esa-sceva/satcom-chunk-collection huggingface dataset:
# Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction
## 3 DM-CPDP Approach
### Selection of Multi-Source Datasets  
To select the appropriate data sets, the earth mover's distance (EMD) is used to measure the similarity between the sets. The EMD is defined as the minimum amount of work that required to convert a data distribution to another, i.e., assuming \(X\) is a warehouse containing \(n\) mound and \(R\) is a warehouse with \(p\) empty pits. The similarity between datasets is the minimal cost to move \(n\) mound in \(X\) to \(p\) pits in \(R\).  
In this stage, the dataset can be represented as \(X=\{(x_{1},w_{x_{1}}),\ldots,(x_{i},w_{x_{i}}),\ldots,(x_{n},w_{x_{n}})\}\). \(w_{xi}\) is the weight of \(x_{i}\). When using the EMD method, we assume each sample \(x_{i}\) is a mound with the quality \(w_{xi}\). Similarly, the prototype set is represented as \(R=\left\{(p_{1},w_{p_{1}}),\ldots,(p_{j},w_{p_{j}}),\ldots,(p_{r},w_{p_{r}})\right\}\). \(w_{pj}\) is the weight of \(p_{j}\). In the EMD method, we assume each sample \(p_{j}\) is an empty pit with the volume of \(w_{pj}\).  
We consider that the quality of each sample in the same set is equal and the total mass is equal to the total capacity, and the conditions are shown in (8). \(d_{ij}\) is the cost of moving \(x_{i}\) to \(p_{j}\), and the calculation method as shown in (9). \(k(x_{i},p_{j})\) comes from the matrix (7). We consider the cost function \(D_{XR}\) from \(X\) to \(R\) as the similarity between the source data set and the prototype set, that is, the similarity between the source data set and the target set. The cost function \(D_{XR}\) refers to the Equation (10).  
\[\left\{\begin{array}{l}w_{x_{i}}=\frac{1}{n}\\ w_{p_{j}}=\frac{1}{r}\\ \sum\limits_{i=1}^{n}w_{x_{i}}=\sum\limits_{j=1}^{r}w_{p_{j}}=1\\ \end{array}\right. \tag{8}\]  
\[d_{ij}=1-k\big{(}x_{i},p_{j}\big{)} \tag{9}\]  
\[D_{XR}=min\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{r}d_{ij}f_{ij} \tag{10}\]  
\(f_{ij}\) defines the flow from \(x_{i}\) to \(p_{j}\). The method aims to find an optimal solution \(\left\{f_{ij}\right\}\) to minimize the overall cost function, and it is subjected to the following constraints:  
\[\left\{\begin{array}{l}f_{ij}\geq 0\quad,\quad 1\leq i\leq n\,;\,1\leq j \leq r\\ \sum\limits_{j=1}^{r}f_{ij}=w_{x_{i}}\quad,\quad 1\leq i\leq n\\ \sum\limits_{i=1}^{n}f_{ij}=w_{p_{j}}\quad,\quad 1\leq j\leq r\\ \sum\limits_{i=1}^{n}\sum\limits_{j=1}^{r}f_{ij}=\min\{\sum\limits_{i=1}^{n}w_ {x_{i}},\sum\limits_{j=1}^{r}w_{p_{j}}\}\end{array}\right. \tag{11}\]  
The method of data set selection is shown in Algorithm 3. In step 2-6, we calculate the cost function of converting the source domain data set into the same data distribution as the prototype set. In step 8-10, the values of cost function \(D(X_{Su},R)\) are sorted in descending order, and the first \(\alpha\) datasets are selected as the training set.
