esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Performance Analysis
#### 4.5.1 Performance Analysis Based on YOLO Object-Detection Dataset  
The analysis of the YOLO object-detection dataset is depicted in Figure 7. Figure 7a denotes the accuracy analysis of FP-SFOA-DQN-FL. For time step = 100 s, the accuracy of the FP-SFOA-DQN-FL at epochs 20, 40, 60, 80, and 100 is 0.783, 0.825, 0.825, 0.841, and 0.853. The loss analysis of FP-SFOA-DQN-FL is given in Figure 7b. The loss of FP-SFOA-DQN-FL at epochs 20, 40, 60, 80, and 100 is 0.206, 0.206, 0.188, 0.184, and 0.181 for time stamp = 160 s. Figure 7c denotes the MSE analysis of FP-SFOA-DQN-FL. For time step = 120 s, the MSE of FP-SFOA-DQN-FL at epochs 20, 40, 60, 80, and 100 is 0.180, 0.173, 0.163, 0.162, and0.152. The RMSE analysis of FP-SFOA-DQN-FL is given in Figure 7d. The RMSE of FP-SFOA-DQN-FL at epochs 20, 40, 60, 80, and 100 is 0.463, 0.462, 0.441, 0.441, and 0.436 for time stamp = 200 s. Figure 7e denotes the FPR analysis of FP-SFOA-DQN-FL. For time step = 100 s, the FPR of FP-SFOA-DQN-FL at epochs 20, 40, 60, 80, and 100 is 0.206, 0.193, 0.191, 0.180, and 0.179. The mean average precision analysis of FP-SFOA-DQN-FL is given in Figure 7f. The mean average precision of FP-SFOA-DQN-FL at epochs 20, 40, 60, 80, and 100 is 0.812, 0.819, 0.835, 0.866, and 0.870 for time stamp = 140 s.
# M2 ER: Multimodal Emotion Recognition Based on Multi-Party Dialogue Scenarios
## 2 Detailed Design
### Multimodal Emotion Recognition Based on Attention Mechanism  
Building an effective multimodal fusion model is a crucial step in multimodal sentiment recognition. We adopted a feature-level fusion approach to combine the emotional features extracted from the text, audio, and video modalities obtained by the aforementioned models. Current research on multimodal sentiment recognition often relies on extracting a large number of features to identify emotion. However, directly concatenating these features can lead to the curse of dimensionality, and there is no distinction in their importance, which may result in the overshadowing of relatively significant features. Furthermore, there are often correlations among the features from the text, audio, and video modalities. Additionally, it is observable that people express emotion differently in real-life scenarios, but existing multimodal fusion models often overlook this phenomenon. The attention mechanism can be used in neural networks to achieve more effective resource allocation. Based on these issues, M2 ER explores an attention-based multimodal fusion model.  
Our fusion model consists of three main parts: (shown in Figure 2).  
1. Cross-Modal Attention Interaction--_Part 1_: This module utilizes cross-modal attention to capture the intermodal relationships and obtain the feature representation of the interaction between different modalities.
2. Multimodal Attention Fusion--_Part 2_: This module employs the attention mechanism to determine the importance of each modality in the final fusion classification. It obtains the weight distribution of each modality's features in the fusion process and performs the fusion accordingly.
3. Finally, the fused multimodal features are passed through the softmax classification layer for emotion recognition.
# Distributed Generation Cluster Division Method Considering Frequency Regulation Response Speed
## 3 Comprehensive Cluster Division Index Considering Frequency Regulation Response Speed
### Functional Index Considering Frequency Regulation Speed
#### 3.2.2 Active Power Balance Index  
In the case of uneven installed capacity of distributed generation, in order to achieve collaborative planning, the net power balance degree can be used to classify clusters. The value indicates the degree of matching between the installed capacity and output power of the existing distributed power supply and the load. The active balance degree, which describes the net power characteristics of a cluster based on a typical time-varying scenario of the network, is as follows:  
\[P_{i}=1-\frac{1}{T}\sum\limits_{t=1}^{T}\left|\frac{P_{clu}(t)_{i}}{\max(P_{ clu}(t)_{i})}\right|, \tag{11}\]  
\[\varphi_{P}=\frac{1}{c}\sum\limits_{i=1}^{c}P_{i} \tag{12}\]  
where \(P_{i}\) is the active balance degree of cluster \(i\); \(P_{clu}(t)_{i}\) is the net power characteristic of cluster \(i\), which can be obtained based on typical time-varying scenarios at each node; \(T\) is the duration of a typical time-varying scenario; and \(\varphi_{P}\) is the active balance index.  
