esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
As shown in Figure 2, our network is divided into three parts: the backbone, encoder, and decoder. Modules composed of multi-scale convolutional attention (MSCA) and a feed-forward network (FFN) are used to extract semantic information as the backbone. The encoder, consisting of multiple Spatial Extraction Modules (SEMs), takes semantic information as input, further extracts spatial information, and fuses it with the semantic information. In the encoder, our work adopts the concept of MaskFormer [10], applying a method previously used for instance segmentation to semantic segmentation. We utilize a dual-branch architecture, where one branch learns masks and the other learns an instance activation map, applying multiple loss functions to constrain each branch separately. However, to adapt it for semantic segmentation, we modified the inference steps, resulting in improved performance.
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 3 Simulation Platform Design
### Upstream Network
#### 3.1.2 Background Voltage Distortion  
Voltage distortion, a common PQD, occurs due to nonlinear devices causing nonsinusoidal voltage drops while maintaining periodicity. These distortions are evaluated by using harmonic components through Fourier analysis. Nonlinear devices are present at all voltage levels of the power system, thus leading to an expected level of background voltage distortion transferred from the upstream network.  
In LV networks, background harmonic distortion from the medium-voltage (MV) network is modeled using a constant harmonic voltage source (Thevenin voltage), as detailed in Table 3 for the LV side of distribution transformers in Germany [19]. Because the delta winding of MV/LV transformers blocks zero-sequence components, and due to limited measurement data, only the most significant harmonics with dominant positive or negative sequences (5 th, 7 th, 11 th, and 13 th harmonics) are considered. Higher-order harmonics are excluded from the analysis because they have smaller magnitudes and inconsistent measurement availability in distribution networks.  
While specific data on background voltage distortion in industrial LV networks are scarce, data from public LV networks, as reported in Table 3, are used to model the background distortion. This approach is justified because industrial LV networks are often connected to public LV networks. Consequently, data from public networks can reasonably represent the background voltage distortion experienced in industrial settings.
## 2 Materials and Methods
### Methodology  
#### 2.3.1 Construction of VMFDI  
The VMFDI was established based on a modified three-dimensional spatial distance model. The spatial distance model, which uses Euclidean distance to measure the absolute distance between points in multidimensional space, has been widely applied in remote sensing for various environmental assessments, including drought monitoring and ecological studies [36; 37; 38]. The VMFDI was constructed using the following steps:  
Data preprocessing: First, outliers in the variables VPD, SM, and GOSIF were removed using the cumulative probability dichotomy method [38]. Then, these variables were normalized to a range of 0-1 to address the differences in their value scales (Equation (1)).  
\[\mathrm{NVar(i,j)}=\frac{\mathrm{Var(i,j)-Var(i)_{min}}}{\mathrm{Var(i)_{max}- Var(i)_{min}}} \tag{1}\]  
where \(\mathrm{NVar(i,j)}\) represents the normalized value of the \(\mathrm{j^{th}}\) month for the \(\mathrm{i^{th}}\) variable (VPD, SM, or SIF). \(\mathrm{Var(i,j)}\) represents the original value, and \(\mathrm{Var(i)_{min}}\) and \(\mathrm{Var(i)_{max}}\) are the minimum and maximum values of the \(\mathrm{i^{th}}\) variable.  
Spatial distance model: The VMFDI was calculated using a three-dimensional spatial distance model, where the variables VPD, SM, and SIF were set as axes X, Y, and Z, respectively (Figure 3). The driest point D (1, 0, 0), representing the highest VPD and the lowest SM and SIF, served as the reference point, while the wettest point W (0, 1, 1) represents the lowest VPD and the highest SM and SIF. The dry-wet line connecting points D and W defined the range of drought conditions.  
Figure 3: The concept of the VMFDI in a three-dimensional space model. A principle map of the VMFDI. The reference point D (1, 0, 0) is the driest point, where the value of the VMFDI is 0. Point W (0, 1, 1) is the wettest point, where the value of the VMFDI is \(\sqrt{3}\).  
VMFDI calculation: The Euclidean distance from any point in the hyperspace to point D (\(1,0,0\)) was calculated, with the distance representing the relative drought value. The formula for the VMFDI is as follows:  
\[\text{VMFDI}=\sqrt{\left(\text{NVPDM}_{\text{Max}}-\text{NVPD}\right)^{2}+ \left(\text{NSM}-\text{NSM}_{\text{Min}}\right)^{2}+\left(\text{NSIF}-\text{ NSIF}_{\text{Min}}\right)^{2}} \tag{2}\]  
where NVPD, NSM, and NSIF represent the normalized values of the VPD, SM, and SIF, respectively. \(\text{NVPD}_{\text{max}}\) refers to the maximum normalized values of VPD, while \(\text{NSM}_{\text{min}}\) and \(\text{NSIF}_{\text{min}}\) refer to the minimum normalized values of SM, and SIF, respectively. The VMFDI ranges from \(0\) to \(\sqrt{3}\), with higher values indicating wetter conditions and lower values indicating drier conditions.
