esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Diagnosis of RIS and BS for RIS-Aided Millimeter-Wave System
## 1 Introduction
Initialization:\(\mathbf{s}^{0}=\mathbf{0}_{N\times 1};\mathbf{\bar{s}}^{0}=\mathbf{0}_{M\times 1};i=0\). for\(0\leq i\leq N_{iter}-1\)do
1 \(\mathbf{1}_{j}=\mathbf{1}_{j}\);
2. \(\mathbf{\boldsymbol{\theta}}^{0}=\mathbf{\bar{y}}\);
3. \(\mathcal{I}=\left[\right]\);
4 for\(\frac{||\mathbf{\boldsymbol{\theta}}||_{2}^{2}}{||\mathbf{\boldsymbol{ \theta}}||_{2}^{2}}\geq\xi\)do
5 4. Estimate the index of non-zero element of RIS blockage \(\mathbf{\bar{s}}^{i+1}\); \(\mathcal{I}_{j}=\underset{j\in[1,M]}{\arg\max}\,||\,\,(\mathbf{A}_{\cdot;j})^{ \text{H}}\mathbf{\boldsymbol{\theta}}^{j}||_{2}^{2}\);
6 5. Update the index: \(\mathcal{I}=[\mathcal{I},\mathcal{I}_{j}]\);
7 6. Estimate the non-zero element: \(\mathbf{\bar{s}}_{\mathcal{I}}^{i+1}=(\mathbf{A}_{\cdot;\mathcal{I}}^{\text{ H}}\mathbf{A}_{\cdot;\mathcal{I}})^{-1}\mathbf{A}_{\cdot;\mathcal{I}}^{\text{H}} \mathbf{\bar{y}}\);
8 7. Update error vector: \(\mathbf{\boldsymbol{\theta}}^{i+1}=\mathbf{\bar{y}}-\mathbf{A}_{\cdot;\mathcal{I }}\mathbf{\bar{s}}_{\mathcal{I}}^{i+1}\)
9 8. \(j=j+1\);
10 end for
11
12 end for
13. \(\mathbf{b}=\mathbf{1}_{N\times 1}+\mathbf{s}^{i}\), \(\mathbf{\overline{b}}=\mathbf{1}_{M\times 1}+\mathbf{\bar{s}}^{i}\); Output:\(\mathbf{b}\) and \(\mathbf{\overline{b}}\). ```  
**Algorithm 1** Proposed algorithm.
## 6 Proposed Scheme
### Smart Grid Device Registration Process  
The \(SD_{i}\) must register with trusted authority \(TA\) to receive the power management services. Figure 6 describes the SG device registration process of proposed scheme and the steps of this process are given below.  
**Step 1:**: \(TA\) generates a random number \(x_{i},a_{i}\) for \(SD_{i}\). After that, \(TA\) computes \(RID_{i}=h(ID_{i}||a_{i})\), \(X_{i}=h(RID_{i}||K_{i}||x_{i})\), \(A_{i}=X_{i}\oplus h(RID_{i}||a_{i})\), and \(B_{i}=h(RID_{i}||X_{i})\) and stores \(\{x_{i},RID_{i}\}\) in secure database. Finally, \(TA\) sends \(\{A_{i},B_{i},a_{i}\}\) to \(SD_{i}\).
**Step 2:**: After receiving the message, \(SD_{i}\) computes \(C_{i}=h(ID_{i}||B_{i})\oplus a_{i}\) and stores \(\{A_{i},B_{i},C_{i}\}\) in the memory.
# A Novel Variable Step Size Incremental Conductance Method with an Adaptive Scaling Factor
## 5 Discussions and Limitations
### Tracking Accuracy  
As indicated by the simulation and experimental results, the proposed novel VSS INC MPPT technology can improve the steady-state oscillation comparing with conventional VSS INC MPPT method; therefore, the tracking accuracy will be better. According to the experimental results, the proposed method can improve the tracking accuracy by 2.1% and 3.5% under the tested increasing and decreasing irradiance level conditions, respectively.
