esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Fusion and Multimodal Integration
### Multimodal Data Representation  
#### Approaches for Representing Data from Different Modalities  
Representing multimodal data involves integrating information from various sources or modalities into a unified format. This cohesive representation allows models to learn complex relationships and patterns across data types. Several common approaches are used to represent multimodal data [40]:  
* **Feature concatenation**: This approach combines the features from different modalities into a single vector, which the model then processes. It is particularly effective when the features from various modalities are similar in size and scale.
* **Canonical correlation analysis (CCA)**: CCA is a statistical technique that investigates the relationship between two sets of variables. It identifies highly correlated linear combinations of features from each modality [41]. This method is ideal when the different modalities provide complementary information, helping to align them effectively. \[\mathbf{X}_{1}\cdot\mathbf{a}_{1}=\mathbf{X}_{2}\cdot\mathbf{a}_{2}\] (11) where \(\mathbf{X}_{1}\) and \(\mathbf{X}_{2}\) are data matrices from the two modalities, and \(\mathbf{a}_{1}\) and \(\mathbf{a}_{2}\) are the corresponding weight vectors.
* **Multimodal autoencoders**: These specialized neural networks are designed to compress data from different modalities into a lower-dimensional space [42]. Each modality is processed via a separate encoder, and the encoded outputs are combined before decoding. This allows the model to learn a shared latent representation of the multimodal data.  
\[\mathbf{z}_{\text{fusion}}=f_{\text{fusion}}(\mathbf{z}_{1},\mathbf{z}_{2}, \ldots,\mathbf{z}_{m}) \tag{12}\]  
where \(\mathbf{z}_{i}\) represents the encoded features from modality \(i\), and \(\mathbf{z}_{\text{fusion}}\) is the resulting fused representation.
# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
## 4 Simulations and Results
### Experiment 1: Spoofing Extraction Effects Based on the Double-Difference Carrier Phase
In order to achieve effective deception, the spoofing interference source needs to transmit deception signals for a long time so it can appropriately increase the number requirement of observation points that meet the above conditions (for example, the double-differenced carrier phase observations of 10 consecutive observation points are less than the set threshold), or meet the continuous observation distance requirement while meeting the continuous observation points requirement (for example, the double-differenced carrier phase observations of 100 m consecutive observation distance are less than the set threshold), enabling the method to achieve deception interference confirmation over a large observation range, so as to eliminate or weaken the influence of multi-path on the effectiveness of the proposed method to a certain extent.
# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 3 Material and Method
### Online (Learning-and-Test) Phase
Instead of applying a strict accuracy threshold, the best-performing model from each category was chosen, based on its ability to achieve the highest accuracy while maintaining computational efficiency. This approach ensured that the most effective algorithms, based on their predictive accuracy, were included in the subsequent analysis while less performant models were excluded. Figure 7: Online phase (Time Dif corresponds to room-to-room transition time,? represents the unknown target room). Among the supervised learning algorithms, Fine Tree (a variant of Decision Tree Classifiers), Efficient Logistic Regression, Weighted K-Nearest Neighbors (k-NN), Linear Support Vector Machine (SVM), Wide Neural Network, and Ensemble Bagged Trees yielded the most promising results and were subsequently compared across both datasets. In the following subsections, each method is described according to its characteristics.