## 3 Proposed Methodology
### Proposed Architecture Using GloVe and DL Classifiers  
In this section, we presented our proposed architecture using GloVe embeddings of varying dimensions (50d, 100d, 200d, and 300d) with BiLSTM, 1D CNN, and MLP for offensive language detection (OLD), as illustrated in Figure 6. The implementation follows Algorithms 2-4. In the case of the 1D CNN classifier seven-layer architecture, the first layer is the GloVe embedding layer, followed by two convolutional layers with ReLU as activation function, one dense layer with ReLU as activation, and a dropout layer with a rate of 0.1. The final layer is a dense layer with softmax as an activation layer. In the case of BiLSTM, we have used the dense layer (activation function = ReLU), the dropout rate is 0.5, and the classification layer uses softmax as an activation function. Similarly, in the case of the MLP, we have used an embedding layer, a flatten layer, two dense layers (one has ReLU as an activation function and the other has softmax), and a dropout layer with a rate of 0.2.  
```
Input: Preprocessed data Loop for \(n\) epochs:
1. Load Glove Embeddings
2. for embedding_dimension in [50; 100; 200; 300]: (a) Add embedding layer with Glove embeddings (dimension = embedding_dimension ) (b) Add encoder layer (c) Add bidirectional LSTM layer (d) Add global max pooling layer (e) Add batch normalization layer (f) Add dropout layer (rate = 0.5) (g) Add dense layer (activation function = ReLU) (h) Add dropout layer (rate = 0.5) (i) Add dense layer (activation function = ReLU) (j) Add dropout layer (rate = 0.5) (k) Add dense layer (activation function = softmax) 3. Train the model
4. Evaluate the model
5. Evaluate the model Output: Classification results (hate, offensive, neither hate nor offensive)
```  
**Algorithm 2** Hate Speech Classification Using GloVe Embedding with BiLSTM.  
```
Input: Preprocessed data Loop for \(n\) epochs:
1. Load Glove Embeddings
2. for embedding_dimension in [50; 100; 200; 300]: (a) Add embedding layer with Glove embeddings (dimension = dimension = embedding_dimension ) (b) Add Conv1D (activation = relu) (c) Add Conv1D (activation = relu) (d) Add Global Max Pooling layer (e) Add dense layer (activation function = ReLU) (f) Add dropout layer (rate = 0.1) (g) Add dense layer (activation function = softmax) 3. Train the model
4. Evaluate the model Output: Classification results (hate, offensive, neither hate nor offensive)
```  
**Algorithm 3** Hate Speech Classification Using GloVe Embedding with 1D Convolution Layer.  
```
Input: Preprocessed data Loop for \(n\) epochs:
1. Load Glove Embeddings
2. for embedding_dimension in [50; 100; 200; 300]: (a) Add embedding layer with Glove embeddings (dimension = embedding_dimension ) (b) Add dropout layer (rate = 0.2) (e) Add dense layer (activation function = softmax) 3. Train the model
4. Evaluate the model Output: Classification results (hate, offensive, neither hate nor offensive)
```  
**Algorithm 4** Hate Speech Classification Using GloVe Embedding with MLP.
## 5 Conclusions  
We proposed a self-supervised learning-based method that can train a denoising neural network for LDCT without paired images. The proposed method uses Bernoulli sampling to generate degraded versions of the projection images and reconstruct the 3D image, and the denoising neural network is trained to restore the image dropped out by Bernoulli sampling in the projection image domain. The proposed method can mitigate the over-smoothing problem in conventional methods with small data. To verify the performance of the proposed method, we compared its performance with various counterpart methods, including deep learning-based methods (\(N2V\), \(N2S\), and \(S2S\)) and non-learning methods  
Figure 6: Artifacts in the results of the counterpart deep learning-based methods. All images are in the coronal view. Red boxes in the images indicate the expanded regions.  
Figure 7: Peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) versus the number of iterations. Both the PSNR and SSIM increased with the number of iterations and gradually converged.  
