esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation
## 2 Methodology
### Segmentation
#### 2.2.3 Modified Intuitionistic Fuzzy C-Means (MIFCM)  
Modified Intuitionistic Fuzzy C-Means (MIFCM) [20] is the variant of the conventional Intuitionistic Fuzzy C-Means (IFCM) [18], and it is based on an intuitionistic fuzzy set. In MIFCM, the input data is clustered by optimizing the following objective function shown in Equation (20)  
\[J=\sum\limits_{j=1}^{n}\sum\limits_{i=1}^{c}\beta_{ij}^{m}\;d_{H}(x_{j},v_{i}) \tag{20}\]  
where \(x_{j}\) represents \(j\)th voxel, \(v_{i}\) refers to \(i\)th cluster center, \(m\) refers to the fuzzification value, \(\beta_{ij}\) refers to the MIFCM membership value of \(j\)th voxel to \(i\)th cluster, and \(d_{H}(x_{j},v_{i})\) is the modified Hausdorff distance between \(j\)th voxel to \(i\)th cluster center.  
Similar to Fuzzy C-Means, MIFCM optimizes the objective function iteratively by updating the membership and cluster centers. The MIFCM membership value is updated using equation  
\[\beta_{ij}=\mu_{ij}+\pi_{ij} \tag{21}\]  
where \(\mu_{ij}\) is the membership value and \(\pi_{ij}\) is the hesitation value. The membership value \(\mu_{ij}\) is computed as follows:  
\[\mu_{ij}=\frac{1}{\sum\limits_{k=1}^{c}\left(\frac{d_{H}\left(x_{j},v_{i} \right)}{d_{H}\left(x_{j},v_{k}\right)}\right)^{\frac{2}{m-1}}} \tag{22}\]  
The hesitation value \(\pi_{ij}\) is the combination of the membership and the non-membership value, and it is computed as:  
\[\pi_{ij}=1-\mu_{ij}-\eta_{ij} \tag{23}\]  
where \(\eta_{ij}\) is the non-membership value. To compute the non- membership value, [PERSON]'s and [PERSON]'s intuitionistic fuzzy complement generators are used and the value is computed using Equations (24) and (25), respectively.  
\[\eta_{ij}=\frac{1-\mu_{ij}}{1+\alpha\mu_{ij}} \tag{24}\]  
\[\eta_{ij}=(1-(\mu_{ij})^{\alpha})^{\frac{1}{\alpha}} \tag{25}\]  
where \(\alpha>0\) is constant.  
In this paper, we employed both [PERSON]'s and [PERSON]'s complement generators. MIFCM using [PERSON]'s function is named MIFCM_S and similarly MIFCM using [PERSON]'s function is named MIFCM_Y. Furthermore, cluster centers are updated using Equation (26).  
\[v_{i}=\frac{\sum\limits_{j=1}^{n}\beta_{ij}^{m}x_{j}}{\sum\limits_{j=1}^{n}\beta_ {ij}^{m}} \tag{26}\]  
MIFCM is an iterative process, and it stops when the convergence criteria are satisfied (i.e., the difference between the objective function value of successive iterations is less than the user-specified stopping criteria value).
## 4 Discussion  
The dual pseudo Bessel beams were generated using the designed metasurface with the proposed method. From the simulated normalized E-field amplitude distribution, we can see two pseudo Bessel beams propagate along the directions of \(\theta_{1}=30^{\circ},\ \varphi_{1}=0^{\circ}\) and \(\theta_{2}=30^{\circ},\ \varphi_{2}=180^{\circ}\) simultaneously, and the energy is divided into two almost equal parts along the desired directions. The energy along the propagation axes is nearly remaining stable, while the simulated and experimental measurement normalized E-field amplitudes at the different distances have the tendency of decreasing gradually, which means the energy is attenuating when propagating. We can calculate the transmission efficiency of the dual pseudo Bessel beams generated by the metasurface at different transmission distances to further research the energy distribution of the dual pseudo Bessel beams. As shown in Figure 10, the transmission efficiency (\(\eta\)) of the metasurface can be defined as the ratio of the pseudo Bessel beam power (\(P_{1}\)) to the total power (\(P_{0}\)) radiated by the metasurface, i.e., \(\eta=P_{0}/P_{0}\), and the values of both \(P_{0}\) and \(P_{1}\) are the flux integral of the Poynting vector.  
As the beams we generate are the dual pseudo Bessel beams, it is necessary to add the power of the two beams obtained at the same transmission distance. Here we use five different reference planes to calculate the power of the dual beams at different transmission distances. The distances from the metasurface are the same as that of the E-field observation planes mentioned, so the transmission  
Figure 10: Schematic diagram of the efficiency calculation method.  
