esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Infrared Image SR Experiment Without Noise  
In the experiment, the LR infrared images without noise were generated by applying the down-sampling operator (two-fold, three-fold, four-fold). To evaluate the performance objectively, PSNR and SSIM were calculated at various levels of the super-resolving operator (correspondingly, x2, x3, and x4) and are listed in Table 1.  
In the SRR of the LR images with the two-fold downsampled operator, the PSNR of the FTV4L\({}_{p}\) method is the highest among the sets of results; however, the average PSNR of the six pictures is slightly higher than TGV, as well as being higher than the other methods. Furthermore, the SSIM of FTV4L\({}_{p}\) is good only in the image of birds; however, the overall SSIM is not as good as the TV4 and TGV methods, even though TV4 has a lower PSNR in four pictures. As the super-resolving operator increases, the results of FTV4L\({}_{p}\) are superior to the other algorithms. In terms of the computation time, the Bicubic method takes the shortest time, while MFT method takes the longest time, and the proposed method takes almost the same time as the TV4 and FTV4 methods. For a visual comparison, we used the LR infrared images of birds, passerby, and a figure, which are downsampled by four-fold in Figure 4.  
Based on the results of the reconstruction of SR infrared images in Figures 5-7, the edges of images reconstructed using the TV method are relatively clearer than the edges of images reconstructed using the Bicubic and MFT methods; however, staircase artifacts can be observed in the smooth regions of the images. The TGV, TV4, and FTV4 methods lead to improvements in terms of the staircase artifacts; however, many obvious \"spots\" remain in the bird and passerby images. In contrast, our proposed method performs better than the other four methods in terms of preserving image edges, as well as reducing staircase artifacts and \"spots\" effects. Figure 3: Comparisons of the convergence between the fast ADMM and plug-and-play ADMM. White Gaussian noise was added to the images: (**a**) birds, (**b**) streets, (**c**) crossroads, (**d**) buildings, (**e**) passerby, and (**f**) figure.
## 3 Results and Discussion
### Overhead Position  
The observation of molten pool behavior with a longitudinal direction in the overhead position is shown in Figure 3(a). The dynamic arc imaging synchronization of waveforms is shown in Figure 3(b). In the overhead welding position, the direction of gravity is opposite to the flat position. The tail of the molten pool in the overhead position affected the strong backward flow because the reverse gravity direction combined with the backward flow. Hence, more molten metal was accumulated in the tail of the molten pool and reverse gravity extracts the molten pool into the top surface direction. These molten pool flow patterns increased the bead height as shown in Figure 5.  
[PERSON] et al. [22] established a unified 3D model to investigate the formation mechanism of a humping bead in high-speed GMAW and found that the strong backward fluid metal flow in the weld pool was the major reason for the humping formation. The combined effect of large arc pressure and droplet impact caused severe depression of the molten pool surface at the arc center [23]. The WFR of 9 m/min increased the values of droplet frequency (217.3 HZ) as shown in Figure 6. Increasing the droplet frequency caused more molten metal flow into the tail of the molten pool. Therefore, a strong backward flow weakens the molten pool layer in the arc center and breaks the molten pool bridge. Hence, increasing the WFR caused the humping bead formation, as shown in Figure 7.  
Figure 3: Synchronization of (**a**) arc images and (**b**) welding signals with a wire feed rate (WFR) of 7 m/min.  
Figure 4: Synchronization of (**a**) arc images and (**b**) welding signals at a WFR of 7 m/min.  
Figure 5: Characteristics of overhead welding position.
