esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Optical Fiber Breakage as an Indication of Cracking of Structural Elements  
The appearance of structural cracks can be indicated by the breakage of optical fibers that are attached to the element or embedded in it and cross the crack. A fiber break causes a sharp decrease in the intensity of optical radiation passing through the fiber, which is easily detected by appropriate instruments. This method was proposed and applied to detect cracks in steel and aluminum components, as well as in composite materials, as early as the 1980s [46; 47]. However, it was believed that this method produced poor results in detecting structural damage [48]. This opinion may be the result of the following:  
* First, the optical coupling between the two parts of a broken fiber depends not only on their axial displacement but also on many random factors, including the angle between the crack and the fiber axis, the shear displacement of the two parts, and the roughness of their end faces. Therefore, signal loss cannot be uniquely related to the expansion of the crack.
* Second, optical fibers, as a brittle material, break at slightly different structural crack widths each time (this property is discussed in detail in the following sections).
* Third, the pioneering work did not demonstrate a practical cost-effective implementation of this method. These might be the three main reasons as to why it has never been used to detect structural damage in buildings.
## 1 Introduction
Conversely, our scheduling strategies were developed in accordance with different coupling states of the satellite so that the observation targets could be selected based on the satellite state. Additionally, our proposed method aims to address the mission scheduling problem for EOS while the research in [32] was for a relay satellite. [PERSON] [33] suggested a case-based reasoning method in the scheduling problem, whereas our proposed method is a rule-based reasoning method. The focus in [33] was on the task schedulability while we focused on addressing the mixed-integer programming problem. In this paper, we defined the satellite multi-subsystem coupling scheduling problem and proposed a reasoning-based scheduling method (RS) for AEOS with multi-subsystem coupling. The two contributions of this study are:  
1. A satellite coupling state prediction method was proposed. We analyzed the coupling relationship between satellite observation, data transmission, and electricity subsystems. Subsequently, we determined the different coupling states of AEOS with each subsystem as the main limiting factor and sorted the variables that reflected the satellite coupling states, and then proposed a state prediction method for these variables;
2. RS was established. Based on the observation-priority scheduling strategy in previous research [34; 35], we proposed two planning strategies including the data transmission-priority scheduling strategy and electricity-priority scheduling strategy. We also constructed reasoning rules that corresponded to different scheduling strategies for different satellite coupling states. The remainder of this paper is organized as follows. Section 2 defines the satellite multi-subsystem coupling scheduling problem, while Section 3 describes the satellite multi-subsystem coupling analysis process, RS, and heuristic methods. Furthermore, Section 4 presents the simulation working conditions and results. The discussion is carried out in Section 5, and Section 6 concludes this paper.
# Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks
## 6 Performance Evaluation
### Methodology  
To comprehensively validate the proposed deployment algorithm, a series of experiments are carried out using the MATLAB simulator. The experiments are carried out using an Intel Core i5 dual-core CPU with a frequency of 2.3 GHz, 4 GB of memory, and a Windows 7 operating system. 300 \(\times\) 300 m square region is considered, where \(N\) sensor nodes are deployed to monitor the area. Such sensor nodes are divided into mobile and stationary nodes. The stationary nodes are deployed randomly in a monitored field, and the sink node is placed at (300 m, 0 m). We provide the simulation results for the heterogeneous network, including sensor nodes with different sensing and battery energies. The initial energy of each sensor node is set between 1 to 19,160 J. it is assumed that a 35 m sensing range and the energy consumption during node movement is 1 J/m [8; 27].  