# Fire Behavior and Adhesion of Magnesium Phosphate Coatings for the Protection of Steel Structures
###### Abstract  
This paper presents the main properties of magnesium phosphate cements (MPCs) to be used as coatings for passive fire protection of steel structures. The influence of various additions, i.e., waste glass powder, fly ash, a styrene-acrylic dispersion, and expandable graphite, on the fire behavior and the adhesion to steel substrates of magnesium phosphate coatings is presented in this paper. The setting time of studied cements is extended when magnesia, the main component of MPCs, is partially replaced with fly ash or/and waste glass powder. The mineralogical composition of these cements, before and after thermal treatment at 1050 \({}^{\circ}\)C, was assessed by X-ray diffraction and could explain the changes in compressive strength, volume, and mass recorded for the thermally treated specimens. The studied magnesium phosphate coatings have a good adherence to the steel substrate (assessed by a pull-off test) both before and after direct contact with a flame (fire test) and decrease the temperature of the steel substrate by 30% with respect to the one recorded for the uncoated steel plate.  
fire protection; magnesium phosphate cement; steel; additions; adhesion strength Article
# Fast Coherent Video Style Transfer via Flow Errors Reduction
## 3 Methods
### Loss Functions for Image Sharpness  
Over a long period of video processing, especially for time-lapse videos, some points in frames are propagated from the beginning to the end and the copied pixel values gradually lose their quality, which results in the loss of image quality. To prevent image degeneration, we adopt the Perceptual Losses [10] and a Pixel Loss into the proposed network. The Perceptual Losses constrain the differences between high-level feature representations of the generated image \(\widehat{x}^{t}\) and the current per-frame stylized result \(f_{s}^{t}\). The Pixel Loss preserves the pixel values between the generated image \(\widehat{x}^{t}\) and \(f_{s}^{t}\) in the RGB domain.  
The Perceptual Losses contain a Content Loss \(\mathcal{L}_{con}(\widehat{x}^{t},f_{s}^{t})\), Style Loss \(\mathcal{L}_{sty}(\widehat{x}^{t},f_{s}^{t})\) and Total Variation Regularization \(\mathcal{L}_{tv}(\widehat{x}^{t})\), which can be formulated as follows:  
\[\mathcal{L}_{percz}(\widehat{x}^{t},f_{s}^{t})=\alpha\mathcal{L}_{con}( \widehat{x}^{t},f_{s}^{t})+\beta\mathcal{L}_{sty}(\widehat{x}^{t},f_{s}^{t})+ \gamma\mathcal{L}_{tv} \tag{2}\]  
where \(\alpha\), \(\beta\) and \(\gamma\) are the weights of three loss terms, respectively. In our experiments, the ratio of \(\alpha/\beta\) close to 0.3 produces better image quality, and we set \(\gamma=1e-3\) (default in [10]) in all experiments. The Pixel Loss is defined as the mean square error between the generated image \(\widehat{x}^{t}\) and current stylized result \(f_{s}^{t}\):  
\[\mathcal{L}_{pixel}(\widehat{x}^{t},f_{s}^{t})=\frac{1}{D}\sum_{i,j}^{D}( \widehat{x}^{t}_{(i,j)}-f_{s(i,j)}^{t})^{2} \tag{3}\]  
where \(D=3\times H\times W\) denotes the total number of pixels in the input image and \(H\times W\) denotes the height times width. In this work, we refer to Perceptual Losses and Pixel Loss as our Sharpness Losses which ensure the image's sharpness during the entire video style transfer process. The Sharpness Losses are the combination of Perceptual Losses and Pixel Loss, which is defined as:  
Figure 9: Initialization generation. \(M^{t}\) is a single channel per-pixel mask which is obtained from mask generation. Note that the generated \(\widehat{x}^{t}_{init}\) contains much fewer errors than the warped image \(w^{t}\) in purple and red rectangles, which leads to much fewer iterations needed to compensate for correct pixel values.  
\[\mathcal{L}_{sharpness}=\mathcal{L}_{perce}+\kappa\mathcal{L}_{pixel} \tag{4}\]  
where \(\kappa\) denotes the weight for Pixel Loss.