## 2 Methodology  
### SBAS-InSAR Technology Based on the Linear Model  
The basic principle of SBAS-InSAR technology is to generate time series deformation based on a least square method, with the deformation maps generated by multi-single D-InSAR as the input observation dataset [23; 24]. Firstly, as N + 1 SAR images obtained at dates (\(t_{0}\),..., \(t_{n}\)), covering the same area, M interferometric pairs can be produced following the inequality \(\frac{N}{2}\leq M\leq\frac{N(N-1)}{2}\). Then, one SAR image can be selected as the super master image. The unwrapped phase at pixel \((x,r)\) in the \(j^{th}\) interferogram with the time period from dates \(t_{A}\) to \(t_{B}(t_{B}>t_{A})\), can be expressed as [25]  
\[\begin{array}{l}\delta\phi_{j}(x,r)=\phi_{B}(x,r)-\phi_{A}(x,r)\\ \approx\frac{4\pi}{\lambda}[d(t_{B},x,r)-d(t_{A},x,r)]+\frac{4\pi}{\lambda} \frac{B_{\perp j}\Delta z(x,r)}{rj\pi i\theta_{j}}+\Delta\phi_{APS}^{j}(t_{B},t_{A},x,r)+\Delta\phi_{res}^{j}(x,r)\end{array} \tag{1}\]  
where \(j\in(1,\,\ldots\,,\,M)\) is the order number of interferometric pairs. M is the total number of interferometric pairs. \((x,r)\) defines the SAR coordinates along range and azimuth direction, respectively. \(\lambda\) is the radar wavelength (3.2 mm for X-band TerraSAR-X data); \(d(t_{B},x,r)\) and \(\mathrm{d}(t_{A},x,r)\) are line-of-sight (LOS) cumulative deformation, referred to as low-pass (LP) deformation component, at dates \(t_{A}\) and \(t_{B}\), respectively. \(B_{\perp j}\) is the spatial baseline and \(\Delta z\) represents the DEM residuals. \(r_{j}\) represents the center range between the sensor and ground point. \(\Delta\phi_{APS}^{j}\) is the phase contribution related to atmospheric delay. \(\Delta\phi_{res}^{j}\) is the residual phase, including phase noise, orbit error, and high-pass (HP) deformation component [22].  
If the linear model is utilized to model the functional relationship between deformation and settlement rate, \(d\) in Equation (1) can be expressed as  
\[d(t_{B},x,r)-d(t_{A},x,r)=\sum_{j=p+1}^{q}v_{j}\Big{(}t_{j}-t_{j-1}\Big{)} \tag{2}\]  
where \(p\) and \(q\) represent the index of master image at date \(t_{A}\) and slave image at date \(t_{B}\), respectively, for i-th interferometric pair. The unknown parameters here include elevation corrections \(\Delta z\) and linear rates \(v_{j}\) over different periods, which can be estimated through a least squares method. Obviously, the coefficient matrix of Equation (2) is a singular matrix. In order to solve the singular mathematical problem, a singular value decomposition (SVD) algorithm is used here [26]. The linear rates of each temporal unit can be estimated through the SVD algorithm. Consequently, the deformational component can be obtained by integrating the velocity of each period, and the final time series deformation results can be obtained [27; 28; 29].
## 5 Experimental Results and Analysis
### Experimental Details  
The experiments were conducted in an environment configured with both advanced software and hardware components to ensure optimal performance. The software environment included Ubuntu 20.04 as the operating system, Python 3.8 for programming, PyTorch 1.10.0 as the deep learning framework, and CUDA 11.3 for GPU acceleration. On the hardware side, the setup featured an RTX 4090 GPU (NVIDIA, Santa Clara, CA, USA) with 24 GB of memory, coupled with an AMD EPYC 9654 96-Core Processor (AMD, Santa Clara, CA, USA), providing substantial computational power for efficient data processing and model training.  
Setting of HyperParameters: For the training phase, we initialized the model with a learning rate of 0.01, which was progressively reduced during training, with the final learning rate set to 1% of the initial rate (0.0001). The stochastic gradient descent (SGD) optimizer was employed with a momentum of 0.937 and a weight decay of 5 \(\times\) 10\({}^{-4}\) to help prevent overfitting. A warm-up strategy was applied over the first 3 epochs, during which the momentum started at 0.8, and the bias learning rate was set to 0.1. This warm-up period helped the model stabilize before reaching the main training phase. The batch size was set to 64 to ensure both training efficiency and stability. Additionally, several loss functions were used to enhance detection accuracy: a box loss gain of 7.5, classification loss gain of 0.5, DFL (distribution focal loss) gain of 1.5, and a keypoint object loss gain of 1.0. These carefully tuned hyperparameters ensured robust performance in detecting student behaviors across various classroom settings.  
