esa-sceva/satcom-chunk-collection huggingface dataset:
# Trajectory Classification and Recognition of Planar Mechanisms Based on ResNet18 Network
## 2 Sample Construction  
As shown in Figure 1, the trajectories generated by a planar four-bar mechanism in regular and inverted configurations with the same parameters are entirely different. Therefore, after determining the mechanism type, it is necessary to distinguish between the regular and inverted installations.  
In recent years, deep learning has achieved remarkable success in image recognition and classification tasks, significantly improving accuracy. Therefore, this paper employs a  
Figure 1: Planar four-bar mechanism positive and negative mounting trajectory diagram. (**a**) Positive mounting trajectory. (**b**) Negative mounting trajectory.  
deep learning approach for trajectory classification and recognition. Samples are saved in the form of images, as depicted in Figure 2.  
The saved images have a size of 128 \(\times\) 128 pixels. Before training, data augmentation operations are applied to enhance the network's generalization performance and robustness, enabling the trained network to handle samples of varying sizes, angles, and brightness. Specifically, the data augmentation operations employed include the following:  
(1) A random rotation operation is applied to the samples with rotation angles in the range of \([-10^{\circ},10^{\circ}]\).  
(2) A random horizontal flipping operation is applied to the samples.  
(3) Standardization is performed on each channel of the sample images, setting the mean to 0.5 and the standard deviation to 0.5.
# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 2 Review of Fake News Detection Approaches and Multimodal Learning Methods
### Explainable AI in Misinformation Detection  
Explainable Artificial Intelligence (XAI) aims to enhance transparency and interpretability of model decisions, playing crucial roles in safety-critical applications [20,58]. In fake news detection, [PERSON] et al. [22] proposed the LIME method that explains black-box model predictions through local approximation. [PERSON] et al. [23] developed the SHAP framework that quantifies feature contributions to predictions based on game theory principles.  
Research on explainability for multimodal fake news detection is gradually emerging. [PERSON] et al. [59] demonstrated model attention focus when processing vision-language tasks through attention visualization. [PERSON] et al. [60] proposed Grad-CAM techniques that generate class activation maps to explain convolutional neural network decision processes. For fake news detection, [PERSON] et al. [40] provided interpretable detection evidence for COVID-19 information detection through the DGExplain generative explanation framework.  
However, existing explainability methods primarily focus on single modalities or simple multimodal fusion, lacking deep interpretability capabilities for social signals such as user comments. Furthermore, cross-modal consistency analysis and interpretable mining of community intelligence remain important directions to be explored [61,62].  
Our proposed UC-CMAF framework introduces cross-modal interpretability mechanisms that not only display attention distributions of image-text consistency differences but also reveal evidence signal sources of collective intelligence through user comment importance scoring, achieving truly multimodal interpretable detection pathways.
## 2 Materials and Methods
### Optimization
#### 2.6.2 Modeling with ANN  
ANNs are designed to learn the complex relationships between input parameters (P1, P2, P3) and output parameters (mass, peak force (PF), and absorbed energy (AE)).  
As shown in Figure 10, the architecture of the ANN model is composed of 3 neurons in the input layer, 15 neurons in the hidden layer, and 3 neurons in the output layer. The Levenberg-Marquardt back-propagation algorithm was used for training the network [52].  
Figure 10: ANN.  
The dataset was randomly divided into training (70%), validation (15%), and test (15%) sets. This division was performed to increase the generalization ability of the model and to avoid overfitting.
## 5 Results and Discussion  
The following sections present the results of the GNSS prediction methodology. Additionally, a procedure for validating these predictions using true values was designed. A GNSS receiver was used to measure the number of satellites visible at a number of distinct locations on Maynooth University campus and this validation data was then compared with the predicted values and the error sources were explored. This enabled assessment of the proposed methodology of applying existing prediction software, 2D polygons and low-density elevation data in ArcMap's viewshed analysis.
