esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Training FNNs
### The One Hidden Layer FNN Architecture  
An ANN has two types of basic components, namely, neuron and link. A neuron is a processing element and a link is used to connect one neuron with another. Each link has its own weight. Each neuron receives stimulation from other neurons, processes the information, and produces an output. Neurons are organized into a sequence of layers. The first and the last layers are called input and output layers, respectively, and the middle layers are called hidden layers. The input layer is a buffer that presents data to the network. It is not a neural computing layer because it has no input weights and no activation functions. The hidden layer has no connections to the outside world. The output layer presents the output response to a given input. The activation coming into a neuron from other neurons is multiplied by the weights on the links over which it spreads, and then is added together with other incoming activations. A neural network, in which activations spread only in a forward direction from the input layer through one or more hidden layers to the output layer, is known as a multilayer feedforward network. FNN, which is also known as Multi-Layer Perceptions (MLP), is an attractive approach due to its high capability to forecasting and classification [4]. In FNNs, signals flow from the input layer through the output layer by unidirectional connections, the neurons are connected from one layer to the next, but not within the same layer. Figure 2 shows an example of a feedforward network with one hidden layer. In Figure 2, \\(R\\), \\(N\\), and \\(S\\) are the numbers of input, hidden neurons, and output, respectively; \\(iw\\) and \\(hw\\) are the input and hidden weights matrices, respectively; \\(hb\\) and \\(ob\\) are the bias vectors of the hidden and output layers, respectively; \\(x\\) is the input vector of the network; \\(ho\\) is the output vector of the hidden layer; and \\(y\\) is the output vector of the network. The neural network in Figure 2 can be expressed through the following equations:  
\\[ho_{i}=f(\\sum_{j=1}^{R}iw_{i,j}.x_{j}\\ +\\ hb i)\\text{, for }i=1,..,N, \\tag{11}\\]  
\\[y_{i}=f(\\sum_{k=1}^{N}hw_{i,k}.ho_{k}\\ +\\ obi)\\text{, for }i=1,..,S, \\tag{12}\\]  
where \\(f\\) is an activation function.  
When implementing a neural network, it is necessary to determine the structure in terms of number of layers and number of neurons in the layers. The larger the number of hidden layers and nodes, the more complex the network will be. A network with a structure that is more complicated than necessary over fits the training data [39]. This means that it performs well on data included in the training set, but may perform poorly on data within a testing set.
## 2 System Model with Collision Design and Analysis of Our Proposed System
### Avoiding Collisions Between Clusters
#### 2.2.1 Problem of Spatial-Temporal Uncertainty  
The delay in submerged acoustical transmission renders the temporal and spatial characteristics of communication unpredictable in underwater wireless communication networks (UWCNs). According to [45], the time for communicating with data packets, the time for transmitting communicating data packets, and the propagation latency of the Rx node are all impacted by the uncertainty in the spatial and temporal components that are transmitted in UWCNs. The uncertainty in spatial-temporal factors considers the location of the Rx node and the moment at which the Tx anchor node delivers the signal when determining the status of a channel in a UWCN. The transmission time of the Tx anchor node and propagation latency have an impact on the collision of communicating data packets, which occurs when two communicating data packets arrive at the Rx node at the same time. Additionally, the distance between the communicating nodes introduces uncertainty into the channel's state, and collisions can still happen even if other communicating nodes in the cluster communicate with one another independently. High propagation latency often has the potential to lead to collisions in UWCNs.  
Figure 3 illustrates the example of spatiotemporal uncertainty. Both communicating node A and communicating node C might send communicating data packets at the same time in a particular scenario. On the other hand, the time of the data packets arriving at the Rx node might shift if the propagation latencies of the communicating nodes are not the same. Additionally, communicating node B could come across a collision if node A and communicating node C communicate data packets at distinct times.  
Figure 3: Example of transmitting data packets with collision or no collision at the same and different times.  
Figure 2: Example of a collision between system clusters.
# Image Super-Resolution Based on CNN Using Multilabel Gene Expression Programming
## 3 The Proposed Method
### Multifeature Representation of Images
#### 3.1.2 Texture Feature  
Texture is a visual feature that reflects homogeneous phenomena in an image, and it reflects the surface structure organization and arrangement properties of an object surface with slow or periodic changes. Texture is a pattern produced by the gray or color of the target image in space in a certain form [17]. From the perspective of texture, the image can be roughly divided into three cases: first, the gray distribution has a certain periodicity (even if the gray change is random, it also has certain statistical characteristics, and may be in a larger area repeatedly); second, the basic components that make up the sequence are regular rather than random; third, the texture of each part in the texture area shows roughly the same size, structure, and image, and is uniformly distributed as a whole.  
