frac{\left(\ln\gamma-\mu_{v}\right)^{2}}{2\sigma_{v}^{2}}\right)\right), \tag{2}\]  
where \(\mu_{u}=2\mu_{1}+\ln\bar{\gamma}_{1,1},\sigma_{u}=4\sigma_{1}^{2},\mu_{v}=2\mu _{1}+\ln\bar{\gamma}_{1,2}\), and \(\sigma_{v}=4\sigma_{1}^{2}\). Consequently, the log-normally distributed PDF of \(\gamma_{1}\) can be approximated with the Gamma distribution as  
\[f_{\gamma_{1}}(\gamma)=\Delta_{1}\Big{(}\frac{\varphi}{\Phi}\Big{)}^{\varphi} \gamma^{\varphi-1}\exp\left(-\frac{\gamma\varphi}{\Phi}\right)+\Delta_{2} \Big{(}\frac{\varphi^{\prime}}{\Phi^{\prime}}\Big{)}^{\varphi^{\prime}}\gamma ^{\varphi^{\prime}-1}\exp\left(-\frac{\gamma\varphi^{\prime}}{\Phi^{\prime}} \right), \tag{3}\]  
where \(\Delta_{1}=\frac{(1-\rho)}{\Gamma(\varphi)}\) and \(\Delta_{2}=\frac{\rho}{\Gamma(\varphi^{\prime})}\). Here, \(\varphi=\left(e^{\sigma_{u}^{2}}-1\right)^{-1}\) and \(\varphi^{\prime}=\left(e^{\sigma_{v}^{2}}-1\right)^{-1}\) represent the shadowing parameters while \(\Phi=e^{\mu_{u}}\Big{(}\frac{1+\varphi}{\varphi}\Big{)}^{1/2}\) and \(\Phi^{\prime}=e^{\mu_{v}}\Big{(}\frac{1+\varphi^{\prime}}{\varphi^{\prime}} \Big{)}^{1/2}\) are the shadowing area mean power of the Gamma distribution [12].
# Investigation of the EWT-PSO-SVM Model for Runoff Forecasting in the Karst Area
## 4 Results and Discussion
### Discussion
#### 4.6.2 Compared with Previous Studies in Runoff Forecasting  
In this study, a comprehensive evaluation index, M\({}_{\text{R}}\), was obtained by ranking and calculating five evaluation indicators to verify the performance of the models. The M\({}_{\text{R}}\) of the EWT-PSO-SVM model was 0.68, while the SVM model was only 0.24. A larger M\({}_{\text{R}}\) indicates a better predictive capability of the developed model. The developed model reduced the maximum forecasting absolute error in runoff forecasting by 43.12%. More accurate forecasting of large flow events can help reservoirs to release water before floods occur, ensuring efficient utilization of water resources. Moreover, the forecasting performance of the three models was also investigated under a monthly structure. The M\({}_{\text{R}}\) of the EWT-PSO-SVM (M) model reached 0.67, higher than the PSO-SVM(M) and SVM (M) models with an M\({}_{\text{R}}\) of 0.33 and 0, respectively, indicating that the developed model has the best forecasting performance under the new data structure input. In previous studies on runoff forecasting in karst basins, process-driven models were commonly used. However, due to the complexity of karst basins, the ability of runoff forecasting was limited. [PERSON] et al. [40] used the Xianjiang model for runoff forecasting studies in karst basins, and the \(NSE\) value was only 0.847 in Guilin. The \(NSE\) of the developed EWT-PSO-SVM model in this study reached 0.86, indicating that the developed model performs well in runoff prediction of karst watersheds.  
However, the developed model shows relatively unsatisfactory results in terms of the \(MAPE\) index, which is different from the work of other scholars' studies. The study by [PERSON] et al. [41] indicates that the forecasting performance of the hybrid model with \"decomposition-forecasting-reconstruction\" is better than that of the single model in terms of the \(MAPE\) index. The one reason may be due to the runoff data in this study being from karst basins. The unique geological structure of karst watersheds gives them stronger water storage characteristics [42], which means that a small amount of rainfall is difficult to directly convert into runoff, ultimately leading to low values in the sequence having no obvious statistical features, which in turn affects the accuracy of the series decomposition. Therefore, the regularity of the runoff series under small flow events is more difficult to capture, which leads to poor forecasting performance. In addition, the LSTM was used in [PERSON]'s study, while the SVM was used in this study, and it is possible that the different model types caused the difference in results. Therefore, accurate runoff forecasting in karst basins is a sustained challenge, and it is worthwhile to further explore the forecasting performance of different hybrid models.
# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 2 Materials and Methods
### Vegetation Indices Calculation and Selection
#### 2.2.2 Vegetation Indices Selection Algorithm
On this basis, RF-ML further considers the differences between multiple labels, so it is more suitable for the multi-label classification task. The calculation formula is shown in Equation (1). \[W(X_{j})=\frac{W_{dYX}(X_{j})}{W_{dY}}-\frac{W_{dx}(X_{j})-W_{dYX}(X_{j})}{c-W _{dY}} \tag{1}\]  
where \(c\) is the total number of iterations, i.e., the total number of randomly selected samples. For the \(j\)-\(th\) sample \(X_{j}\), the weight \(W_{dX}\), the tag weight \(W_{dY}\), and the joint weight \(W_{dYX}\) calculation methods are shown in Equations (2)-(4). \[W_{dX}(X_{j})=\sum_{i=1}^{c}\sum_{z=1}^{k}diff(X_{j},E_{i},EK_{z})\times d(E_{ i},EK_{Z}) \tag{2}\]  
\[W_{dY}=\sum_{i=1}^{c}\sum_{z=1}^{k}mld(E_{i},EK_{z})\times d(E_{i},EK_{z}) \tag{3}\]  
\[W_{dYX}(X_{j})=\sum_{i=1}^{c}\sum_{z=1}^{k}mld(E_{i},EK_{z})\times diff(X_{j}, E_{i},EK_{z})\times d(E_{i},EK_{z}) \tag{4}\]  
where \(k\) represents the number of neighbors, i.e., the total number of \(EK\) calculated for each iteration. The \(d\) is used to represent the distance between two samples. The Euclidean distance between all the samples is calculated and the first \(k\) samples are taken as the neighbor sample set \(EK\) of the current sample after sorting by distance. The \(diff(X_{j},E_{i},EK_{z})\) means the difference in characteristic \(X_{j}\) between sample \(E_{i}\) and \(EK_{z}\). The \(mld(E_{i},EK_{z})\) uses the Hamming Distance (HD) to measure the difference between sample \(q\) labeled \(Y_{a}\) and \(Y_{b}b\), and its calculation method is shown in Equation (5). \[mld(E_{i},EK_{z})=HD(Y_{a},Y_{b})=\frac{|Y_{a}\cup Y_{b}|-|Y_{a}\cap Y_{b}|}{q} \tag{5}\]  
The overall flow of the RF-ML algorithm is shown in Figure 4. First, the algorithm selects \(c\) samples by random sampling, where \(c\) is the total number of samples, and \(k\) neighbor weights are calculated for each sample. Second, the k nearest neighbor (KNN) algorithm is used to calculate the \(k\) nearest neighbors for each sample. KNN is an instance-based classification algorithm that determines sample categories by measuring the distance between samples. We used the KNN algorithm to calculate the Euclidean distance between samples and sort the first \(k\) nearest neighbor samples of the current sample. Based on the characteristics and labels of these samples, the importance weights of \(M\) vegetation indexes were calculated, respectively. Finally, the calculated weight was weighted to the corresponding vegetation index by point multiplication through the softmax function to realize the significant attention weighting.
## 3 Proposed Solution to Predict the AoA
### Beamforming and Precoding Strategy
#### 3.4.3 Predictive Precoding with Slot-Level Responsiveness  
The use of DE-ACSW-R predictive AoA models allows for the beamforming vectors to be computed one or more time slots in advance, enabling proactive beam steering. Let \(\hat{\theta}_{u}(t+\Delta)\) be the predicted AoA at a future time, \(t+\Delta\). The precoder \(w_{u}(t+\Delta)\) is constructed from \(\hat{\theta}_{u}(t+\Delta)\). Moreover, it is applied at the scheduled transmission time without waiting for CSI feedback or explicit uplink measurements.  