Figure 9: Experimental measurement normalized E-field amplitude distribution of the dual pseudo Bessel beams on the observation plane with a size of 1.2 m \(\times\) 0.6 m and a distance to the probe of 0.6 m.  
distances are 400 mm, 600 mm, 800 mm, 1000 mm, and 1200 mm, respectively. The result shows that on the reference plane has a distance of 400 mm, and the total calculated power value of the two beams is 0.474. As the power value \(P_{0}\) on the metasurface can be obtained as 0.8 by integration, so the efficiency of the dual pseudo Bessel beams at 400 mm is about 59.2%. The efficiency at the latter distances was also calculated by the same method as being 36.8%, 32.6%, 27%, and 25%, respectively. It can be seen that the efficiency changes with the change in transmission distance, and it maintains a relatively high value considering that the reference planes are far away from the metasurface; that is, a high efficiency can be obtained at different transmission distances in accurately controllable propagating directions when using the proposed method to generate pseudo Bessel beams. As the energy can be allocated in multiple directions, devices at multiple different locations can be charged simultaneously. Furthermore, because the generated pseudo Bessel beams have uniform energy distributions within a certain range, the location requirements for the devices are also reduced.
# Dual-Modality Transformer with Time Series Imaging for Robust Epileptic Seizure Prediction
## 4 Experiments
### Experimental Settings
#### 4.1.2 Implementation Details  
All experiments were implemented using the PyTorch 2.1.0 framework and conducted on an NVIDIA RTX 4090 GPU (Nvidia, Santa Clara, CA, USA)). For the teacher model (dual-modality), we set the feature dimension d_model to 128 and used 8 attention heads in the Transformer encoder. The EEG data were processed using a sliding window of 40 time steps, and the images were resized to 224 x 224 pixels. We employed the Adam optimizer with an initial learning rate of 1 \(\times\) 10\({}^{-4}\) and a batch size of 32. The models were trained for 20 epochs with an early stopping patience of 5 epochs. For knowledge distillation, we set the temperature parameter to 2.0 and the distillation loss weight \(\alpha\) to 0.5.  
For the frequency analysis branch, we utilize two 1D convolutional layers with kernel sizes of 3 and stride of 1. The first layer expands the channel dimension from 1 to 32, while the second layer further increases it to 64. Each convolutional layer is followed by batch normalization and ReLU activation. The output is then adaptively pooled to match the Transformer branch's dimension. The image processing stream implements a modified ResNet-18 architecture pre-trained on ImageNet. We remove the final classification layer and add a custom projection layer that reduces the 512-dimensional features to 128 dimensions. The Vision Transformer path processes 16 x 16 pixel patches with a Transformer encoder containing six layers and eight attention heads. The embedding dimension is maintained at 128 throughout the network to facilitate feature fusion. The SYNI module utilizes scaled dot-product attention with a temperature parameter of 2.0. The fusion weights are learned through a two-layer neural network with hidden dimension 256 and ReLU activation. All attention computations employ a dropout rate of 0.1 during training. The final MLP classifier consists of two layers with dimensions [128; 64; 5], where five corresponds to the number of classes.  
During training, we employ the AdamW optimizer with an initial learning rate of 1 \(\times\) 10\({}^{-4}\) and weight decay of 1 \(\times\) 10\({}^{-4}\). The learning rate is reduced by a factor of 0.5 when validation performance plateaus for 50 epochs. We use a batch size of 32 and train for a maximum of 200 epochs with an early stopping patience of 20 epochs. All experiments were conducted on an NVIDIA RTX 4090 GPU (Nvidia, Santa Clara, CA, USA) with 24 GB memory. The complete training process typically requires approximately 14 h.
# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 5 Experiments
### Evaluation Metrics
The calculation formulas are as follows:  
\[\text{MOCA}=1\ -\ \frac{\text{FP}_{\text{type}}-\text{FN}_{\text{type}}+\text{TYPE }_{\text{s}}}{\text{GT}} \tag{41}\]  
\[\text{IDP}= \frac{\text{TP}_{\text{type}}}{\text{TP}_{\text{type}}+\text{FP}_{ \text{type}}} \tag{42}\]  
\[\text{IDR}= \frac{\text{TP}_{\text{type}}}{\text{TP}_{\text{type}}+\text{FN}_{ \text{type}}} \tag{43}\]  
\[\text{IDF}_{1}\ =\frac{2\text{TP}_{\text{type}}}{2\text{TP}_{\text{type}}+\text{FP}_{ \text{type}}+\text{FN}_{\text{type}}} \tag{44}\]  
In this context, \"type\" refers to the category of the detected vessel, while FP\({}_{\text{type}}\), FN\({}_{\text{type}}\), TP\({}_{\text{type}}\), and GT represent the number of vessels of each type that are incorrectly classified as positives, incorrectly classified as negatives, correctly classified as positives, misallocated vessel types, and the total number of ground truth instances, respectively. Higher values of MOCA, IDP, IDR, and IDF\({}_{1}\) indicate better fusion performance and improved consistency in vessel detection. * The Evaluation of Data Fusion  
In the evaluation of data fusion performance, we performed a frame-by-frame analysis of the five videos in the test set, using fusion accuracy, IDP, IDR, and the IDF\({}_{1}\) score as evaluation metrics. The specific calculation formulas are as follows:  
\[\text{FA}= \frac{\text{FP}_{\text{mmsi}}}{\text{GT}_{\text{mmsi}}} \tag{45}\]  
\[\text{IDP}= \frac{\text{TP}_{\text{mmsi}}}{\text{TP}_{\text{mmsi}}+\text{FP}_{ \text{mmsi}}} \tag{46}\]  
\[\text{IDR}= \frac{\text{TP}_{\text{mmsi}}}{\text{TP}_{\text{mmsi}}+\text{FN}_{ \text{mmsi}}} \tag{47}\]  
Figure 5:0  
In the data fusion evaluation, we replaced the ship type with the ship's MMSI, which represents the identity information of the tracked vessel. FN\({}_{\text{mmsi}}\), FP\({}_{\text{mmsi}}\), and GT\({}_{\text{mmsi}}\) represent the number of MMSI incorrectly classified as negative examples, the number of MMSI incorrectly classified as positive examples, and the number of MMSI true ground truth values, respectively. Additionally, we also combine IDP, IDR, and IDF\({}_{1}\) as metrics for fusion accuracy; a better fusion model is characterized by higher FA, IDP, IDR, and IDF\({}_{1}\).
# A BiGRU Model Based on the DBO Algorithm for Cloud-Edge Communication Networks
## 4 Simulation Experiment and Result Analysis
### Experimental Equipment and Software Environment  
(1) Hardware Parameters  
1. CPU: Intel i7-7700 HQ with 4 physical cores, 8 processing threads, 2.8 GHz base frequency, which can be boosted to a maximum of 3.8 GHz by Intel Turbo Boost technology, and 6 MB Intel Smart Cache cache size (Intel, Santa Clara, CA, USA).  
2. Memory: 8 GB DDR4 2400 MHz.  
(2) Software Environment  
1. Programming Language: Python 3.10  
2. Deep Learning Framework: Pytorch 2.1.2 CPU  
(3) Experimental Process  
1. Execution times: about 16 h  
2. Possible issues and solutions: During our experiments, we closely monitored memory usage and encountered some memory usage challenges when training the deep-learning models. We found cases where the memory usage was close to the upper limit. To address these issues, we took the following steps: (1) Optimise the data loading and preprocessing process to reduce memory usage by using data generators and reducing the storage of intermediate data in the preprocessing step. (2) Appropriately adjust the batch size of the model to ensure that each training iteration does not consume too much memory. (3) Memory analysis tools are used to monitor memory usage and adjust the model structure and training strategy as needed.
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
## 2 Ground RFI Impacts on GEO SAR Signal  
The ground RFI is the most common case which often derives from ground implemented radio electronic devices and ground-based radar systems. Thus, we analyse its impact on GEO SAR in this section. A sketch of a GEO SAR system in the presence of RFI is shown in Figure 3. The received  
Figure 2: Comparisons between the GEO SAR swath and the LEO SAR swath and their covered RFI signals (SMOS RFI occurrence probability data in East Asia for 20170902 \(\pm\) 7 days [31], red regions stand for the high probability of RFI occurrence).  
RFI signal will raise the noise floor and degrade the Signal-to-Interference Ratio (SIR). Generally, the definition of the SIR is [32].  
\[SIR=\frac{P_{s}}{P_{RFI}}, \tag{1}\]  
where \(P_{RFI}\) is the power of the RFI and \(P_{s}\) is the power of the received effective GEO SAR signal. Based on the radar equation [29], \(P_{s}\) can be written as  
\[P_{s}(\theta_{d},\psi)=\frac{P_{a}{A_{1}}^{2}F^{2}(\theta_{d},\psi)L_{loss}^{2 }}{4\pi\lambda^{2}R^{4}}\sigma_{0}\rho_{a}\rho_{g}, \tag{2}\]  
where \(P_{a}\) is the average transmitted power of the GEO SAR signal, \(A_{t}\) is its antenna aperture area, \(F\) represents the gain, \(\theta_{d}\) and \(\psi\) represent the pointing direction of the antenna, \(L_{loss}\) is the one-way total loss, \(\sigma_{0}\) is the backscatter coefficient of the target, \(\lambda\) is the wavelength, \(R\) is the slant range and \(\rho_{a}\) and \(\rho_{g}\) are azimuth resolution and the ground range resolution, respectively.  
