esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal View Estimation Algorithm and Evaluation with Deviation Angle Analysis
## 2 Related Work
### Evaluation  
The quality evaluation of estimated viewpoints depends on labeled images and is primarily conducted through visual observation or quantitative indicator measurement.  
\begin{table}
\begin{tabular}{c c c} \hline
**Reference** & **Statistical Feature** & **Geometric Feature** \\ \hline
[19] & Histogram of features & Contour \\
[20] & Step ray; unbiased distance estimator & Central-projection analysis \\
[21] & A statistical optimization & Rotation matrix \\ \hline \end{tabular} HOG is the Histogram of Oriented Gradient.  
\end{table}
Table 1: \(Cont\).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Method** & **Gen. Data** & **Model** \\ \hline
[14] & 19-layer VGGNet, based on AlexNet & Y & Fish, head, tree, etc. \\
[22] & Semi-supervised, simple CNN & Y & Airplane, car, and chair \\
[24] & Self-supervised, Rectification Network & Y & Duck, cat, iron, etc. \\
[26] & A CNN with shared lower layers & Y & Bus, car, chair, etc. \\
[27] & R-CNN model & Y & Airplane, boat, and car \\ \hline \end{tabular} Gen. Data means that new image data are generated.  
\end{table}
Table 2: The examples of CNN models used in references.  
Regarding the accuracy of quantitative evaluation estimation, the method based on minimizing the empirical loss proposed in [22] is representative. Minimizing empirical loss means minimizing the total error between the estimated viewpoint direction and the actual viewpoint direction. In 2018, [PERSON] et al. [28] proposed a novel method of viewpoint quality evaluation (VQE), which utilizes texture distortion as an evaluation metric. The texture stretch and object fragment are taken as the main distortion factors. In 2010, [PERSON] et al. [29] established a benchmark for the evaluation of best view selection algorithms. The benchmark consists of the preferred views of 68 3D models provided by 26 human subjects. In 2019, [PERSON] et al. [30] presented a novel viewpoint evaluation method combining geometric and visual perception information. In 2022, [PERSON] et al. [31] predicted the viewpoint similarity for rotated novel objects based on the projected position shifts of surface points as the object rotates.  
Considering the outstanding performance of deep learning methods in image classification problems and their early exploration in viewpoint estimation problems, we adopt a new evaluation method combining a CNN and viewpoint estimation to achieve image-based viewpoint estimation.
# No-Reference Image Quality Assessment Based on Image Multi-Scale Contour Prediction
## 3 Approach
### Model Architecture  
The MSIQA model consists of two main modules: (1) a multi-scale contour prediction network to simulate the response of human eyes on an image's contours at different distances, and (2) a central attention peripheral inhibition module to simulate the mechanism of the receptive field of retinal ganglion cells. We use four inception [31; 32; 33] modules with the same structure to build the contour prediction network. Each layer has a batch normalization (BN) [34] and a rectified linear unit (ReLU) [35]. After the inception module, the PixelShuffle [36] method is used to upscale the input feature to the same size as the input image. In the second stage of training, the outputs of different inception modules are first fused and combined with the central attention peripheral inhibition module, then fed into the convolutional layer and two fully connected layers.
## 2 Materials and Methods
### Mathematical Models/Theoretical Analysis  
A theoretical approach based on energy balance equations has been used to analyze the numerical results and to obtain the thermal performance of DSTSC.  
Solar air collectors are typically analyzed via two coefficients of performance [17], the heat exchange effectiveness \(\varepsilon_{HX}\) and the solar collector efficiency \(\eta\).  
The heat exchange effectiveness is defined as:  
\[\varepsilon_{HX}=\frac{T_{outlet}-T_{ambient}}{T_{absorber\ plate}-T_{ambient}} \tag{1}\]  
where: \(T_{outlet}\) is the outlet air temperature [\({}^{\circ}\)C]; \(T_{ambient}\) is the ambient air temperature [\({}^{\circ}\)C]; and \(T_{absorber\ plate}\) is the temperature of absorber plate [\({}^{\circ}\)C].  
