esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Feature Fusing Based on Cross Attention Mechanism (SCF-Net)
Specifically, the concepts of query, key, and key originate from the \"query-match-retrieve\" mechanism commonly used in information retrieval systems. The Query vectors are responsible for querying relevant information by matching with the Keys, while the Values provide the actual information that will be integrated into the output representation. Our SC feature is derived from the electromagnetic scattering characteristics of the target, containing rich target information, which directly reflect object-level properties. Therefore, the SC feature is particularly well-suited to serve as \(V\). In detail, as shown in the upper part of Figure 3, CNN features \(\overset{\rightharpoonup}{\mathbf{f}}_{CNN}\) are set as \(Q\), while scattering center features \(\overset{\rightharpoonup}{\mathbf{f}}_{sc}\) are set as \(K\). The initial similarity matrix is calculated as follows:  
\[\mathbf{M}_{s}=\overset{\rightharpoonup}{\mathbf{f}}_{CNN}\bullet\overset{ \rightharpoonup}{\mathbf{f}}_{sc}^{T} \tag{3}\]  
where \((\ )^{T}\) is the vector transposition operation. The shape of \(\mathbf{M}_{s}\) is \(256\times 256\). Then, the primeval similarity matrix needs to be normalized. \[\dot{\mathbf{M}}_{S}=softmax\left(\frac{\mathbf{M}_{s}}{\sqrt{d_{k}}}\right) \tag{4}\]  
where \(d_{k}\) is the dimension of \(k\), which is also equal to the dimension of \(q\). \(\dot{\mathbf{M}}_{s}\) measures the importance of different semantic channel of \(\overset{\rightharpoonup}{\mathbf{f}}_{sc}\) in the feature fusion process, which can be used as a weight of the feature vector. Finally, the fused feature is outputted as follows:  
\[\overset{\rightharpoonup}{\mathbf{f}}=\varphi_{fc}\left(\dot{\mathbf{M}}_{s} \bullet\overset{\rightharpoonup}{\mathbf{f}}_{sc}\right) \tag{5}\]  
where the size of \(\dot{\mathbf{M}}_{s}\bullet\overset{\rightharpoonup}{\mathbf{f}}_{sc}\) is \(256\times 1\). \(\varphi_{fc}\) is a fully connected layer operation that transfers the size of the feature to \(128\times 1\) and improve the fitting ability of the network. Through this feature fusion method, the target features contained in the radar echoes are fully utilized to enhance the classification performance of our method on SAR targets, while increasing the stability of the network features to suppress the impact of catastrophic forgetting.
## 4 Fatigue Damage Analysis
### Finite Element Analysis
#### 4.1.2 Load-Stress Relationship  
The analysis results show that the element nodes close to fastener holes bear heavier stress. The element stress values near the fastener holes were obtained by post-processing. The nodes with the most severe stresses near the fastener holes were selected as the fatigue hot spots. The von Mises equivalent stress was used to calculate the fatigue damage accumulation. Figure 8 shows the stress distribution of the finite element model when the purlin distance is 600 mm. The subsequent fatigue analysis adopted the hot spot stress for the calculation.  
Nine groups of finite element models with a purlin distance from 600 mm to 1000 mm were established. After loading and analysis, the relationships between hot spot stress and pressure of different purlin distance models are obtained, as shown in Figure 9. The hot spot stress of nine groups of finite element models increases with an increase in purlin distance. With graded loading, the hot spot stress of each model increases gradually in the bilinear model.
