esa-sceva/satcom-chunk-collection huggingface dataset:
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 5 Results
### GANs in Histological Image
#### 5.4.4 Synthetic Image Generation  
Synthetic image generation is a strategy that is particularly valuable in pathology, where obtaining annotated images is resource-intensive [100]. Building on this concept, [PERSON] et al. [61] employed a Conditional GAN (cGAN) model for both image generation and transformation tasks using semantic label maps. Implemented on an NVIDIA TITAN RTX GPU with 24 GB VRAM, their approach achieved strong quantitative results: Structural Similarity Index (SSIM) of 0.9066 \(\pm\) 0.065 and Mean Squared Error (MSE) of 0.0076 \(\pm\) 0.003. [PERSON] et al. [78] introduced a novel Vision Transformer-based model, ViT-P2P, specifically for semantic-to-image generation. The study evaluated image realism using alternative perceptual quality metrics: Natural Image Quality Evaluator (NIQE) = 8.94 \(\pm\) 1.62, Multi-scale Aesthetic Image Quality Assessment (MANIQA) = 0.51 \(\pm\) 0.03, and Perceptual Aesthetics Quality Predictor (PAQ2 PIQ) = 70.22 \(\pm\) 1.76.  
[PERSON] et al. [66] proposed a GAN-based model trained on an NVIDIA GTX TITAN X GPU with 12 GB RAM. Their model, designed for synthetic data augmentation, reported a Frechet Inception Distance (FID) of 142.84. In [PERSON] et al. [95] developed a high-definition image-to-image translation model (Pix2 Pix-GAN) for synthetic histological image generation. Running on an NVIDIA 2080 GPU, the model achieved a mean perceptual quality score of 4.9379 with a standard deviation of 0.2692.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 8 Conclusions and Future Direction  
In large-scale CR-WSNs, clustering is an effective way to achieve goals like obtaining information on local spectrum, efficient resource allocation, interference mitigation, PU protection, coverage and deployment cost reduction, network scalability, communications overhead reduction, an extended lifetime, robustness, and better data collection and aggregation. Clustering is very important in CR-WSNs because it provides a topology control approach to reduce transmission overhead and to exploit data aggregation among a large number of CR-WS nodes. Clustering in CR-WSNs is different from conventional clustering algorithms in WSNs in various ways. CR-WS nodes in CR-WSNs are responsible for channel sensing, channel allocation, data collection and data aggregation, including other cognitive tasks. Cluster heads in clustering help data aggregation and fusion, which intensely reduces the number of nodes taking part in packet transmission, and prolongs the overall network lifetime.  
In a small indoor or a favorable area, where the BS is very close to the sensors (typically, just one hop), a flat CR-WSN can be advantageous. That is because the BS is at least two hops away after clustering. Clustering also increases cluster formation and maintenance overhead.  
One critical step in clustering is to deploy the CHs uniformly to conserve energy, and mitigate interference with the PU of the channel. If cost, size, and energy are not the issue, most existing clustering algorithms for WSNs, which are primarily focused on scalability and energy conservation, can be tailored to CR-WSNs. The primary focus of the algorithm should be protection of the PU's right to access the channel. Otherwise, designing CR-WSNs with only the CH having a CR module can be the best way.  
Clustering in CR-WSNs brings several unique challenges to deployment, such as mitigating interference with PUs, ensuring connectivity among WS nodes, determining optimal cluster sizes, etc. Clustering in CR-WSNs is still in its infancy, and several key issues are yet to be addressed, such as interference mitigation, the optimal number of CHs and backup CHs, cooperative sensing with and without clustering, among others. Although the optimum number of clusters in WSNs has been addressed by many researchers, to the best of our knowledge, there is no such work on CR-WSNs.  
We provide the impetus required for further research to solve energy conservation, load balancing, PU protection, and efficient bandwidth utilization in CR-WSNs.  
This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01058751) and in part by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-R2718-16-0035) supervised by the IITP (National IT Industry Promotion Agency). Both authors wrote the paper and read and approved the final manuscript. The authors declare no conflict of interest.