Considering the thermal noise, we can define the SINR as  
\[SINR=\frac{P_{s}}{P_{RFI}+P_{th}}, \tag{3}\]  
where \(P_{th}\) is the power of the thermal noise, which can be written as [32]  
\[P_{th}=k_{B}T_{th}B_{g}, \tag{4}\]  
where \(k_{B}=1.38\times 10^{-23}J/K\) is the Boltzmann constant, \(T_{th}\) is the thermal noise temperature and \(B_{g}\) is the bandwidth of the GEO SAR system.
# A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks
## 2 Related Works
### Synthesis of the State-of-the-Art  
In the previous subsections, some of the most relevant IEEE 802.15.4 handover proposals were presented according to the main strategy used to improve the handover process. In this section, these proposals are classified according to the remaining axes. Figure 2 classifies the reported proposals according to the four classification axes previously defined.  
Regarding the strategy adopted for handover, most of the above studies anticipate the selection of a more appropriate coordinator when degradation of the connection between the MN and the coordinator is detected [8; 11; 18; 19; 20; 21; 22; 23; 24; 25; 26]. Another relevant characteristic is that one of the main modifications to the association mechanism is to reduce the number of scanned channels in the case of loss of connectivity [9; 11; 15; 16; 17],while some other schemes try to reduce the association time.  
In terms of the metrics to select a suitable time instant to perform the handover, it can be seen from Figure 2 that the vast majority of the studies are based on RSSI [19; 20; 21; 25; 26; 28] and/or LQI [8; 11; 15; 18; 20; 23; 24; 25; 27] for handover decision-making. Other works use the residual energy of the mobile nodes as a criterion [18; 20; 22; 23; 24; 26]. Some other studies that use the direction of movement or position of the mobile node as a decision criterion can also be found [11; 21; 26]. Finally, refs. [21; 28] consider the distance from the mobile node to the coordinator and others [18; 20; 26] the path from the mobile node to the coordinator. Some of these proposals are based on multiple criteria [11; 18; 20; 26], and the remainder considers only one, or at most two, criteria to select the best instant for the handover decision.  
It is clear that the cluster-tree topology was predominantly selected, followed by a mesh topology. This is an expected result, as mesh and cluster-tree topologies make it possible to cover larger areas. Regarding whether the decision on the handover is taken by the mobile node or the coordinator, it was observed that in a large number of works is the mobile node that initiates this process. Some other  
Figure 2: Synthesis of the reported proposals.  
schemes propose an initiation process involving the coordinator node [8; 9; 14; 17]. Several studies were also found in which decision making was distributed between the MN and the coordinator [22; 24; 25].  
Through a systematic review of the literature, this section raised state-of-the-art proposals for improving the IEEE 802.15.4 standard association mechanism, providing the theoretical basis necessary to improve the handover process. This study showed that it is necessary to take a step forward in the proposals for IEEE 802.15.4 association mechanisms.
# Ferroelectric Materials: A Novel Pathway for Efficient Solar Water Splitting
## 4 Ferroelectric Materials for Photoelectrochemical (PEC) Water-Splitting Devices
### (4) PbTiO\({}_{3}\)
#### Quaternary Metal Oxides  
Quaternary metal oxides, especially double perovskite materials featuring ferroelectric behavior, have been identified as promising candidates for solar energy to electricity conversion because of their efficient charge separation, which results from suitable control of the polarization-induced internal electric field [103; 104]. Bi\({}_{2}\)FeCrO\({}_{6}\) (BFCO) exhibiting multiferroic properties plays a role as a photocathode with a narrow bandgap (1.9-2.1 eV), and an appropriate conduction band position, which shows a twofold increase of the photocurrent density after negative poling, as presented in Figure 8 [105]. Recently, [PERSON] et al. performed many studies on Pb(Zr,Ti)O\({}_{3}\) (PZT) and revealed that ferroelectric PZT films deposited on indium tin oxide (ITO)-coated quartz glass [106; 107] along with PZT films decorated with Ag nanoparticles on an ITO coated Si-pn\({}^{+}\) junction [108] can function as stable and effective photocathodes for water splitting. More importantly, polarization switching can drive the photogenerated electrons transfer process, which then results in significantly enhanced PEC activity.