## 2 The Models
### A Modified Model  
Let us investigate the next differential system:  
\[\left\{ \begin{array}{rcl}\frac{dx}{dt}&=&y\\ \frac{dy}{dt}&=&x-x^{3}-\epsilon\Bigg{(}Ay|y|^{p-1}-\sum_{j=1}^{N}\!\!g_{j}\sin( j\omega t)\Bigg{)}\end{array} \right. \tag{10}\]  
For fixed \(\epsilon=0\), the outcome Hamiltonian of the model (10) is  
\[H(x,y)=\frac{1}{2}y^{2}-\frac{1}{2}x^{2}+\frac{1}{4}x^{4}.\]  
A saddle point exists at the origin, which is centered at \((\pm 1,0)\), and a couple homoclinic trajectories are set by (see Figure 6):  
\[ \begin{array}{l}x_{0}(t)=\pm\sqrt{2}sech\,t\\ y_{0}(t)=\mp\sqrt{2}sech\,t\,tanh\,t.\end{array}  \tag{11}\]  
We refer to [8; 9; 10; 11; 20] for more details. The homoclinic integral of Melnikov is set by  
\[M(t_{0})=\int_{-\infty}^{\infty}\!\!y_{0}(t)\Bigg{(}Ay_{0}(t)|y_{0}(t)|^{p-1} -\sum_{j=1}^{N}\!\!g_{i}\sin(j\omega(t+t_{0}))\Bigg{)}\,dt, \tag{12}\]  
where the expressions \(x_{0}(t)\) and \(y_{0}(t)\) are circumscribed by Equation (11). We will demonstrate the next statement, where \(p=2\) and \(N=1\). **Proposition 1**. : _If \(p=2\) and \(N=1\), then the zeroes of the Melnikov function \(M(t_{0})\) are obtained as roots of the following equation:_  
\[ \begin{array}{rcl}M(t_{0})&=&\frac{16}{15\sqrt{2}}A+\frac{1}{30\sqrt{2}}e^{- it_{0}\omega}\Big{(}15g_{1}\omega PolyGamma[0,\frac{1}{4}-\frac{i\omega}{4}] \\ &&+15e^{2 it_{0}\omega}g_{1}\omega PolyGamma[0,\frac{1}{4}-\frac{i\omega}{4}]- 15g_{1}\omega PolyGamma[0,\frac{3}{4}-\frac{i\omega}{4}]\\ &&-15e^{2 it_{0}\omega}g_{1}\omega PolyGamma[0,\frac{3}{4}-\frac{i\omega}{4}]+ 15g_{1}\omega PolyGamma[0,\frac{1}{4}+\frac{i\omega}{4}]\\ &&+15e^{2 it_{0}\omega}g_{1}\omega PolyGamma[0,\frac{1}{4}+\frac{i\omega}{4}]- 15g_{1}\omega PolyGamma[0,\frac{3}{4}+\frac{i\omega}{4}]\\ &&-15e^{2 it_{0}\omega}g_{1}\omega PolyGamma[0,\frac{3}{4}+\frac{i\omega}{4}] \Big{)}=0.\end{array}  \tag{13}\]  
Figure 6: Double homoclinic orbit [20]. Here, \(PolyGamma[n,z]\) is the \(n\)th derivative of the _digamma function_\(\psi^{(n)}(z)\), i.e., \(\psi^{(0)}(z)=\frac{\Gamma^{\prime}(z)}{\Gamma(z)}\) and \(\Gamma(z)=\int_{0}^{\infty}t^{z-1}e^{-t}\,dt\)[26]. As an illustration, the equality \(M(t_{0})=0\) (for \(p=2\); \(N=1\); \(A=1.01\); \(\omega=0.25\); \(g_{1}=0.9\)) is depicted in Figure 7a. The roots in interval \((0,26)\) are as follows: \(2.42651\); \(22.7062\). For \(p=2\); \(N=1\); \(A=1.05\); \(\omega=0.1\); \(g_{1}=0.916\), \(M(t_{0})\) has no roots (Figure 7b). From Proposition 1 (see also Figure 7), the reader may formulate the [PERSON]'s condition for chaotic behavior of the dynamical model. Let us demonstrate the next statement, where \(p=2\) and \(N=2\). **Proposition 2**.
# Novel Extensions to Enhance Scalability and Reliability of the IEEE 802.15.4-DSME Protocol
## 4 Mirror D-DSME Extension
In fact, the adoption of the _miGTS_ allows working with several GTSs lower than the one that would be needed using the standard retransmission mechanisms.