Data Augmentation: We employed several data augmentation techniques during training, including HSV space transformation (Hue \(\pm\) 1.5%, Saturation \(\pm\) 70%, Value \(\pm\) 40%), horizontal and vertical translation (\(\pm\)10%), scaling (\(\pm\)50%), 50% probability of horizontal flip, and Mosaic augmentation applied at 100%. These techniques enhanced the model's robustness to varying target scales, orientations, and class imbalance.
## 2 Related Work
### Task Offloading Algorithm
After a certain number of learning sessions, the deep network parameters reach convergence, at which point the task offloading strategy achieves better results than greedy algorithms, heuristic algorithms, and metaheuristics and does not increase the time required for decision making due to the complexity of the model. However, this method relies on a large amount of sample data, having the same problems as traditional DRL of low utilization of samples, slow learning speed, and poor adaptability. In DAG task offloading, different applications correspond to different DAG applications, and, when users use other applications, the corresponding network parameters are invalidated because of the change in application characteristics. Touse the task offloading module on different DAG tasks, the network must be retrained for each type of DAG task. This takes a long time and is hard to do in real life. The meta reinforcement learning (MRL) framework [21] is able to use the experience of a series of different tasks to accelerate the learning of new tasks as a way to achieve rapid adaptation to different tasks. Generally speaking, MRL is a two-stage learning process: the first stage is outer-loop learning, which aims to learn the common experience of a series of different tasks and requires more computational resources; the second stage is inner-loop learning, which builds on the common experience learned in the outer loop and fine-tunes the experience according to the task under its responsibility to make the strategy better fit its task [22]. There are numerous advantages to using MRL to solve the computational offloading problem in MEC systems. First, because mobile devices run different applications, their offloading policies are not always the same, and MRL technology allows mobile devices to use the learning experience of other devices to quickly adjust their own offloading policies, unlike reinforcement learning, which requires retraining. Second, the system's outer-loop learning, which is performed on the more powerful edge server, and the system's inner-loop learning, which can be performed on mobile devices, are both performed on the more powerful edge server. This makes full use of the edge server's and mobile devices' resources. Inner-loop training can be performed on mobile devices with limited computation and data since it just requires a few simple steps and a small quantity of sampled data. This learning strategy makes full use of all the capabilities in the system and can speed up the training. As a result, this element of the research has great practical significance.
## 5 Conclusions  
This review aimed to address the characteristics and formation process of the microalgae-bacteria consortium, commonly known as a MABA, as well as the research conducted using this consortium with several reactors' configurations. Studies demonstrate that MABAs effectively remove COD, TN, and TP, with continuous flow reactors exhibiting lower installation and operating costs. In sequential batch reactor configurations, better extracellular polymeric substance formation is observed, resulting in more stable and sedimentable granules, whereas higher concentrations of microalgae do not favor good aggregate formation. However, high-rate ponds are the most widely used configurations on a full scale, allowing for good sedimentation of the biomass and the production of value-added products from the microalgae.  
Light intensity plays a crucial role in system operation, as aggregates assimilate CO2 during the light period. The dark phase, in the form of HCO3\({}^{-}\), facilitates the generation of biomass associated with extracellular polymeric substances. However, there is a pressing need to thoroughly investigate the performance and stability of microalgae-bacteria biomass resulting from varying reactor designs and applied operating conditions. This is essential for identifying systems with optimal financial and operational viability for real-scale implementation. It is worth nothing that achieving a more stable microalgae-bacteria consortium results in aggregates less prone to disintegration. This not only enhances nutrient removal capabilities but also enables the extraction of value-added products. Lastly, the main challenge in operating these systems is maintaining the microalgae and making operational adjustments to the reactors. These adjustments aim to increase the sedimentability of the biomass generated from effluent treatment, facilitating biomass recovery. Once separated and harvested, this biomass can serve as an alternative source of energy, animal feed, fertilizer production, and biofuel generation.  