# Machine Learning in Medical Triage: A Predictive Model for Emergency Department Disposition
## 3 Results
It provides a measure of the average deviation between the predicted probabilities and the actual class labels. A lower RMSE indicates better model performance. Log Loss: The log loss is 0.5005, which evaluates the model's performance by measuring the logarithmic difference between the predicted probabilities and the actual class labels. A lower log loss indicates greater model accuracy. Mean Per-Class Error: The mean per-class error is 0.2939, representing the average error rate across both classes. It provides insights into the model's performance in correctly classifying instances for each class. A lower mean per class error suggests better class separation. Area Under the Curve (AUC): The AUC is 0.8256, indicating the model's ability to discriminate between positive and negative classes. A higher AUC value signifies better overall model performance (Figure 1). Area Under the Precision-Recall Curve (AUCPR): The AUCPR is 0.8722, which measures the trade-off between precision and recall. A higher AUCPR value suggests a better model. To address concerns regarding the performance of our predictive model as illustrated in Table 3, particularly the issue of false negatives, it is imperative to clarify the intended use of our model within a clinical setting. Our model is designed as a decision-support tool, intended to augment but not replace the expert judgment of medical professionals. The primary role of this model is to assist emergency department staff by providing a statistical assessment that must be considered alongside a comprehensive clinical evaluation. Decisions on patient disposition (admission or discharge) ultimately reside with the attending healthcare providers, who can evaluate the broader context of a patient's medical condition. To visualize the distribution of predictions versus actual values, we can use a bar plot to compare the count of each class in the actual and predicted results (Figure 2). This plot provides a visual comparison between the distribution of the actual and predicted classes, enabling us to evaluate the model's performance in correctly identifying different class labels. Using the varimp() function in H20, we can perform a variable importance analysis for each classification model. For the GBM model, the six highest-ranked variables are acuity, waiting hours, concomitant drugs, age, means of arrival/transport, and race (Figure 3). Based on the H2O GBM predictions, a correlation heatmap was generated, enabling the visualization of the correlations between different features in the dataset (Figure 4). Figure 3: Variable importance analysis of GBM model. Figure 2: Counts of actual vs predicted classes. The bar plot illustrates the counts of actual and predicted classes in the classification model. Understanding these correlations helps in identifying relationships between different physiological measures. For example, the positive correlation between the acuity and heart rate can be critical in triaging patients based on their vitals.
# Inactivation of Contaminated Fungi in Rice Grains by Dielectric Heating
## 3 Results and Discussion
### Study of Relative Electric Field Intensity and Temperature on Heating System  
The effect of relative electric field intensities and temperatures (heating time-temperature histories and energy consumption-temperature histories) was investigated at the selected frequency of 40.68 MHz (Figure 6). The result showed that the heating time-temperature histories and energy consumption-temperature histories were nonlinear. The graph also indicates that the heating time and energy consumption decreased when electric field intensities increased. The optimal electric field intensity was 450 kV/mbased on the highest ratio (26.26 folds at 90 \({}^{\circ}\)C) between the heating time comparison of 450 kV/m and 150 kV/m for each temperature (35 min, and 47.25 \({}^{\circ}\)C/min). The differences in the heating time are because of various heating mechanisms [49]. It has been reported that dielectric materials convert electric energy to heat when placed into an electromagnetic field in the range of radio and microwave frequencies [43; 50]. Similar to the earlier work, heating rate and heating time changed with electrode gaps [51; 52; 53]. Moreover, the optimal electric field intensity was 450 kV/m based on the highest of ratio (26.26 folds at 90 \({}^{\circ}\)C) between the energy consumption comparison of 450 kV/m and 150 kV/m for each temperature (0.33 kWh/g). This result showed that the increased electric field intensity allowed less energy in the dielectric heating system. Similar to the earlier work, energy comparison changed with electrode gaps (1.98 folds at 80 \({}^{\circ}\)C) [49].
# The Effect of Particle Type and Size on CoCr Surface Properties by Fine-Particle Shot Peaning
## 1 Introduction
While there have been previous studies on shot peening of CoCr alloys, they have mainly focused on optimizing the shot peening parameters such as peening intensity, shot size, and coverage, without considering the effect of particle type and size on the surface properties. Therefore, the proposed study aims to fill this knowledge gap by comparing the effect of different particle types and sizes on surface morphology, surface roughness, and surface hardness of the CoCr alloy. The results of this study can provide valuable insights into the optimization of shot peening parameters for the CoCr alloy and improve our understanding of how particle type and size can influence its surface properties.
## 3 Results
### Comparison of Single Angular MISR Cameras  
Scenario 3 assessed the contribution of each off-nadir viewing angles individually (both forward and aft), including all four spectral bands, to determine their ability to predict the forest structural parameters and analyze off-nadir information content. The results showed that for both structural parameters the best angular configurations were either the nadir (An) or a small view angle close to nadir (Table 2, Figure 5b). Only a marginal difference in estimation error was observed for the H\({}_{\text{mean}}\) parameter for the Af camera (rRMSE = 37.29%) compared to the An nadir camera of Scenario 1 (rRMSE = 37.36%). For the CC parameter, the An nadir camera produced a lower error than any of the off-nadir cameras. For both parameters, the model performance generally decreased as the view angle increased (further from the nadir) with no apparent difference for the fore and aft view angles (Scenario 3) (Figure 5b). The only statistically significant differences observed between Scenario 3 was between the Cf and Da cameras, (\(F_{1,7340}\) = 254.30, \(p\) < 0.05) for both parameters.  