## 5 Comparison and Discussion Between KANs and MLPs
### Prediction Errors and Discussion  
The training/test set ratio for MLPs was 1:1, with half of the samples randomly selected for training and the other half for testing. However, MLP performance is influenced by the random seed. To mitigate randomness, MLP-A/B/C were trained 20 times sequentially, with average values, standard deviations of error metrics, and runtime recorded in Table 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Category** & **Error** & **Average** & **STD** \\ \hline \multirow{8}{*}{MLP-A} & \multirow{8}{*}{Train} & MSE & 0.0262 & 0.0514 \\  & & MAE & 0.0957 & 0.0897 \\  & & sMAPE & 18.3045 & 11.2367 \\  & & MaxAE & 0.6628 & 0.2226 \\  & & R\({}^{2}\) & 0.9319 & 0.1341 \\  & & Adjusted R\({}^{2}\) & 0.9317 & 0.1345 \\ \cline{2-5}  & \multirow{8}{*}{Test} & MSE & 0.0259 & 0.0508 \\  & & MAE & 0.0957 & 0.0890 \\  & & sMAPE & 19.0729 & 11.4347 \\  & & MaxAE & 0.6283 & 0.2481 \\  & & R\({}^{2}\) & 0.9304 & 0.1358 \\  & & Adjusted R\({}^{2}\) & 0.9302 & 0.1362 \\ \cline{2-5}  & \multirow{8}{*}{Train} & Time & 6.1766 & 0.1811 \\ \cline{2-5}  & & MSE & 0.1284 & 0.1923 \\  & & MAE & 0.2060 & 0.2033 \\  & & sMAPE & 30.6012 & 22.7596 \\  & & MaxAE & 0.9980 & 0.6303 \\  & & R\({}^{2}\) & 0.6644 & 0.4966 \\ \cline{2-5}  & \multirow{8}{*}{Test} & MSE & 0.1272 & 0.1874 \\  & & MAE & 0.2064 & 0.2036 \\  & & sMAPE & 31.3365 & 22.8278 \\  & & MaxAE & 0.9181 & 0.6418 \\  & & R\({}^{2}\) & 0.6608 & 0.5053 \\  & & Adjusted R\({}^{2}\) & 0.6598 & 0.5069 \\ \hline \multirow{8}{*}{MLP-B} & \multirow{8}{*}{Train} & Time & 7.1237 & 0.2635 \\  & & MSE & 0.0064 & 0.0096 \\  & & MAE & 0.0506 & 0.0421 \\  & & sMAPE & 12.2323 & 7.2257 \\  & & MaxAE & 0.5859 & 0.1206 \\  & & R\({}^{2}\) & 0.9830 & 0.0255 \\  & & Adjusted R\({}^{2}\) & 0.9830 & 0.0256 \\ \hline \multirow{8}{*}{MLP-C} & \multirow{8}{*}{Train} & MSE & 0.0064 & 0.0093 \\  & & MAE & 0.0512 & 0.0419 \\ \cline{1-1}  & & sMAPE & 12.8230 & 7.3307 \\ \cline{1-1}  & & MaxAE & 0.5565 & 0.1856 \\ \cline{1-1}  & & R\({}^{2}\) & 0.9831 & 0.0244 \\ \cline{1-1}  & & Adjusted R\({}^{2}\) & 0.9830 & 0.0245 \\ \hline \multicolumn{2}{c}{Runtime} & Time & 6.8627 & 0.2627 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Prediction errors of MLP-A/B/C.
###### Abstract  
Recently, a layer-stacked ESN model named deep echo state network (DeepESN) has been established. As an interactional model of a recurrent neural network and deep neural network, investigations of DeepESN are of significant importance in both areas. Optimizing the structure of neural networks remains a common task in artificial neural networks, and the question of how many neurons should be used in each layer of DeepESN must be stressed. In this paper, our aim is to solve the problem of choosing the optimized size of DeepESN. Inspired by the sensitive iterative pruning algorithm, a neuronal similarity-based iterative pruning merging algorithm (NS-IPMA) is proposed to iteratively prune or merge the most similar neurons in DeepESN. Two chaotic time series prediction tasks are applied to demonstrate the effectiveness of NS-IPMA. The results show that the DeepESN pruned by NS-IPMA outperforms the unpruned DeepESN with the same network size, and NS-IPMA is a feasible and superior approach to improving the generalization performance of DeepESN. The newly proposed method has broad application prospects in real-time systems.  