## 2 Materials and Methods
### Correlation  
According to [PERSON] et al. [21], correlation analysis is a statistical approach used to determine the strength of a relationship between two variables or features. Additionally, this analysis assesses the direction and degree of correlations between variables or features. There are various correlation measures, the first being Spearman's product-moment. Because it can quantify correlations on interval scales and ratios, this measure is sometimes referred to as parametric statistics. By comparison, other correlation coefficients, such as the spearman rank correlation coefficient, the Kendall correlation coefficient, and the gamma correlation coefficient, are referred to as nonparametric statistics.  
Other research on correlation measurement includes those by [PERSON] et al. [22], who introduced a method called the Maximal Information Coefficient (MIC). As [PERSON] [23] observe, MIC is both popular and contentious; it is asserted that MIC lacks state-of-the-art. Researchers enhanced and refined their study with [PERSON] et al. [24] and [PERSON] et al. [25], which is a combination approach of MIC and Total Information Coefficient (TIC). Their strategy resulted in establishing a library, which was explored in works by [PERSON] et al. [26]. [PERSON] et al. [27] also proposed the Power Predictive Score (PPS) method for assessing the level of relationships. This method arose due to various problems with correlation; there are some cases of relationships that are not found when using conventional correlation methods. This method is open-source, and they provide a library that can be downloaded and used for free.  
This study used correlation to determine the correlation between features as the preliminary stages in eliminating the feature. At this stage, the correlation value between two features is rated, and then ten features with a high correlation value between features are eliminated. This method, also known as Correlation Based Feature Selection (CSF), evaluates more than one attribute and analyzes the relationship between features. In CSF, a good feature combination has substantially linked characteristics with output but not with one another [28]. Eliminating correlation between features from existing features is expected to result in unique features. [PERSON] et al. [29] define multicollinearity as the presence of a strong relationship between two or more variable predictors. According to [PERSON] et al. [30], feature selection is a process that entails identifying the most meaningful subset of characteristics for targeted concepts by excluding unnecessary features. Besides that, it can result in decision-making errors, but it can also jeopardize the legitimacy of the conclusions or outcome. The most straightforward method for determining this is building and examining correlation matrix tables.
## 3 Multi-Layer Security Approach Based on Vulnerability Indices
### The Blockchain Architecture-Based Public Defense
This consensus protocol can guarantee new transactions being added to the network in such a way that has no conflict with the other transactions within the system. Henceforth, the fresh transactions are located in a block that needs to be submitted to the blockchain process with the aim of confirming through the validation mechanism. The cryptographic process is one of the most significant parts of blockchain technology, which is considered to get a safe environment for data and records exchanged among nodes. For more clarification, it is needed to describe the data structure in each node. The block is established in each node in order to retain a growing list of records made by the distributed database of the blockchain and there is a need to secure the network against malicious hackers by using continual verification. In other words, each node joined to the network is able to view its block including the list of transactions inserted in the ledger. Figure 3 shows a block shape in blockchain technology. Focusing on this figure, it can be realized that a block made by a node consists of the transactions, timestamp, the data related to the previous block, and a tree chart of transactions named Merkle root. As mentioned before, it is necessary that the data block is secured by each node to make an obstacle for the invalid nodes. To do so, all nodes should reflect an encryption system according to the data label of the block for generating current/amenable hash addresses (HAs). To elaborate on the generation process of the previous/current HAs, in a network are some nodes, each node in iteration \(i\) bounds up to generate a HA (current HA) considering its hash function and keeps an HA (previous HA) made by the adjacent nodes in iteration \(i\)-1. Each node saves both the recent and current HAs into its data block. This trend makes two general results including validity and confirmation of the data block. It is significant to mention that the HAs are generated in range of the 32-bit compounded words concerning different hash functions [44], that is, SHA-512, SHA-384, SHA-256, SHA-224, and SHA-1, consisting of letters and numbers [0-9, A-F].
## 8 Expected altimeter performances  
### Measurements objectives and principle  
The main parameters to retrieve from return waveforms are (see Figure 7):  
- the range, standing for the distance measurement between the satellite and the sea surface,  
- the significant wave height (SWH),  
- the backscatter coefficient \(\sigma 0\) which is directly related to the surface wind,  
- thermal noise and antenna mispointing can also be derived from the measurement.  
To estimate these parameters on sea surfaces, the [PERSON]'s model [35] is commonly used as it is an analytical formulation of the return power depending on these different parameters.  