Figure 5: (**a**) Relative root mean square error (rRMSE) for MISR BRF models Scenario 1 (Nadir 4 bands, blue, green, red and NIR) and Scenario 2 (single spectral bands—blue, green, red and NIR—with all view angles) for mean tree height (H\({}_{\text{mean}}\)) and canopy cover (CC) and (**b**) Relative root mean square for MISR BRF models Scenario 1 (Nadir 4 bands, blue, green, red and NIR) and Scenario 3 (single view angles—Af, Bf, Cf, Df, Aa, Ba, Ca and Da—with all four spectral bands) for H\({}_{\text{mean}}\) and CC.
## 3 Mechanically Tunable PhC Cavity Sensors
### Discussion  
As discussed above, there are mainly three working mechanisms used for mechanically tunable PhC cavity sensing applications, including probe perturbation, deformable cavity as well as mode splitting methods. Table 3 summarizes a comparison of mechanically tunable PhC with various photonic configurations, sensing principles, measurable factors, remarks and sensing applications. Most sensing applications based on mechanically tunable PhC cavities essentially utilize the principle of displacement sensing. The target physical signals, such as force, stress, magnetic field, etc., are converted to the displacement of an opto-mechanical sensor. In order to optimize the sensing performance, the optimized mechanical design is required for efficient conversion from the target signal to on-chip displacement. Besides, simultaneous optimization on the optical and mechanical components in PhC cavities should be well-designed with a higher opto-mechanical coupling efficiency to maximize the variation of optical readout. However, its sensitivity is still limited by thermal-electronic noise [104] in the readout circuits and the operational bandwidth only reaches up to a level of kHz. Considering sensitivity and bandwidth, another opto-mechanical system driven by radiation force is expected to behave well and will be discussed in next section. In the future, with further integration with PICs, mechanically tunable micro/nano PhC cavities are expected to evolve with compact and feasible measurement apparatus and find more promising applications in optical communications.  
Figure 11: (**a**) Schematic of the magnetic field sensor based on coupled PCN cavity [14]; (**b**) schematic of coupled PCN cavity to detect lateral shearing optical force [14].  
Figure 12: Schematics of the strain sensors: (**a**) double-layer PhC cavity using an atomic force microscope (AFM) tip contacting the center of microcavity to apply strain [12; 102]; (**b**) displacement sensor using a double-membrane PhC slab integrated with electro-optical readout [10; 103].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**PhC Cavity**} & \multicolumn{1}{c}{**Principle**} & \multicolumn{1}{c}{**Tunable Movement**} & \multicolumn{1}{c}{**Factor**} & \multicolumn{1}{c}{**Remarks**} & \multicolumn{1}{c}{**Sensing**} \\ \hline \multirow{4}{*}{Single PhC} & FCN & Probe perturbation & In-plane translating & \(\lambda\)\({}^{*}\) & Small wavelength tuning range; & \multirow{2}{*}{Displacement [8,42]} \\  & \multirow{2}{*}{2D PhC} & & & and \(Q\)\({}^{*}\) & Moderate optical loss & & Displacement [8,42] \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & \multirow{2}{*}{2D PhC} & & & & & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of sensing applications based on mechanically tunable PhC cavities.