reservoir computing; deep echo state network; neuronal similarity-based iterative pruning merging algorithm; chaotic time series forecast
# N-Heterocyclic Carbene-Gold(I) Complexes Targeting Actin Polymerization
## 3 Results
### AuL4 and AuL7 Interface with the Normal Intracellular Actin Organization
Considering that LA can trigger the F-actin disassembly, we performed a depolymerization assay, in order to determine whether our compounds could act as actin depolymerizing agents. Actin polymerization was allowed for one hour, under the same experimental conditions used in the previous assay, then **AuL4** or **AuL7** were added, at the concentration of 5 \(\upmu\)M, and the reactions were monitored for one more hour. Negative (only vehicle and CB, 5 \(\upmu\)M) and positive (LA, 5 \(\upmu\)M) control reactions were performed, as well. Our results showed that the LA curve sensibly decreased until a value of 16,800 RFU in the first 8-9 min, meaning that the actin depolymerization was occurring (Figure 7). The initial value was around 28,000 RFU and after 9 min, the curve decreased slowly until the final value of 14,000 RFU. The negative control reactions (only vehicle and CB, 5 \(\upmu\)M) showed no relevant changes, maintaining the same initial value and indicating that the F-actin did not depolymerizes. The curves relative to **AuL4** and **AuL7** showed significant reduction in RFU values with respect to the control reactions, suggesting that they did not accelerate the depolymerization reaction. To summarize, these data proved that both **AuL4**  
Figure 6: In vitro actin polymerization assay. The effect of compounds **AuL4** and **AuL7** (5 \(\upmu\)M) on in vitro actin polymerization was examined. DMSO was used as a negative control (CTRL). Latrunculin A (LA) and cytochalasin B (CB), were employed as reference molecules and used at the concentration of 5 \(\upmu\)M. **AuL4** and **AuL7**, incubated with the labeled rabbit muscle actin, demonstrate a direct effect on actin polymerization reaction. The graphic was obtained measuring the fluorescence (Ex/Em: 365/410 nm) given by actin filaments assembly in a kinetic mode for 1 h at room temperature. Data are representative of three independent experiments; standard deviations (SDs) are shown. and **AuL7** are actin polymerization inhibitors with an efficacy similar to CB, used at the same concentrations and under the adopted experimental conditions, and do not accelerate actin depolymerization, contrarily to LA.
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 3 AIoT Applications in PV Systems: A Comprehensive Review
### Applications of AI in PV Systems
#### 3.1.2 Predictive Maintenance
By addressing challenges like dynamic weather variations and regular shadowing, the unshared convolutional model demonstrated superior performance, showcasing the potential of CNNs for accurate anomaly detection in PV systems. [PERSON] et al. [103] further advanced predictive maintenance strategies by introducing a big data-driven framework for PV plants. Their dual-model architecture combined a Supervision-Diagnosis Model (SDM), which employed Self-Organizing Maps (SOMs) for detecting operational deviations, and a Short-Term Fault Prediction Model (FPM), which utilized a Pattern Recognition Neural Network (ANN) to forecast specific fault classes. Tested on six PV plants, the system demonstrated the ability to predict faults up to 7 days in advance with high sensitivity and specificity, enhancing reliability and reducing downtime. To address the scalability challenges of large PV plants, [PERSON] et al. [104] proposed a hybrid ML approach integrating K-Means clustering and LSTM networks. The K-Means algorithm clustered operational patterns based on meteorological data, while LSTM accurately predicted deviations in electrical currents for precise anomaly detection. This hybrid approach demonstrated superior performance over conventional ANN models, offering a scalable and cost-effective solution for predictive maintenance in large-scale PV systems. Similarly, [PERSON] et al. [105] developed a data-driven routine combining XGBoost, One-Class SVM, and the Facebook Prophet algorithm. Their methodology analyzed performance trends to generate predictive maintenance alerts up to 7 days in advance. Validated on a 1.8 MW PV power plant, the system achieved a sensitivity of 92.9% and a predictive accuracy of 99.4%, demonstrating its robustness in detecting underperformance conditions such as inverter shutdowns and string disconnections. Table 6 provides a comparative overview of these studies, highlighting their methodologies, data requirements, fault types addressed, key contributions, and limitations.
# A Study on the Analysis of the Effects of Passenger Ship Abandonment Training Using VR
## 4 Results of Video and VR Passenger Ship Abandonment Training
### Comparison of Educational Effects of Video and VR Training  
Immediately after the training, 20 examination questions were prepared in relation to the contents of the 20 items on the passenger ship abandonment training content listed in Table 2; the participants in Group 1 and Group 2 were then examined based on their knowledge of the items. In the examination immediately after training, the score of Group 1 (the video-based training group) was higher by 2.3 points (on average) than that of Group 2. After 30 days, the 60 participants in Group 1 and Group 2 took an examination with the same questions; the score of the group with the video-based training method was 68.7 points, whereas the score of the group with the VR-based training method was 71.5 points. Clearly, the score of the VR Group 2 was higher by 2.8 points.  
Regarding the decrease in scores over time, after 1 month, the \"retention of knowledge\" was higher in the VR group than in the video group; that is, the score difference was 3.5 for the VR group, whereas the score difference was 9.1 for the video group, confirming that the long-term effect on the training content using VR-based methods proved to be more effective. This result shows that training using VR was equivalent or more effective compared to the video-based training currently employed in MET. It can be argued that, in some respects, VR-based training serves as a superior training tool. Figure 8 shows the first and second results of the examinations of Group 1 and Group 2.
