esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Demand-Side Management Classifications and Their Roles in Cost Minimization
### Classification of DSM
#### 2.1.2 Load Scheduling  
In this type of DSM, consumer demand is regulated by employing energy efficient algorithms and optimal demand side management schemes. The optimality of these schemes and algorithms is generally measured in terms of minimization of the peak-to-average power ratio (PAPR), generation cost, and consumers' electricity bills. PAPR is reduced by shifting the high power rating appliances to off-peak hours [108]. Load scheduling is further classified into subclasses which include DSM based on energy pricing, DSM based on energy consumption scheduling, and DSM based on home energy management. These are discussed in detail in the upcoming subsection, as shown in Figure 4.  
Consumer demand plays an important role in energy management systems. Various DSM schemes have been utilized to manage consumer load. A dynamic programming based DSM scheme was suggested by the authors of [89; 109]. In this algorithm, the consumer's profit is maximized and the overall cost of production by using the smart distributed network is minimized. The demand during peak hours is one of the critical and most important factors in DSM. The authors of [110] used a fuzzy system approach to reduce the consumers' peak hours demands. In this fuzzy system based DSM, the authors gave input in the form of demand during peak hours at one end of the system and get the the corresponding DSM as the output of the system. Similarly, the authors of [111] proposed a DSM scheme considering multiple utilities based on game theory. The game consists of two levels; at the lower level, i.e., an evolutionary game, the consumers manage their demands and power prices correspondingly and at the upper level, the problem of multiple suppliers is modeled as a non-cooperative game. There are plenty of research articles available in the literature with the aim of minimizing costs, consumers' electricity bills, and the peak to average power ratio, etc, by employing efficient DSM schemes. These research articles along with the objectives achieved and advantages associated with each article are summarized in Table 7. To achieve these objectives, authors have utilized different  
Figure 4: Classification of DSM. optimization approaches. The various optimization approaches employed in DSM schemes along with the relevant references are shown in Figure 5.  
Load scheduling is further classified into subclasses. The details for these subclasses are provided as follows.
# Chaotic Vibration Prediction of a Laminated Composite Cantilever Beam
###### Abstract  
The deep learning method of the recurrent neural network (RNN) is applied to predict the chaotic vibrations of a laminated composite cantilever beam. The RNN model converts time series data into a multi-step supervised learning format and normalizes it using MinMaxScaler. The cantilever structure is subjected to an evenly distributed load, and a series of chaotic vibrations are observed corresponding to different amplitudes and angular velocities of the load. Then, the RNN data-driven model is applied to predict chaotic vibrations, and the chaotic vibration prediction of RNN is evaluated. The prediction results are primarily evaluated using two metrics: mean absolute error (MAE) and root mean square error (RMSE). The analysis results show that the maximum MAE is 0.041 and the maximum RMSE is 0.067. Even under perturbed initial conditions, the RNN model maintained high prediction accuracy, with a maximum MAE of 0.022 and RMSE of 0.038, highlighting its robustness and reliability in predicting chaotic vibrations. The error analysis indicates that the RNN accurately predicts chaotic vibrations with a high degree of precision.  