The solar efficiency collector can be calculated with the following expression:  
\[\eta=\frac{c_{p,air}\ m_{air}(T_{outlet}-T_{ambient})}{I_{T}\ A_{S}} \tag{2}\]  
where: \(T_{outlet}\) is the outlet air temperature [\({}^{\circ}\)C]; \(T_{ambient}\) is the ambient air temperature [\({}^{\circ}\)C]; \(c_{p,air}\) is the specific heat capacity of air [J/kg\(\cdot\)K]; absorber plate area [m\({}^{2}\)].  
In this paper, we also analyzed the performance of the solar collector via the heat transfer coefficient. The heat transfer coefficient (\(h\)) can be assessed with the following expression:  
\[h=\frac{q}{\Delta T} \tag{3}\]  
where: \(q\) is the heat flux [W/m\({}^{2}\)] and \(\Delta T\) is the difference in temperature between the solid surface and surrounding fluid area [K].
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
## 3 Interleaver
### Quadratic Polynomial Permutation Interleaver  
Equation (18) presents a QPP interleaver defined by the polynomial.  
\[\pi(i)=\left(f_{1}\cdot i+f_{2}\cdot i^{2}\right)\text{ }mod\text{ }L \tag{18}\]  
where:  
\(i\) is the position index of the bit before interleaving with \(0\leq i\leq L-1\);  
\(L\) corresponds to block size;  
\(\pi(i)\) is an interleaved position corresponding to the position \(i\);  
\(f_{1}\) and \(f_{2}\) are the coefficients that define the permutation.  
The conditions for \(f_{1}\) and \(f_{2}\) are defined in extenso in [35]. Table 2 presents the coefficients\(f_{1}\) and \(f_{2}\) for some \(L\) lengths.
## 4 Choice of Polymeric Carriers
### Sodium Alginate  
Sodium alginate is a polysaccharide abundant in the cell walls of brown algae, and also present in the walls of certain bacterial species, such as _Pseudomonas_ and _Azotobacter_[32]. This polymer consists of \(\beta\)-d-mannuronate and \(\alpha\)-l-galuronate residues whose carboxylate groups carry a net negative charge [32]. Alginate is applied in many fields, namely, pharmaceutical, food, and agricultural [33; 34]. Among its applications, it is considered the substrate of choice for the encapsulation of PGPMs in consideration of its biodegradability, biocompatibility, availability, nontoxic nature, relatively low cost, and ability to withstand acidic soil conditions and trap a large number of microbial cells and allow their slow and progressive diffusion [35; 36]. Water-soluble alginate forms an irreversible hydrogel after reacting with acids or salts containing divalent cations [37]. Guluronic-acid-based alginate has more ion affinity than mannuronic-acid-based alginate, and the gel matrix is formed after diffusion of cations into the alginate solution after exchange of Na\({}^{+}\) with Ca\({}^{2+}\) and the formation of an ion bridge between the two chains [38]. However, it has been reported that the presence of certain antigelling agents, such as Mg\({}^{2+}\), or chelating agents, such as citrate, can affect the integrity of alginate capsules [39]. The combination of alginate with other polymers, such as chitosan and gelatin, seems to be the solution [24].
