esa-sceva/satcom-chunk-collection huggingface dataset:
# False Information Detection via Multimodal Feature Fusion and Multi-Classifier Hybrid Prediction
## 3 False Information Detection Method
### Early Fusion  
We obtained text features \(T_{i}\) and image features \(V_{i}\). In order to obtain the interactive relationship between the text and the image, this paper uses a deep autoencoder to interact with text and image features to obtain multimodal joint features. The deep autoencoder will convert the high-dimensional feature vector into a low-dimensional feature vector through a function. The low-dimensional feature vector will contain most of the main features of the data, and some tiny features may be lost, so as to eliminate irrelevant features in the features. This module firstly inputs the features of text and images into the encoder for encoding. After the features of text and images enter the encoder, they will be compressed into low-dimensional feature vectors through two linear layers, and then add the encoded two feature vectors by dimension. Finally, the added feature vector is input into the two decoders to obtain two outputs. The specific calculation process is as follows:  
\[T_{encoder1}=Linear(T_{i}) \tag{3}\]  
\[T_{encoder2}=Linear(T_{encoder1}) \tag{4}\]  
\[T_{encoder3}=Linear(T_{encoder2}) \tag{5}\]  
\[V_{encoder1}=Linear(V_{i}) \tag{6}\]  
\[V_{encoder2}=Linear(V_{encoder1}) \tag{7}\]  
\[V_{encoder3}=Linear(V_{encoder2}) \tag{8}\]  
\[F_{co}=add(T_{encoder},V_{encoder}) \tag{9}\]  
\[T_{decoder3}=Linear(F_{co}) \tag{10}\]  
\[T_{decoder2}=Linear(T_{decoder3}) \tag{11}\]  
\[T_{decoder1}=Linear(T_{decoder2}) \tag{12}\]  
\[V_{decoder3}=Linear(F_{co}) \tag{13}\]  
\[V_{decoder2}=Linear(V_{decoder3}) \tag{14}\]  
\[V_{decoder1}=Linear(V_{decoder2}) \tag{15}\]  
\(Linear()\) is a linear transformation function, which transforms the dimension of the feature vector, and \(add()\) is an addition operation, which adds the two input feature vectors according to the corresponding dimensions. \(T_{encoder}\in R^{e_{i}}\) is the feature of the text feature \(T_{i}\) processed by the encoder, and \(V_{encoder}\in R^{e_{i}}\) is the feature of the image feature \(V_{i}\) processed by the encoder. \(T_{encoder}\), \(V_{encoder}\), \(F_{co}\in R^{e_{i}}\), \(e_{i}\) is the dimension of the feature vector processed by the encoder. \(T_{decoder}\), \(V_{decoder}\in R^{d_{i}}\) is the feature vector decoded by the \(T_{encoder}\), and \(V_{encoder}\), \(d_{i}\) is the dimension of the feature vector.  
The \(T_{encoder}\) and \(V_{encoder}\) are only used when training this module. This module obtains the prediction error by comparing the output of the decoder with the input of the encoder, and then backpropagation is performed to train the deep autoencoder and gradually improve the accuracy of autoencoding, and finally, the trained model is used as the early fusion module of the model in this paper. In this paper, the \(F_{co}\) after the addition of \(T_{encoder}\) and \(V_{encoder}\) is taken as the joint features of multimodality.
