esa-sceva/satcom-chunk-collection huggingface dataset:
# Removal of Color-Document Image Show-Through Based on Self-Supervised Learning
###### Abstract
Furthermore, the structure and attributes of the show-through layer often resemble the content of the foreground image, posing challenges in removing unnecessary information from the show-through layer while preserving the content and details of the foreground image with reduced redundancy. So far, many studies have adopted methods based on generative adversarial network (GANs) [2] to restore document images or natural images. [PERSON] et al. [3] proposed a robust end-to-end framework called document-enhancement GAN, which effectively restores severely degraded document images using conditional GAN [4]. However, these methods still require the use of a large number of paired datasets to train the model for good experimental results. It is difficult to find real, clear images that match the degraded document images. In addition, [PERSON] et al. [5] proposed a single-image dehazing method based on cycle GAN (CycleGAN) [6], which uses unpaired hazy images for training and constrains the process of haze removal through double-cycle consistency loss. This method is suitable for removing fog images in natural scenes, but for document images, few people adopt the approach of double-cycle consistency loss. Therefore, the removal of show-through effects in document images faces several challenges:  
1. The foreground and show-through parts in document images are highly similar because they both comprise document content. Without additional prior knowledge, it is difficult to accurately recover the desired foreground images. 2. In real-world scenarios, there is a lack of authentic images that match the show-through images, making supervised training infeasible. As a result, most document image processing algorithms rely on synthetic training data. However, there exists a gap between synthetic and real-world data, which limits the performance of these algorithms. To address these challenges, this paper proposes a self-supervised-learning-based method for removing show-through effects in document images. This method, called the CycleGAN-based color document show-through-removal network (CDSR-CycleGAN), eliminates show-through effects without requiring paired datasets. The network framework adopts a two-stage structure and employs double-cycle consistency loss and pseudo-similarity loss constraints to guide the process of removal of image show-through. Twodatasets, each consisting of different show-through mixing ratios, are constructed for training the document show-through-removal task. The remaining parts of this article are organized as follows: Section 2 presents the related work; Section 3 introduces the proposed method; Section 4 presents the experimental results; Section 5 discusses the findings; and Section 6 concludes the paper.
## 4 Model Construction
### Layout Aesthetics and Visual Cognitive Mapping Model
#### 4.2.2 Criteria Weights  
The conventional gray H-convex correlation algorithm overlooks indicator weights when delineating the similarity process of data sequence curves. This section employs the ICRITC method to ascertain criteria weights, formulate criteria-weighted data sequences, and enhance the optimization of model data input.  
Step 1. Create a sample data matrix. Setting the number of samples as \(m\) and the number of aesthetic indicators as \(n\), and the sample data matrix used for interface evaluation is:  
\[Z\ =\ \big{[}r_{ij}\big{]}_{m\times n} \tag{10}\]  
Step 2. Standardize data. The data need to be pre-standardized in order to eliminate differences in the scale and positive and negative attributes of the indicators.  
Positive criteria are calculated as:  
\[t_{ij}\ =\ \frac{x_{ij}-\min\big{\{}x_{ij}\big{\}}}{\max\big{\{}x_{ij}\big{\}}- \min\big{\{}x_{ij}\big{\}}} \tag{11}\]  
Negative criteria are calculated as:  
\[t_{ij}\ =\ \frac{\max\big{\{}x_{ij}\big{\}}-x_{ij}}{\max\big{\{}x_{ij}\big{\}}- \min\big{\{}x_{ij}\big{\}}} \tag{12}\]  
Step 3. Calculate the correlation coefficient. The correlation coefficient between the \(i\) and \(j\) criteria was calculated as:  
\[r_{ij}\ =\ \frac{\sum\limits_{h\ =\ 1}^{n}\big{(}t_{hj}-\overline{t}_{i} \big{)}\Big{(}t_{hj}-\overline{t}_{j}\Big{)}}{\sqrt{\sum\limits_{h\ =\ 1}^{n}\big{(}t_{hi}-\overline{t}_{i}\big{)}^{2}}\sqrt{\sum \limits_{h\ =\ 1}^{n}\big{(}t_{hj}-\overline{t}_{j}\big{)}^{2}}} \tag{13}\]  
where \(t_{hi}\) and \(t_{hj}\) are the values of the \(i\)th and \(j\)th indicators in the \(h\)th evaluation sample, and \(\overline{t}_{i}\) and \(\overline{t}_{j}\) are the mean values of the \(i\)th and \(j\)th criteria in all samples.  