# Fast Segmentation Algorithm for Cytoid Macular Edema Based on Omnidirectional Wave Operator
###### Abstract
The wave algorithm is a segmentation method based on image pixel features, which is much faster than level set, graph theory, and other methods. It has outstanding performance in retinal layer segmentation and can also be used in denoising applications while preserving boundaries [17]. According to the gradual change in the gray value of the pixels from the target boundary, the wave algorithm can effectively reduce the influence of random speckle noise and is suitable for the OCT image process. The algorithm has high precision in retina interlaminar segmentation. Compared with manual segmentation, the deviation is less than 1.5 pixels. It is both an accurate and fast segmentation method. The wave algorithm has outstanding performance in retinal layer segmentation. However, when being applied to CME segmentation, the wave algorithm has the following problems: (a) the complete contour cannot be extracted; (b) the algorithm operation direction is fixed, and so is the extracted curve direction. Aiming at the shortcomings of the wave algorithm, this paper describes an algorithm that can extract the CME contour in all directions. The direction adjustment function of the algorithm endows the operator with omnidirectional process capabilities, which can realize full-contour detection for any figure of a closed curve and extract the complete target boundary. In addition, the proposed algorithm does not rely on the gradient information of the target boundary and realizes the accurate extraction of the target contour under speckle noise. Moreover, other process algorithms are added, including enhancing the contrast of images and contour integration. The former brings higher accuracy for CME detection; the latter filters the interference information of the choroid through connected domain screening, to avoid the wrong results. Compared with other algorithms, the proposed algorithm has higher robustness, higher accuracy, and faster operation speed, which can ensure the automatic segmentation of CME and help promote the development of ophthalmology and clinical medicine.
# A Fairness Index Based on Rate Variance for Downlink Non-Orthogonal Multiple Access System
## 4 Power Allocation under Fairness Constraints
### Joint Power Allocation Intra and Inter Cluster  
The intra and inter cluster power allocation algorithms are optimization algorithms under the given inter cluster and intra cluster power allocation coefficient, respectively. The intra and inter cluster power allocation can be iteratively carried out alternately, so that the system can obtain the maximum throughput that meets the minimum fairness condition. Based on the above ideas, this paper proposes a joint intra and inter cluster power allocation algorithm, where \(\epsilon\) represents the iterative error tolerance.  
```
Input: \(P_{t}\), \(\varphi_{\text{c}}\), \(\{\alpha_{n}^{s},\alpha_{n}^{w}\}_{n=1}^{N}\), \(H\), \(W\), \(n_{0}\), \(\epsilon\), \(i=0\) Output: \(\{p_{n},\alpha_{n}^{s},\alpha_{n}^{w}\}_{n=1}^{N}\)
1: initialize \(\{p_{n}^{0}=P_{t}/N\}_{n=1}^{N}\), execute Algorithm 1 and get \(\left\{\left(\alpha_{n}^{s}\right)^{0},\left(\alpha_{n}^{w}\right)^{0}\right\}_ {n=1}^{N}\), calculate \(R_{sum}^{0}\);
2:\(i=i+1\);
3: bring \(\left\{\left(\alpha_{n}^{s}\right)^{i-1},\left(\alpha_{n}^{w}\right)^{i-1} \right\}_{n=1}^{N}\) into Algorithm 2 and get \(\left\{p_{n}^{i}\right\}_{n=1}^{N}\);
4: for \(n=1\): \(N\);
5: substitute \(\left\{p_{n}^{i}\right\}_{n=1}^{N}\) into Algorithm 1 and get \(\left(\alpha_{n}^{s}\right)^{i}\), \(\left(\alpha_{n}^{w}\right)^{i}\);
6: end;
7: calculate \(R_{sum}^{i}\);
8: if \(\left|R_{sum}^{i}-R_{sum}^{i-1}\right|>\epsilon\), turn to step 2;
9: output \(\left\{p_{n}^{i},\left(\alpha_{n}^{s}\right)^{i},\left(\alpha_{n}^{w}\right)^{i} \right\}_{n=1}^{N}\);
10: end.