**Author Contributions:** Conceptualization, J.S.; methodology, J.S., A.G.d.S.N., T.M. and M.S.; formal analysis, J.S., I.M., M.T.K. and R.S., investigation, J.S., A.G.d.S.N., T.M., M.S. and I.M.; visualization, J.S., A.A. and L.F.; writing--original draft, J.S.; writing--review and editing, J.S., M.T.K., R.S., A.A. and L.F.; resources, A.A. and L.F.; project administration, M.T.K., R.S., A.A. and L.F.; supervision, M.T.K., R.S., A.A. and L.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors wish to acknowledge the support obtained from Science and Technology Foundation of Pernambuco (FACEPE, Brazil) and by the Foundation for Science and Technology (FCT, Portugal).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Asynchronous Partial Gaussian Approximation Detection Algorithm for Uplink-Grouped MIMO-SCMA System
###### Abstract
[22] proposed a partially active message passing based on the Gaussian approximation principle and a sliding window is introduced to determine what user stays active during each detection. The combination of MIMO and SCMA can enhance the performance greatly but it also adds computing complexity at the same time. Ref. [23] proposed a multi-dimensional space-time block code (MSTBC)-aided downlink MIMO-SCMA, which has almost the same complexity but better performance. In [24], a low-complexity codebook design algorithm based on the cross-entropy method and low-complexity MIMO-SCMA codeword detectors based on depth-first and breadth-first tree-search algorithms was proposed. Ref. [25] proposed a new scheme, named spatial modulation sparse code multiple access (SM-SCMA). At the receiver, a low-complexity joint message-passing algorithm is proposed. Targeting the problems above, this paper constructs a grouped-detection scheme, in which users with similar delays are grouped together for the ease of joint detection. First, we assume that the scheme in the same group is perfect synchronization, which is to say that the delays of users' information in the same group are exactly the same and delays are different between different groups. A message-passing algorithm based on serial propagation (SP-MPA) is proposed and utilized to propagate the updated information to the next symbol as the initial information. This method can effectively cut down the bit error rate because it sufficiently utilizes the same information without recalculation. Then, we consider the imperfect synchronization of users in the same group. In this scheme, performance undergoes a sharp degradation because of the interference of the delayed symbols. Therefore, we propose a partial Gaussian approximation-aided (PGA) algorithm to compensate the error and improve the transmission accuracy when ensuring lower complexity compared to original SP-MPA in the MIMO-SCMA system. The contents of this paper are outlined as follows. Section 2 represents the asynchronous SCMA system model. Section 3 describes the detailed process of asynchronous detection using SP-MPA. The imperfect scheme in the same group and the process of PGA are illustrated in Section 4. Section 5 shows the final results of these algorithms, such as bit error rate and complexity. Finally, conclusions are drawn in Section 6.
## 4 Experiments and Analysis
### Experimental Results and Analysis  
Experiments were undertaken to verify the classification performance of the proposed SSECRF algorithm. At the same time, the SSECRF result was compared with the result of the SSE feature set input into the SVM classifier, and the SSECRF result was also compared with the other existing methods.  
.2.1 Validity Analysis of the Multi-Feature Fusion Framework for the High-Resolution Visible Imagery  
In order to verify the effectiveness of the features extracted from the visible imagery (VDVI, texture feature, and object-based feature map), the features were individually fused with the visible data and classified by the SVM algorithm.  