## 4 Results
### Deep-Seated Gravitational Slope Deformations
Moreover, observing Figure 9a, additional kinematic domains can be identified, i.e., a central sector, presenting ground LOS velocities from \(-0.8\) to \(-0.5\) cm/year, and a sector in the west portion of the gravitational phenomenon, characterized by LOS velocities from \(-0.57\) to \(-0.25\) cm/year. Analyzing the ground deformation time series available for several targets within the phenomenon, we observe a substantially constant deformation rate, represented by a linear trend; however, several accelerations, related to the later spring-summer period, are observed. Specifically, five targets distributed within different Beauregard domains, are presented (Figure 8b,c). The higher displacement of \(-3.7\) cm (away from the satellite), for the observed period from June 2004 and October 2010, was registered by the target B1 located in correspondence to the main scarp of the DsGSD, while the target B5 located in correspondence to the toe of the DsGSD, close to the dam, registered a maximum displacement of \(-1.07\) cm. Instead, a displacement comparable to what was observed for the B1 target, is registered by target B4, with a maximum of \(-3.5\) cm. Figure 9: (**a**) Mean deformation velocity map of Quart DsGSD case study, for Envisat ascending dataset. (**b**) Time series corresponding to the targets signed by the **white** dots in the map. The DsGSD of Quart is located on the north side of the broad middle portion of the principal valley. This phenomenon covers an area of about 13 km\({}^{2}\) from 530 m a.s.l. at the valley bottom up to 2200 m a.s.l., in the terminal part of the main DsGSD scarp. In [10], the complex evolution of this phenomenon is described, and the ongoing deformation was attributed to an interaction between gravitational movements with glacial and fluvial processes operating over a long time period. The mean deformation velocity map obtained with GPOD-SBAS processing confirms the ground deformation trend outlined in the literature [10] (Figure 9a) and allows for the identification of ground LOS velocities in the upper and central portions of about \(-0.8\) and \(-0.6\) cm/year, respectively, while lower sectors appear relatively stable over time. Furthermore, in this case, the ground deformation time series generally present a constant deformation rate and show local acceleration during the summer months; the maximum displacement of \(-5.25\) cm for the observed period was registered by the upper sector of the DsGSD (Figure 9b); going down to the valley bottom, the central sector presents a maximum displacement of \(-1.9\) cm, up to \(-0.2\) cm in the lower sector of the slope.
## 3 Results and Discussion
### Photocatalytic Degradation Mechanism  
Based on the above outcomes, PCD mechanism for MB degradation by Ce doped MnFe\({}_{2}\)O\({}_{4}\) NCs is shown in Figure 10. When passing through the visible light on the nano-catalysts surface, the electrons, e-, become excited from the valence band (VB) to the conduction band (CB), along with producing h\({}^{+}\) in VB. The CB excited electrons of MnFe\({}_{2}\)O\({}_{4}\) NCs become combined cerium and electron-hole (e-h) pair recombination rates become lower, hence the PCD process improves [57; 58; 59; 60]. The CB electron becomes combined with a dissolved oxygen molecule to form anions of peroxide radicals (\(\bullet\)O\({}_{2}\)\({}^{-}\)) and similarly created \(\bullet\)OH, owing to the reaction amongst h\({}^{+}\) and OH\(-\) ions. MB decomposes into a simpler molecule in the presence of \(\bullet\)OH and \(\bullet\)O\({}_{2}\)\({}^{-}\) species into CO\({}_{2}\) and H\({}_{2}\)O. The appropriate PCD mechanism is listed in the following equations:  
\[\text{MnCe}_{\text{x}}\text{Fe}_{2-\text{x}}\text{O}_{4}+\text{ Visible Light}\rightarrow\text{e}^{-}\left(\text{CB}\right)+\text{h}^{+}\left(\text{VB}\right)+ \tag{4}\]  
\[\text{h}^{+}\left(\text{VB}\right)+\text{H}_{2}\text{O}\rightarrow\text{H}^{ +}+\bullet\text{OH} \tag{5}\]  
\[\text{e}^{-}+\text{O}_{2}\rightarrow\bullet\text{O}_{2}\text{}^{-} \tag{6}\]  
\[\left[\text{MB Dye}\right]+\bullet\text{O}_{2}\text{}^{-}+\bullet\text{OH} \rightarrow\text{H}_{2}\text{O}+\text{CO}_{2} \tag{7}\]
## 3 Results and Discussion
### Algorithm Performance for LST Forecasts
#### 3.1.1 The First Case-Study: The Pedemontana Motorway
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{**Station Point**} & \multicolumn{2}{c}{**Hourly Solar**} & \multicolumn{2}{c}{**Moving Average (of the Previous**} \\  & & **Radiation as Input** & **3 h) of Solar Radiation as Input** \\ \hline
**Nash-Sutcliffe** & SP2 & 0.65–0.69 & 0.64–0.68 \\
**Efficiency (NSE) [–]** & Ponte Olona & 0.58–0.62 & 0.56–0.61 \\
**Gorla** & 0.62–0.65 & 0.60–0.63 \\
**Root Mean Square** & SP2 & 3.00–3.20 & 3.00–3.20 \\
**Error (RMSE) [\({}^{\circ}\)C]** & Ponte Olona & 3.30–3.40 & 3.30–3.50 \\
**Pearson Correlation** & SP2 & 0.90 & 0.89–0.90 \\
**coefficient (r) [–]** & Ponte Olona & 0.90 & 0.89 \\
**Correlation (r) [–]** & Gorla & 0.90 & 0.90 \\
**Determination** & SP2 & 0.80–0.81 & 0.80–0.81 \\
**coefficient (R2) [–]** & Ponte Olona & 0.81 & 0.79–0.80 \\
**coefficient (R2) [–]** & Gorla & 0.81 & 0.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistical indexes during cloudy days between observed and forecasted LST values by the MEC algorithm using the hourly solar radiation and the 3 h-moving average as input.