## 2 Problem Formulation
### Mutual Information for Finite-Alphabet Inputs  
When a linear unitary transform \(\boldsymbol{U}_{R}^{\text{H}}\) is applied on the receiving signal \(\widetilde{\widetilde{\boldsymbol{y}}}\), the MIMO model in (1) is equivalent to a model with channel matrix \(\boldsymbol{U}_{R}^{\text{H}}\widetilde{\widetilde{\boldsymbol{H}}}\) and noise \(\boldsymbol{U}_{R}^{\text{H}}\widetilde{\widetilde{\boldsymbol{w}}}\)[16], which is written as  
\[\mathbf{y}=\boldsymbol{U}_{\text{R}}^{\text{H}\widetilde{\boldsymbol{y}}}= \boldsymbol{\Sigma}_{\text{R}}^{1/2}\boldsymbol{H}_{\text{WG}}\boldsymbol{ \Sigma}_{\text{T}}^{1/2}\boldsymbol{U}_{\text{T}}^{\text{H}\widetilde{ \boldsymbol{x}}}+\boldsymbol{w} \tag{3}\]  
where \(\boldsymbol{H}_{\text{WG}}=\boldsymbol{U}_{\text{R}}^{\text{H}}\widetilde{ \widetilde{\boldsymbol{H}}}_{\text{WG}}\boldsymbol{U}_{\text{T}}\) and \(\boldsymbol{w}=\boldsymbol{U}_{R}^{\text{H}}\widetilde{\widetilde{\boldsymbol{ w}}}\) is selected equiprobably from \(N_{T}\)-dimension constellation consisted of \(N=\prod_{I=1}^{N_{T}}N_{I}\) vectors, where \(\boldsymbol{N}_{\boldsymbol{I}}\) denotes the number of symbols in the \(i\)-th discrete constellation \(\Omega_{I}\). The mutual Information for Finite-Alphabet inputs between \(\boldsymbol{x}\) and \(\mathbf{y}\) is given by [22]  
\[\begin{split}\mathcal{I}(\widetilde{\widetilde{\boldsymbol{x}}}; \widetilde{\widetilde{\boldsymbol{y}}})=\mathcal{I}(\boldsymbol{U}_{T}^{\text {H}\widetilde{\boldsymbol{x}}};\boldsymbol{y})\\ =\log_{2}N+\frac{1}{N}\sum\limits_{m=1}^{N}\mathbb{E}_{ \boldsymbol{H}_{\text{WG}},\boldsymbol{w}}\left\{\log_{2}\left[\exp\left(- \frac{||\boldsymbol{w}||^{2}}{\sigma^{2}}\right)/\sum\limits_{k=1}^{N}\exp \left(-\frac{||\boldsymbol{c}_{mk}+\boldsymbol{w}||^{2}}{\sigma^{2}}\right) \right]\right\}\end{split} \tag{4}\]  
where \(\boldsymbol{c}_{m,k}=\boldsymbol{\Sigma}_{\text{R}}^{1/2}\boldsymbol{H}_{ \text{WG}}\boldsymbol{\Sigma}_{\text{T}}^{1/2}\boldsymbol{U}_{\text{T}}^{ \text{H}}\boldsymbol{d}_{m,k}\) and \(\boldsymbol{d}_{m,k}=\boldsymbol{q}_{m}-\boldsymbol{q}_{k}\cdot\boldsymbol{q}_ {m}\) and \(\boldsymbol{q}_{k}\) are the \(m\)-th and \(k\)-th points in the constellation of \(\widetilde{\widetilde{\boldsymbol{x}}}\), and \(||\boldsymbol{\bullet}||\) stands for the Euclidean norm of the variable \(\bullet\).  
Since the statistical Channel State Information (CSI) is varying much slower than instantaneous \(\widetilde{\widetilde{\boldsymbol{H}}}\), and can be obtained by channel estimation, this work assumed the statistical CSI was a perfectly-known constant. Consequently, the problem was to calculate the average mutual information with given statistical CSI.
# RACF: A Multimodal Deep Learning Framework for Parkinson's Disease Diagnosis Using SNP and MRI Data
## 4 Discussion
### Study Limitations and Future Directions  
While the RACF framework has exhibited strong performance in PD diagnosis, several opportunities for refinement remain. First, the current validation using 424 PPMI samples, while statistically robust, underscores the inherent challenges of obtaining paired MRI-SNP datasets in clinical practice. While stratified cross-validation partially addresses this limitation, future studies should prioritize expanding the dataset through multi-cohort collaborations incorporating longitudinal data across diverse populations to enhance generalizability. Second, although the framework effectively integrates structural MRI and SNP data, we aim to extend its capabilities by systematically incorporating clinical progression markers, such as UPDRS scores and olfactory testing results, and leveraging Graph Neural Networks (GNNs) to model biomarker association networks, thereby improving disease staging precision. Finally, external validation across diverse populations, particularly in multi-ethnic cohorts with varying disease trajectories, remains imperative to ensure broad clinical applicability.