## 2 Materials and Methods  
### Dataset  
The province of Istanbul was selected as the study area, as it exhibits land cover (LC) diversity throughout time and reduces the obstacles by upgrading historical aerial photographs within the context of SR. Historical orthophoto images with varying dates and spatial resolution were provided by the General Directorate of Mapping, affiliated with the Ministry of National Defense, Republic of Turkiye. These orthophotos are grayscale images, as presented in Figure 1. The resolutions of these orthophotos are different from each other, as they were produced from photographs taken with different photogrammetric techniques in various years. Data with ground sampling intervals of 10 cm, 30 cm, and 40 cm were used. Because the computational cost of generating data over the entire orthophoto in tiff format is high, all orthophotos were divided into four equal parts. Among these, the one from 1982 had a higher resolution than the others; thus, each four parts were divided again into four more equal parts.  
Four different LC categories were determined: bare land, farmland, residential areas, and forested regions. The image from 1954 mainly consists of bare land and forestry. The image from 1968 contains more residential areas than the image from 1954. The image from 1982 contains more residential areas than the others, and is rich in other categories as well. The orthophoto images included in the training and testing were derived from the identical parts extracted from each orthophoto. The random clipping approach was used to create photos with a maximum overlap rate of 10%. Thus, the shifting approach to data augmentation was used between the randomly cropped photographs, and the image similarity was preserved to a specified level to prevent the data group from overfitting. Because the orthophoto from 1982 was much richer in terms of content than the others, most of the photographs were derived from it. The 1954 and 1968 images were fewer in number, as they are from older years and have fewer pixels covering same area. The amount of data was increased by additionally applying horizontal and vertical flipping to the images derived from the 1954 and 1968 orthophotos. An important nuance when creating a training set from this type of data is the number of images. Although the aforementioned criteria and geometric transformations exist, the limiting factors mentioned previously increase the visual similarity as the number of photographs increases. Therefore, the total number of images included in training was limited to 1000, the majority of which belonged to 1982. Numerically, 700 images belonged to 1982 and the rest belonged to the other years.
## 4 Conclusions  
We have proposed a new method for processing the spectra of tilted TFBG gratings to determine the SRI value. The use of Gabor filters processes the optical spectra in a very convenient way, enabling the use of simple methods in subsequent stages of signal processing algorithms. Such simple filters eliminate the need for operations such as denoising or baseline correction of the measured spectra. This is a big practical advantage. The log-Gabor filter has a better enhancement and separation effect of optical spectra, and consequently, it better highlights the wavelengths between leaked and non-leaked modes.  
The basic concept of filter selection was to limit the spectrum to a single frequency associated with the occurrence of cladding modes. If the cladding modes were equidistant from each other, such a signal would have a spectrum characteristic of a periodic signal. There would be the first and subsequent harmonics. Four such harmonics can be seen in the spectrum of the analysed TFBG grating. However, since the distances between the modes decrease with increasing wavelength, the first harmonic of the cladding modes does not create a single frequency, but a chirp. Therefore, in the frequency spectrum, individual harmonics are broadened and partially overlap. Therefore, simply adjusting the filter to the first or subsequent harmonics is not sufficient. A filter that is too narrow does not cover the frequency of the entire chirp. Broadening the filter results in the signal transfer not only of the selected harmonic, but also the partial transfer of the next harmonic. As a result, the calculated derivative of the envelope will not have a clear maximum. The filter band should therefore be selected so that it does not cover another harmonic. The main frequency of the filter can be taken as the frequency of the maximum amplitude of the frequency spectrum for a given harmonic.  
The proposed spectrum analysis method improves the quality of determining the SRI coefficient value by estimating the change in the cut-off wavelength position. This is the result of precisely and adaptively extracting the frequency range in which the optical spectra are modified by the cut-off wavelength value.  
This work was supported by the Lublin University of Technology (grant number: FD-20/EE-2/301).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The author declares no conflicts of interest.
## 3 Construction of OD Passenger Flow Prediction Deep Learning Model
### Tcn-Attention-LSTM Model  
The OD passenger flow of urban rail transit gathers at various stations, which has a strong correlation with the spatial dimension of the stations, leading to mutual influence of OD passenger flow within a certain spatial range. At the same time, OD passenger flow exhibits certain periodic changes in the time dimension, such as the tidal pattern of morning and evening peak OD passenger flow analyzed earlier, which also includes a short-term autocorrelation of OD passenger flow, such as the short-term OD passenger flow changes caused by sudden changes in preceding passenger flow. Therefore, it is necessary to capture all features of OD passenger flow data through a combination model, considering the short-term autocorrelation and long-term periodicity of passenger flow demand. A temporal convolutional network (TCN) model and long short-term memory (LSTM) were used as the core, and an attention mechanism was introduced to construct a TCN-Attention-LSTM model for predicting the OD passenger flow of urban rail transit.  