deep learning; neural networks; RNN; chaotic vibration prediction; laminated composite beam; cantilever structure Article
## 3 Results
### Analysis of the Effect of Post-processing  
The post-processing method was designed to eliminate the connected components (in the segmentation yielded by the previous step) smaller than a certain threshold. To evaluate the effect of post-processing routines, we compared the segmentation performance obtained before and after the post-processing step. The ensemble model performed reasonably well in all the experiments; however, post-processing step further improved the segmentation (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Performance Measures** & \multicolumn{3}{c}{**Scanner-Specific**} & \multicolumn{3}{c}{**Scanner-Agnostic**} & \multicolumn{3}{c}{**Across-Scanner**} \\ \cline{2-7}  & **I** & **II** & **III** & **Combined** & **I** & **II** & **III** \\ \hline Dice score & 90.54 & 92.75 & 91.03 & 92.02 & 90.34 & 91.36 & 90.25 \\ Accuracy & 89.65 & 91.98 & 89.63 & 91.78 & 88.76 & 90.87 & 89.87 \\ Sensitivity & 88.78 & 93.39 & 92.89 & 93.98 & 92.43 & 93.69 & 92.59 \\ Specificity & 90.23 & 90.68 & 90.87 & 90.65 & 87.43 & 88.36 & 91.78 \\ AUC & 0.916 & 0.917 & 0.899 & 0.907 & 0.891 & 0.907 & 0.799 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of the ensemble model in terms of various performance measures in scanner-specific, scanner-agnostic, and across-scanner configurations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance Measures** & \multicolumn{3}{c}{**Scanner-Specific**} & \multicolumn{3}{c}{**Scanner-Agnostic**} \\ \cline{2-5}  & **I** & **II** & **III** & **Combined** \\ \hline Dice score & 81.93 & 81.65 & 82.65 & 82.65 \\ Accuracy & 80.76 & 82.65 & 80.76 & 79.76 \\ Sensitivity & 79.23 & 80.32 & 83.34 & 82.34 \\ Specificity & 82.87 & 82.65 & 81.87 & 81.87 \\ AUC & 0.816 & 0.815 & 0.809 & 0.813 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of ResNet model in terms of various performance measures in scanner specific and scanner-agnostic configurations. I, II, and III correspond to Dataset-I, Dataset-II, and Dataset-III.
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
## 4 Theoretical Background and Simulation Methodology
### Choice of Approach to Channel Capacity Calculation  
The NS-3 simulator allows two methods of channel capacity calculation. Methodologies of both algorithms were compared and described by demonstration results in order to choose the method for the simulations.
# STAVOS: A Medaka Larval Cardiac Video Segmentation Method Based on Deep Learning
## 4 Experiment
### Comparative Experiments  
Due to the limited size of the medaka dataset, both the test and validation sets consist of only 1-2 videos. Consequently, the dataset lacks diversity, resulting in experimental outcomes being significantly dependent on the specific videos used in the dataset. Therefore, we chose to assess the framework's performance using the DAVIS2016 dataset instead of the medaka dataset. However, the test set for DAVIS2016 is not publicly available and is only provided for online testing during challenge competitions. Therefore, in this experiment, we can only use the DAVIS2016 validation set as our evaluation benchmark. Due to DAVIS2016 being a dataset for VOS and U-Net being a static image segmentation network, U-Net cannot participate in the current comparison.  
Figure 5: Validation set J&F curves during training with different batch sizes. “J&F” represents the average values of Jaccard (J) Per-Sequence and Boundary (F) Per-Sequence for the entire validation set.  
We conducted 400 rounds of training on the DAVIS2016 training set for both the TBD and STAVOS models. Subsequently, we tested them on the DAVIS validation set, consisting of 20 video sequences. The results are presented in Table 2, where STAVOS outperforms TBD in 15 video sequences, leading to an overall improvement in J&F by 0.032. Especially in video sequences where TBD exhibits poorer segmentation performance, such as \"breakdance\" and \"scooter-black\", STAVOS demonstrates a significantly superior segmentation effect. This is because, for more complex video objects, the STA module can exert greater advantages. It enables more precise segmentation of edges, which aligns with the original design purpose of STAVOS.
# Novel Fault Injection Attack without Artificial Trigger
## 4 Experiment
### Experimental Results  
The experimental results are provided in Table 2. The used data correspond to 16-byte ciphertext information. The marked byte as * denotes the byte affected by the fault injection attack. Here, \(\medriangle\) is the resulting value without the fault being injected; \(\medriangle\)\(\medriangle\) correspond to the resulting values of one column in the 9 th round MixColumns affected by a fault; \(\medriangle\)\(\medriangle\) represent the resulting values when more than two columns are affected. Table 3 represents different fault types from \(\medriangle\) to \(\medriangle\). As a result of the experiment, we obtained 15 fault injection ciphertexts of Fault Type1, eight fault injection ciphertexts of Fault Type2, 15 fault injection ciphertexts of Fault Type3, and 17 fault injection ciphertexts of Fault Type4.  
Figure 8: Electromagnetic fault injection experimental setup and fault injection location on the board.  
Figure 7: **(a) Electromagnetic trace of AES; (b) Electromagnetic trace of 8 th, 9 th, and 10 th AES.**In this experiment, a one-byte fault injection attack was executed successfully 55 times out of the 1000 conducted fault injection attacks overall. Three fault injection ciphertexts used for analysis could be obtained for each Fault Type by executing 250 fault injection attacks, on average. As a result, we found the 16-byte 10-round key using the fault-injected ciphertexts using the attack algorithm described in Section 3.2.