As for sensing, it is assumed that each sensor acquires 1 KB of data per second. According to [8], the energy dissipation for data acquisition is defined in Equation (37), where \(NB\) is the number of bytes of data acquired. \(P_{dac}\) is the required power for data acquisition, and \(D_{so}\) is the duration of a single operation. In this work, we have adopted the same values in [8]. \(NB\) = 1 KB, \(D_{so}\) = 0.135 \(\upmu\)s, and \(P_{dac}\) = 300 mW.  
\[E_{acq}=NBP_{dac}D_{so} \tag{37}\]  
As for data communication between two nodes, we have adopted the energy consumption model, and the model of lossy links in WSNs is based on [28]. Besides, each node runs the AODV routing protocol [29] to send and forward data packets. When running simulation, data traffic in the network is generated according to a Poisson process with a mean parameter, \(\lambda\). The simulation parameters are listed in Table 5.
## 3 Results and Discussions
#### Quantitative Comparison of MFO with DFO and PSO Algorithms
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Response**} & \multirow{2}{*}{**Statistics**} & \multicolumn{8}{c}{**MFO**} \\ \cline{3-10}  & & & **RMSE** & & **MAE** & & & \(R^{2}\) & \\ \cline{3-10}  & & **Total** & **Training** & **Testing** & **Total** & **Training** & **Testing** & **Total** & **Training** & **Testing** \\ \hline \multirow{10}{*}{L} & Mean & 0.0647 & 0.0628 & 0.0713 & 0.0556 & 0.0540 & 0.0631 & 0.9825 & 0.9807 & 0.9842 \\  & StDev & 0.0008 & 0.0008 & 0.0009 & 0.0009 & 0.0008 & 0.0008 & 0.0100 & 0.0108 & 0.0126 \\  & Minimum & 0.0631 & 0.0613 & 0.0697 & 0.0544 & 0.0527 & 0.0614 & 0.9631 & 0.9635 & 0.9606 \\  & Q1 & 0.0641 & 0.0620 & 0.0706 & 0.0548 & 0.0534 & 0.0625 & 0.9744 & 0.9713 & 0.9708 \\  & Median & 0.0648 & 0.0627 & 0.0713 & 0.0556 & 0.0540 & 0.0631 & 0.9837 & 0.9785 & 0.9865 \\  & Q3 & 0.0652 & 0.0633 & 0.0718 & 0.0565 & 0.0547 & 0.0638 & 0.9908 & 0.9906 & 0.9973 \\  & Maximum & 0.0660 & 0.0642 & 0.0730 & 0.0569 & 0.0554 & 0.0642 & 0.9998 & 0.9999 & 0.9996 \\  & Range & 0.0028 & 0.0029 & 0.0033 & 0.0025 & 0.0027 & 0.0029 & 0.0367 & 0.0365 & 0.0390 \\ \hline \multirow{10}{*}{D} & Mean & 0.0804 & 0.0843 & 0.0697 & 0.0668 & 0.0693 & 0.0597 & 0.9767 & 0.9771 & 0.9810 \\  & StDev & 0.0011 & 0.0012 & 0.0009 & 0.0010 & 0.0010 & 0.0008 & 0.0121 & 0.0128 & 0.0125 \\  & Minimum & 0.0787 & 0.0821 & 0.0678 & 0.0653 & 0.0675 & 0.0583 & 0.9603 & 0.9600 & 0.9600 \\  & Q1 & 0.0792 & 0.0834 & 0.0689 & 0.0659 & 0.0684 & 0.0590 & 0.9658 & 0.9667 & 0.9712 \\  & Median & 0.0804 & 0.0846 & 0.0699 & 0.0668 & 0.0695 & 0.0598 & 0.9743 & 0.9750 & 0.9809 \\  & Q3 & 0.0813 & 0.0853 & 0.0704 & 0.0675 & 0.0701 & 0.0603 & 0.9844 & 0.9927 & 0.9945 \\  & Maximum & 0.0824 & 0.0861 & 0.0711 & 0.0683 & 0.0707 & 0.0611 & 0.9997 & 0.9970 & 0.9997 \\  & Range & 0.0037 & 0.0039 & 0.0033 & 0.0031 & 0.0032 & 0.0028 & 0.0394 & 0.0371 & 0.0398 \\ \hline \multirow{10}{*}{W} & Mean & 0.0796 & 0.0830 & 0.0681 & 0.0657 & 0.0680 & 0.0586 & 0.9775 & 0.9804 & 0.9829 \\  & StDev & 0.0015 & 0.0012 & 0.0011 & 0.0011 & 0.0011 & 0.0011 & 0.0134 & 0.0120 & 0.0092 \\  & Minimum & 0.0771 & 0.0804 & 0.0665 & 0.0639 & 0.0663 & 0.0572 & 0.9604 & 0.9609 & 0.9609 \\  & Q1 & 0.0784 & 0.0820 & 0.0669 & 0.0646 & 0.0669 & 0.0575 & 0.9670 & 0.9708 & 0.9769 \\  & Median & 0.0795 & 0.0831 & 0.0681 & 0.0657 & 0.0680 & 0.0581 & 0.9699 & 0.9812 & 0.9832 \\  & Q3 & 0.0810 & 0.0840 & 0.0689 & 0.0668 & 0.0688 & 0.0596 & 0.9929 & 0.9903 & 0.9877 \\  & Maximum & 0.0818 & 0.0848 & 0.0702 & 0.0677 & 0.0701 & 0.0605 & 0.9999 & 0.9999 & 0.9999 \\  & Range & 0.0046 & 0.0044 & 0.0037 & 0.0039 & 0.0038 & 0.0033 & 0.0396 & 0.0390 & 0.0390 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Statistical analysis of output obtained by the DFO algorithm. The comparison of the performance measures of the proposed method using MFO (PM-MFO) and the existing method using Feed Forward Back Propagation (EX-FFBP) by [PERSON] et al. [1] is illustrated in Figure 10. It is understood that, for the tested data set, the
# A Throughput Analysis Using a Non-Saturated Markov Chain Model for LTE-LAA and WLAN Coexistence
## Abbreviations  
Notations for our proposed throughput study.  
\begin{tabular}{l l} Notation & Description \\ \(P[k_{1}^{\prime},k_{2}^{\prime}|k_{1},k_{2}]\) & Transition probability from state \((k_{1}^{\prime},k_{2}^{\prime})\) to state \((k_{1},k_{2})\) \\ \(b(0,k_{2})\) & Stationary probability of being in backoff state \((0,k_{2})\) \\ \(b(-1,k_{2})\) & Stationary probability of being in post-backoff state \((-1,k_{2})\) \\ \(W\) & Contention window size of LTE-LAA eNB \\ \(P_{cca}\) & CCA threshold of LTE-LAA eNB \\ \(q\) & Probability of at least one packet awaiting transmission at the start of a timeslot \\ \(\tau\) & Probability that the LTE-LAA eNB is attempting transmission in a timeslot \\ \(\tau_{iw}\) & Probability that WLAN nodes located in the CCA range are attempting \\  & transmission in a timeslot \\ \(P_{alle}\) & Probability that the medium will be sensed as idle in a timeslot \\ \(p\) & Collision probability of LTE-LAA eNB \\ \(p_{w}\) & Probability of a collision among WLAN nodes located in the CCA range \\ \(R_{I}\) & Distance between LTE-LAA eNB and the outer boundary of the interfering \\  & region \\ \(R_{cca}\) & Radius of the CCA range of LTE-LAA eNB \\ \(R_{lan}(\cdot)\) & Data rate of LTE-LAA eNB \\ \(R_{W}\) & Data rate of a WLAN transmission \\ \end{tabular}
## 3 Materials and Methods
### Multi-Scale Classifier  
In the previous works about pollen classification, classifiers that were based on CNN architectures were needed to normalize the resolutions of the input images [35; 36; 37]. However, the resolutions of the obtained images after deblurring still varied greatly in this study, as shown in Table 3. The operation for normalized resolutions could lead to serious image distortion and affect the classification performance. Inspired by multi-scale features, such as image pyramids [38], scale-space representations [39] and coarse-to-fine approaches [40], we adopted the idea of multi-scale resolution to develop an easy-to-implement and efficient pollen image classification architecture.  