This proactive approach addresses the latency and coherence time challenges in high-mobility scenarios and is particularly beneficial when fronthaul delays exist between the O-DU and O-RU.
# Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network
## 3 Methods
### Dual Social Graph Attention Network  
The current methods for constructing pedestrian interaction feature graphs are primarily based on a \(360^{\circ}\) panoramic perspective. As shown in Figure 2, in a pedestrian interaction scenario, there are six pedestrians labeled 1-6, each with distinct motion characteristics such as position, speed, and direction.  
Figure 2: (**a**) Pedestrians in scene; (**b**) omni-directional information aggregation; (**c**) pedestrian directed interactions; (**d**) directed information aggregation.  
The construction of interaction graphs aims to incorporate these features while reflecting real-world interactions. Mainstream methods quantify interactions by considering the influence of all surrounding pedestrians and constructing undirected interaction graphs. The feature extraction process, as illustrated in Figure 2a,b, involves abstracting the six pedestrians into graph nodes and calculating the interaction features between these nodes. For example, in Figure 2b, each node (labeled 1-6) undergoes feature computation. Taking pedestrian 3 as an example, this pedestrian serves as the target node for feature aggregation, while pedestrians 1, 2, 4, 5, and 6 act as neighbor nodes providing interaction features. In the undirected graph construction shown in Figure 2b, node 3 considers the features of all the pedestrians in the scene.  
However, in reality, pedestrians have spatial relationships (front, back, left, right) that significantly influence their trajectory choices and must be considered and quantified. As shown in Figure 2c,d, using pedestrian 3 as an example, pedestrians tend to pay more attention to those in front of them, whether based on their field of view or trajectory direction. This aligns with the principles of social psychology and reflects an asymmetric interaction that is closer to a directed relationship. Regrettably, most existing methods fail to adequately account for this aspect and lack the quantification of such relationships.  
In real pedestrian movement trajectories, pedestrian behavior is complexly influenced by various social factors. Therefore, when modeling pedestrian trajectories, it is necessary to consider the impact of various factors on pedestrian behavior. Pedestrians are both independent individuals and sometimes exist in groups, which requires us to consider the interaction relationships between pedestrians from both individual and group levels. For individual interactions, we use the directed social attention function for precise quantification to obtain the interaction relationships between different individuals. Groups are ubiquitous in crowds and can be regarded as an important whole in social relationships. Therefore, we use the group attention function to extract and quantify the influence of groups from the crowd.
# Risk-Aware Wireless Positioning in
## 2 Preliminary: Various Wireless Positioning Schemes  
In this section, we summarize various wireless positioning schemes such as the received signal strength (RSS) scheme [9; 10; 11; 12], the angle of arrival (AoA) scheme [13; 14; 15; 16], the time of arrival (ToA) scheme [17; 18; 19; 20], the time difference of arrival (TDoA) scheme [21] and the fingerprinting scheme [22; 23]. In addition, there are some schemes that using hybrid solutions for the positioning [24; 25; 26; 27; 28].
## 6 Application of the Optimization Techniques: Routing Protocols
### Hierarchical Routing Protocols
#### 6.3.1 LEACH (Low Energy Adaptive Clustering Hierarchy)  
In LEACH the role of the cluster head is periodically transferred among the nodes in the network in order to distribute the energy consumption. The performance of LEACH is based on rounds. Then, a cluster head is elected in each round. For this election, the number of nodes that have not been cluster heads and the percentage of cluster heads are used. Once the cluster head is defined in the setup phase, it establishes a TDMA schedule for the transmissions in its cluster [20]. This scheduling allows nodes to switch off their interfaces when they are not going to be employed. The cluster head is the router to the sink and it is also responsible for the data aggregation. As the cluster head controls the sensors located in a close area, the data aggregation performed by this leader permits to remove redundancy.  
A centralized version of this protocol is LEACH-C [21]. This scheme is also based on time rounds which are divided into the set-up phase and the steady-phase. In the set-up phase, sensors inform the base station about their positions and about their energy level. With this information, the base station decides the structure of clusters and their corresponding cluster heads. Since the base station posses a complete knowledge of the status of the network, the cluster structure resulting from LEACH-C is considered an optimization of the results of LEACH.