Active balance refers to the coordination of the combination of nodes in the network in order to realize the autonomous capacity of the cluster. According to the time-varying power output characteristics of the nodes, the nodes are used to complement each other, i.e., source-source complementarity between renewable energy sources and source-load complementarity between them and the load in order to mitigate the volatility and intermittency of the renewable energy output while achieving a certain balance in the cluster power. The active balance index quantifies the degree of balance of all power sources and loads within a cluster over a period of time, while the cluster frequency regulation response speed index measures the degree of power balance at the current moment, and the two complement each other.
## 2 Principle
### Calibration of Measuring System  
The camera calibration method based on the 2D planar target is used to calibrate the left and right cameras separately, and then the system is calibrated with the calibration results of two cameras. Since the filters are installed in front of both two cameras, the signal-to-noise ratio of the obtained target image under the condition of visible light illumination is low. Therefore, two extra UV light sources with a power of 10 W are used to assist the camera calibration process with the enhanced contrast of the target image.
# Model for the Prediction of Rain Attenuation Affecting Free Space Optical Links
## 4 Multiple Scattering Effects
### Multiple Scattering Gain  
The multiple scattering gain is defined as the ratio between the multiple scattering transmissivity and the Beer-Lambert transmissivity expressed on a dB scale, i.e.,  
\[G_{ms}=10\log_{10}\left(\frac{\tau_{ms}}{\tau_{BL}}\right), \tag{16}\]  
where  
\[\tau_{BL}=e^{-\gamma L}. \tag{17}\]  
In the following, \(G_{ms}\) is calculated as a function of the path length \(L\) and fitted with a power-law expression, that is,  
\[G_{ms}=aL^{b}. \tag{18}\]  
The coefficients \(a\) and \(b\) depend on the rain rate for a given DSD shape. Therefore, by taking into account the multiple scattering gain, the adjusted path attenuation can be written as follows:  
\[A_{ms}(L,R)=A(L,R)-G_{ms}(L,R), \tag{19}\]  
where \(A\) is the attenuation calculated through the model described in Section 2 or through one of the analytical methods listed in Section 3.
## 2 Optimization Models  
### Wake Model  
For the wind farm optimization, an analytical wake model that quantitatively describes the wake effect should be incorporated into the mathematical formulations. In this paper, the PARK model is applied to quantify the average wind speed in the wake and wake radius behind an upstream wind turbine rotor. Its major characteristic is illustrated in Figure 1[20].  
The original PARK model is proposed by [PERSON] et al. [21]. The wake model assumes a linear wind expansion after the wake passes the wind turbine rotor. \(r_{x}\) indicates the wake radius at the axial distance of \(x\), which is given by:  
\[r_{x}=r_{0}+\alpha x \tag{1}\]  
Based on the theory of momentum conservation, the wind velocity \(v_{x}\) at an axial distance of \(x\) downstream of the wind turbine rotor can be calculated by:  
\[v_{1}=v_{0}\left[1-2a\left(\frac{r_{0}}{r_{0}+\alpha x}\right)^{2}\right] \tag{2}\]  
where \(v_{0}\) is the free stream wind speed and \(v_{x}\) is the wind speed in the wake at the axial distance of \(x\). Notice that the parameter \(r_{0}\) in the equations is the downstream rotor radius (indicating the radius right behind the wind turbine rotor). According to the actuator disk theory, \(r_{0}\) is calculated based on the wind turbine rotor radius \(R\) as:  
\[r_{0}=R\sqrt{\frac{1-a}{1-2a}} \tag{3}\]  
where \(a\) is axial induction factor depicting the induction percent of wind speed reduction. In Equations (1) and (2), \(\alpha\) is the entrainment constant that is associated with the surface roughness (\(z_{0}\)) and hub height (\(l\)), with the equation of:  
\[\alpha=\frac{0.5}{\ln\left(\frac{h}{z_{0}}\right)} \tag{4}\]  
Figure 1: PARK wake model.  
The velocity deficit (_Vel_def_) between two wind turbines is calculated by [22]:  
\[Vel\_def=2a\left(\frac{r_{0}}{r_{0}+\alpha x}\right)^{2}\sqrt{\frac{A_{overlap}}{A _{rotor}}} \tag{5}\]  
where \(A_{overlap}\) is the overlapping between the wake and wind turbine rotor. It equals zero without wake interaction and it equals rotor area \(A_{rotor}\) with the full wake interaction. For the partial wake interaction, it is calculated according to the relative wind turbine positions [23].  