# Digital-Twin-Based Monitoring System for Slab Production Process
## 2 Related Work
### Defect Detection Techniques  
In recent years, defect detection technology has been a popular research direction for many scholars. Significant progress has been made not only in the steel industry but also in various other fields. In the steel industry, X-ray, infrared imaging technology, and ultrasonic technology are commonly used for defect detection [20; 21; 22; 23; 24]. [PERSON] et al. [20] compared the advantages and disadvantages of different non-destructive imaging techniques, including X-ray, in detecting defects in sintered layers. [PERSON] et al. [22] used enhanced ultrasound technology to detect defects in transplanted liver perfusion. [PERSON] et al. [24] constructed a camera ultrasound data fusion (CUlfuse) model for detecting surface defects on rails. With the increasing maturity of deep learning technology, many scholars have introduced neural networks into defect detection in the manufacturing industry. [PERSON] et al. [25] proposed a deep-learning-based method for automatic recognition and tracking of steel products for the intelligent manufacturing of steel. [PERSON] et al. [26] developed a new image synthesis method for defect image generation in the steel manufacturing process.  
The above-mentioned methods have provided crucial technical support for improving product quality and the reliability of industrial production in the steel industry. Among them, deep learning has the advantage of being more intuitive and faster compared to other defect detection methods. Conventional detection algorithms rely on various sensors and detection devices, which lack stability and are insufficient for meeting the high-precision requirements of defect detection in different smelting plants.
# An Organizational Perspective on Robotic Process Automation Adoption and Usage Factors
## 4 Discussion  
Due to the existing gap in the theoretical foundation regarding RPA from an organizational perspective, one of the research proposals was to explore the propensity for the adoption and use of RPA from an organizational point of view. The research is based on the combination of the TOE and DOI theories to explain the adoption and use of RPA.  
The obtained results confirm that Compatibility is a significant antecedent of the adoption and use of RPA, and Relative Advantage, based on the results obtained in both  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dependent** & \multirow{2}{*}{**Independent Variable**} & **Path Coefficient** & \multirow{2}{*}{**R2** (**Pilot Test**)} & **Path Coefficient** & \multirow{2}{*}{**R2** (**Full Model**)} \\
**Variable** & & & **(Pilot Model)** & & **(Full Model)** \\ \hline \multirow{8}{*}{RPA Adoption} & Relative Advantage & 0.132 & & 0.070 & \multirow{8}{*}{0.657} \\  & Compatibility & 0.198 & & 0.284 & \\ \cline{1-1}  & Complexity & \(-\)0.027 & & 0.044 & \\ \cline{1-1}  & Technology Competence & \(-\)0.031 & 0.608 & 0.055 & 0.657 \\ \cline{1-1}  & Managerial Obstacles & 0.048 & & 0.019 & \\ \cline{1-1}  & Environmental Pressure & 0.005 & & 0.002 & \\ \cline{1-1}  & Control Variable & 0.115 & & 0.133 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: PLS results. Source: own elaboration.  
the pilot test and the full model, also appears to be statistically significant. Both antecedents belong to the category of technological factors. The relevance of the compatibility of the RPA solution with the company's existing systems is highlighted as a key factor influencing the propensity for adoption. Additionally, the organization's propensity to adopt RPA depends on the expectation of gaining a competitive advantage, whether through increased sales, cost reduction, decreased paperwork, or the ability of RPA to assist with quick data capture and analysis. Despite the fact that the remaining antecedents did not prove to be statistically significant in this study, the combined use of DOI and TOE as theoretical foundations for the model has proven to be useful, as these antecedents together explain a substantial variation in the adoption and use of RPA (R\({}^{2}\) = 0.657). In fact, a significant contribution of this research is the combination of DOI and TOE to explain the propension for RPA adoption.
## 3 Results and Discussion
### Equivalent Viscous Damping Ratio (\(\xi\)eq)  
An equivalent viscous damping ratio was calculated to quantify further and understand the energy dissipation of the NEWRC bridge column and frame. It is estimated that in a typical RC structure, the elastic damping ratio \(\left(\xi_{\text{eq}}\right)\) accounts for around 5% of the critical damping. In the present study, the equivalent viscous damping ratio \(\left(\xi_{\text{eq}}\right)\) was calculated using the method provided in [38]:  
\[\left(\xi_{\text{eq}}\right)=\frac{1}{2\pi}\frac{S_{ABC}+S_{CDA}}{S_{OFD}+S_{ OBE}} \tag{4}\]  
In Equation (4), \(S_{ABC}\) and \(S_{CDA}\) represent the areas enclosed by the shaded hysteresis loop, and \(S_{OFD}+S_{OBE}\) is the energy stored in the triangle, as shown in Figure 13. In general, each test specimen's equivalent viscous damping ratio increases gradually as the displacement increases. Furthermore, in Figure 13, from comparing the equivalent viscous damping ratio for all the column specimens, it is clear that BMRC1 has a higher \(\xi_{\text{eq}}\). Compared to other NEWRC columns. In Figure 13, the equivalent viscous damping ratio for BMRC1 and NEWRC1 is compared, which shows that, before the yielding stage, both specimens have similar \(\xi_{\text{eq}}\). However, after the yielding stage, they follow a different pattern since the \(\xi_{\text{eq}}\) for BMRC1 is significantly higher than that of NEWRC1. The results suggest that material properties had an apparent influence on the energy dissipation capacity of the columns.  