## 2 Related Work
### Action Recognition Methods
#### 2.2.3 Skeleton-Based Methods  
RGB+D data contain more information, which is conducive to more accurate action recognition. However, more background noise is introduced into the samples. To solve this problem, [PERSON] et al. [29] proposed for the first time to extract human skeleton coordinates from RGB+D data, model them as curves in lie group, and combine them with dynamic time warping for action recognition. [PERSON] et al. [30] attempted to apply Recurrent Neural Network (RNN) to skeleton sequences to simulate the action trajectories of bones and joints in time series, and their work inspired the research on skeleton-based deep learning methods. Since Spatial Temporal Graph Convolutional Networks (ST-GCN) was proposed in [6], it has become one of the most popular methods in the field of human action recognition to process human skeleton data by the convolution neural network of a spatiotemporal graph. With ST-GCN as a basis, a series of action recognition methods, such as Action-structure Graph Convolutional Networks (AS-GCN) [31] and Two-Stream Adaptive Graph Convolutional Networks (2s-AGCN) [32], were proposed.  
As previously mentioned, ST-GCN, AS-GCN, 2s-AGCN, and many other state-of-the-art skeleton-based human action recognition methods have achieved the leading results in the field of human action recognition, and they offer great inspiration for solving the problem of basketball action recognition. Most of these methods directly take the skeleton graph constructed from joint point coordinate data as input and use a graph convolution neural network to extract action features. In this way, end-to-end action recognition can be realized without human intervention. However, totally relying on a neural network to extract motion features from the 3D coordinates of all human joint points consumes a lot of computing resources and learning time, and learning noise data is difficult to avoid.
# Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network
## 3 A Network Traffic Prediction Framework Incorporating Prior Knowledge  
### Problem Definition  
The problem of predicting network traffic in a single-feature time-series scenario can be described as follows:  
* A time series is a node \(v\), which only has one feature \(X_{t}\) at a given moment \(t\).
* The prediction problem refers to the information that is used to predict the next moment \(X_{t+1}\) from historical data \((X_{1},X_{2},\ldots,X_{t})\), where \(S_{1,\ldots,t}\) is the relevant characteristic obtained from the historical data \((X_{1},X_{2},\ldots,X_{t})\); that is, to find the information \(F(.)\) that satisfies: \[X_{t+1}=F(X_{1},X_{2},\ldots,X_{t}|S_{1,\ldots,t})\] (1)
## 4 Numerical Studies  
Three methods commonly used in slope stability analysis were employed and compared: the limit equilibrium analysis (LEA), strength-reduction finite element analysis (SRFEA), and finite element limit analysis (FELA).  
**Limit equilibrium analysis:** The LEA is widely used due to its simplicity and long history. The slope stability analysis is conducted with curved slip lines (e.g., Bishop, Janbu, Spencer) to get the factor of safety (FoS). This method has been implemented in some commercial software, such as Roccience [71]. Although this method does not satisfy the overall equilibrium conditions, e.g., several assumptions regarding the interslice forces need to be made in advance, it is easy to handle and has been validated in case studies [5; 54].  
**Strength reduction finite element analysis:** The finite element code Plaxis [72] was used for the slope stability analysis of the case study in this application. The FoS was obtained by the strength-reduction method (SRM) [32]. In this approach, the shear strength parameters (c and \(\varphi\), if a Mohr-Coulomb failure criterion is assumed) were reduced until the slope failure occurred [73]. The definition of the FoS is:  
\[\text{FoS}=\frac{\text{c}}{\text{c}_{\text{red}}}=\frac{\tan\varphi}{\tan \varphi_{red}} \tag{4}\]  
where c and \(\varphi\) are the actual material strength parameters, and cred and \(\varphi_{\text{red}}\) are the reduced parameters for which no equilibrium can be found any more.  
**Finite element limit analysis:** A FELA is based on the theorems of plasticity developed by [PERSON] et al. [74]. In the past decades, the finite element formulations of the upper- and lower-bound theorems of plasticity were significantly developed and successfully applied to slope stability problems [37; 38; 39]. The method can calculate the upper and lower bound of the FoS based on the formulations developed by [PERSON] [37]. For instance in the software application OptumG2 [75], a common definition for the FoS involves the shear strength of the material related to the strength that will cause the slope collapse, which is analogous to the strength-reduction method elaborated in the finite element method (as illustrated in the above paragraph).