Something to point out is the large sensitivity of Ka-band echoes to satellite mispointing, due to the small antenna aperture, which entails a high attenuation and deformation of the return waveform. This is shown on Figure 8. The platform pointing accuracy is thus required to be better than \(\pm\) 0.2\({}^{\circ}\) at 3\(\sigma\), which is something that has been proved to be fully achievable in flight on microsatellite platforms such as the CNES MYRIADE series.  
Figure 7: [PERSON]’s model with associated parameters.  
The next sections will deal with a variety of aspects of the performance budget.
## 3 Experimental Section
### HPLC Analysis of Carotenoid and Quinone  
Samples redissolved in hexane were dried at speedvac, redissolved in ethyl acetate (100 uL for cells and 50 uL for medium samples) and immediately analyzed by HPLC. Quali-quantitative analyses were carried out using an Agilent 1100 Series HPLC system (Agilent Tecnologies, Waldbronn, Germany) as described by [PERSON] _et al._[25] but slightly modified. Carotenoid and quinones were separated using a reverse-phase C\({}_{30}\) column (5 um, 250 \(\times\) 4.6 mm) (YMC Inc., Wilmington, NC, USA) with mobile phases consisting of methanol (A), 0.2% ammonium acetate aqueous solution/methanol (20/80 _v/v_) (B), and _tert_-methyl butyl ether (C). The elution was as follows: 0 min, 95% A and 5% B; 0 to 12 min, 80% A, 5% B, and 15% C; 12 to 42 min, 30% A, 5% B, and 65% C; 42 to 60 min, 30% A, 5% B, and 65% C; 60 to 62 min, 95% A, and 5% B. The column was re-equilibrated for 10 min between runs. The flow rate was 1.0 mL/min, and the column temperature was maintained at 25 \({}^{\circ}\)C. The injection volume was 10 L. Absorbance was registered by diode array at wavelengths of 475 nm for carotenoids and 275 nm for quinones. Carotenoids and quinones were identified by comparing their retention times and UV-vis spectra to authentic standards. An example of chromatographic profile of carotenoids and quinones in _A. annua_ cell cultures was reported in Figure 5. Carotenoid standards were purchased from Sigma-Aldrich (Milan, Italy), Cayman Chemicals (Ann Arbor, MI, USA), and Extrasynthese (Genay CEDEX, France).  
**Figure 5.** HPLC analysis of _Artemisia annua_ suspension cell extracts recorded at 475 nm (**A**) and at 275 nm (**B**). Cells were treated with 50 mM DIMEB for three days. Peaks in (**A**) are (1) violaxanthin; (2) neoxanthin; (3) chlorophyll b; (4) lutein; (5) zeaxanthin; (6) \(\beta\)-carotene. Peaks in (**B**) are (1) Q9; (2) Q10.
# Hide45: A Method for Optimal Payload Data Hiding in Base45 Encoded Strings
## 5 Proposed Algorithm
### Optimization Method
#### 5.1.1 Problem Description  
Given a function that needs to be optimized in the presence of constraints, one might think to solve the problem by means of combinatorial optimization [42]. In fact, there is the need to maximize the average payload (2) by assigning a certain number of bits \(n_{i}\) to the \(i\)-th binary configuration without exceeding the number of available configurations (3).  
As a preliminary, let us say we only consider the most frequent (for each file type) Base45 configurations: baseline for our optimization is, in fact, the one-bit assignment to each one of the \(45^{3}-2^{16}=25,589\) most frequent configurations [23]. In this setting suppose, without loss of generality, that we sort all configurations in non-increasing order of their frequency \(f_{i}\), such that \(f_{i}\geq f_{j}\) when \(i\leq j\). Then we can define the conditional probability to find a (frequent) configuration \(i\) as:  
\[e_{i}=\frac{f_{i}}{\sum_{j=0}^{25,589-1}f_{j}}\qquad 0\leq i<25,589, \tag{5}\]  
that is, the probability to find configuration \(i\), subject to \(i<25,589\), in any file of a given type having a specific frequency distribution.  