## 3 Maintenance and Lifespan Management
### Maintenance-based Asset Management
#### 3.2.2 Maintenance and Risk Management  
Relating to maintenance and risk management, [PERSON] [198], with two risk levels, implements a planning model in an electrical system. [PERSON] and [PERSON] (2004) [199] design the methodology to restore electricity supply from the management of diffuse risks. [PERSON] et al. [200] propose an optimal method based on ANN; the authors propose a simple neuronal architecture, where their training has a reduced computational cost.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & Maintenance and Lifespan Management: Developing the Means that Enable Signals to be Digitally Acquired \\ \cline{2-3}
**Summary of Developing the Means that enable Signals to be** & \multicolumn{2}{c}{**Optical Digital**} \\ \cline{2-3}
**Digitally Acquired** & \multicolumn{2}{c}{**Transformers/Intelligence/Capturing**} & \multicolumn{2}{c}{**Development and Implementation**} \\ \cline{2-3}
**Sensors and Parameters** & \multicolumn{2}{c}{**of the IEC 61850**} \\ \hline
**Standardization and** & \multicolumn{2}{c}{} \\ \cline{2-3}
**normalization** & \multicolumn{2}{c}{-} & [174; 175; 176; 177; 178] \\ \hline
**Partial Discharges** & [180; 181; 182; 183; 184; 185; 186; 187; 188; 189] & - \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of Developing the Means that enable Signals to be Digitally Acquired.  
[PERSON] and [PERSON] (2010) propose a mechanism to identify the layout of the sequence of activities for distribution component repair based on statistical techniques. The relationship between preventive and corrective maintenance can be approached from an optimization problem perspective, and its solution can be based on a particle swarm optimization algorithm, as presented by [PERSON] et al. (2020).  
Interesting work is the one presented by [PERSON] et al. (2003), the authors define a complete program to inspect distribution facilities and optimally; the method is based on a complex algorithm. The algorithm is based on two sections: estimation of inspection interval and optimal schedule generation. The algorithm manages to reduce the cost of inspection by 14%, and also guarantees the stability of the system.  
By means of a laser monitoring, it is possible to perform the maintenance of distribution lines, as presented by [PERSON] et al. (2004). These data are crossed checked with GIS and control information.  
[PERSON] et al. (2005) present a novel maintenance program from decision-varying Markov models, and [PERSON] et al. (2006) combine Markov with a modified genetic algorithm.  
To summarize, Table 8 shows all the work described in this section. In the rows are shown different areas (asset management and risk management) while in the columns is shown the application rate on maintenance based on asset lifecycle management. As can be seen in the previous table, maintenance and asset management is reinforced by AI and other simpler techniques. Therefore, these management tasks will be essential for operators, and the use of advanced techniques essential for its implementation and use.
## 6 Conclusions
The results indicated that the position RMS of the GC-PPP-RTK/INS/vision tightly coupled integration were 0.08 m and 0.09 m in the horizontal and vertical directions, respectively, which could fully meet the demands of vehicle navigation in urban areas. However, the introduction of vision significantly improved the positioning performance in both the horizontal and vertical directions for T02. The RMS of GC-PPP-RTK/INS/vision reached 0.83 m and 0.91 m in the horizontal and vertical directions, respectively. It improved the positioning accuracy by 54.9% and 7.1% in the horizontal and vertical directions, respectively, compared with GC-PPP-RTK/INS. Additionally, the velocity and attitude estimation performance were also analyzed in this paper. The inclusion of vision improved the velocity performance by more than 25% in the north, east, and down directions in a GNSS-challenged environment. As for attitude, there was no obvious difference with vision in the roll and pitch angles, but GC-PPP-RTK/INS/vision performed much better in the estimation of the yaw angle. The improvements brought about by vision were more than 30% in both tests. The results show that GC-PPP-RTK/INS/vision integration with a cascading filter performs best in the position, velocity, and attitude estimations compared with the other solutions. Multi-GNSS, INS, and vision can play their respective roles and achieve complementary advantages in vehicle navigation in urban areas. However, navigation performance in real-time still deserves further study. [PERSON], [PERSON] and [PERSON] carried out the research and the experiment. [PERSON] helped to solve the atmosphere augmentation products and UPD products; [PERSON] and [PERSON] analyzed the results and drafted the paper. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 42174029. The GNSS observation, region atmosphere augmentation products, UPD products, IMU, and vision measurement data can be accessed from s[[ftp://59.172.178.34:10016](ftp://59.172.178.34:10016)]([ftp://59.172.178.34:10016](ftp://59.172.178.34:10016)) (accessed on 10 August 2022), username: tmp-user, password: Multi-sensor@FUSING. For example, input the command \"sftp -P 10016 [EMAIL_ADDRESS]\". The authors declare no conflict of interest.