The model structure is shown in Figure 10. The processed OD passenger flow dataset is selected for data extraction, which is first normalized, and then the TCN is used to extract the features of the OD passenger flow data and time data. After that, the output data are sent to the attention layer. When the attention mechanism layer further extracts the features, the output of the attention mechanism layer is used as the input vector of the LSTM for training. In this way, the change law of the OD passenger flow is captured, and the prediction results are obtained.  
Figure 9: Attention calculation process.
## 3 Results and Discussion  
E-Nose Discrimination of the Ripening Stage of \"Candonga\" Strawberries and Correlation Analysis with VOC Pattern  
Four lots of 12 \"Candonga\" strawberries were obtained pooling the fruit collected for each harvest time (H1, H2 and H3) at the two different ripening stages (half-red and red), obtaining a total of 12 samples for the half-red and 12 samples for the red fruit. Since 11 technical replicates were performed for each sample, a data set composed of 264 observations and 10 variables (sensor responses) was investigated by univariate data analysis and the results are reported in Table 1. All sensors, except for S4 and S10, resulted in us being able to distinguish the two maturity stages. The same data set (264 observations and 10 variables) was mean centred and investigated by PCA. A model with two principal components with R\({}^{2}\) = 0.95 and Q\({}^{2}\) = 0.81 was obtained. In particular, in the score scatter plot of the model shown in Figure 1, where half-red samples are indicated in green and red samples are reported in red, the data variation due to technical replicates (indicated by the circles) was smaller than the biological variability (the medians of the technical replicates of the same sample are indicated by the triangles). Moreover, the samples at different ripening stages were easily classified into two groups based on their degree of ripeness (red and half-red). Interestingly, half-red samples were spread over a smaller area than the region of the plot relevant to the red strawberries. This result indicates a larger variability among the red than the half-red fruit. The PCA results indicated that there was a potential relationship among the e-nose signals and the ripeness of fruit, as the e-nose device responded sensitively and selectively to the modification in the patterns of aroma VOCs from strawberries at different ripening stages. Overall, 57 volatile compounds were previously identified by HS-SPME/GC-MS analysis of the \"Candonga\" strawberry samples at two different ripening stages (half-red and red) and at three different harvest times (H1, H2 and H3) (Table S1) [20]. As reported by [20], PCA performed on volatiles, quality traits and phenolic com-pounds highlighted that only the red samples were directly correlated to volatile components, as VOCs clearly increased both in number and amount during ripening. In particular, volatiles with positive impact on the consumers' acceptance, including butyl butyrate, ethyl hexanoate, hexyl acetate, nonanal, terpen and lactones, were positively associated to the red-H1 and red-H2 strawberries, while volatiles with negative coefficients related to consumer liking, including isopropyl butyrate, isoamyl butyrate and mesifurane, were directly correlated to the red-H3 samples  
All these VOCs were correlated with the data obtained by e-nose on the same fruit samples. The heatmap representing the correlation between VOCs and e-nose is reported in Figure 2.
# Enhanced Message Replication Technique for DTN Routing Protocols
###### Abstract  
Delay-tolerant networks (DTNs) are networks where there is no immediate connection between the source and the destination. Instead, nodes in these networks use a store-carry-forward method to route traffic. However, approaches that rely on flooding the network with unlimited copies of messages may not be effective if network resources are limited. On the other hand, quota-based approaches are more resource-efficient but can have low delivery rates and high delivery delays. This paper introduces the Enhanced Message Replication Technique (EMRT), which dynamically adjusts the number of message replicas based on a node's ability to quickly disseminate the message. This decision is based on factors such as current connections, encounter history, buffer size history, time-to-live values, and energy. The EMRT is applied to three different quota-based protocols: Spray and Wait, Encounter-Based Routing (EBR), and the Destination-Based Routing Protocol (DBRP). The simulation results show that applying the EMRT to these protocols improves the delivery ratio, overhead ratio, and latency average. For example, when combined with Spray and Wait, EBR, and DBRP, the delivery probability is improved by 13%, 8%, and 10%, respectively, while the latency average is reduced by 51%, 14%, and 13%, respectively.  