## 3 Hardware Architecture of the Proposed STFT Processor
### Hardware Architecture of the Proposed STFT Processor
#### 3.2.1 Windowing Module (WM)  
If the conventional R4 MDC architecture is applied to the STFT processor, there is a problem in that the area required is higher owing to the first DMM that reconstructs the data before the first R4 BM input [25]. In the case of 1024-point R4 MDC FFT, 3072 delay elements are required for the subsequent R4 BM (R4 BM1). However, in the proposed STFT processor, the WM converts the input single data stream into four 75% overlapped data streams and reconstructs the data with an alignment pattern corresponding to that required by the first R4 BM as shown in Figure 8. Therefore, in the  
Figure 6: Output pattern of the proposed STFT processor to support variable overlap ratios (green arrow: 75%, blue arrow: 50%, red arrow: 25%, purple arrow: 0%).  
Figure 7: Hardware architecture of the proposed STFT processor.  
proposed architecture, a DMM for performing data alignment for the first R4 BM was not used. In this way, the required number of delay elements is only 768, thereby enabling an implementation with lower complexity. In addition, since the proposed WM should support variable lengths of 16/64/256/1024 points, we use multiplexers to determine the required data alignment pattern for 16/64/256/1024 points.
## 3 Data and Application
### Method 2: Time-Frequency Analysis (e.g., Wavelet Transform)  
This algorithm performs change detection on a synthetic nonlinear signal using Continuous Wavelet Transform (CWT). It computes the CWT of the signal, decomposing it into frequency components across different scales. A threshold is then applied to the wavelet coefficients to detect significant changes in the signal. Changes exceeding the threshold are identified, and the results are visualized as an image, with time on the x-axis and frequency on the y-axis. Darker regions in the image represent higher magnitudes of change.  
The first example related to the CWT is displayed in Figure 6.  
Figure 4: Change detection using mean and standard deviation method. (**Top**) Synthetic nonlinear signal plotted against time and shown in blue. This signal exhibits complex behavior over time, characterized by variations and fluctuations in amplitude. (**Bottom**) The red line represents the results of change detection using the mean and standard deviation method.  
We have displayed the result of the second example in Figure 7.
## 4 Results and Discussion
### Fit of Durations Simulation
#### 4.2.1 Cardenillo Hydroelectric Project  
The evaluation was performed in months 10, 20, 30, 40, 50, and 60 after starting the simulated execution of the project, and in months 10, 8, 6, and 1 before the simulated completion of the project. Figure 2 presents the temporal evolution of how the EVM and CTCR methods fit each other in their forecasts of the final completion time of the Cardenillo project, which is in accordance with the ten thousand scenarios in the simulation model.  
It is apparent that from the first months of execution, the CTCR methodology displays greater probability of fit than the EVM method. This indicates that CTCR predicts the period to completion of the Cardenillo project more efficiently than EVM. It is apparent that in month 10 before completion of the project, CTCR has a probability of 64.1% of correctly predicting the project's period of completion, while in the same period, EVM achieves a probability of only 34.7%. Similarly, in month 6 prior to completion, CTCR shows a probability of 99.3% compared with 97.4% for EVM.
## 2 Materials and Methods
### Compressive Sensing Method Based on Separable Approximate Sparse Reconstruction  
The compressive sensing method based on separable approximate sparse reconstruction is a framework algorithm to solve the regularization method. According to different L-p norm regularization problems, different parameters and different criteria can be selected to solve the optimization problem. For the layover solution model from Equation (6), the layover problem is transformed into the L1-L2 norm optimization problem, and the corresponding compressive sensing method based on separable approximate sparse reconstruction is solved in an iterative manner. The algorithm can be expressed as [24; 25]:  
\[\gamma^{t+1}\in\underset{z}{\text{argmin}}\bigg{\{}\|z-u^{t}\|_{2}^{2}+\frac{ \lambda_{k}}{\alpha_{t}}\|z\|_{1}\bigg{\}} \tag{15}\]  
\[u^{t}=\gamma^{t}-\frac{1}{\alpha_{t}}\
abla\|G-M\gamma^{t}\|_{2}^{2} \tag{16}\]  
where \(\
abla\) denotes the differential operation.  