## 1 Introduction
Despite the prospects of the involvement of the satellite component in the provision of IoRT services in 5G networks, there are still reservations like issues of long propagation delay, power consumption, inter-operation of terrestrial and satellite segments, channel model etc. which have been raised as issues affecting satellite systems. However, recent advances in satellite communication technology have dispelled most of these misconceptions on the usage of satellite systems. Most of these issues have been addressed using new techniques or/and from architectural solutions. Satellite networks are nowadays capable of providing 0.9999 availability [21; 22]. Nevertheless, as acknowledged in [21], in order to achieve an effective and reliable IoRT communications via satellite, just as the case for terrestrial networks, several issues are still open and need solutions. Some of these issues are but not limited to Radio Interference and Resource Management (RIRM) functions including spectrum management for IoRT devices in future 5G satellite networks as identified in [21]. An overview of the challenges of spectrum management for wireless IoT networks is presented and a regulatory framework is recommended in [23], the Narrowband IoT (NB-IoT) spectrum management system, which is a narrowband system based on LTE, is introduced for M2M communications in [24], while [25] investigates the effect of NB-IoT on existing LTE cellular network. While there are some notable works done on spectrum management in 4G and emerging 5G terrestrial networks, to the best of the authors' knowledge, no spectrum management investigation or framework have been proposed in the literature for IoRT in 5G satellite networks. The peculiar nature of satellite networks and IoRT, as compared to terrestrial and general IoT devices, compels the need for a study of spectrum management in this scenario. It is on this premise that the focus of this research study is on spectrum management for IoRT devices in 5G satellite networks. A new scheduling scheme is also introduced in order to investigate the considered spectrum management schemes. The remainder of this paper is organized as follows. Section 2 presents the system description. The considered spectrum management schemes to be investigated are presented in Section 3. Section 4 presents a new scheduling scheme and existing one that are used for the investigation. The simulation model and numerical results are presented in Sections 5 and 6, respectively. Section 7 concludes this paper.
# Structural Correlation Based Method for Image Forgery Classification and Localization
## 6 Experimental Results
### Image Retrieval Results  
To evaluate the performance of the proposed image forgery clustering algorithm, we carry out the experiments to estimate \(MAP\) of image retrieval in 3 different scenarios related to cluster formation of Algorithm 1. In the first case, only one query is performed to compose the cluster. In the second case, the second query is performed to augment the retrieved results. In the third case, the cluster refinement using structural correlation of Algorithm 1 is conducted after two queries to form the image forgery cluster. We denote these cases by case \(A\), case \(B\), and case \(C\), respectively. Figure 6 shows the retrieval performance of 3 above-mentioned cases in 3 datasets. It is clear that \(MAP\) significantly increases from case \(A\) to case \(C\) in all 3 datasets to prove the efficiency of the proposed image retrieval based clustering algorithm.  
We present the average ratios that the host image of the query is retrieved in 3 cases in Table 1. The results ensure that by using image forgery clustering algorithm, we can generally retrieve the host images of query images into the clusters.
## 3 Methodology
### Algorithm Description
3:Split the \(DTS\) in to \(DTS_{TrainingData}\) and \(DTS_{TestingData}\)
4:for each layer of \(DNNGRU\)do
5:\(DTS^{\prime}_{Train}\) = Pre-processing of \(DTS_{TrainingData}\)
6:\(DNNGRU_{Training}\) = Train the model using \(DTS^{\prime}_{Train}\)
7:\(Z=Bias+W_{1}X_{1}+W_{2}X_{2}+W_{3}X_{3}+.......+W_{n}X_{n}\)
8:\(U_{t}=(W^{z}+X_{t}+U^{z}h_{t-1}+B_{z})\)
9:\(r_{t}=\sigma(W^{(r)}+X_{t}+U^{(r)}h_{t-1})\)
10:\(h^{\prime}_{t}=\tanh(WX_{t}+r_{t}\odot(U)h_{t-1})\)
11:\(h_{t}=Z_{t}\odot h_{t-1}+(1-Z_{t})\odot h^{\prime}_{t}\)
12:endfor
13:\(DTS^{\prime}_{Test}\) = Pre-processing of \(DTS_{TestingData}\)
14:\(DNNGRU_{Testing}\) = Test the model using \(DTS^{\prime}_{Test}\)
15:while True do
16:\(PredictAttack\to DNNGRU_{TModel}(DTS^{\prime}_{Test})\)