## 5 TSN Scheduling Problem Formulation
### TSN Scheduling Problem Formulation Assumptions
#### 5.3.3 Assumptions on the Forwarding Mode  
Many TSN switches support two different forwarding modes. In the store-and-forward mode, the switch processes a frame only after its full reception. In the cut-through mode, the switch starts processing the frame after only the necessary information is received (i.e., its header). Refs. [34, 37, 74] assumed that all switches are in store-and-forward mode while [40, 112] assumed the switch delay to be 4 \(\upmu\)s, which is equivalent to a cut-through mode.
## 4 Results
### Visual Results of Segmentation Outputs--BDD100K Dataset  
Qualitative results include segmentation maps that delineate objects like vehicles, pedestrians, and road signs. Attention heatmaps illustrate the mechanism's ability to focus on relevant regions. Figure 3, provided above, demonstrates the results of a segmentation task in the proposed system. The first image represents the original input taken from the study's dataset, showcasing a real-world urban environment. The second image shows ground truth segmentation, where different regions (such as roads, vehicles, buildings, and trees) are labeled with distinct colors to represent various semantic classes. The third image displays the output of the proposed segmentation method, which appears to align with the ground truth closely. This comparison highlights the model's effectiveness in accurately identifying and segmenting critical objects and regions in the scene, such as road surfaces, vehicles, and background elements, which is essential for applications like autonomous driving and urban scene understanding.  
The training and validation curves shown in Figures 4 and 5 indicate that the proposed model learned effectively and achieved high performance. The training accuracy rapidly increased and stabilized close to 100%, while the validation accuracy consistently remained around 94-96%, demonstrating good generalization. The training loss decreased steadily to near zero, showing efficient convergence, while the validation loss initially dropped and then fluctuated slightly, which aligns with observations in prior segmentation studies, where the complexity of dense prediction tasks often leads to mild loss instability during validation [19, 35]. Overall, the model exhibited fast and stable learning with minimal overfitting, highlighting the robustness and effectiveness of the Adaptive Ensemble Attention mechanism within the GAN framework.  
Figure 4: Train Accuracy BDD100K dataset.  
Figure 5: Validation Loss BDD100K dataset.  
Figure 3: Segmentation results of the proposed method for the BDD100K dataset.
## 4 Experiment
### Results of Off-Grid Arrays  
#### 4.1.1 Performance of Chaotic Initialization  
In this part, different chaotic initialization methods are used to design the off-grid sensor array, and the convergence speed and beampattern performance are analyzed. Five chaotic mapping functions, including Logistic, Henon, Kent, Sin, and Tent, are tested under two termination conditions.  
Under the first termination condition, which depends on the maximum number of iterations \(t_{max}\), the search accuracy of five chaotic initialization methods is compared. The parameter settings and the obtained PSLLs are shown in Table 2, where the PSLL is the minimum value among 10 experiments. The beampatterns of the obtained array are shown in Figure 5. The iterative process of five chaotic initialization methods are shown in Figure 6. It can be seen that the search accuracy is improved after chaotic initialization compared to the original WOA (denoted as Origin in Figures 5 and 6). In addition, the Kent initialization method yields the array with the lowest PSLL.  
Under the second termination condition that the algorithm stops if the PSLL remains unchanged beyond 100 iterations, the convergence speed of five chaotic initialization methods is compared. The numbers of iterations are shown in Table 2. It can be seen that Origin\(<\)Kent\(<\)Sin\(<\)Tent\(<\)Logistic\(<\)Henon in terms of the convergence speed.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Chaotic Initialization** & **Parameter Settings** & **PSLL (dB)** & **Iterations** \\ \hline Logistic & \(\alpha=3.9,\tilde{s}_{0}=0.5\) & \(-19.96\) & 659 \\ Henon & \(\beta=1.4,\zeta=0.3,\tilde{s}_{0}=0\) & \(-19.63\) & 451 \\ Kent & \(\mu=0.625,\tilde{s}_{0}=0.45\) & \(-19.98\) & 800 \\ Sin & \(\eta=0.867,\tilde{s}_{0}=0.5\) & \(-19.30\) & 733 \\ Tent & \(\gamma=14.1,\tilde{s}_{0}=0.1\) & \(-19.77\) & 488 \\ Origin & - & \(-18.65\) & 897 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PSLLs and the numbers of iterations for five chaotic initialization methods.  