The Fourier power spectrum method is used to measure the texture characteristics of the image. Let the texture image be _f(x, y)_; its Fourier transform can be expressed by Equation (2).  
\[\rm F(u,v)=\int\int_{-\infty}^{\infty}f(x,y)\exp[-j2\pi(ux+vy)]dxdy \tag{2}\]  
The definition of the power spectrum of the two-dimensional Fourier transform is shown in Equation (3):  
\[|F|^{2}=FF^{\star} \tag{3}\]  
where \(F^{\star}\) stands for the conjugate of \(F\). The power spectrum \(|F|^{2}\) reflects the nature of the entire image. If the Fourier transform is expressed in polar form, i.e., \(F(r,\theta)\) form, then the energy on the circle \(r\) from the origin is  
\[\Phi_{r}=\int_{0}^{2\pi}[\rm F(r,\theta)]^{2}d\theta. \tag{4}\]  
From research on the energy in the small fan-shaped region in the angle \(\theta\) direction, the law of this energy changing with the angle can be obtained by Equation (5):  
\[\Phi_{\theta}=\int_{0}^{\infty}\bigl{|}F(r,\theta)\bigr{|}^{2}dr. \tag{5}\]  
Figure 2: Color histogram of an image (RGB channel).  
When a texture image runs along \(\theta\) and there are many lines, edges, etc., in the direction \(\theta+\frac{\pi}{2}\), i.e., in a right-angle direction to \(\theta\), the energy is concentrated. If the texture does not show directionality, there is no directionality in the power spectrum. Therefore, the \(|F|^{2}\) value reflects the directionality of the texture.
## 3 Results
### Simulating Experimental Data Using MC
#### 3.3.2 Modeling Phantoms with Actively Pumped Flow
Input values for \(h_{0}\) and \(V_{1}\) were sought by running a set of simulations that varied \(h_{0}\) (between 0-0.05 cm) and \(V_{1}\) (between 0.04 and 0.001 cm/s) to find values that matched experimental measurements for phantom \(D_{0}F_{1}\) with lowest \(\chi^{2}\) residuals. From these set of initial simulations, the tissue model with \(h_{0}=0.02\) cm and \(V_{1}=5.7\times 10^{-3}\) cm/s, produced the lowest \(\chi^{2}\) residuals relative to experimental data acquired in phantom D0F1. Once \(h_{0}\) and \(V_{1}\) were known, required inputs for all 12 tissue models (\(D_{0}F_{1}-D_{3}F_{1}\), \(D_{0}F_{2}-D_{3}F_{2}\) and \(D_{0}F_{3}-D_{3}F_{3}\)) were generated by scaling them across phantoms. The (scaled) input values of the coefficients (sought as inputs) for all the 3-layered tissue models constructed are shown in in Table 2.  
Figure 12 shows comparisons of experimental measurements (symbols), diffusion theory fits with shear-flow model (dashed black lines) and the MC simulations (solid lines) for one representative measurement, for each of the 12 phantoms with active flow. Data in Figure 12 are organized such that moving across a row, changes channel depth (\(D_{0}\): Column 1; \(D_{1}\): Column 2; \(D_{2}\): Column 3; \(D_{3}\): Column 4) while moving down a column changes channel flow rate (\(F_{1}\): Row 1; \(F_{2}\): Row 2; \(F_{3}\): Row 3). Figure 12: Comparisons of experimentally measured data (circles), diffusion theory-based fits (dashed line) and MC predictions (solid line) for the 12 phantoms with actively pumped. (**a**–**d**) show these data when flow rate was \(F_{1}=1.44\) mL/h for depths \(D_{0}\)–\(D_{3}\). ((**e**–**h**) show the same sequence of depths at flow rate \(F_{2}=3.6\) mL/h and (**i**–**l**) for flow rate \(F_{3}=5.76\) mL/h). As seen in Figure 11, the data generated by the MC simulations matched experimental measurements as well as the fitted theoretical curves (\(\chi^{2}\) residuals are shown in each figure). Thus, the MC tissue model used could accurately represent the experimental phantom system used.