17:if the value predicted = 0 then
18: Return Benign
19:else
20: Return attack type
21:endif
22:endwhile
```  
**Algorithm 1** Proposed hybrid detection framework.
# Medical Named Entity Recognition Fusing Part-of-Speech and Stroke Features
## 5 Conclusions  
In this study, an NER method fusing part-of-speech and stroke features is designed for medical text. In the embedding layer, the part-of-speech data and stroke data of words are embedded as multiple features to distinguish words with different semantics, and the features of stroke information are extracted by CNN using two convolution kernels with different sizes. After that, the part-of-speech and stroke feature vectors are spliced with the word vector, respectively, and input into the BiLSTM and CRF layer for training, wherein the word vectors are obtained by the BERT pre-training model. From the experimental data, it can be seen that using the BERT pre-training model to obtain word vectors makes the entity recognition effect better because BERT is a depth model with high semantic information extraction capabilities that was trained using a vast amount of unlabeled data. Moreover, using the BiLSTM layer can extract features better than not using it, which makes the results better. The embedding layer uses BERT pre-training models, the encoder uses BiLSTM, and the decoder uses CRF, which shows better performance than other combinations. The part-of-speech and stroke features used in the model suggested in this paper, as part of the word features, make it simpler to distinguish between words with different semantics while also making it easier to identify similar words. This makes it possible to more accurately identify the boundaries of long medical entities and improves the identification effect of medical entities, providing better services for subsequent tasks such as building medical knowledge maps. Additionally, the difference in data volume between various categories of samples is balanced by adding a class-weighted loss function.  
Our research is limited in its ability to process long sentences since the BERT processing sequence is limited to a certain length. Additionally, because our model processes input text using a character-based approach, text features lack word information. In further studies, we will strengthen our model, to address these issues and investigate the impact of additional semantic elements and labeling strategies on boundary word recognition.  
Conceptualization, F.Y. and H.L.; methodology, F.Y.; validation, Y.W., S.W., C.S. and P.F.; investigation, J.Z.; writing--original draft, F.Y.; writing--review and editing, F.Y. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hunan Province (2021)J30456, 2021)J30734), the Open Research Project of the State Key Laboratory of Industrial Control Technology (No. ICT2022B60), and the National Defense Science and Technology Key Laboratory Fund Project (2021-KJWPDL-17), the National Natural Science Foundation of China (61972055).  
Not applicable.  
The authors declare no conflict of interest.
# An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction
## 2 Preliminary
### Radial Basis Function  
Given \(N_{t}\) neuron centers \(\mathbf{c}^{(l)}=\left[c_{1}^{(l)},c_{2}^{(l)},\ldots,c_{m}^{(l)}\right]\) with the associated responses \(y^{(l)},l=1,2,\ldots,N_{t}\), Radial Basis Function (RBF) can be represented as [8]  
\[\hat{y}_{RBF}=\sum\limits_{l=1}^{N_{t}}\alpha_{l}\phi\left(\mathbf{x},\mathbf{c }^{(l)}\right), \tag{10}\]where \(\alpha_{l},l=1,2,\ldots,N_{t}\) is the output layer weights. \(\mathbf{x}\in\mathbb{R}^{m}\) is the input vector with the \(j\)th element denoted as \(x_{j}\). \(\phi(\cdot)\) is the basis function with respect to the radial distance \(r\left(\mathbf{x},\mathbf{c}^{(l)}\right)\) between \(\mathbf{x}\) and \(\mathbf{c}^{(l)}\). In this study, multiquadric basis function is used, which is formulated as  
\[\phi\left(\mathbf{x},\mathbf{c}^{(l)}\right)=\sqrt{\left(r\left( \mathbf{x},\mathbf{c}^{(l)}\right)\right)^{2}+d^{2}} \tag{11}\] \[r\left(\mathbf{x},\mathbf{c}^{(l)}\right)=\left\|\mathbf{x}- \mathbf{c}^{(l)}\right\|=\left(\sum\limits_{j=1}^{m}\left(x_{j}-c_{j}^{(l)} \right)^{2}\right)^{\frac{1}{2}},\]  
where \(d\) is the shape parameter. In the RBF interpolation model, the neuron centers are the same as the training points, and therefore the RBF output at each center is the same as its known function value as  
\[\hat{y}\left(\mathbf{c}^{(l)}\right)=y^{(l)},l=1,2,\ldots,N_{t} \tag{12}\]
## 7 Challenges and Limitations
### Computational Demand  
[PERSON] et al. [22] highlighted that their hybrid CNN-LSTM model required substantial processing power, potentially limiting its application in resource-constrained environments such as small-scale recycling facilities.