(bilateral filtering, _TV_, and _NLM_). Quantitatively, the results of our experiments showed that our proposed method outperformed the counterpart denoising methods by at least 4.47 dB in terms of PSNR value on the SPARE challenge dataset and the in-house lithium polymer dataset. In particular, we solved the problem of generating artifacts and the over-smoothing when applying the denoising method in the projection domain. This method can be applied to various tomographic systems in medical and industrial areas.  
Conceptualization, Y.-J.H.; methodology, Y.-J.H.; software, Y.-J.H.; validation, Y.-J.H.; formal analysis, Y.-J.H.; investigation, Y.-J.H.; resources, Y.-J.H.; data curation, Y.-J.H.; writing--original draft preparation, Y.-J.H.; writing--review and editing, H.-J.Y.; visualization, Y.-J.H.; supervision, H.-J.Y.; project administration, H.-J.Y.; funding acquisition, H.-J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT, and Future Planning (2020R1A2C1007081).  
Not applicable.  
The authors declare no conflict of interest.
# Zebrafish Larval Melanophores Respond to Electromagnetic Fields Exposure
## 4 Discussion
### Static Magnetic Field  
Zebrafish embryos/larvae exposed continually to the static magnetic field (40 mT) did not show increased mortality but showed a slight delay in development and a reduction in swimming activity and circling motion. In addition, there was slight evidence of malformations in the spine development, and thus, curved larvae could appear. Pericardial oedemas were also evident in developing larvae [5]. Moreover, treated zebrafish larvae did not show any differences in the developing melanophores, one of the three pigment cell types responsible for zebrafish stripes [16]. These data agree well with those of [PERSON] et al. [10] on zebrafish embryos exposed from fertilization to 24 hpf to a 9 T static magnetic field, which resulted in neither lethal nor teratogenic effects. However, the static magnetic field delayed the developmental pace of the whole animal, as indicated by slower hatching, pharyngeal development, and body growth, altered gene expression, and worse performance in behavior tests compared to control. Whether the development-delaying effect is caused by the interference of the static magnetic field in microtubule and spindle positioning during mitosis, especially in early cleavages [10], remains an open question. Shorter exposures (e.g., 2 h) associated with stronger static magnetic fields (e.g., 14 T)--as requested in magnetic resonance imaging (MRI)--results in stronger biological effects (e.g., otolith fusion), suggesting a possible role of magnetic field intensities in the manifestation of the biological effect [17].
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 4 Sensitivity Analysis of Smoothing Probability
### Observation Parameter Variation  
The sensitivity function for the backward probability \(p(\mathbf{y}_{\mathbf{e}}^{t+1:T}|x_{v}^{t})(\theta_{o})\) for the observation parameter variation \(\theta_{o}=o_{r,s}\) can be represented as follows:  
\[p(\mathbf{y}_{\mathbf{e}}^{t+1:T}|x_{v}^{t})(\theta_{o})=AO^{t+1}(\theta_{o}) p(\mathbf{y}_{\mathbf{e}}^{t+2:T}|x_{z}^{t+1})(\theta_{o})\]  
For \(t=T-1\), \(p(\mathbf{y}_{\mathbf{e}}^{T+1:T}|x_{z}^{T})(\theta_{o})=1\), the recursive probability expression reduces to the following  
\[p(\mathbf{y}_{\mathbf{e}}^{T:T}|x_{v}^{T-1})(\theta_{o})=AO^{t+1}(\theta_{o})\]The sensitivity analysis of the backward probability sensitivity function \(p(\mathbf{y}_{\mathbf{e}}^{t+1:T}|x_{v}^{t})(\theta_{o})\) for observation parameter variation \(\theta_{o}=\sigma_{r,s}\) is summarized in Algorithm 6.  
```
0:\(A,O,\Gamma,\mathbf{e}\), and \(\theta_{o}\)
0:\(B^{T},\cdots,B^{t}\)
1:\(\tilde{O}\gets O\)
2:\(\tilde{O}_{r,s}\gets 0\)
3:\(\tilde{O}_{r,i}\leftarrow\tilde{O}_{r,i}\cdot/\left(1-\sigma_{r,s}\right)\)
4:\(\tilde{O}\gets O-\tilde{O}\)
5:\(\tilde{O}_{r,i}\cdot/\sigma_{r,s}\)
6:\(B^{T}\gets ones(n,1)\)
7:for\(k=T-1\) to \(t\)do
8:\(\tilde{O}^{k+1}\gets diag(\tilde{O}_{r,\mathbf{e}+1})\)
9:\(\tilde{O}^{k+1}\gets diag(\tilde{O}_{r,\mathbf{e}+1})\)
10:\(Btmp_{1}\leftarrow\left[A*\tilde{O}^{k+1}*B^{k+1},zeros(n,1)\right]\)
11:\(Btmp_{2}\leftarrow\left[zeros(n,1),A*\tilde{O}^{k+1}*B^{k+1}\right]\)
12:\(B^{k}\gets Btmp_{1}+Btmp_{2}\)
13:Return\(B^{T},\cdots,B^{t}\)
```  
**Algorithm 6** This computes the coefficients of the backward probability sensitivity function \(p(\mathbf{y}_{\mathbf{e}}^{t+1:T}|x_{v}^{t})(\theta_{o})\) with \(\theta_{o}=\sigma_{r,s}\) in backward matrices \(B^{k}\) for \(k=T,\cdots,t\) using SMF.