## 4 MHAR-OLSR Protocol
### CPN MHAR-OLSR Model
#### 4.2.3 Sub-Model \"Paths_Classification\"  
The existing classification methods use the number of jumps, the state of the link [34], or the energy consumption [35] to rank the calculated paths. In our study, we exploit the nature of the nodes that make up the detected routes as a classification measure.  
The CPN net shown in Figure 5 illustrates the main transitions of the classification phase:  
* From the \"Found path tree\" port place where the calculated paths are saved from the shortest to the longest according to their number of hops, one route is chosen among them.
* In the \"browse_list_hops\" transition, the identifiers of all selected path components will be extracted.
* The \"Path identification\" (Figure 6) substitution transition exploits the received information to identify this route being processed. And the \"Next Path\" transition is fired  
Figure 4: Identification organizational chart [14].  
enough times to recover all paths from the \"Paths identified\" place to the \"Found path tree\" place.
* Each route will have an identifier and a tag indicating its class. These represent its quality, efficiency, and availability for sending data.  
To give a clearer idea of the path identification phase, the flowchart (Figure 7) shows the label values from 1 to 7 assigned to each route based on the identifiers of its components:  
* A path composed of FANET nodes (ID-Node = 3) will have an identifier 1 (Path-Id = 1),
* The presence of MANET nodes (ID-Node = 1) and at least one FANET node gives an identifier 2 for this path,
* A route having an identifier 3 will link nodes of at least all MANET, VANET (ID-Node = 2), and FANET network categories,  
Figure 5: CPN model of path classification.  
Figure 6: Path identification module.  
Figure 7: Path classification flowchart.  
Each \"Path-Id\" indicates the route quality that ensures sending a specific type of data. For example, identifier \"1\" represents that this path is the fastest and most reliable, then it is reserved for critical data. While Path-Id = 7 is given to the longest and least reliable itinerary where informative data can be transmitted. This type of classification allows us to have several paths with the same identifier.
# ISAR Imaging Analysis of Complex Aerial Targets Based on Deep Learning
## 3 CapsNet Assists ISAR Imaging
### Training of the CapsNet  
Regarding the whole structure of the CapsNet, in this section, we discuss its training processing, including the generation of its input and reference. The inputs of the network area series of STFT spectra of the simulated radar signals are generated according to (5), and the number of scatterers \(f\) is randomly selected from 1 to 5. To consider the noise effect, random Gaussian noise is added to the simulated signals with a signal-to-noise ratio (SNR) changing from 5 to 10 dB. STFT spectra of the simulated signals are computed according to (9) as the input of the network. In our setting, the input image is of size \(256\times 256\) and the reference image is expected to be of \(512\times 512\) since two times up-sampling operations are implemented in the proposed network. The reference image is generated according to (9).  
The network is trained to approximate the function, which can transform the noisy low-resolution spectral image to a noiseless ideal image with a high resolution. After training, the loss results of CapsNet are shown in Figure 4. The parameter settings of CapsNet are summarized in Table 1.  
Figure 4: The loss of CapsNet.
## 2 Materials and Methods
### Data Acquisition Module
#### 2.3.3 Model-Eye Eccentricity Infrared Image Acquisition System  
During eccentric photorefraction, it is difficult for human eyes to maintain an exact position relative to the camera aperture. According to Equation (1), the relative position distance affects the accuracy of refractive error measurement. Therefore, we designed a 3D coordinate motion platform to mount the model eye and move it within a 3D space relative to the camera, covering the ranges of y: [950 mm, 1050 mm], x: [\(-50\) mm, \(50\) mm], and z: [\(-20\) mm, \(30\) mm]. Figure 9 illustrates this process in detail.  
Figure 8: **(a,b)** Objective model-eye structure diagrams. **(c)** Objective simulation of the actual-eye diagram.  
Figure 7: Eccentric light position. The horizontally arranged 0\({}^{\circ}\), 60\({}^{\circ}\), and 120\({}^{\circ}\) light groups form three sets of radial eccentricity variation images, which include the changes in the crescent bright area within the pupil under the same angle but different eccentric light sources. The vertically arranged light groups form a set of axial variation images with the same eccentricity, showing the changes in the crescent bright area within the pupil under different angles but with the same eccentricity.  
As shown in Figure 10, there is a significant difference between the eccentric infrared images of real eyes and model eyes. They share similar light intensity distributions under eccentric light illumination, but the model eyes are more distinct.  