In Figure 13, energy dissipation between NEWRC1, NEWRC2, and NEWRC3 is compared with each other. It is seen that, before the 4% drift ratio, the \(\xi_{\text{eq}}\) for all the specimens are similar. However, after the 4% drift ratio, the \(\xi_{\text{eq}}\) for NEWRC1 is slightly lower than that of the other two specimens. It might be because of the strength degradation after longitudinal rebar buckling. Additionally, in Figure 13, the \(\xi_{\text{eq}}\) for NEWRC4 is compared with NEWRC1. From the results, it is evident that an increase in axial load ratio had only a minor influence on the equivalent viscous damping ratio of the columns. Furthermore, in Figure 13, the \(\xi_{\text{eq}}\) for NEWRC5 is compared with NEWRC1; at every drift ratio, the \(\xi_{\text{eq}}\) for NEWRC5 is slightly lower than that of NEWRC1. Hence, the replacement of transverse reinforcement strength had only a minor impact on the equivalent viscous damping ratio of the columns. On the other hand, Figure 13 also shows the equivalent viscous damping ratio for NEWRCF. It can be observed that the equivalent viscous damping ratio of the frame increased steadily with the increase in drift ratio. Hence, it can be concluded that the equivalent viscous damping ratio capacity of the NEWRCF is satisfactory. Therefore, it is evident that the spacing of transverse reinforcement must be controlled strictly in the seismic design of high-strength bridge columns.
## 2 Background  
### Binaural Perception by Humans and Machines  
For humans and machines alike, spatial perception of audio sources in the horizontal plane is facilitated by such binaural cues as interaural time difference (ITD), interaural level difference (ILD), and interaural coherence (IC) [2]. In addition, spectral cues help to localize audio sources in the median plane [16]. Since real-world binaural recordings of music typically contain a large number of spatially distributed and simultaneously sound-emitting sources, the above cues get confounded, particularly under reverberant conditions. Hence, the automatic classification of complex audio scenes in binaural recordings of music is not a trivial task.  
An important limitation of binaural listening (both in humans and machines), resulting in localization errors between front and back hemispheres, is caused by the ambiguity of binaural cues--a mechanism referred to as a cone-of-confusion effect [17]. To reduce the influence of the above effect, humans exploit involuntary micro head movements [18].
## 4 Results and Discussions
### LoRaWAN(r) Coverage Analysis for Optimal GW and EN Height Planning in a Rural Farm Area  
One of the challenging tasks for the LoRaWAN(r) GW mounted on a drone during operation is identifying optimal configurations, specifically for Tx/Rx height, to guarantee reliable communications between the GW and ENs in the network. As such, this section analyzes the coverage of the deployment area based on Cloud-RF(r) simulations at different GW and EN height configurations, as shown in Figure 17. The simulations were based on the fine-tuned ITM PL model and used the configurations specified in Table 5. To simplify the simulation and the discussion, only seven ENs (water quality sensors located in the river) have been considered at this stage and the results can be easily generalized to all other ENs.  
Figure 15: Aerial view of the location (Cyberjaya, Malaysia) for drone speed measurement.  
Figure 16: Performance of LoRa multi-channel gateway against different drone speeds.  
Figure 17: LoRaWAN® coverage prediction in a rural farm area (Pahang, Malaysia) using the fine-tuned ITM PL model in Cloud-RF® based on different GW and EN height configurations. (**a**) GW in idle state (on the ground) and EN at 1 m AGL. (**b**) GW at 50 m AGL and EN at 1 m AGL. (**c**) GW at 100 m AGL and EN at 1 m AGL. (**d**) GW at 150 m AGL and EN at 1 m AGL.  