Figure 5: Beampatterns of the obtained array based on five chaotic initialization methods.  
Here, the Kent initialization method is adopted in GEWOA because it has the best search accuracy and faster convergence speed compared with the original method. Considering that the search accuracy is the most important criterion for array design, the selection is reasonable.
## 7 Results and Discussion  
### PCA-EHMM  
Table 2 shows the PCA-EHMM prediction performance for specific values (\(k\) = 7-12). As previously stated, the principal components after complete decomposition are organized from left to right in order of importance. As a result, reducing the number of principal components from the right has no effect on their strength. In this vein, the performance of PCA-EHMM was verified for \(k\) = 7-12 (rather than \(d\) = 28), which reduced the computational cost imposed by PCA on EHMM. This is because the higher the dimension, the more complex the EHMM process, where \(k\) represents the dimension of EHMM.  
From Table 2, one can notice that the performance of PCA-EHMM improves as the value of \(k\) increases from 7 to 9. For example, at \(\chi=80\), there is a performance improvement of 0.20%, 0.21%, 0.20%, and 0.21% in \(\mathcal{S}_{e}\), \(\mathcal{S}_{p}\), \(\mathcal{P}\), and \(\mathcal{F}1\), respectively, as \(k\) increases from 7 to 8. Likewise, at \(\chi=80\), there is a performance improvement of 0.21%, 0.23%, 0.22%, and 0.22% in \(\mathcal{S}_{e}\), \(\mathcal{S}_{p}\), \(\mathcal{P}\), and \(\mathcal{F}1\), respectively, as \(k\) increases from 8 to 9. Therefore, increasing the value of \(k\) does not result in a significant improvement in the prediction performance of PCA-EHMM; instead, it increases the computing cost of the EHMM process. Thus, \(k=9\) is chosen as the ideal number for \(k\) because it strikes a suitable compromise between performance gain and computational time complexity. Notwithstanding, this value of \(k\) is large enough to impose a high computational burden on EHMM. As such, MRE-EHMM is introduced in this article, as discussed earlier.  
Figures 2-5 depict the PCA-EHMM performance as \(k\) increases in size. One can notice how the curves of each figure flatten as \(k\) increases. This clearly shows that there is no significant increase in the performance of PCA-EHMM, even if all derived principle components are used (that is, \(d=28\)). Also, it is important to mention that the performance of PCA-EHMM improves as the training size \(\chi\) increases from 70% to 80%. This performance improvement is evident in the four performance metrics considered.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{\(k=7\)} \\ \hline \(\chi\) (\%) & \(\mathcal{S}_{e}\) (\%) & \(\mathcal{S}_{p}\) (\%) & \(\mathcal{P}\) (\%) & \(\mathcal{F}1\) (\%) \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 2: PCA-EHMM performance as a function of \(k\).  
Figure 4: PCA-EHMM precision versus dimension performance.  
Figure 3: PCA-EHMM specificity versus dimension performance.  
Figure 2: PCA-EHMM sensitivity versus dimension performance.
# Internet of Things (IoT) and the Energy Sector
## 4 IoT in the Energy Sector  
Today, the energy sector is highly dependent on fossil fuels, constituting nearly 80 % of final energy globally. Excessive extraction and combustion of fossil fuels has adverse environmental, health, and economic impacts due to air pollution and climate change to name a few. Energy efficiency, i.e., consuming less energy for delivering the same service, and the deployment of renewable energy sources are two main alternatives to diminish the adverse impacts of fossil fuel use [12; 13].  
In this section, we discuss the role of IoT in the energy sector, from fuel extraction, operation, and maintenance (O&M) of energy generating assets, to T&D and end use of energy IoT can play a crucial role in reducing energy losses and lowering CO2 emissions. An energy management system based on IoT can monitor real-time energy consumption and increase the level of awareness about the energy performance at any level of the supply chain [15; 113]. This section discusses the application of IoT in energy generation stages first. Then, we continue with the concept of smart cities, which is an umbrella term for many IoT-based subsystems such as smart grids, smart buildings, smart factories, and intelligent transportation. Next, we discuss each of the above-mentioned components separately. Finally, we summarize the findings of this section in Tables 2 and 3.