This implies that we need to design a method that preserves the prominent bright-region features in the model-eye images while avoiding the noise that may be indistinguishable between model- and real-eye images. This noise affects the model's performance when tested on real data after being trained on simulated data. To mitigate this impact, this study fine-tunes the model-eye pretrained model using real-human-eye data. This transfer learning approach transfers the features learned from the model-eye domain to the real-human-eye domain, thereby improving the performance of the target task.
## 3 Results
### Detailed Analysis of Developments in Selected Specialties
#### 3.7.10. Telemedicine and eHealth in Nursing  
The research explores eHealth literacy among nursing students and has outlined the requirements for telemedical systems in home monitoring, particularly in rural areas with limited care access [155]. Surveys have examined nurses' views on eHealth and patient empowerment through digital solutions, noting the value of eHealth despite implementation barriers [156]. Research has studied nursing students' clinical education during COVID-19, advocating tele-education integration into curricula [157]. Cross-sectional studies have evaluated eHealth literacy among nursing students in Poland and Sweden, showing varying yet positive literacy trends in digital healthcare [158]. Nursing research has evolved from a system design to workforce needs and education. The COVID-19 pandemic has accelerated the use of remote learning in nursing education. eHealth literacy among students remains vital for telemedicine in patient care, highlighting the need to prepare nurses for digital healthcare.
# Biosensors Based on Bivalent and Multivalent Recognition by Nucleic Acid Scaffolds
## 3 Biosensors Based on Multivalent Binding Modes
### Biosensors Based on DNA Chain Elongation  
One of the easiest ways to express multiple ligands using nucleic acids is to linearly connect numerous ligand-modified oligonucleotides with certain distances of one another. To achieve such chain elongation, the DNA HCR is a powerful tool. In 2016, [PERSON] et al. utilized a DNA-programmed HCR for the construction of multivalent binding scaffolds bearing cell-binding aptamers and quantum dots (QDs) to detect target cancer cells [100]. To build such scaffolds, two metastable HCR hairpins were prepared. Each hairpin possesses an overhang to which QD-modified DNA or an aptamer can hybridize (Figure 13). In the presence of an initiator, HCR occurs, and the desired QD-aptamer polymers (QAPs) with multiple aptamers are obtained. As expected, the QAPs showed photoluminescence signals that were four times stronger than those of QD-aptamer monomers (QAMs). Furthermore, the QAPs afforded clearer fluorescence images of target cells (CCRF-CEM) than the QAMs owing to their multivalent binding properties and multi-QD-based signal amplification. This study suggests that the HCR strategy is advantageous for the design of multivalent interaction-based biosensors in terms of target selectivity and detection sensitivity. Recently, this method has been used to detect circulating tumor cells [101, 102].  
In the same year, [PERSON] and [PERSON] introduced the RCA strategy [103] and molecular beacons for the efficient detection of B16 cells [104]. RCA is a DNA amplification technique that utilizes circular templates and polymerases to produce a long single-stranded DNA of a repetitive sequence [105]. In this work, linear-type long-chain DNA structures were obtained through RCA polymerization with Phi29 polymerase, and the fixed complementary DNA (FC DNA) and beacon-type nucleolin aptamers were alternatively hybridized to the RCA products. Subsequently, this complex bound to cells via multiple nucleolin-aptamer interactions and allowed for the very sensitive detection of B16 cells with increased signal  
Figure 13: Construction of QD-aptamer polymers (QAPs) via an HCR-based strategy for cancer cell detection. Adapted with permission from _Anal. Chem._ 2016, 88, 9355–9358, doi:10.1021/acs.analchem.6b02864. Copyright (2016) American Chemical Society [100].  
intensity (Figure 14). Both the unfolding ability and fluorescence intensity of the molecular beacons were significantly improved by RCA polymerization compared to those of the short single-chain system. Because RCA is a simple and practical method, this strategy for multivalent systems can be directly applied to various nucleic acid-based sensory studies and clinical diagnostics of cancer cells.  
In addition to these examples, the nucleic acid elongation concept has been used for other designs. For example, CEM cells have been recognized and visualized using DNA nanowires [106], and nanocentipedes have been constructed using the HCR for the imaging of specific cells (SMMC-7721 cells) [107].