Three different GW heights were considered in addition to the idle state, where the GW was placed on the ground, to justify the need for such system requirements. Additionally, the coverage of each of the seven sensing nodes was simulated separately at minimal heights to justify the need for the drone-based system further (Figure 17a). Based on the coverage results shown in Figure 17, it can be observed that GW height has a significant impact on overall achievable coverage. Therefore, this also justifies the need for a drone-based system to guarantee reliable communications.  
To further illustrate the impact of GW and EN height, the predicted RSSI at each of the seven ENs was extracted from the simulations and plotted as shown in Figure 18. Based on the plot, it can be seen that switching the GW height to 50 m would result in an RSSI that is slightly above the LoRaWAN(r) receiver sensitivity level. Meanwhile, increasing the GW height to 100 m or 150 m would result in an average improvement from 3 dB to 7 dB. On the other hand, changing the EN height showed an additional minor improvement.  
Finally, to compensate for other signal-degrading factors that are not considered in the Cloud-RF(r) simulations, such as temporal fading and dense foliage impact, we can assume that the optimal RSSI required for the system should be kept 10 dB above the minimum  
Figure 18: Predicted RSSI using the fine-tuned ITM PL model in Cloud-RF(r) with various GW and EN height configurations.  
sensitivity level. As such, it can be concluded that the optimal GW and EN heights are 150 m AGL and 1 m AGL, respectively, for the final deployment area.
# Pathological Gait Classification Using Early and Late Fusion of Foot Pressure and Skeleton Data
## 5 Proposed Models
### Single-Modals (MViTv2_Base and MViTv2_Small) for Foot Pressure Dataset
#### 5.1.1 Data Augmentations  
Insufficient training data can lead to improper training or overfitting problems. Data augmentation, achieved by generating modified data from the original dataset, can effectively augment the dataset's size to mitigate the issue of data scarcity. The foot pressure dataset utilized in our experiment also possesses a relatively limited amount of data (14 to 40 samples). For each foot pressure image, the following successive transforms were performed:  
* Flip/mirror the image with a probability of 0.5.
* Rotate the image with a random angle from 15 to 45 (in degrees).  
For MViTv2_base and MViTv2_small, the results both without and with augmentation, specifically flipping and rotating images, were evaluated as detailed in Table 2. Without any augmentation, an accuracy of 71.74% for MViTv2_base and 70.83% for MViTv2_small were achieved. For flipping, random horizontal flips with a probability of 0.5 were employed, resulting in an accuracy of 72.43% for MViTv2_base and 73.47% for MViTv2_small. Additionally, random rotations were applied at angles of 15, 30, and 45 degrees, respectively. With a probability of 0.5, these rotations were applied, and the highest accuracy was achieved with a 30-degree rotation: 76.46% for MViTv2_base and 78.24% for MViTv2_small. Notably, applying the 30-degree rotation augmentation led to a substantial accuracy increase of 7.41% (from 70.83% to 78.24%) for the MViTv2_small model.  
Figure 3: Proposed single-modals (**a**) MViTv2_base and MViTv2_small for foot pressure dataset, and (**b**) single-modal ST-GCN [40] for the skeleton dataset.
## 3 Results
### Correlations and RDA Analysis  
Based on Pearson correlation analysis, FAME indicators were significantly and positively correlated with chemical parameters (TC, TN, and POXC) and EAs related to C (BG) and P (PME) cycling. C:N ratio was negatively correlated with total FAME, bacterial FAMEs (total bacteria, GramPos, GramNeg, and Actino), SaproFungi, and Fungi (Figure 3).  
Redundancy analysis (RDA) was performed on the enzyme activities and physiochemical parameters used as environmental variables (Figure 4a). The first and second axes accounted for 33.59% and 2.47% of the total variation between enzyme activities and soil C and N pools, respectively. The EAs were, significantly correlated with TN (F = 3.35, \(p\) = 0.002), soil moisture (F = 2.77, \(p\) = 0.061) and TC (F = 2.02, \(p\) = 0.098) at a \(p\)-value of less than 0.1 based on Monte Carlo permutation tests.  