## 2 Materials and Methods
### Methods
#### 2.3.4 Relative Contributions of Factors  
To quantify the contributions of various factors to GPP, standardized ridge regression was employed. Initially, both GPP and the influencing factors were standardized to eliminate the effects of unit differences in the original regression coefficients. The standardization was performed using the following formula:  
\[GPP_{i}^{\prime}=\frac{GPP_{i}-\mu}{\sigma}, \tag{7}\]  
where \(GPP_{i}\) represents the total GPP for the \(i\)-th year, and \(\mu\) and \(\sigma\) are the mean and standard deviation of the GPP dataset, respectively. Precipitation, temperature, solar radiation, and forest cover were also standardized using the same method. Ridge regression analysis was then applied to assess the influence of individual factors on GPP using the following equations:  
\[X=\big{[}\textit{Tem}^{\prime},\textit{ Pre}^{\prime},\textit{ }\textit{ PAR}^{\prime},\textit{ }\textit{ Tree}^{\prime}\big{]}, \tag{8}\]  
\[\beta=\Big{(}X^{T}X+\lambda I\Big{)}^{-1}X^{T}GPP, \tag{9}\]  
where \(GPP^{\prime}\), \(\textit{Tem}^{\prime}\), \(\textit{Pre}^{\prime}\), \(\textit{PAR}^{\prime}\), and \(\textit{Tree}^{\prime}\) are the standardized values of GPP, temperature, precipitation, solar radiation, and forest coverage, respectively. \(X\) is the matrix of independent variables, \(\lambda\) is the regularization parameter, \(I\) is the identity matrix, and \(\beta\) represents the standardized ridge regression coefficient. Compared to linear regression, ridge regression effectively addresses the multicollinearity problem among different variables [23; 24].  
The relative contributions of each factor to GPP variation were calculated using the following equation:  
\[C_{i}=\frac{|\beta_{i}|}{|\beta_{1}|+|\beta_{2}|+|\beta_{3}|+|\beta_{4}|}, \tag{10}\]  
where \(C_{i}\) represents the relative contribution of the i-th factor to GPP variation, and \(\beta_{i}\) is the regression coefficient of the corresponding factor in the multiple linear regression model. This calculation allows for the determination of the relative importance of each factor in influencing GPP variations. A higher value of \(C_{i}\) indicates a greater impact of that particular factor on GPP changes in the study area.
# Clinical Applications of Optical Coherence Angiography Imaging in Ocular Vascular Diseases
## 3 Retina Vein Occlusion  
In retinal vein occlusion (RVO), stagnation of blood flow in retinal venous occlusion causes elevated venous and tissue pressure, tissue hypoxia, and extravasation of blood constituents. Age, raised intraocular pressure, and systemic vascular risk factors such as hypertension, hyperlipidemia, diabetes, and smoking are common predisposing risk factors. There are three types of vein occlusions: central, branch, and hemi-retinal vein occlusion [45; 46; 47]. Clinically, retina vein occlusions usually begin at an arteriovenous crossing and are characterized by dilation and tortuosity of the affected venous segment, flame-shaped, dot and blot hemorrhages, retina edema, and cotton wool spots (Figure 4A). Macular edema and neovascularization are common complications that can develop [45]. Retinal vein occlusions have been previously divided into ischemic and non-ischemic forms with certain definitions [48]. However, the increasing use of anti-vascular endothelial growth factor injections for the treatment of macular edema impacts peripheral ischemia, making these definitions less clear during the course of treatment [49; 50].  
Figure 4: A case of fresh superior-temporal branch retinal vein occlusion. **(A)**: Color fundus photos depicting tortuous venous segments with flame-shaped, dot and blot hemorrhages and cotton wool spots. En face OCTA montage **(B)** and FA **(C)** depict associated collateral vessel formation (red arrows), which are blocked by the dense hemorrhage on fundus photos. Yellow arrows indicate areas obscured by the extensive retinal hemorrhage on fundus photo, OCTA and FA.