## 1 Introduction
For image classification, DL models are built to train, validate, and test thousands of images of different types for accurate prediction. These models consist of many layers through which a CNN transforms the images using functions such as convolution, kernel initialization, pooling, activation, padding, batch normalization, and stride. The combination of image-feature engineering and ML classification has shown remarkable performance in terms of medical image analysis and classification. In contrast, CNN adaptively learns various image features to perform image transformation, focusing on features that are highly predictive for a specific learning objective. For instance, images of benign and malignant tissues could be presented to a network composed of convolutional layers with different numbers of filters that detect computational features and highlight the pixel pattern in each image. Based on these patterns, the network could use sigmoid and softmax classifiers to learn the extracted and important features, respectively. In DL, the \"pipeline\" of CNN's processing (i.e., from inputs to any output prediction) is opaque, performed automatically like a passage through a \"black box\" tunnel, where the user remains fully unaware of the process details. It is difficult to examine a CNN layer-by-layer. Therefore, each layer's visualization results and prediction mechanism are challenging to interpret. The present paper proposes a pipeline for tissue image classification using DL and ML techniques. We developed two lightweight CNN (LWCNN) models for automatic detection of the GP in histological sections of PCa and extracted the non-handcrafted texture features from the CNN layers to classify these using an ensemble ML (EML) method. Color pre-processing was performed for enhancing images. To carry out a comparative analysis, the two types of hand-designed [29] features, such as the opposite color local binary patterns (OCLBP) [30] and improved OCLBP (IOCLBP) [30] were extracted and pre-trained models (VGG-16, ResNet-50, Inception-V3, and DenseNet-121) [31] were used for EML and DL classification, respectively. To avoid the complexity and build lightweight DL models, we used a few hidden layers and trainable parameters, and therefore, the models were named LWCNN. The DL models were trained several times on the same histopathology dataset using different parameters and filters. For each round of training, we fine-tuned the hyperparameters, optimization function, and activation function to improve the model performance, including its accuracy. Binary classification is critical for PCa diagnosis because the goal of the pathologist is to identify whether each tumor is benign or malignant [32]. We generated a class activation map (CAM) using predicted images and created a heat map to visualize the method by which the LWCNN learned to recognize the pixel pattern (image texture) based on activation functions, thus interpreting the decision of the neural network. The CAM visualization results of the training and testing were difficult to interpret because CNNs are black-box models [33; 34].
# Minimum-Cost-Based Neighbour Node Discovery Scheme for Fault Tolerance under IoT-Fog Networks
## 3 Materials and Methods
### Cost Model  
The following equation can be used to obtain the cost of Fog control and access nodes:  
\[\mathrm{C}_{f}=\sigma_{c}\mathrm{r}_{i}+\sum_{j\in c}\sigma_{j}\mathrm{d}_{ij} \tag{15}\]  
where \(\sigma_{c}\) is the selectivity of the Fog control nodes, and \(\sigma_{j}\) represents the selectivity of all possibly linked neighbouring candidate Fog access nodes for task-offloading. Here, the selectivity of all Fog access/control nodes should be maintained as one (\(\sigma_{j}=1\) and \(\sigma_{c}=1\)); \(\mathrm{r}_{i}\) indicates the number of hops between the source Fog control node \((\mathrm{r}_{i}=0)\) and \(\mathrm{d}_{ij}\) denotes the minimum number of hops between the Fog control nodes and the Fog access node. To estimate the minimal cost of a Fog network, a minimum of three Fog control nodes are considered to be necessary for efficient cost estimation, which can be obtained by selecting the minimum number of hops between the nodes. The minimum-cost Fog access node is selected for task-offloading via the minimum-cost-based neighbour candidate discovery method for efficient task-processing. In some cases, the source server itself has minimum-cost fog access nodes and can be selected using Equation (16). If the source server has busy nodes, then it will select the minimum-cost fog access nodes from the neighbouring server using Equation (17):  
\[\mathrm{sel}(\mathrm{C}_{f})=\min\left\{\sigma_{c}\mathrm{r}_{i}+\sum_{j\in c }\sigma_{j}\mathrm{d}_{ij};\mathrm{Source}\ \mathrm{F}_{s}\right\} \tag{16}\]  
\[\mathrm{sel}(\mathrm{C}_{f})=\min\left\{\sigma_{c}\mathrm{r}_{i}+\sum_{j\in c }\sigma_{j}\mathrm{d}_{ij};\mathrm{NeighbourF}_{s}\right\} \tag{17}\]