Equation (15) can be solved by the following Equation (17):  
\[\underset{z}{\text{argmin}}\bigg{\{}\|z-u^{t}\|_{2}^{2}+\frac{\lambda_{k}}{ \alpha_{t}}\|z\|_{1}\bigg{\}}=soft\bigg{(}u^{t},\frac{2\lambda_{k}}{\alpha_{t} }\bigg{)} \tag{17}\]  
The soft function can be expressed as:  
\[soft(u,a)=sign(u)\text{max}\{|u|-a,0\} \tag{18}\]  
Let \(s^{t}=\gamma^{t}-\gamma^{t-1}\), \(p^{t}=\
abla\|G-M\gamma^{t}\|_{2}^{2}-\
abla\|G-M\gamma^{t-1}\|_{2}^{2}\), then  
\[\alpha_{t}=\frac{\left(s^{t}\right)^{T}p^{t}}{\left(s^{t}\right)^{T}s^{t}} \tag{19}\]  
The iteration condition can be judged as:  
\[\left|\gamma^{t}-\gamma^{t-1}\right|<\delta \tag{20}\]  
Formulas (15) and (16) belong to the category of iterative soft threshold shrinkage (IST). Strictly speaking, the sparse reconstruction algorithm based on separable approximation belongs to the accelerated algorithm of the IST algorithm. The main difference between IST algorithms lies in the different selection and acceleration methods, and the method based on separable approximate sparse reconstruction has a faster convergence speed than other IST algorithms [24].
## 6 Results and Discussions
### Performance of the Models  
The load torque is the main parameter used to evaluate the load-carrying performance of a turbine shrink disk; the load torque relates to the following parameters: contact pressure, friction coefficient, length of mating surfaces and diameter of shaft. The corresponding load torque can be obtained from the contact pressure. The load-carrying performance of the turbine shrink disk can be directly judged by comparison to the related torque. A calculation equation of the loading torque can be deduced from Equation (1).  
\[M=\frac{\mu P\pi d^{2}l}{2} \tag{13}\]  
Using the curving integral of the mating surface between the shaft and the sleeve, the load torque of the turbine shrink disk can be obtained by combining it with Equation (13). The results of the load torque on each model and the relative error compared with model 1 (ideal model) are shown in Table 4.  
As shown in Table 4, compared with Model 1 (ideal model), the relative error of the load torque of Model 2 is 4.4%, which shows that the minimum assembly clearance has little influence on the load torque and can meet the engineering design requirements. The relative errors of Model 4 and 5 are 4.8% and 5.0%, respectively, which shows that the machining deviation range can also meet the engineering requirements. However, the relative error of Model 3 reaches 23.5%, i.e., the maximum assembly clearance has a great effect on the load-carrying performance. Moreover, as the deviation of propel stroke between the design value and the practical value increases, the greater the weakening  
\begin{table}
\begin{tabular}{c c c} \hline
**Model Number** & **Torque (kN-m)** & **Relative Error (\%)** \\ \hline
1\# & 3396 & 0.0 \\
2\# & 3245 & 4.4 \\
3\# & 2599 & 23.5 \\
4\# & 3233 & 4.8 \\
5\# & 3565 & 5.0 \\
6\# & 2675 & 21.2 \\
7\# & 3033 & 10.7 \\ \hline \end{tabular}
\end{table}
Table 4: Load torque of each model and relative error.  
Figure 8: Distribution of the contact pressure on each mating surface under different propel strokes. (**a**) Mating surfaces of shaft and sleeve; (**b**) mating surfaces of sleeve and inner ring; (**c**) mating surfaces of inner ring and outer ring.  
effect on load torque becomes. The full axial movement of the outer ring can guarantee the normal operation of the turbine shrink disk.