```  
**Algorithm 3**Joint power allocation inter and intra cluster under fairness constraints
# A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
## 3 Signal Model and Time-Frequency Transformation
### Time-Frequency Transformation  
The common time-frequency transformation methods include the short-time Fourier Transform (STFT), the Wigner-Ville distribution (WVD), the Choi-Williams distribution (CWD), the pseudo Wigner-Ville distribution (PWVD), the smoothed pseudo Wigner-Ville distribution (SPWVD), and so on [41; 42]. The SPWVD can express the radar signals in detail and effectively prevent cross-interference over other methods, and is adopted in this paper [43]. The SPWVD transformation is written as  
\[S(t,f)=\iint x(t-\mu+\tau/2)\cdot x^{*}(t-\
u-\tau/2)\cdot h(\tau)g(\mu)e^{- 2\pi f\tau}d\mu d\tau \tag{2}\]  
where \(*\) is the complex conjugate; \(h(\tau)\) and \(g(\mu)\) represent window functions; and \(x(t)\) is the analytic signal \(y(t)\).  
Through SPWVD time-frequency transformation, 2184 types of dual-component radar signals under different SNRs are acquired. To save space, Figure 1 only illustrates four types of time-frequency images (TFIs) of dual-component radar signals under different SNRs. Therein, Figure 1 represents the overlap of EQFM and SFM at \(-2\) dB; Figure 1 represents the overlap of EQFM at \(-2\) dB and SFM at \(12\) dB; and Figure 1 represents the overlap of EQFM and SFM at \(12\) dB.  
Figure 1 demonstrates that SNR exerts an essential effect on TFIs. The higher SNR results in obtaining clearer TFIs. It means that the more detailed features are extracted for the clearer TFIs, and better classification performance can be achieved in the following analyses.
# A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
## 5 System Model
### Hyperparameter Tuning  
When creating deep learning models, hyperparameter tuning is an essential step. The configuration parameters that a model is trained on and structured with are called hyperparameters. Hyperparameters, in contrast to parameters, which are discovered from the data during training, are preset before training and direct the learning process. The goal of fine-tuning these hyperparameters is to identify the optimal combination that maximizes the model's performance [61]. We independently carried out the hyperparameter optimization procedure for every deep network in our experiment. These were the primary hyperparameters:  
* Dropout: This serves as a regularization technique aimed at mitigating overfitting by randomly selecting cells within a layer and nullifying their output based on a specified probability.
* Number of layers: The neural network's depth, which represents its number of layers. Deeper networks have the potential to overfit but can also capture more complex patterns. Achieving the proper depth is critical.
* Epochs: How many times is the neural network used to pass the whole dataset back and forth during training? Epoch determines how frequently the model views the complete dataset. Underfitting may result from a lack of epochs, and overfitting may result from an excess of epochs.
* Number of neurons in each layer: The quantity of neurons in every network layer regulates the model's ability to recognize patterns. Underfitting can occur from having too few neurons, while overfitting can occur from having too many.
* Batch size: The total number of training instances used in a single iteration impacts how quickly and effectively training uses memory. Greater batch sizes may result in quicker training, but they also demand more RAM.
* Learning rate: The step size while attempting to minimize a loss function at each iteration. The model might overshoot the minimum but converge quickly if the learning rate is high. A low learning rate can cause slow convergence.
* Lookback period: The lookback period refers to the number of previous time steps that are taken into consideration by the model when generating predictions. The model becomes more complex when the lookback period is extended, but it also enables the model to capture longer-term dependencies and patterns. If the lookback period is too short, the model might miss important temporal relationships.
# Parallel Ghost Imaging with Extra Large Field of View and High Pixel Resolution
## 1 Introduction
This research realizes parallel ghost imaging with an ultra-large field of view, high pixel resolution, and high experimental efficiency. We believe that this framework will lay a foundation for the practical application of ghost imaging.
# Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images
## 3 Results
### Scenario 2: Field Area
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**RGB**} & \multicolumn{6}{c}{**R**} \\ \hline  & \multicolumn{3}{c}{**Field**} & \multicolumn{6}{c}{**Field**} \\ \hline  & STARFM & FSDAF & HCM & Bicubic & & STARFM & FSDAF & HCM & Bicubic \\ RMSE & 0.0302 & 0.0286 & **0.0266** & 0.0479 & RMSE & 0.0337 & 0.0321 & **0.0308** & 0.0560 \\ PSNR & 30.4069 & 30.8705 & **31.5069** & 26.3870 & PSNR & 29.4494 & 29.8801 & **30.2383** & 25.0355 \\ AD & 0.0206 & 0.0194 & **0.0168** & 0.0335 & AD & 0.0233 & 0.0220 & **0.0196** & 0.0400 \\ CC & 0.4967 & 0.5025 & **0.5121** & 0.4223 & CC & 0.9300 & 0.9360 & **0.9394** & 0.8047 \\ SAM & 1.4415 & 1.2646 & **1.0583** & 1.7460 & SSIM & 0.8661 & 0.8723 & **0.8926** & 0.5412 \\ SSIM & 0.8902 & 0.8966 & **0.9156** & 0.6574 & ERGAS & 9.5117 & 9.0515 & **8.7089** & 15.8107 \\ ERGAS & 8.3115 & 7.8876 & **7.3729** & 13.2696 & Q2N & 0.8602 & 0.8712 & **0.8967** & 0.5391 \\ Q2N & 0.8439 & 0.8559 & **0.8929** & 0.5461 & & & & & \\ \multicolumn{3}{c}{**(a)** Metrics for all 3 bands} & \multicolumn{6}{c}{**(b)** Metrics for Red band} \\ \hline  & \multicolumn{3}{c}{**G**} & \multicolumn{6}{c}{**B**} \\ \hline  & \multicolumn{3}{c}{**Field**} & \multicolumn{6}{c}{**Field**} \\ \hline  & STARFM & FSDAF & HCM & Bicubic & & STARFM & FSDAF & HCM & Bicubic \\ RMSE & 0.0286 & 0.0274 & **0.0259** & 0.0464 & RMSE & 0.0279 & 0.0260 & **0.0225** & 0.0400 \\ PSNR & 30.8846 & 31.2412 & **31.7407** & 26.6616 & PSNR & 31.0748 & 31.7023 & **32.9749** & 27.9594 \\ AD & 0.0195 & 0.0187 & **0.0166** & 0.0328 & AD & 0.0189 & 0.0176 & **0.0141** & 0.0276 \\ CC & 0.9180 & 0.9240 & **0.9292** & 0.7820 & CC & 0.8760 & 0.8895 & **0.9150** & 0.7418 \\ SSIM & 0.8827 & 0.8868 & **0.9047** & 0.6042 & SSIM & 0.8619 & 0.8713 & **0.9065** & 0.6407 \\ ERGAS & 8.0951 & 7.7694 & **7.3551** & 13.1633 & ERGAS & 7.1583 & 6.6595 & **5.7564** & 10.2467 \\ Q2N & 0.8522 & 0.8607 & **0.8885** & 0.5361 & Q2N & 0.8054 & 0.8209 & **0.8782** & 0.5155 \\ \multicolumn{3}{c}{**(c)** Metrics for Green band} & \multicolumn{6}{c}{**(d)** Metrics for Blue band} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of prediction results of using Planet and WV images for the scenario depicted in Figure 8.  
Figure 8: Two Planets images at 19 July and 27 July and one WV image at 18 July are fused to generate a prediction. The prediction is then compared to a WV image collected on 26 July 2017.
# Information Security Applications in Smart Cities: A Bibliometric Analysis of Emerging Research
## 4 Findings and Discussion
#### 4.1.1 Cluster 1 (Red): Smart Power Grid in Smart Cities  
One of the applications of information security is related to smart power grid maintenance in smart cities. A smart power grid can offer support to a smart communications grid since society increasingly requires information transfer infrastructure in daily activities [65]. Over the years, utilities have invested in communication networks to improve awareness of the power grid assets and to control, automate, and integrate the service delivery systems. The key point of integrating systems and working in real-time is connectivity. Most of the time, the web facilitates systems integration and benefits society with this support.  