Step 4. Calculation of the Gini coefficient between criteria:  
\[\delta_{i}\ =\ \frac{\sum\limits_{j\ =\ 1}^{n}\sum\limits_{k\ =\ 1}^{n}\big{|}t_{ij}-t_{ih}\big{|}}{2n\sum\limits_{j\ =\ 1}^{n}t_{ij}} \tag{14}\]  
where \(\delta_{i}\) is the Gini coefficient for the \(i\)th criteria and \(\delta_{i}\in[0\,\ 1]\).  
Step 5. Calculate the conflict factor. To address the issue of positive and negative correlations between criteria, the correlation coefficients are taken as absolute values. The formula for this calculation is:  
\[v_{i}\ =\ \sum_{j\ =\ 1}^{n}\big{(}1-\big{|}r_{ij}\big{|}\big{)} \tag{15}\]  
Step 6. Calculation of the informativeness of the criteria:  
\[C_{i}\ =\ \delta_{i}v_{i} \tag{16}\]  
Step 7. Calculation of criteria weighting:  
\[W_{i}^{g}\ =\ C_{i}/\sum_{i\ =\ 1}^{n}C_{i} \tag{17}\]
# NewApproach to Predict the Motion Characteristics of Single Bubbles in Still Water
## 4 Conclusions  
The physical motion of single bubbles in the still water is the main focus of this study, as it plays a significant role in engineering applications, especially for marine engineering.  
Since machine learning has been widely used in different fields of study, it is utilised in this research for both regression and classification problem-solving. The major aim of this study is to develop prediction models for the final velocity, drag coefficient, and shape of single bubbles.  
For final velocity prediction model development, feed forward back propagation neural network was selected to establish the relationship between the dimensionless parameters (Re number, Eo number, and We number) and bubble final velocity. Such a model has achieved the R\({}^{2}\) value of 0.83 and RMSE value of 0.0518, indicating that the model has the ability to predict the bubble final velocity with a considerably high level of accuracy.  
While developing the bubble drag coefficient prediction model, the FBNN approach was chosen. The dimensionless parameters (Re number, Eo number, and We number) were kept as the input and the bubble drag coefficient was reserved as the output. The R\({}^{2}\) and RMSE value of the model is 0.92 and 0.1534, respectively, meaning that the model has achieved a good prediction performance. It is found that the predicted values from the developed model have a higher accuracy if compared to the values from the empirical formulas proposed by [PERSON] (2002) and [PERSON] (2010).  
On the other hand, KNN, logistic regression and random forest algorithm were used to develop the prediction model for bubble shape. The performance of each approach was evaluated and the logistic regression prediction model has achieved the best performance.  
It is believed that the output models developed in this study can be beneficial to the engineers, scientists, and hydrologists while dealing with the problems related to the motion of single bubbles in still water.  
Conceptualization, B.D. and C.J.; methodology, Y.T.; formal analysis, B.D. and Y.T.; writing--original draft preparation, R.J.C.; writing--review and editing, S.H.L. and R.J.C.; supervision, C.J.; funding acquisition, B.D.  
This research was funded by the National Natural Science Foundation of China, NSFC Grant Nos. 51979015, 51509023, 51839002 and 51879015 and Natural Science Foundation of Hunan Province, China, Grant No. 2018 JJ3535.  
The authors would like to thank the School of Hydraulic Engineering, Changsha University of Science and Technology for the facilities provided for their experiments.  
The authors declare no conflict of interest.