From Table 2, it can be seen that the use of only the visible imagery can obtain a desirable classification result. However, the overall accuracy (OA) and kappa coefficient are improved greatly using the composite of VIS + Tex. + Obj. + VDVI (i.e., the spatial-spectral feature set), indicating that the extracted features are effective in the classification task.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **VIS 1** & **VIS + VDVI 2** & **VIS + Obj. 3** & **VIS + Tex. 4** & **VIS + Tex. + Obj.** & **VIS + Tex. + Obj. + VDVI** \\ \hline
**OA** & 82.3218 & 82.4446 & 81.8945 & 81.7489 & 84.9826 & 85.1477 \\
**Kappa** & 0.7438 & 0.7452 & 0.7427 & 0.7401 & 0.7835 & 0.7857 \\ \hline \hline \end{tabular}  
* VIS is visible imagery. 2 Visible Difference Vegetation Index (VDVI) is regarded as the VDVI feature map. 3 Obj represents the object-based feature map. 4 Tex. represents the texture feature map calculated with the visible imagery.  
\end{table}
Table 2: Accuracy assessment for the visible imagery with different combinations of extracted features, classified by support vector machine (SVM).  
After separate fusion of some of the features extracted from the visible data, the accuracy of certain classes increases or decreases slightly, as shown in Figure 4. This indicates that some combinations of feature maps are sensitive to specific classes. For instance, when adding the object-based feature in visible imagery, trees and grey roofs can be identified well, but the accuracy of vegetation is decreased. Finally, it can be expected that the OA will be further improved by integrating the emissivity information retrieved from the thermal infrared hyperspectral imagery data.
# Concrete Dam Deformation Prediction Model Research Based on SSA-LSTM
## 3 SSA-LSTM Based Dam Deformation Prediction Model
### Prediction Accuracy Evaluation Index  
To assess the accuracy of the predictions, this model employs three evaluation metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). These metrics are used comprehensively to provide a comprehensive assessment of the model's performance. The specific calculation formula is shown in Equations (17)-(19).  
\[\text{MAE}(y_{i},\hat{y})=\frac{1}{m}\sum_{1}^{m}|y_{i-}\hat{y}| \tag{17}\]  
\[\text{MAPE}(y_{i},\hat{y})=\frac{1}{m}\sum_{1}^{m}\frac{|y_{i-}\hat{y}|}{|y_{i }|} \tag{18}\]  
\[\text{RMSE}(y_{i},\hat{y})=\sqrt{\frac{1}{m}\sum_{1}^{m}(y_{i-}\hat{y})^{2}} \tag{19}\]  
where \(\hat{y}\) represents the predicted value of the \(i\)-th sample, \(y_{i}\) represents the corresponding true value, and \(m\) represents the total number of data to be predicted.  
MAE reflects the actual situation of prediction error, MAPE evaluates the model accuracy, and RMSE is used to evaluate the prediction accuracy. For example, smaller values of the three parameters indicate higher accuracy of the prediction model and a better prediction effect.
## 3 Results
### Calculation and Comparison of Friction Coefficient  
For better comparison of recorded friction forces, friction coefficients were calculated (Figure 2b). As expected, the highest friction coefficient was found at a speed of 1.5 mm/s (0.59 \(\pm\) 0.12) followed by 2.0 mm/s (0.45 \(\pm\) 0.11), 1.0 mm/s (0.27 \(\pm\) 0.11) and 0.5 mm/s (0.26 \(\pm\) 0.11). The lowest friction coefficient was calculated for a pulling speed of 0.1 mm/s (0.24 \(\pm\) 0.13). Statistical analysis revealed that friction coefficients obtained at 1.5 and 2.0 mm/s are significantly higher compared to 0.1 mm/s, while friction coefficient at 0.5 and 1.0 mm/s showed no significant difference.  
Taken together, obtained data clearly indicate a significant impact of the pulling speed on friction forces and friction coefficients. Friction coefficients are significantly increased when pulling speed raises. Interestingly, significant highest values were recorded at 1.5 mm/s followed by 2.0 mm/s and significant lowest values occur at 0.1 mm/s.