# Experimental Evaluation of the Packet Reception Performance of LoRa
## 2 Related Work
Furthermore, given the SNR, it will comprehensively consider the impact of physical layer parameters and the packet length on the performance of LoRa to optimize the parameter configuration, thereby reducing the time and energy consumption of successful communication and ultimately achieving a balance of reliability, delay, and energy consumption.
# Controlled Fault Current Interruption Scheme for Improved Fault Prediction Accuracy
## 3 Validation of Half-Cycle Elimination Prediction Algorithm  
### Simulation Verification of Over-Zero Prediction Accuracy  
In order to test the prediction accuracy of the half-period elimination method, this paper firstly adopts the simulation method to test, and sets the basic parameters as \(\phi=40^{\circ}\), \(\alpha=110^{\circ}\), \(i(0)=1\), and \(I_{\text{pk}}=5\); the time constant is set to 45 ms, according to the standard time constant [22]. In order to simulate the actual working conditions, the sampling frequency is set as 10 kHz, 40 dB of noise are added, and the sampling time is set to 15 ms. Each test is simulated 10,000 times. The underlying expression can be represented by Equation (12).  
\[i_{\text{test}}(t)=5\cdot\sin(100\pi t+70^{\circ})+(1-5\cdot\sin(70^{\circ})) \cdot\text{e}^{-\frac{t}{0.085}} \tag{12}\]  
Considering the relay protection response time, opening time, and optimal arc burning time, the target zero-crossing point is set to be the sixth zero-crossing point after the occurrence  
The analytical simulation test results show that the half-cycle elimination method exhibits good prediction accuracy and reliability under different operating conditions. The prediction error for the sixth over-zero point after a fault is consistently controlled within 0.2 ms with the addition of 40 dB noise, a sampling frequency of 10 kHz or more, and a sampling time of more than 15 ms.  
1. The effect of the time constant on prediction error: The test shows that the prediction error increases gradually with the increase in time constant, but the error remains within 0.2 ms in the range of 100 ms.
2. Sampling frequency on prediction accuracy: Sampling frequency significantly affects the prediction accuracy. When the frequency is increased from 10 kHz to higher, the error is further reduced, and the improvement in accuracy tends to level off above  
Figure 4: Zero prediction error versus sampling frequency.  
Figure 5: Zero point prediction error versus fault phase angle.  
Figure 6: Zero prediction error versus sampling time.  
40 kHz, which verifies the positive effect of high frequency sampling on the accuracy of the algorithm.
3. Influence of fault phase angle: The test covers a range of fault phase angles from 1\({}^{\circ}\) to 360\({}^{\circ}\), and the prediction errors fluctuate at specific phase angles; however, they are all within 0.2 ms, which indicates that the algorithm has a strong adaptability in dealing with different phase angles.
4. Sampling time constraints: Too short a sampling time affects the algorithm's accuracy, but when the sampling time exceeds 17 ms, the error stabilizes and is sufficient to meet the needs of engineering applications at 15 ms.
# Deep Learning for Laryngopharyngeal Reflux Diagnosis
## 3 Results
### Transfer Learning  
The transfer learning techniques was leveraged to enhance the performance of the backbone network ResNet18 and ResNet18 CBAM. The backbone network was pretrained on the image classification task with the large ImageNet dataset. Then we fine-tuned it on our laryngeal image dataset in two distinct ways: (1) The first residual block was frozen as the feature extractor and learned the weights of following layers with relatively small learning rate \(10^{-4}\)(denoted as ResNet18 + freeze and ResNet18 CBAM + freeze). (2) All the weights of the entire network were modified with learning rate \(10^{-4}\)(denoted as ResNet18 + finetune and ResNet18 CBAM + finetune).  