## 5 Experiments
### Visualization  
To further visually understand and evaluate the proposed model in this paper, we visualized the ground truth and the model's predictions. As shown in Figure 6, we selected two nodes from the METR-LA dataset, Node 89 and Node 101, and displayed their data for an entire day on 11 June 2012 (from the test set). These two nodes displayed distinct traffic patterns. For example, Sensor 89 showed traffic congestion only during the morning peak hours, while Sensor 101 experienced traffic congestion not only during the morning peak hours but also during the evening peak hours. The results indicate that DSTGFCN can capture different traffic patterns of different nodes. Additionally, we can observe that the ground truth curves were highly irregular with significant fluctuations. Our model could effectively adapt to these abrupt trend changes and make predictions that closely  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**15 min**} & \multicolumn{3}{c}{**30 min**} & \multicolumn{3}{c}{**60 min**} \\ \cline{2-10}  & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** \\ \hline DSTGFCN & **2.53** & **4.97** & **6.52\%** & **2.88** & **6.03** & **7.79\%** & **3.28** & **7.13** & **9.59\%** \\ w/o Dg & 2.70 & 5.37 & 7.07\% & 3.15 & 6.59 & 8.91\% & 3.68 & 7.87 & 11.16\% \\ w/o Fus & 2.56 & 5.08 & 6.55\% & 2.92 & 6.07 & 8.05\% & 3.33 & 7.16 & 9.95\% \\ w/o Res & 2.62 & 5.27 & 6.82\% & 3.03 & 6.35 & 8.43\% & 3.45 & 7.45 & 10.32\% \\ w/o Fus \& Res & 2.63 & 5.39 & 6.95\% & 3.01 & 6.41 & 8.35\% & 3.41 & 7.47 & 10.07\% \\ Dg2 Sg & 2.71 & 5.40 & 7.03\% & 3.16 & 6.53 & 8.84\% & 3.70 & 7.81 & 11.16\% \\ Dg w/o X & 2.54 & 5.07 & 6.67\% & 2.91 & 6.13 & 8.24\% & 3.32 & 7.21 & 10.08\% \\ Dg w/o T & 2.55 & 5.10 & 6.70\% & 2.93 & 6.17 & 8.21\% & 3.36 & 7.34 & 10.07\% \\ Dg w/o E & 2.66 & 5.38 & 7.07\% & 3.01 & 6.36 & 8.52\% & 3.39 & 7.35 & 10.17\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiments on the METR-LA dataset.  
approximate the ground truth as much as possible. However, specific local details in the predictions may be less accurate due to random solid noise, such as the sudden and significant fluctuations during the morning peak hours in Figure 5(a).  
To visually observe the dynamic spatial correlations in the traffic road network, we selected 25 nodes from the METR-LA dataset. We visualize their dynamic adjacency matrices for two time periods in Figure 7. It can be observed that the dynamic adjacency matrices changed over time. For instance, Node 20 and Node 5 exhibited a strong correlation at 9:00, but their correlation weakened at 18:00. Additionally, Node 18 showed a similar correlation with other nodes in both periods. This indicates that specific road segments have similar traffic patterns during peak hours. These findings demonstrate that DSTGFCN effectively constructs dynamic adjacency matrices to capture the dynamic topological relationships in the traffic road network.  
Figure 6: Visualization of the predicted and true values.  
Figure 7: Dynamic adjacency matrices for two time slots.
# Effects of Movement for High Time-Bandwidths in Batched Pulse Compression Range-Doppler Radar
## 3 Doppler Effect Modelled in Its Scaling Relation
### Effect of a Constant Effective Velocity on an Instantaneous Broadband Waveform Processing
According to Equation (12), it is rather an identical consideration of a moving target that changes its bistatic range linearly over the coherent processing interval. Its particular impact (Section 5) on classical range-Doppler processing can become quite severe and is often underestimated. Sometimes this spread is wrongly associated to an assumed change of the effective velocity. The particular impact of this bandwidth-dependent effect on a classical processing scheme will be addressed in Section 5.
## 2 Modeling and the Proposed Method
### Estimation of Attitude Angle
#### 2.2.1 The Size of the Target Is Known  
For the cube-shaped target with size \(L\times L\times L\), the intersection of the axis of rotation and the upper surface of the cube is represented by \(A\). One vertex of the upper base surface is \(B\) (see Figure 1). The unit vector of LOS is expressed as \(\overrightarrow{\mathbf{n}}=[0,\)\(\sin\)\(\alpha\), \(\cos\)\(\alpha\)]. The included angle between \(\overrightarrow{\mathbf{OB}}\) and \(\overrightarrow{\mathbf{n}}\) is \(\gamma\). \(R_{0}\) is the distance between the radar and the target. According to the cosine theorem, the distance between point \(B\) and the radar is given by  
\[r(t)=\sqrt{\left|OB\right|^{2}+R_{0}^{2}-2\left|OB\right|R_{0}cos\gamma} \tag{12}\]  
Generally, the relation \(\left|OB\right|\ll R_{0}\) is satisfied; namely, the distance between the radar and the target is much larger than the target size. So, Equation (14) can be further reduced to  
\[r(t)\approx R_{0}-\left|OB\right|cos\gamma=R_{0}-\overrightarrow{OB}\cdot \overrightarrow{n} \tag{13}\]  
where \(cos\gamma=\frac{\overrightarrow{OB}\cdot\overrightarrow{n}}{\left|OB \right|}\). Point \(B\) rotates around the rotation axes, and the variation of \(\overrightarrow{OB}\) with time is expressed as  
\[\overrightarrow{OB}=(\left|AB\right|cos2\pi f_{s}t,\ \left|AB\right|sin2\pi f_{s}t,\ \left|OA\right|)^{T} \tag{14}\]  
The distance between points \(A\) and \(B\) is \(\left|AB\right|=\sqrt{2}L/2\). Note that in the above derivation, the initial phase angle is set as 0.  