On the other hand, the web environment allows targeted attacks and attempts to break into the system. The North American Electric Reliability Corporation [335] highlighted compliance concerns in strengthening essential cybersecurity across the entire power system and emphasized that this requires a series of cybersecurity concerns [87; 88; 336; 337].  
For some authors, the smart grid needs to be observed and measured before being controlled and automated [338]. To that end, the automation of the power substation helps utilities add sophisticated protection and control functions while offering more visibility into the performance and integrity of the network infrastructure. Also, it is essential to note that the resilience of physical and electrical networks must also be improved according to the flow of information, as critical operations can cause failures or can be combined with physical attacks to create a blackout [339].  
A reliable smart grid requires layered protection applications that consist of a cybernetic infrastructure that limits adversary access and limits the operation of the transmission accurately during an attack.
## 3 Background
### Security Requirements
#### 3.3.3 Availability  
The number of vehicle owners has increased in the past ten years. As such, there is a high likelihood of a high number of IoV users within a particular region at the same time. For this reason, network breakdowns cannot be ruled out, especially during peak hours when everyone is rushing to go home or to work. Hence, an effective system will be available at all times to all legitimate users. The entire IoT system is based on information dissemination. For this reason, information should be made available when needed; otherwise, if there are delays, it might be of no help. It is for this reason that group signature was developed to solve the availability issue.  
Figure 1: Security requirements in IoV.
## 5 Grasp
### Latency GRASP
#### 5.1.1 Construction Phase  
During the first step of the GRASP, namely construction phase, a valid solution must be built. That is, interconnect all the network nodes with valid transmission paths according to the transmission levels shown in Table 1 until every node is capable of reaching the sink node. Algorithm 2 shows the code of the method responsible for such matter. Starting from an empty set as shown in line 1, we define a loop in lines 2-16 in which each iteration is responsible for adding a new node to the solution until the size of the problem is reached. To decide which node to include in the current step, firstly the cost of adding each node outside both the partial solution and the critical nodes subset \(Perms_{i}\) is computed as seen in lines 4-6. When all the costs are calculated, a Restricted Candidate List (RCL) is built by adding only the nodes with a cost inside the range [\(C_{min}\), \(\alpha\) * (\(C_{max}-C_{min}\))] (lines 10-14). These thresholds represent the minimum and the maximum _adding_ cost for the nodes yet outside the solution. To finalize, a random node is extracted from the RCL and added to the solution.  
The objective function of the problem is defined inside the function responsible for calculating the cost of adding an element to the current solution, as seen in line 5.  
```
1:\(\alpha\): randomness level specific for GRASP
2:
3:\(G\): node graph
4:\(G\leftarrow\emptyset\)
5:while (\(|G|<\) problemSize) do
6:\(Node_{costs}\leftarrow\emptyset\)
7:for\(Node_{i}\
ot\in G\)and\(Node_{i}\
otin Perms_{i}\)do
8:\(Costs_{i}\leftarrow\) costOfAdding(\(Node_{i}\), \(G\))
9:endfor
10:\(RCL\leftarrow\emptyset\)
11:\(C_{min}\leftarrow\) minCost(\(Costs\))
12:\(C_{max}\leftarrow\) maxCost(\(Costs\))
13:for\(Cost_{i}\in Costs\)do
14:if (\(Cost_{i}\leq C_{min}+\alpha*(C_{max}-C_{min})\)) then
15:\(RCL\gets Node_{i}\)
16:endif
17:endfor
18:\(G\leftarrow\) selectRandomNode(\(RCL\))
19:endwhile
20:return\(G\)
```  
**Algorithm 2** greedyRandomizedSolution(\(\alpha,Perms_{i}\))  
In this construction phase, there is no order in which nodes are added to the solution, or a global condition to test if the insertion is the best possible one. This is why the method is considered greedy because, in each step, the best local cost is chosen as a candidate.