Figure 3: Correlation analysis based on Pearson index. Blue values are negative correlations, red values are positive correlations and white is no significant values. F/B-fungal to total bacterial ratio FAME; G+/G—Gram-positive to Gram-negative bacterial FAME; AMF/SF-Arbuscular mycorrhiza fungi to saprophytic fungi FAME; AMF/TB-Arbuscular mycorrhiza fungi to total bacteria FAME; SF/TB-saprophytic fungi to total bacteria FAME; and cy/pre-cyclopropyl to precursor FAME; AMF-Arbuscular mycorrhiza fungi; SaproFung-Saprophytic fungi; Actino-actinomycetes; GramNeg-Gram-negative; GramPos-Gram-positive; C/N-Total organic carbon to total nitrogen; TN-total nitrogen; TC-total carbon; POXC-permanganate oxidable carbon; moisture-gravimetric soil moisture; MWD-mean weight diameter; AS-arylsulfatase; NAG-glucosaminidase; PME; acid phosphatase, and BG-glucosidase.  
The potential relationships occurring between EAs and the microbial community structure was assessed by RDA using FAME data (Figure 4b). The first and second axes, respectively, accounted for 41.8% and 4.89% of the total variation. Soil enzyme activities, including \(\beta\)-glucosidase (F = 9.20, \(p\) = 0.001), \(\beta\)-glucosidaseaminidase (F = 3.63, \(p\) = 0.024), and acid phosphatase (F = 3.59, \(p\) = 0.035) activities of different biochar treatments were significantly correlated with FAME indicators based on Monte Carlo permutation tests. Results of the RDA between soil nutrients and microbial community composition (FAME indicators) are shown in Figure 4c. The first and second axes accounted for 43.54% and 0.43% of the total variation in microbial community composition and structure. The microbial community composition was not significantly correlated with the physical-chemical parameters.
## 2 Method
### Point Cloud Classification Based on SVM  
SVM [22] is achieved by maximizing the classification interval in the feature space. For non-linear data, SVM maps them into a high-dimensional feature space by a kernel function, which make the data into linear separable data in a high-dimensional feature space. Following this, it realizes a classification by maximizing the interval. In view of the excellent generalization ability of SVM, we use SVM as a classifier for the single point classification in point cloud data. As we know, the correlation between the point cloud single point feature and neighbor points features, and the Gauss kernel function only has one parameter \(\sigma\) and a low model complexity. Thus, in the absence of prior knowledge, the Gauss kernel function is often better than other kernels. Therefore, we choose the Gauss kernel function as the kernel function. Here, the Gauss kernel function of the SVM classifier is defined as follows:  
The fused feature space is X. The selected \(n\)\(d\)-dimensional feature samples \(\{\text{x}_{1},\text{x}_{2},\dots,\text{x}_{n}\}\):  
\[\text{x}_{i}=(x_{i1},x_{i2},\dots,x_{id})^{\prime}e\mathbb{R}^{d} \tag{23}\]  
After the feature transformation, the feature space is Z. We map data in the \(X\) space to the \(Z\) space \(\text{z}_{i}=(z_{i1},z_{i2},\dots,z_{id})e\mathbb{R}^{d}\) via the mapping function \(\phi(x)\). The function \(K(x,z)\) satisfies the condition \(K(x,z)=\phi(x)\cdot\phi(z)\), and the function \(K(x,z)\) is a kernel function, while \(\phi(x)\) is a mapping function. The Gauss kernel function is as follows:  
\[K(X,\mathbf{z})=\sum_{i}^{n}K(\mathbf{x}_{i},\mathbf{z})=\sum_{i}^{n}\exp{(-\frac{\left\| \mathbf{x}_{i}-\mathbf{z}\right\|^{2}}{2\sigma})} \tag{24}\]  
The corresponding decision function is:  
\[f(\mathbf{z},\alpha^{*},b^{*})=sign(\sum_{i}^{n}y_{i}\alpha^{*}\exp{\left(-\frac{ \left\|\mathbf{x}_{i}-\mathbf{z}\right\|^{2}}{2\sigma}\right)}+b^{*}) \tag{25}\]  
The SVM classifier is trained by the features of the training set, and the test set is classified by the trained classifier. The initial classification results for the point cloud in Figure 1b are shown in Figure 1d.