The proposed architecture was achieved using a multi-branch network structure, with each branch receiving images with different resolution scales as their input. By fusing the loss functions of the different branches, we could train the whole architecture end to end. Considering that different pollen datasets have different resolution ranges, our model could also provide a network structure with scalable branches. Therefore, the proposed architecture could be extended to different pollen grain datasets, which would be a significant improvement compared to the existing classification methods for single-resolution images.  
The framework of the proposed architecture is shown in Figure 7. According to the resolution ranges of the pollen grain images, the input images were divided into \(N\) parts, which corresponded to \(N\) branches in the architecture. Each branch took images that were normalized to a uniform resolution as their input. The overall loss was calculated by combining the loss functions of all of the branches. Then, the model could be trained using the backbone network to obtain the parameters that minimizes the overall loss function.  
By assuming that \(a_{n}^{j}\) was the predicted value of the \(n\)-th branch, we obtained:  
\[a_{n}^{j}=soft\max\left(z_{n}^{j}\right)=\frac{e^{z_{n}^{j}}}{\sum_{k}e^{z_{n} ^{k}}}. \tag{1}\]  
where \(j\) represents the \(j\)-th sample and \(z_{n}^{j}\) represents the \(n\)-th branch of the \(j\)-th sample. The loss function of the \(n\)-th branch could be computed using the cross-entropy of \(M\) samples:  
\[l_{n}(y_{n},a_{n})=-\sum_{j=0}^{M}y_{n}^{j}\log a_{n}^{j}. \tag{2}\]  
where \(y_{n}\) and \(a_{n}\) represent the vectors of the true labels and the predicted values of the \(M\) samples, respectively.  
Figure 6: Several low-resolution images and their deblurred counterparts: (**a**) the original images; (**b**) the deblurred images using bilinear interpolation; (**c**) the deblurred images using our deblurring pipeline.  
The overall loss function \(J_{N}\) of the whole model could be obtained using:  
\[J_{N}=\sum_{n=0}^{N}k_{n}\times l_{n}(y_{n},a_{n}). \tag{3}\]  
where \(k_{n}\) represents a coefficient with the value of 0 or 1. When training the \(n\)-th branch, this coefficient was assigned a value of 1, while the \(k\) coefficient of other branches was 0.
# Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement
## 4 Results
### Modeling Consumer Ad Engagement
#### 4.2.2 Identification of Model Shapes Associating Video Features and Engagement  
A series of scatter plots comparing each selected video feature in pairs with the UES-SF dimensions were investigated both visually and statistically to determine the nature of the relationships between them. Visual inspection revealed a potential linear relationship between the low-level visual features and the corresponding dimensions of engagement. For example, the relationship between the video feature Lighting Key and the UES-SF dimension Reward (RW) indicates a linear relationship, as shown in Figure 3a.  
Several cases indicate that there may be more complex dependencies in addition to a simple linear relationship. Considering the U-shaped (or possible inverted U-shaped) relationship between a perceived response and its stimulus, which is often found in studies on human behavior [35; 36], nonlinear modeling was also considered. At this stage of the study, the goal was to identify the nature of the relationship rather than to define an optimal model. A quadratic model was applied as the simplest nonlinear model capable of capturing non-monotonous relations. An example of a possible U-shaped relationship between Standard deviation of Motion and the UES-SF dimension Perceived Usability (PU) is shown in Figure 3b. Figure 3c represents a U-shaped relationship between the mean value of Color Variance and overall User Engagement Score (UES).
## 3 Transmission Protocol Configuration and Resource Allocation Scheme  
Firstly, this paper determines the configuration of the transmission protocol by maximizing the expected throughput of the system. Secondly, for diversified services, this paper introduces utility functions to characterize the satisfaction of services. Finally, the traffic flow resource allocation problem in the integrated system is modelled as a utility maximization problem, which can be decomposed into an independent cell utility maximization problem. By optimizing the transmission power on LP-ABS and the resource allocation of each traffic flow, the network utility is maximized under resource constraints. Figure 3 shows the flow and relationships between the different problems and subproblems of the algorithm design.