For a wind turbine \(i\) that is affected by multiple wakes of up to \(N\) wind turbines, the velocity deficit is given by:  
\[Vel\_def_{i}=\sqrt{\sum_{i=1}^{N}\left(\sqrt{\frac{A^{i}_{overlap}}{A^{i}_{ rotor}}}2a\left(\frac{r_{0}}{r_{0}+\alpha x_{i}}\right)^{2}\right)^{2}} \tag{6}\]  
The final incoming wind speed of the wake-affected wind turbine is calculated by:  
\[v_{i}=v_{0}(1-Vel\_def_{i}) \tag{7}\]
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Ultra-Dense Networks (UDNs)
#### 4.3.1 Unique Features of UDNs  
1. A massive number of SCs and AP (more than or equal to the Ues number). The massive number of SCs can enhance frequency reuse in the same manner that adjacent distance and frequency reuse operate in macro-cells. The dense SCs increase the capacity of the network by offloading the traffic of macro-cell, balancing loads of the network, and minimizing congestion [117; 118].
2. Dense and extensively interconnected cross-tier distribution. This comprises macro-cell, SCs (femto-cell, pico-cell), relay nodes, D2D connections, etc., which boost the network environment's complexity. Due to the multi-tier distribution, the signals of various frequencies are sent throughout the overlapping region (e.g., macro-cell and SC). Furthermore, the proximity of SCs results in a great frequency reuse factor. Thus, the coordination of sophisticated interference is critical to reducing intra-tier interference and inter-tier interference, as well as assisting with resource management [119; 120].
3. Quick access and flexibility of switching (e.g., handovers). In the dense distribution scenario, the mobile UE may often swap the connection among access nodes, to get, the best service, optimal communications, and so on. The performance of high-quality handover (HQHO) is required to hand over smooth and seamless communications [121; 122].
## 4 Discussion: Comprehensive Analysis of the Deep-Learning-Oriented Framework
### Contribution to Seaport Operations' Efficiency and Sustainability
#### 4.1.2 Real-Time Updates and Predictive Maintenance  
Seaport efficiency is greatly enhanced by the provision of real-time information to drivers and operators. Our framework offers real-time updates on parking space availability and traffic conditions. The drivers receive notifications on the available parking spaces closest to their destination, reducing their search time and optimizing operations [47]. Additionally, our system employs predictive maintenance, anticipating maintenance needs based on the equipment usage patterns. This proactive approach prevents breakdowns, ensuring seamless operations and bolstering overall efficiency.
## 2 Principle
### MIMO Hardware Requirements  
Last but not least, this subsection discusses the design and architecture of spatial multiplexing MIMO decoders for field-programmable gate arrays (FPGAs). Conventionally, the optimal hard-decision detection for MIMO systems is the maximum likelihood (ML) detector, which is well known due to its superior performance in terms of BER, but its direct implementation grows exponentially with a higher-level modulation scheme, causing its FPGA implementation to be relatively infeasible [38].  
On the other hand, soft-output generation for a sphere detector can solve the ML detection issue in a computationally efficient manner, for a real-time implementation on a DSP processor at high-performance parallel computing platforms. The scalable architecture of soft-output generation for a sphere detector is presented in Figure 11, which requires further micro-architecture optimizations and trade-offs [39].  
Figure 10: The orthogonal frequency-division multiplexing (OFDM) FDE equalizer complexity requirements compared with other MIMO approaches [37].
## 3 Proposed Optimal Orchestration Process
### Algorithm Performance Analysis
#### 3.4.1 Network Performance Evaluation  
The framework demonstrated exceptional performance in slice orchestration, achieving an average deployment time of 1.8 s, with 92.5% resource allocation accuracy. Service function chain setup was completed within 215 ms, while scaling operations responded to demand changes within 480 ms. These metrics represent significant improvements over traditional orchestration approaches.  
Resource utilisation metrics showed consistent efficiency, maintaining average CPU utilisation at 78.5% and memory utilisation at 82.3%. Network bandwidth efficiency reached 86.7%, indicating effective resource management while maintaining sufficient headroom for demand spikes.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 3 NP Properties and Characterization Methods
### Chemical Properties and Their Characterization of MNPs
#### 3.2.2 Nuclear Magnetic Resonance (NMR)  
Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical technique commonly used for the characterization of magnetic nanoparticles. While NMR has some limitations when it comes to studying nanoscale materials directly, it can provide valuable information about the structure, composition, and interactions involving nanoparticles.  