Our aim is then to maximize the _expected payload_, that is:  
\[\text{maximize}\sum_{i=0}^{25,589-1}e_{i}n_{i}; \tag{6}\]  
we note that maximizing (6) is equivalent to maximize (2) over the 25,589 most frequent configurations since:\[\text{maximize}\ \sum_{i=0}^{25,589-1}e_{i}n_{i} \Leftrightarrow \text{maximize}\ \sum_{i=0}^{25,589-1}\frac{f_{i}}{\sum_{j=0}^{25,589-1}f_{j}}n_{i}\] \[\Leftrightarrow \frac{1}{\sum_{j=0}^{25,589-1}f_{j}}\cdot\text{maximize}\ \sum_{i=0}^{25,589-1}f_{i}n_{i}\] \[\Leftrightarrow \text{maximize}\ \sum_{i=0}^{25,589-1}f_{i}n_{i}\]  
given that the term \(\sum_{j=0}^{25,589-1}f_{j}\) is a constant and so we can remove it from the equation.  
The only modification required on constraint (4) is the sum index that becomes \(25,589-1\) from the old \(2^{16}-1\); this is the same thing we already did in (6).  
It is easy to see that the objective function (6) under the one-bit assignment hypothesis [23] equals to 1, since \(n_{i}=1\) for all of the \(i\) most frequent configurations. Our optimization approach, then, aims to maximize this value: if we can find an assignment with an objective value above 1, it means that there is a possible gain w.r.t. the one-bit baseline.
## 4 Results and Discussion  
### Model Selection  
After the data were divided into the children group and the older adults group, the system was trained with linear regression, random forest, and XGBoost. For selection of the optimal model for each group, the R\({}^{2}\) value in Equation (1), which calculates the ratio of the variance of the predicted value to the variance of the actual value as an index, was used. The closer the value of this index is to 1, the more accurate it is. The ultimately derived R\({}^{2}\) is shown in Table 3. XGBoost was selected as the final model, as it had a large R\({}^{2}\) value for most illnesses.  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}(t_{i}-y_{i})^{2}}{\sum_{i=1}^{n}\bigl{(}t_ {i}-\overline{t_{i}}\bigr{)}^{2}} \tag{1}\]  
\(t_{i}\): actual value, \(y_{i}\): expected value.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**a. Children**} \\ \hline
**Illness** & **Linear Regression** & **Random Forest** & **XGBoost** \\ \hline Acute bronchitis & 0.010 & 0.501 & **0.504** \\ Common cold & 0.242 & 0.610 & **0.632** \\ Rhinitis, tonsillitis & 0.013 & 0.447 & **0.498** \\ Inner ear inflammation & 0.147 & 0.239 & **0.338** \\ Chronic bronchitis & **0.452** & 0.221 & 0.271 \\ Intestinal inflammation & 0.117 & 0.138 & **0.162** \\ Influenza, pneumonia & 0.075 & 0.109 & **0.153** \\ Eczema, dermatitis & 0.147 & 0.237 & **0.282** \\ Chicken pox & 0.085 & 0.012 & **0.089** \\ Conjunctivitis & 0.090 & 0.142 & **0.199** \\ \hline \multicolumn{4}{c}{**b. Older adults**} \\ \hline High blood pressure, heart disease & 0.655 & **0.842** & **0.842** \\ Common cold & 0.343 & 0.545 & **0.558** \\ Esophageal inflammation, gastritis & 0.552 & 0.728 & **0.739** \\ Acute bronchitis & 0.262 & 0.475 & **0.477** \\ Eczema, dermatitis & 0.465 & 0.606 & **0.635** \\ Chronic bronchitis & 0.439 & 0.564 & **0.589** \\ Rhinitis, tonsillitis & 0.342 & 0.473 & **0.505** \\ Brain disease & 0.284 & 0.340 & **0.341** \\ \hline \hline \end{tabular} Bold text means the best performance when comparing the three models.  
\end{table}
Table 3: Comparison of R\({}^{2}\) value for each machine-learning model.
## 3 Results and Discussion
### Soil Water Change in Slopes under Natural Evaporation Conditions
#### 3.3.1 The Law of Total Variation in Slope Soil Water under Natural Evaporation Conditions
The findings suggest that vegetated slopes have relatively low water retention capacity, which worsens with steeper slope ratios.
## 5 Maintenance Decision  
A maintenance decision describes the O&M vision of maintaining the health and safety of assets in the whole life cycle. The specific repair process includes inspection, repair, upkeep, and renewal of the systems, subsystems, and components. In order to improve maintenance efficiency and reduce cost, maintenance strategies have also shown many types [294]. Figure 13 depicts the evolution of maintenance strategy in the maritime field.  