Combined with the formula of the micro-Doppler effect, the Doppler frequency shift is now given by  
\[f_{D}=\frac{2}{\lambda}\frac{dr(t)}{dt}=\left(\frac{4}{\lambda}|AB|\pi f_{s} sin\alpha\right)cos2\pi f_{s}t \tag{15}\]  
One can see that \(f_{\rm D}\) is a cosine function of time with the amplitude  
\[\left|f_{max}\right|=\frac{4}{\lambda}|AB|\pi f_{s}sin\alpha \tag{16}\]  
When \(f_{s}\) is acquired, the attitude angle can be solved as  
\[\alpha=arcsin\left(\frac{\left|f_{max}\right|\lambda}{4|AB|\pi f_{s}}\right) \tag{17}\]  
Equation (15) is associated with the scattering of the vertex. In the case of laser radar, the received signal is generated as a superposition of waves scattered from the countless scattering elements on the cube surface. As a result, the envelope of the 2D TF image corresponds to the scattering of the vertex. This means that once we obtain the envelope of the TF image by edge extraction and acquire the peak value of the envelope, we can then estimate the attitude angle according to Equation (17).
## 4 Discussion
MB solution, which was introduced in some recent articles, was used in the present study owing to its ability to indicate the presence of \(\bullet\)OH. The color of the MB solution was used to detect the \(\bullet\)OH concentrations. The MB cations reacted with \(\bullet\)OH to generate hydroxyl ions and colorless MB radical cations, resulting in a color change from dark blue to colorless [29]. In this study, it was observed that in DW treated with NCP, the color of the MB solution became lighter, and the absorbance decreased. It can be assumed that DW is decomposed by NCP, and a large amount of \(\bullet\)OH is generated. Several studies have been conducted to determine the effects on dental hard tissues; however, the results are highly controversial. Some previous studies have demonstrated that bleaching with HP and CP has harmful effects on dental hard tissue, especially on the mineral composition and microhardness of the tooth. [PERSON] et al. [30] and [PERSON] and [PERSON] [31] reported mineral loss in teeth after peroxide-based bleaching; thus, the use of a peroxide-based bleaching agent can affect the mineral composition of dental hard tissue. [PERSON] et al. [32] reported that the use of a peroxide bleaching agent that included an acid as an activator led to a decrease in the microhardness of the dental hard tissue. In addition, [PERSON] et al. [33] reported that the use of 10% CP resulted in a reduction in the microhardness and an increase in the surface roughness. In this study, changes in the hardness and mineral composition of dentin and enamel could not be observed after DW and NCP treatment. These results mean that the DW and NCP treatment does not have any detrimental effects on the hard tissues of the tooth. Because even low concentrations of HP cause side effects on teeth, alternative materials that do not contain HP are needed for teeth whitening. Considering these results, the present study suggests that DW can act as an effective whitening gel when combined with an optimal plasma device. Therefore, the tooth whitening method using DW and NCP can be a reliable and novel tooth bleaching method that does not affect the properties of the dental hard tissue. Although human teeth were used in this experiment, extracted teeth or the teeth of a cadaver were used. Therefore, for a more accurate tooth whitening effect, the next study should be conducted directly through clinical trials.
## 7 Simulation Results and Discussion
### Experimental Scenario (1): The Impact of Node Density  
The network performance is studied for node densities of 10, 20, 30, 40 and 50. Figure 10 illustrates that the PDR grows clearly with node density, with higher node density indicating more reliable routes to the destination. However, when the node density is fewer than 11 nodes, the PDR for both the fixed GPSR and AFB-GPSR protocols is similar; however, as the node density rises, the AFB-GPSR provides a greater PDR than the GPSR and AODV.  
Figure 9: Simulation network settings.  