NMR can be used for quantitative analysis, allowing the determination of NPs concentration in a sample. This is particularly useful for assessing the synthesis yield.  
Surface functionalization can be analyzed by observing changes in chemical shifts and peak intensities. Ligands or surface coatings on nanoparticles can be characterized by NMR, providing insights into their composition and arrangement.  
Nevertheless, MNPs' size and dispersion can be studied through NMR relaxometry.  
Techniques such as diffusion-ordered spectroscopy (DOSY) can be employed to study the translational diffusion of nanoparticles [106].
# Preddiagnosis of Heart Failure (HF) Using Deep Learning and the Korotkoff Sound
## 3 Materials and Methods  
### System Overview  
The overall structure of this paper is depicted in Figure 1. The KS data were collected by the medical team of the Department of Cardiology, the fourth people's Hospital connected with Zhejiang University, using a 3M Littmann 3200 electronic stethoscope with a sampling frequency of 480 kHz. After data cleaning, all KS signals were downsampled to 4 kHz. We denoised the downsampled KS signals using the Wiener filtering approach to remove friction noise, ambient noise, electromagnetic noise, and human breath sounds.  
The signal segmentation work refers to the heart sound segmentation algorithm combined with the Shannon energy envelope algorithm, signal locating, and segmentation method. KS locationing, unlike heart sounds, does not necessitate a fine distinction of effective signals; however, there will be over-localization during the positioning process, and the positioning results must be re-validated.  
To compare the effect of KS signal segmentation on HF prediagnosis, we extracted CWT and MFCC features from segmented and unsegmented KS signals. The following datasets were obtained: the segmented-based CWT (ST-CWT) data set, the segmented-based MFCC (ST-MFCC) data set, the unsegmented-based CWT (UST-CWT) data set, and the unsegmented-based MFCC (UST-MFCC) data set. In CWT feature extraction, the Morse mother wavelet was employed. When performing MFCC feature extraction, 26 Mel filters were used, the frame length was set to 512, and the frame shift was set to 256; the obtained CWT and MFCC coefficients were converted into 224 \(\times\) 224 images. After image augmentation, they were sent to the convolutional neural network for classification and performance evaluation.
## 4 Results and Discussion
### Figure of Merits  
To evaluate the performance of the proposed framework and carry out its comparison with the existing techniques, we used three metrics that were commonly used in the literature. These metrics were the Spearman rank order correlation coefficient (SROCC), Pearson linear correlation coefficient (PLCC) and Kendall rank order correlation coefficients (KROCC). These metrics are mathematically expressed as follows:  
\[SROCC=1-\frac{6\sum_{i=1}^{N}(v_{i}-p_{i})^{2}}{N(N^{2}-1)} \tag{1}\]  
where \(N\) is the total number of images and \(v_{i}\) and \(p_{i}\) are the ranked actual and predicted quality scores, respectively:  
\[PLCC=\frac{\sum_{i=1}^{N}(x_{i}-\hat{x})(y_{i}-\hat{y})}{\sqrt{\sum_{i=1}^{N}(x_ {i}-\hat{x})}\sqrt{\sum_{i=1}^{N}(y_{i}-\hat{y})}} \tag{2}\]  
where \(N\) is the total number of samples (Images), \(x\) and \(y\) are sample points of the actual and predicted scores, respectively, and \(\hat{x}\) and \(\hat{y}\) are the means of the two different samples:  
\[KROCC=\frac{P-Q}{\sqrt{(n_{[0]}-n_{1})(n_{0}-n_{2})}} \tag{3}\]  
where  
\[n_{0}=\frac{n(n-1)}{2} \tag{4}\]  
\[n_{1}=\sum_{j}\frac{q_{j}(q_{j}-1)}{2} \tag{5}\]  
\[n_{2}=\sum_{k}\frac{r_{k}(r_{k}-1)}{2} \tag{6}\]  
where \(P\) represents the number of concordant pairs, \(Q\) represents the number of discordant pairs, \(q_{j}\) shows the tied samples from the \(j\)th group, which ties with the first quantity, and \(r_{k}\) shows the tied samples from the \(k\)th group, which ties with the second quantity.