# Speed Optimization for Incremental Updating of Grid-Based Distance Maps
## 4 The Methodology
### Algorithm Pseudocode  
#### 4.2.1 Initiate the DM  
Table 1 presents pseudocode for the initialization of a DM, including three functions, i.e., (1) **initialize0** to set aside certain space for required data structures and then initial their values; (2) **setObstacleCell(o)** to register a newly inserted obstacle cell; and (3) **removeObstacleCell(o)** to reset an removed obstacle cell. Upon initialization, all the cells are set to be traversable and undetermined, declaring that there is no obstacle cell, neither in the map, nor in finite distance (lines 1 to 7). When a cell \(o\) is marked as an obstacle cell by calling **setObstacleCell(o)**, it sets \(dist_{o}\) as 0 and refers to itself as the closest obstacle cell, i.e., \(obst_{o}=o\) (lines 8 and 9). Conversely, when \(o\) is freed by calling **removeObstacleCell(o)**, the function **resetCell(o)** resets it to the initial values, i.e., \(dist_{o}=\infty\) and  
Figure 7: Steps of constructing DM-based subgoal graphs with different safety radius. (**a**) Step1: placing subgoals (\(R\)=1); (**b**) Step2: connecting direct-h-reachable subgoals (\(R\)=1); (**c**) a subgoal graph with \(R\)=1.5; (**d**) a subgoal graph with \(R\)=3.  
\(obst_{o}=\emptyset\) (line 11), and _raise\({}_{o}\)_ is set as true (line 12). The function **insert (OPEN, c, d)** inserts a cell \(c\) into _OPEN_ with a priority value \(d\), or updates the priority if \(c\) is already in _OPEN_.
## 6 Numerical Analysis  
In order to assess the performance of the three optimized SMA-LRBs, the seismic responses of the highway bridge equipped with the SMA-based isolators are calculated and compared with those of the LRB isolated and the non-isolated bridges. These responses include the base shear at the piers, the deck acceleration, relative displacement at the mid-span of the bridge, and the mid-span residual deformation. For the purpose of the comparison, five distinctive sets of dynamic time history analyses must be conducted on the (1) the non-isolated bridge, (2) LRB isolated bridge, (3) SMA-LRB isolated with double-cross wires, (4) SMA-LRB isolated with straight wires and finally (5) SMA-LRB isolated with cross wires. All these models are excited under two real and 10 synthetic ground motion records. The detailed characteristics of these records are mentioned in Section 3. These records are exerted on the bridge only in the longitudinal direction, and between the two horizontal components for each record, the one that leads to more critical responses is chosen. It is worth mentioning that the responses of the non-isolated bridge are calculated by analyzing the non-isolated model (phase I) of the benchmark bridge, which was developed and released by [PERSON] et al. [27].
## 3 Methods for Assessing the Aesthetic Quality of Images
### Deep-Learning-Based Method for Assessing the Aesthetic Quality of Images
#### 3.2.6 Aesthetic Assessment Method of Images Based on Multimodal Attention Networks  
In 2020, [PERSON] [90] proposed a multimodal self-collaborative attention network to address challenges such as the heavy reliance on convolution to extract visual features for image aesthetic assessment and the difficulty in capturing the spatial interaction of visual elements in image synthesis. The self-attentive module computes the response of a location by focusing on all locations in the image, thus enabling spatial interaction of visual elements. To model complex image-text feature relationships, a co-attention module is used to jointly perform text-guided visual attention and visually guided textual attention, and then the participating multimodal features are aggregated and sent to a two-layer multilayer perceptron (MLP) for aesthetic value. [PERSON] [91] 2021 proposed an end-to-end multi-output deep learning based on a multimodal graph convolutional network model for joint image aesthetic and sentiment analysis with joint attention. In the model, a stacked multimodal graph convolutional network is proposed to encode features guided by a correlation matrix and a joint attention module is designed to help image aesthetic and sentiment feature representations learn from each other. [PERSON] [92] divided the aesthetic assessment into multiple modalities, evaluated multiple modalities separately, evaluated each modality more accurately, and finally combined the evaluation of the various modalities to obtain an overall feature vector. A method of aesthetic assessment based on the fusion of multiple modalities is proposed.  
The mainstream technique used for image aesthetic-quality assessment at this stage is still based on deep learning methods. Powerful feature representations learnt from large numbers of data have shown increasing performance on recognition, localization, retrieval and tracking tasks, exceeding the capabilities of traditional manual features. However, deep-learning-based image aesthetics-assessment methods have poor interpretability; on the contrary, non-deep learning methods are more interpretable. Non-deep learning approaches rely mainly on low-level features that do not take into account the semantic information of the image and provide a minimal range of aesthetic grades. Deep learning techniques provide better accuracy compared to non-deep learning techniques, focusing on a broader picture of the image, including both low-level features and high-level features. In addition, deep learning methods require large numbers of data for model training, and these datasets are more important than those used in non-deep learning techniques. Deep learning techniques also require more computational resources and time for training and deploying models.