* The first generation maintenance method mainly adopts the run to failure maintenance method. That is, the maintenance process is operated until it breaks down. The typical maintenance practices are routine maintenance (RM), (ii) CM [295].
* Second-generation maintenance is mainly preventive maintenance. With the increasing complexity of MSAE, the maintenance cost increases. The maintenance policies adopted are (i) PM, (ii) time-based maintenance (TM).
* The maintenance strategies adopted during the period 1980-2000 are called the third generation maintenance. The typical maintenance character of this period is mainly predictive maintenance. (i) CBM, (ii) reliability centered maintenance (RCM).
* The traditional maintenance methods are transforming to more proactive types, this is the recent generation maintenance. This generation is highly characterized by the in  
Figure 13: Evolution of maintenance strategies in the marine domain.  
ception of risk-based maintenance (RBM) in addition to RCM, Predictive Maintenance, CBM, and CBM+.
## 3 Results and Discussion
### Cognitive Experience Assessment  
#### 3.3.1 Single Cognitive Load Index Comparison  
Under the premise that the load indexes of the two groups are similar, the control group and the experimental group have less difference in the four factors of perceived brain power consumption, teaching rhythm, satisfaction with their performance and effort in the learning process. Alternatively, in the physical exertion and frustration load indicators, the difference is more than two load units, which is more significant. The cognitive load indexes of each group are shown in Figure 6.  
Figure 6: The cognitive load indexes of each group. (**a**) The cognitive load index and its meaning; (**b**) cognitive load scale index comparison.  
The control group and the experimental group were similar in terms of brain power consumption, teaching rhythm, performance satisfaction and the degree of effort in the process, while the experimental group produced significantly higher physical exertion than the control group, and the analysis in the previous section shows that the teaching test overall effect of the control group was better than the experimental group, though the control group produced significantly more frustration than the experimental group. According to experimental observations, the experimental group students showed more enthusiasm of the VR teaching process. Although it was their first experience with VR teaching devices, they could independently grasp the teaching rhythm and participate in the process practice in the virtual world and show confidence and interest in the construction course. Therefore, even if the experimental group students produced a large amount of physical exertion due to the teaching, and there was no advantage in the subsequent knowledge point test, the pleasant teaching experience can enhance self-confidence and interest, and did not produce the frustration of the control group.
## 3 Materials and Methods
### Proposed Approach: Hydra
#### 3.2.1 Hydra SISR Trunk  
The structure of our SISR model is outlined in Figure 2. As shown in the figure, the model has a shallower depth [21]. The first layer of the network operates on the entire input image (RGB/grayscale), contrary to previous methods [20; 21]. This input image is interpolated, via bi-cubic interpolation, and fed into the batch normalization layer. This layer optimizes and speeds up model training, and improves generalization. Our proposed SISR model has seven convolutional layers with \(3\times 3\) filter size. The number of filters starts as 8 in the first layer and continues to double in subsequent layers. We used zero padding to (1) keep the sizes of all feature maps the same and (2) avoid boundary artifacts due to the convolution operation. We also used ReLU functions with all convolution layers to speed-up model training, resulting in faster convergence. The last three convolutional layers (4 th, 5 th, and 6 th) of the SISR model have dilated convolutional kernels (dilation rate = 2) that capture a wider context, by enclosing a bigger receptive field, at a reduced computational cost. To construct an HR image, the SISR model takes an interpolated LR image as input and predicts the residual image, which contains high-frequency details. As discussed in [21], predicting residual image, instead of non-residual image, leads to faster convergence and superior performance. Finally, the predicted residual image is added back to the input LR image to get the final HR image.  
We trained the model using pairs of (LR,HR) CXR data. Specifically, we used bi-cubic interpolation to down-sample the HR images for training the model following existing methods in the literature [20; 21; 23]. We trained the SISR model using Adam optimizer with \(1\times 10^{-3}\) learning rate, 256 epochs, 64 batch size, and random weight initialization. The model is trained on multiple scales (i.e., 2, 3, 4, and 8) at the same time. Training is carried out to find the optimal parameters by optimizing the learning-loss function, which consists of two terms: MS-SSIM and MAE. The combined learning-loss achieved the best results as it allows to capture the best characteristics of both loss functions. Depending on the number of GPUs, the training time of our SISR model ranges from 21,917.64 to 54,311.21 s.