## 3 Results
### Land Use and Land Cover Distribution
#### 3.3.3 Buffer Areas Around Conservation Units  
In Manaus, buffer zones around Federal sustainable use units experienced a relatively low level of change, with 23 ha deforested and 76 ha degraded. The buffers of Federal full protection units faced more significant pressures, with 183 ha deforested and 3321 ha degraded (Figure 5a). State-level units in Manaus showed varying impacts, with buffers around sustainable use units experiencing 362 ha of deforestation and 1599 ha of degradation, while the buffers of full protection units saw 19 ha deforested and 292 ha degraded (Figure 5b). Local-level units in Manaus exhibited similar deforestation levels in their buffer zones (around 145 ha for both sustainable use and full protection), but degradation was more pronounced around sustainable use units (739 ha against 250 ha for full protection) (Figure 5c).  
Figure 5: Total area of deforestation and forest degradation inside buffer zones around conservation units in both study sites per administrative types. Buffers zones of each management type (full protection and sustainable use) belonging to the (**a**) Federal Level; (**b**) State Level; and (**c**) Local Level.  
Porto Velho displayed different trends, particularly in federal-level units. Buffer zones around Federal sustainable use units experienced higher deforestation (4593 ha) than degradation (3712 ha), contrasting with the general trend. The buffers of Federal full protection units showed lower but still significant changes (189 ha deforested and 1808 ha degraded) (Figure 5a). Buffers of State sustainable use units in Porto Velho faced substantial pressures, with 1270 ha deforested and 2269 ha degraded (Figure 5b). Local-level units in Porto Velho showed minimal changes in sustainable use buffers but more significant impacts around full protection units (127 ha deforested and 497 ha degraded) (Figure 5c).  
Comparing the two areas, Porto Velho's sustainable use units' buffer zones experienced significantly higher levels of both deforestation (5863 ha) and degradation (5981 ha) compared to Manaus (529 ha and 2416 ha, respectively). Conversely, the buffers of full protection units in Manaus showed slightly higher rates of deforestation (349 ha) and degradation (3865 ha) than those in Porto Velho (316 ha and 2305 ha, respectively).  
Across both areas, the primary driver of forest loss in buffer zones was conversion to Agriculture/Pastureland, mirroring the broader regional trend. Federal-level units' buffer zones experienced the largest disturbance, particularly in Porto Velho for deforestation (4782 ha) and in both areas for degradation (5520 ha in Porto Velho, 3397 ha in Manaus).
## 3 Experimental Results and Discussion
### Training and Evaluation of DCNNs
#### 3.2.4 Ensemble Model Results  
The classification accuracy of the deep model can be improved using the ensemble technique. For this purpose, an ensemble of the above-stated deep learning models has been used, utilizing the weights from the Softmax function for voting. The mean values of the performance matrices for every type of dataset obtained for all deep models and the proposed approach are given in Table 4.  
The performance matrices, including accuracy, precision, recall and F1-score, for the developed model are given in Table 4. The voting ensemble approach showed a better outcome compared to the single DCNN models. Although the evaluation metrics for the noiseless dataset using individual deep models are closely related to the results of the ensemble approach, a notable difference between the proposed approach and DCNN models becomes evident when dealing with a noisy dataset. It improved the results for each case and produced the best results for noiseless cases, giving the following values: accuracy (99.98%), precision (99.97%), recall (99.80%), and F1-score (99.85%), while the lowest performance is recorded for 20 dB noise, with accuracy (99.73%), precision (98.23%), recall (97.23%), and F1-score (97.78%), as compared to other cases.  
Figure 14: AlexNet confusion matrices for noisy and noiseless testing datasets.  
The performance of the ResNet-50 model was also evaluated with an SE attention mechanism for PQD classification. The SE mechanism improves feature learning by amplifying important features while suppressing less useful ones. The training results of ResNet-50 with and without the attention mechanism are shown in Figure 15. The model with SE produced comparatively better results than the standard ResNet-50. Both the training and validation accuracy are recorded as 98.71% and 98.40%, respectively, with 0.121% training and 0.144% validation losses. In noisy conditions (20 dB noise), the attention-enhanced model also performed better, giving 95.89% and 93.11% training and validation accuracy, respectively, as compared to the standard ResNet-50. Additionally, accuracy, precision, recall, and F1-score are calculated according to Equations (4)-(7) in Table 4, which demonstrate that ResNet-50 with the attention mechanism consistently delivers better results compared to the baseline model without any attention. However, despite improved performance, the integration of the SE mechanism introduces computational overheads. The attention module requires additional computational resources, leading to increased model complexity and longer training and inference times, as explained in Section 3.3. While the accuracy and feature learning is enhanced, the added computation may impact real-time applications, especially in systems like smart grids where rapid decision-making is crucial. Therefore, while ResNet-50 with the attention mechanism shows improved results, its computational overheads must be carefully considered, particularly in resource-constrained environments.