# Automated Chest X-Ray Diagnosis Report Generation with Cross-Attention Mechanism
## 4 Discussion
Although these data can provide support for the initial diagnosis of the disease, their information content is relatively limited. X-ray images provide a relatively simplified two-dimensional image in the detection of chest and abdominal diseases. For some complex lesions or early minor abnormalities, their performance may not be clear enough, resulting in the prediction accuracy of the model being affected. In contrast, MRI (magnetic resonance imaging) and CT (computed tomography) images provide more detailed structural images, especially in showing chest and abdominal organs, blood vessels, tissue layers, etc., and have significant advantages. These technologies can provide higher-resolution three-dimensional data to help capture smaller structural changes, which is particularly important for the evaluation of complex lesions. Therefore, incorporating MRI and CT images into data sources can greatly improve the model's ability to detect diseases, especially in early screening and diagnosis, providing more accurate information. In future research, we plan to combine more types of medical imaging data, including MRI and CT images, and may even consider combining clinical data (such as genomic data, blood biochemical indicators,etc.) through multimodal learning methods to further improve the generalization ability and prediction accuracy of the model. We believe that as the amount of data increases and the types of data become richer, the performance of the model will be significantly improved, so that it can be better applied to the automated diagnosis of chest and abdominal diseases. Determining how to protect patient privacy and prevent data leakage or improper use during data collection, storage, and use is an important ethical issue. Developers and users need to strictly abide by relevant data protection laws and regulations and adopt anonymization and encryption technologies to ensure data security and patient privacy protection.
## 4 Discussion
### Limitations and Future Perspectives  
The results of this study should be interpreted with caution, as the analysis focused exclusively on games from the Women's EuroLeague during the 2021/22 and 2022/23 seasons, which may limit the generalization of the findings to other competitions, categories, or contexts. Additionally, comparisons with results from other studies were challenging, as this research did not base its criteria on game-related statistics.  
In the future, it would be valuable to expand this type of research to other competitions, categories, and contexts, including national leagues, men's competitions, or youth categories.  
Variables such as bench depth, player rotation, and game pace were not assessed in this study, but it is well known that the depth of player rotation significantly impacts on-court outcomes [13], potentially influencing the final moments of close games, which are characterized by a slower pace and greater score unpredictability [74]. Other factors, such as team ranking or timeout usage, could influence the research outcomes. Therefore, incorporating these variables in future studies could provide complementary insights into the findings obtained.
## 6 Results
### Average Data Packets Received by BS  
Another crucial comparative parameter is the number of data packets received by the base station. The role of each node is to sense and transmit the information to the base station. The total number of data received at the base station helps judge the routing technique's quality. The number of data packets received by BS during the execution of LEACH, LEACH-C, and EOICHD variants for Scenario #1 is 27,493, 43,217, 52,319, 54,253, and 60,450, respectively. Similarly, the number of data packets received by BS during the execution of LEACH, LEACH-C, and EOICHD variants for Scenario #2 is 38,650, 45,350, 51,915, 54,955, 56,479, respectively. Based on the data, it can be understood that EOICHD variants outperform LEACH and LEACH-C protocols. The number of data received at the base station is directly proportional to the network lifetime. As EOICHD variants can prolong network lifetime by keeping a significantly large number of nodes operating, the data received at BS are also comparatively large. From Figure 11, it is evident that the \(EOICHD\)\(3\times 3\) has transmitted maximum data packets to the base station in both the scenarios among all the EOICHD variants.  
Figure 11: Average data packets received at the Base Station (BS) for Scenario #1 and Scenario #2.  
Figure 10: Energy consumption of network over simulation period Scenario #2.
## 4 Simulation Experiment and Result Analysis  
During the simulation experiment, 60 moving targets were set up in the task area. They were uniformly generated around the task area at the beginning of the simulation and then passed through the task area at a constant speed along a straight line starting from the generated position. The moving speed was 20 m/s, and the motion trajectory was a straight line parallel to the grid line. Simulations were conducted under different conditions, such as different planned path lengths and whether yaw costs were added, and data analysis was conducted. In simulation experiments, the performance of sensors (detection distance, detection probability, false alarm probability, etc.) and UAVs (flight speed, yaw rate, etc.) come from typical values in practical scenarios.