# Metallurgical Alchemy: Synthesizing Steel Microstructure Images Using DCGANs
## 2 Materials and Methods
### Training Loop  
The training loop of the DCGAN involves initializing several essential components and iteratively updating the generator and discriminator networks. First, a binary cross-entropy loss function (BCELoss) is initialized to measure the discrepancy between real and synthetic outputs. A set of fixed latent vectors is used to monitor the generator's progress, and smoothed labels for real and synthetic examples (real_label = 0.9, synthetic_label = 0.1) are employed to improve training stability. Adam optimizers are configured to update the parameters of both the generator (G) and discriminator (D) networks.  
Extensive experiments were conducted to determine the optimal hyperparameters for the training process. The details of these hyperparameters are provided in Table 4. The training utilized one GPU, with the data loader using 2 workers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer (Type: Depth-Index)** & **Output Shape** &
\begin{tabular}{c} **Number of** \\ **Parameters** \\ \end{tabular} \\ \hline Sequential: 1-1 & [4, 1, 1, 1] & – \\ Conv2d: 2-1 & [4, 16, 256, 256] & 768 \\ LeakyReLU: 2-2 & [4, 16, 256, 256] & – \\ Conv2d: 2-3 & [4, 32, 128, 128] & 8192 \\ BatchNorm2d: 2-4 & [4, 32, 128, 128] & 64 \\ LeakyReLU: 2-5 & [4, 32, 128, 128] & – \\ Conv2d: 2-6 & [4, 64, 64, 64] & 32,768 \\ BatchNorm2d: 2-7 & [4, 64, 64, 64] & 128 \\ LeakyReLU: 2-8 & [4, 64, 64, 64] & – \\ Conv2d: 2-9 & [4, 128, 32, 32] & 131,072 \\ BatchNorm2d: 2-10 & [4, 128, 32, 32] & 256 \\ LeakyReLU: 2-11 & [4, 128, 32, 32] & – \\ Conv2d: 2-12 & [4, 256, 16, 16] & 524,288 \\ BatchNorm2d: 2-13 & [4, 256, 16, 16] & 512 \\ LeakyReLU: 2-14 & [4, 256, 16, 16] & – \\ Conv2d: 2-15 & [4, 512, 8, 8] & 2,097,152 \\ BatchNorm2d: 2-16 & [4, 512, 8, 8] & 1024 \\ LeakyReLU: 2-17 & [4, 512, 8, 8] & – \\ Conv2d: 2-18 & [4, 1024, 4, 4] & 8,388,608 \\ BatchNorm2d: 2-19 & [4, 1024, 4, 4] & 2048 \\ LeakyReLU: 2-20 & [4, 1024, 4, 4] & – \\ Conv2d: 2-21 & [4, 1, 1, 1] & 16,364 \\ Sigmoid: 2-22 & [4, 1, 1, 1] & – \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the discriminator network architecture.  
In each epoch and for each batch of data, the discriminator is first updated to minimize its classification error by correctly labeling real and generated samples. Then, the generator is updated to maximize the classification error of the discriminator by generating more realistic images that the discriminator classifies as real. During these updates, the losses are calculated, gradients are backpropagated, and network parameters are adjusted. Progress is monitored by periodically printing loss statistics and generating images from the fixed latent vectors to visualize the generator's performance, as will be shown in the Results section. The impact of using different hyperparameters will be further examined in the Discussion section.
## 3 Evaluation and Results  
The test set was evaluated using a single network that was simultaneously trained with all acquisition modalities, which allowed the CNN to learn shared features across all domains. For the single and combined modality, we computed the accuracy and the area under the curve (AUC). Additionally, sensitivity, defined as the rate of correct pre-malignant polyps classified as such; and specificity, defined as the correct rate of benign polyps classified as benign, were calculated as well. The performance of the CADx system and all associated architectures was evaluated and compared to outcomes of the experts and novices.  