delay-tolerant network; network congestion; DTN routing; congestion control; wireless networks; ad hoc networks +
Footnote †: journal: Sensors
# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 2 Materials and Methods
### Inclusion and Exclusion Criteria  
A well-defined selection criterion lays out the guidelines for the inclusion or exclusion of a particular research paper in the systematic review. The selection criterion should be impartial toward the authors' names and institutions. This study includes full-length articles involving GAN for brain MRI. The focus is given to only Q1 or Q2-rated peer-reviewed journals and recent conferences to have high-quality search results. A search for full-text availability reduced the volume of papers. The studies in a language other than English are excluded to obtain a clear perception. We have considered only the MRI modality as the source image for the review and excluded other modalities such as CT, PET, or X-ray. Reports based on the non-human brain are excluded. Review papers, editorials, and book chapters are not included. So, the final count of study papers is n = 145 after applying the inclusion-exclusion criterion as given in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Research Questions** & **Motivation** \\ \hline RQ 1 & What are the applications of GAN-synthesized images for brain MRI? & The question divides the available literature into more clear categories. \\ RQ 2 & What are the most commonly used loss functions in GAN-synthesized image applications for brain MRI? & Loss function affects the training of GAN. \\ RQ 3 & What are the preprocessing steps performed on ground truth brain MRI? & Preprocessing steps performed on ground truth brain MRIs are crucial for the fidelity of the successive GAN operations. \\ RQ 4 & How to compare the existing evaluation metrics for GAN-synthesized brain MRI? & This question encourages a comparative study of available evaluation metrics. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Research questions for this SLR.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Database** & **Query** & **Initial Result** \\ \hline Web of Science & ((MR Imaging) OR (MRI) OR (magnetic resonance imaging)) AND & \\ ((Brain Imaging) OR (Brain Images)) AND (GAN OR Generative & 210 \\ Adversarial Network) & ((MR Imaging) OR (MRI) OR (magnetic resonance imaging)) AND & \\ Scopus & ((Brain Imaging) OR (Brain Images)) AND (GAN OR Generative & 389 \\ Adversarial Network) & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Search query used for paper selection related to MRI brain imaging.
# Hybrid Quantum Neural Network Image Anti-Noise Classification Model Combined with Error Mitigation
## 4 Simulation and Analysis
### Simulation and Analysis
#### 4.2.1 MNIST Accuracy Test  
In this experimental section, we employed the classification model designed in this paper to perform a four-class classification task on the MNIST dataset. Each sample in the MNIST dataset is a grayscale image of size 28 \(\times\) 28 pixels. The dataset used here is MNIST, which is a collection of digit images covering the numbers 0 to 9. In this experiment, we have selected a subset of images containing digits 0 to 3 for conducting a four-class classification. We selected 400 training image samples (100 samples for each of the digits 0, 1, 2, and 3) and 200 test image samples. For the four-class classification task, four measurements and outputs were set at the end of the circuit. The variational quantum circuit was tested with four, five, and six layers. In the benchmark test, optimization was conducted over 100 epochs. As shown in Figure 6, the classification capability does not necessarily improve with an increase in variational blocks in our limited design. The best classification performance was achieved with five layers of variational quantum blocks, reaching a maximum accuracy of 0.98. Therefore, in the subsequent experiments, we consistently adopted five layers of variational quantum blocks, measuring the first four quantum bits for the four-class classification.  
The classification model designed in this paper achieved a classification accuracy of over 0.9 in a relatively short number of epochs. The accuracy obtained in the four-class classification study using the MNIST dataset is not significantly superior to the current best classical classification algorithms. However, the demonstrated classification accuracy and optimization of the model affirm the feasibility of our designed classifier in image classification, showcasing commendable classification performance.