## 4 Hybrid Algorithm Design
### Encoding Method  
The encoding of the solution plays a crucial role in addressing the multi-constraint LRP, as it significantly impacts the algorithm's complexity and effectiveness. This study employs integer coding to depict individuals (feasible solutions), with each individual encompassing one or multiple distribution routes. Each route comprises a set of waypoints and a distribution center, indicating that a distribution vehicle initiates from the distribution center and fulfills the distribution task in accordance with the sequence of waypoints.  
For instance, in the context of the location-routing problem (LRP) involving four candidate distribution centers and ten cabins, the provided example illustrates a feasible solution code for the problem. This solution comprises three distribution routes. The vehicles in route 1 initiate from distribution center 12, carry out the distribution to cabins 6-3-2, and subsequently return to distribution center 12. Notably, distribution center 13 is absent from the feasible solution code, suggesting that it has not been chosen (each vehicle can only serve one distribution center, so three vehicles are needed in Figure 6. When the number of delivery locations increases, there will be a situation where one vehicle serves multiple routes).
## 5 Results and Discussion  
### PSVM Training and Test Results  
The PSVM training and test results are shown in Figure 5a,b, respectively. It can be found that the measured and estimated \(N_{u}\) values obtained from PSVM in the training and test sets agree with each other. The excellent prediction performance of PSVM shows that the SVM model, which adopts PSO algorithm to seek the optimal parameter combination, can capture the complex nonlinear mapping between the seven input variables and ultimate axial capacity \(N_{u}\)[56]. The prediction effect of PSVM is quantitatively evaluated with five statistical indexes. The performance evaluation results of PSVM training and test are seen in Figure 6a,b. Obviously, \(R^{2}\), \(MAPE\), \(MAE\), and \(RMSE\) are close to 1 and \(CEI\) is 0 in both datasets, which indicates that the prediction accuracy of PSVM is relatively high. Moreover, the regions enclosed by five indexes corresponding to the PSVM training and test in the radar chart are almost coincidental, indicating that the trained PSVM model is reasonable.  
Figure 5: Measured and estimated \(N_{u}\) values obtained from PSVM in the (**a**) training set and (**b**) test set.
# A Low-Loss Circuit with High-Pass Low-Pass Broadband Flat Negative Group Delay Characteristics
## 2 Circuit Design and Analysis with High-Pass and Low-Pass Negative Group Delay
### High-Pass Flat Negative Group Delay Circuit
#### 2.1.4 High-Pass Negative Group Delay Circuits with Different \(L_{2}\) Values  
When the values of \(C_{1}\), \(R_{1}\), \(L_{1}\), \(R_{2}\), and \(L_{3}\) in the circuit remain unchanged, as shown in Table 1, and the \(L_{2}\) values are sequentially changed to 0.9 nH, 3.9 nH, and 5.9 nH, we observe the changes in the circuit transfer function and group delay. The circuit results in these three scenarios are shown in Table 5 and Figures 11 and 12. Through observation, it is found that, as the value of inductance \(L_{2}\) increases, the group delay of the high-pass circuit at 0 MHz decreases, but the difference is small. Changing the value of inductance \(L_{2}\) also has a small impact on the bandwidth of the flat negative group delay. At the same time, the insertion loss of the circuit also decreases with
### MCDM Methods for the Assessment of Safety and Sustainability Tasks  
Multi-criteria decision-making (MCDM) methods might be effective in addressing complex decision problems involving multiple objectives and multiple criteria. Various MCDM methods, such as AHP (Analytical Hierarchy Process) [22], COPRAS (COmplex PROportional Assessment) [23], TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution Elimination) [24], PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) [25], ARAS (Additive Ratio ASessment) [26] can be applied to sustainability and safety topics. For instance, [PERSON] et al. [27] employed interval TOPSIS to assess the sustainability of the insulation materials of the buildings. [PERSON] et al. [28] employed COPRAS and WASPAS [29] to assess sustainable decision making in the management of municipal residential building facilities. [PERSON] al. [30] applied WASP to identify the most sustainable solutions for the placement of 40 nearly-zero-energy buildings relative to each other. [PERSON] et al. [31] used SAW [32], COPRAS, TOPSIS, VIKOR [33], and MIVES to analyse the impact of different modern construction techniques on the sustainability of building structures in single-family houses. [PERSON] et al. [34] proposed an emotion recognition methodology for the evaluation of building renovation decisions by integrating BIM and the COPRAS multicriteria evaluation method.  