The best CADx system achieves an accuracy of 95.0% with specificity of 93.3%, sensitivity of 95.6%, and AUC of 0.97. For each input image, the CADx system computes the region for decision-making via Grad-CAM and the malignancy prediction. The average prediction of the three modalities offers the endoscopists with the best possible diagnosis result. The end-to-end framework described above is depicted in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Hyperplastic**} & **Adenoma** & **Sessile Serrated** \\ \hline
**Surface** & & & & \\ \hline  & Presence of mucus & No & No & Yes \\  & Regular/irregular & Regular & Regular or irregular & Regular or irregular \\  & Pseudodepression & No & Yes & No \\  & Depression & No & No & No \\ \hline
**Pit Pattern** & & & & \\ \hline  & Featureless & Yes & No & No \\  & Type & Round & Not round & Round pits \\  & & & & with/without dark \\  & & & & spots \\  & Distribution & Homogenous & Homogenous or & Homogenous or \\  & & & heteregenous without & heteregenous \\  & & & focal loss & \\ \hline
**Vessels** & & & & \\ \hline  & Present? & Yes/No & Yes & Yes/No \\  & Type if present & Lacy & Pericryptal & Pericryptal \\ \hline \hline \end{tabular}
\end{table}
Table 2: The BASIC classification comprises a list of visual BLI features, employed by endoscopists to classify hyperplastic, sessile serrated, and adenomatous polyps. A more in-depth clinical analysis can be found in the study of [PERSON] et al. [38].  
Figure 2: End-to-end framework of the proposed CADx system. The training batch contains independent images of each modality (WL, BLI, and LCI) and at least a representative of each polyp class. The output of the system is a prediction for each single image.
# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
###### Abstract  
With the rapid development of blockchain technology, consensus algorithms have become a significant research focus. Practical Byzantine Fault Tolerance (PBFT), as a widely used consensus mechanism in consortium blockchains, has undergone numerous enhancements in recent years. However, existing review studies primarily emphasize broad comparisons of different consensus algorithms and lack an in-depth exploration of PBFT optimization strategies. The lack of such a review makes it challenging for researchers and practitioners to identify the most effective optimizations for specific application scenarios. In this paper, we review the improvement schemes of PBFT from three key directions: communication complexity optimization, dynamic node management, and incentive mechanism integration. Specifically, we explore hierarchical networking, adaptive node selection, multi-leader view switching, and a hybrid consensus model incorporating staking and penalty mechanisms. Finally, this paper presents a comparative analysis of these optimization strategies, evaluates their applicability across various scenarios, and offers insights into future research directions for consensus algorithm design.  
1
## 4 Experiment Results and Discussion  
### Experimental Device  
This study used two kinds of non-contact excitation methods to simulate single- and multi-frequency excitations. The experimental device is shown in Figure 9.  
Figure 8: Mode shape of the system: (**a**) 1 st order, (**b**) 2 nd order, (**c**) 3 rd order, (**d**) 4 th order, (**e**) 5 th order, (**f**) 6 th order, (**g**) 7 th order and (**h**) 8 th order.  
Figure 9: Configuration of dual joints and one flexible plate.  
The vibration control experiments under the non-contact periodic single-frequency excitations were performed. These experiments were under the conditions of **case-1** (same frequency but diverse amplitudes) and **case-2** (diverse frequencies and diverse amplitudes). The experimental platform is shown in Figure 10. Herein, the selected aluminum plate is larger and more flexible than the previous flexible beam [34]. The excitation frequency is regulated by the motor rotation speed. By adjusting the moving slide table up and down, the magnetic excitation air gap can be adjusted. By changing the intensity of the excitation magnetic fields between the air gaps, diverse excitation amplitudes can be generated.