In this setting, six models were trained: ResNet18, ResNet18 CBAM and their freeze or finetune versions. Except for the learning rate, all hyperparameters were set in the same way as the previous backbone network experiments. The results of accuracy and AUC  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy** & **AUC Value** \\ \hline ResNet18 & 0.688 \(\pm\) 0.089 & 0.704 \(\pm\) 0.095 \\ ResNet18\_SE & 0.697 \(\pm\) 0.086 & 0.715 \(\pm\) 0.123 \\ ResNet18\_CBAM & **0.729 \(\pm\) 0.050** & **0.739 \(\pm\) 0.068** \\ \hline ResNet50 & 0.707 \(\pm\) 0.126 & **0.719 \(\pm\) 0.117** \\ ResNet50\_SE & 0.659 \(\pm\) 0.088 & 0.680 \(\pm\) 0.116 \\ ResNet50\_CBAM & **0.734 \(\pm\) 0.101** & 0.719 \(\pm\) 0.132 \\ \hline ResNet101 & 0.672 \(\pm\) 0.108 & 0.678 \(\pm\) 0.115 \\ ResNet101\_SE & 0.656 \(\pm\) 0.074 & 0.682 \(\pm\) 0.103 \\ ResNet101\_CBAM & **0.679 \(\pm\) 0.110** & **0.691 \(\pm\) 0.096** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test performance of the backbone models (mean \(\pm\) SD).  
Figure 3: Training and testing accuracies of networks. The curves were obtained by averaging the accuracies of three times of five-fold cross validations. (**a**) Networks with different depths. (**b**) Networks with different modules.  
value are shown in Table 3. The ResNet18 + freeze model yielded a higher accuracy and AUC value than its original ResNet18. However, both freeze and finetune variants could not improve the performance of ResNet18 CBAM, as seen with the training procedure shown in Figure 4.
# Diffuse Optical Tomography Using Bayesian Filtering in the Human Brain
## 2 Material and Methods
### DOT and fMRI Statistics  
A new approach for treating DOT volumes as if they were fMRI volumes was used, using the canonical SPM 8 software to obtain cerebral activation maps, as is performed with fMRI images [21]. fMRI and DOT volumes were both filtered to improve the signal to noise ratio, while using a high pass filter based on a discrete cosine transform, with a 64s cut-off period. The regressors were convolved with the canonical HRF functions, according to the design matrix. After estimation, cerebral activation maps were generated by applying a fixed effect model analysis. The cerebral activation maps that were provided by DOT data processing were compared to the cerebral activation maps generated by the fMRI processing method based on the standard General Linear Model (GLM) [22].
## 5 Analysis of Selected Design Variants of the Considered Structure
### Solution III: Cross-Shaped Steel Braces  
Solution III employs cross-shaped steel bracing to enhance the structural stability of the box-type substation, utilizing ST12 material, as depicted in Figure 25c.  
The simulation results, as shown in Table 12, lead to the following conclusions:  
1. Deformation Analysis: The deformation in the Y direction shows little variation across all wall thicknesses, indicating the good overall rigidity of the structure in this direction. In the Z direction, despite the highest stress at the 1.5 mm thickness, its deformation is slightly lower than that of the 2.5 mm and 2.0 mm thicknesses. This suggests that, despite the higher stress, the cross-shaped steel supports still help to control the deformation of the thinner wall panels to some extent.
2. Stress Analysis: Across all thicknesses, the stress in the Z direction increases as the wall thickness decreases. The 2.5 mm thickness exhibits the lowest stress, while the 1.5 mm thickness shows the highest stress. This suggests that the cross-shaped steel supports are less effective at countering stress in the Z direction for thinner walls. The stress variations in the X and Y directions are minimal, indicating that the reinforcement effect of the cross-shaped steel supports is relatively uniform across these directions.  
From the simulation results after adjusting the structure, the stress contour plot for the 2.5 mm-thickness scheme is shown in Figure 26, revealing a significant alleviation of the deformation concentration phenomenon previously observed in the side panels, and a noticeable reduction in stress concentration points at the junction of the side panels and supports. Similar to the stress contour plots for the 2.0 mm- and 1.5 mm-thickness schemes, this demonstrates the universal applicability and effectiveness of the cross-shaped steel bracing solution across box-type substation structures with different thicknesses.