Criteria identification is a fundamental task in any MCDM approach. However, it becomes particularly challenging in the context of multi-criteria tasks, where the identified criteria must be aligned with multiple objectives. Additionally, there is no particular MCDM method that can be used for all types of decision problems. Different MCDM approaches have different assumptions and requirements for the criteria structure, participation of decision makers, and the complexity of the calculations.  
State-of-the-art analysis revealed that there is a lack of studies investigating safety and sustainability aspects of interior staircases installed in single-family houses. This multi-objective task requires careful consideration of the criteria affecting stair safety and sustainability, as well as the appropriately chosen multicriteria decision-making approaches. While selection of a single MCDM method is full of uncertainties, multiple MCDM methods can be applied to improve the accuracy and resilience of the decision-making results [35]. However, it is difficult to decide which of them is most suitable for the research task without additional calculations.
## 3 Methodology
### Training Methods
#### 3.5.2 10-Fold Cross-Validation Approach  
In the 10-fold cross-validation approach, the entire dataset was used for training and validation. After hyperparameter optimization, the models were trained using 10-fold cross-validation with the best parameters identified. Since XGBoost and DNN are prone to overfitting, early stopping was applied to both models. Performance metrics, including precision, recall, accuracy, and F1-score, were averaged across the folds to provide a rigorous evaluation of the model's capabilities.
## 5 Results and Discussions
### Ground-Based Tracking Scenario  
To track the RSO using the ground-based optical sensors, it is crucial to define the sensor positions in the Cartesian frame. For this particular scenario, two ground-based sensors are assumed to be located in Goldstone, California and Canberra, Australia. The geocentric coordinates of these two sites are converted to a Cartesian frame and the sensor positions are tabulated in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cartesian Co-Ordinates** & \(\mathbf{X_{i}}\) (**km**) & \(\mathbf{Y_{i}}\) (**km**) & \(\mathbf{Z_{i}}\) (**km**) \\ \hline
**Sensor 1** & 1880.82 & 13.78 & 6221.92 \\ \hline
**Sensor 2** & \(-1502.52\) & \(-5.24\) & 6356.15 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ground-based sensor positions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Orbital Parameters** & **Sensor 1** & **Sensor 2** & **Cartesian Coordinates** & **Sensor 1** & **Sensor 2** \\ \hline a (km) & 6878 & 6878 & \(\mathrm{X_{i}}\) (km) & 6456.74 & 3843.01 \\ \hline e & 0.001 & 0.001 & \(\mathrm{Y_{i}}\) (km) & \(-367.63\) & \(-891.28\) \\ \hline i (deg) & 99 & 99 & \(Z_{i}\) (km) & 2321.12 & 5627.35 \\ \hline \(\omega\) (deg) & 20 & 20 & VX (km/s) & \(-2.6\) & \(-6.31\) \\ \hline \(\Omega\) (deg) & 0 & 0 & VY (km/s) & \(-1.12\) & \(-0.66\) \\ \hline \(\theta\) (deg) & 0 & 36 & VZ (km/s) & 7.07 & 4.21 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sensor orbital state elements.  
Figure 11: Uncertainty volumes around the tracked RSO in space-based scenario.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cartesian Coordinate** & \(\mathbf{x_{t}}\) (**km**) & \(\mathbf{y_{t}}\) (**km**) & \(\mathbf{z_{t}}\) (**km**) & **R (km)** \\ \hline
**RSO-state parameters** & 5397.7 & \(-2446\) & 7908.3 & 2265.65 \\ \hline
**Total Error (\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{ }}}}}}}}}}}}}\)\) & 4.7 & 10.46 & 20.36 & 9.12 \\ \hline \hline \end{tabular}
\end{table}
Table 5: RSO position estimates and errors.  
The sensor positions in Table 6 are used as inputs in the tracking algorithm to calculate the RSO position estimates and the corresponding errors listed in Table 7.  
An uncertainty ellipsoid, as shown in Figure 12, can be generated using the mathematical framework presented in Sections 2.2 and 2.3 to represent the navigation errors, tracking errors, and total error for the ground-based tracking scenario.