# A Study of Forest Phenology Prediction Based on GRU Models
## 3 Results and Analysis
### Model Performance Evaluation  
In order to better validate the accuracy of the proposed model and evaluate the goodness of fit of the model within the sample, performance evaluation was conducted on the GRU neural network model. The adaptive moment estimation method was used for optimization, and the loss function was evaluated using the MSE, RMSE, and MAE methods. The results of the GRU model for the three preset points are compared in Table 1.  
Model validation is an important step to evaluate the performance of a system by comparing actual data with predicted data. As shown in Table 1, the MSE of preset point 1 is 9.055 \(\times\)\(10^{-5}\), RMSE is 9.516 \(\times\)\(10^{-3}\), and MAE is 7.174 \(\times\)\(10^{-3}\); the MSE of preset point 3 is 12.91 \(\times\)\(10^{-5}\), RMSE is 11.36 \(\times\)\(10^{-3}\), and MAE is 8.241 \(\times\)\(10^{-3}\); the MSE of preset point 5 is 8.241 \(\times\)\(10^{-5}\), RMSE is 7.313 \(\times\)\(10^{-3}\), and MAE is 5.351 \(\times\)\(10^{-3}\). The high goodness-of-fit between the predicted values and the real values indicates that the GRU model can achieve accurate predictions. The MAE and RMSE are absolute errors and are not affected by the size of GEI. The small differences in performance indicators between the GRU model and the comparative evaluation suggest that the GRU model can achieve precise predictions.
# Design of a Heterogeneous-Based Network Intrusion Detection System and Compiler
## 3 Materials and Methods
### Design of CPU-Based Intrusion Detection and Rule Compilation
#### 3.3.2 Snort Module  
The Snort module, composed of the Snort system and PCIe drivers, is responsible for receiving data packets and rule IDs transmitted via PCIe, performing operations such as data integration, reassembly, and storage. Through preprocessing plugins, the data and rule IDs are fed into the Snort system for comprehensive matching detection. The rule set used for compilation in this system is identical to that used for offloading, and rule ID indexing ensures matching accuracy and consistency. Deploying the Snort system as the final detection layer is driven by two reasons: first, Snort rule matching requires not only hits on five-tuple and fast-pattern fields but also verification of other complex fields in the rules, necessitating the software Snort's capability to handle complex rule matching; second, Snort's high accuracy ensures the overall system detection precision, avoiding false positives. Since the data volume received via PCIe accounts for only 8-10% of the total traffic (approximately 8-10 Gbps), which is significantly below the CPU's load threshold, Snort efficiently performs final alerting, logging, traffic statistics, and returns abnormal packet IDs for problematic session termination under this lightweight scenario.
## 3 The Proposed Network Model  
The model is set as a two-line operation on visible light image (RGB) and corresponding infrared image (IR). As shown in Figure 5, it is composed of a feature extraction layer, an information extraction block and a reconstruction block. The feature extraction layer extracts the shallow features of the original low-resolution image and the visible light image by two 3 \(\times\) 3 convolution operations followed by activation functions. The information extraction block introduces the feature mapping attention mechanism in the RGB line to extract the important feature information from the visible light image and transfer it between channels to supplement the missing information of IR. The dense connection module is introduced in the IR line to extract image features, which can more fully extract IR edge, texture and other information. Finally, the extracted RGB and IR feature information are fused in the reconstruction block, and the global feature information is integrated to complete the IR reconstruction.
## 4 Proposed Methodology: Dynamic Context-Aware Transformer (DCAT)
### System Model
#### 4.1.1 ViT Encoder in DCAT  
In the DCAT model, the Vision Transformer (ViT) serves as the encoder, transforming the visual content of images into a format that can be effectively processed for caption generation. ViT's unique approach to image processing, which treats images as sequences of patches, enables it to capture a detailed understanding of both local features and the broader context of the visual content.  
\[I_{\text{patches}} =\text{Split}(I,P) \tag{5}\] \[X_{\text{embed}} =\text{Embed}(I_{\text{patches}})+E_{\text{pos}}\] (6) \[X_{\text{transformed}} =\text{ViT}(X_{\text{embed}}) \tag{7}\]  
These equations represent the process of splitting the image into patches, embedding them, and then processing through the ViT's self-attention mechanism, allowing the model to understand and encode the visual information thoroughly.