On the other hand, in Figure 11, when the network density increases (i.e., more nodes are added), the resultant E2 ED of AFB-GPSR achieves nearly the same E2 ED in GPSR-2s; but AODV indicates the highest E2 ED values when compared to GPSR-1s. This occurs due to the excessive route discovery and maintenance procedure of AODV mechanism. Furthermore, when density expands, throughput improves significantly. AFB-GPSR outperforms GPSR, whereas the route in AFB-GPSR exposes more reliable beacon intervals than GPSR at fixed beacon intervals, as illustrated in Figure 12. In addition, because AODV operates without loops and is scalable to a large number of nodes, it has the maximum throughput in comparison to GPSR and AFB-GPSR in two examples of 10 and 20 node densities, although it drops less than the other protocols as the node density rises.  
Figure 11: End-to-end delay vs. node density.  
Figure 10: Packet-delivery ratio vs. node density.
# From Cyber-Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs
## 5 Overview of the AI Market and Current State of the Telecommunications Industry
### Current State of the Telecommunications Industry
#### 5.1.3 Content-Based Applications and Social Networking  
In addition to connected television and content applications such as TikTok, further undercutting their traditional business model has been the rise of OTT communications: (i) applications for social media networking, such as Facebook; (ii) applications for internet telephony, such as Skype; (iii) applcations for micro-blogging, such as Twitter, Posterous, FriendFeed, etc.; (iv) applications for instant messaging, such as WhatsApp, Facebook Messenger, WeChat, Viber, etc.). All these applications most predominantly consist of real-time content, hence they exhibit different traffic patterns compared to the usual voice, text messaging, emails, as well as web surfing [4]. These applications take up a huge share of the communications market, such that users no longer rely on carrier networks as much as they used to. The need to continuously invest in next-generation infrastructure and increasing commoditization of offerings is leading the telecommunication sector to the perfect storm. As digital players such as Google Fiber expand to infrastructure, carrier networks are becoming more worried about their survival in the wireless marketplace [87]. In order to improve their chances of survival and remain profitable, many communication service providers (CSPs) have begun embracing additional and sometimes unconventional revenue streams. For instance, Verizon attempted to increase its reach through media via its Verizon Media Group Holding in recent years to give it multiple additional revenue channels beyond traditional telecommunications offerings [88].
# Farm Monitoring System with Drones and Optical Camera Communication
## 3 Proposed Scheme
### Algorithm  
The goal of the proposed algorithm is to achieve the approximately shortest trajectory ensuring data transmission from all sensor nodes. The concept of the trajectory control algorithm is shown in Figure 5. The proposed algorithm is summarized in Algorithm 1. The proposed algorithm consists of the following steps.
## 3 Result and Discussion
### Comparison with the Previous Study
#### 3.4.1 Comparison of Ultimate Load Capacity  
This section compares the ultimate load capacity of the various panel configurations. It is confirmed that the addition of infill material significantly improves the load capacity of the panel to act as a structural component in modular construction. The load capacity of the hollow panels was in the range of 6 kN to 13 kN (single-cell to multi-cell panels), which can be safely adapted for temporary flooring applications such as formworks and in temporary housing, such as refugee shelters, as floors according to the AS 2327-2017 standard code [34]. Considering the floor slabs with SGCC infill and reinforcement, the ultimate load ranged between 39 kN and 98 kN (single-cell to multi-cell slabs), which shows a significant increase, highlighting their potential as structural floor slabs.  
It can be seen in Figure 14 that a hollow single-cell floor panel reported only a 6 kN ultimate load, which, when filled with SGCC reinforced in BFRP, reported a six-fold increase in the ultimate load capacity. Moreover, the SGC with chopped basalt fibre reinforced in BFRP reported a further 7% increase in the ultimate load for single-cell floor panels compared with samples without chopped basalt fibre additions with better ductility. A double-cell hollow panel reported an ultimate load of 8.59 kN which is eight times higher  
Figure 13: Strain mapping of triple-cell panels.  
when filled with SCGC, chopped basalt fibres, and BFRP bars. This shows the relevance of infill material in improving structural performance. A similar trend was reported by triple-cell panels, where the ultimate load was increased seven-fold, reporting a peak load of 98 kN for panels with infill.  
In summary, the presence of infill material significantly improves the performance of these panels for advanced modular slab systems. Along with the improvement in load capacity, the presence of an infill material offers a significant increase in ductility and stiffness properties.