esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method  
The overall architecture of MINTFormer is illustrated in Figure 1. MINTFormer comprises a Heterogeneous encoder, a Demodulate Bridge, a multi-Scale Sampling Decoder, and an aggregation output head. In MINTFormer, the MaxViT module initially performs the encoding operation. For any input feature map with a fixed dimension, the MaxViT stem stage encodes the feature map into patches using a convolution operation with a kernel size of \(7\times 7\) and a stride of 4. The resulting patches then enter the MaxViT encoder for further encoding. The feature map produced by MaxViT is subsequently filtered by the Demodulate Bridge to eliminate redundant information. The filtered feature map is then fed into the multi-Scale Sampling Decoder for decoding. The resulting prediction map is scaled to the dimension of the feature map received by CSWin through the OC-S module and weighted with the feature map about to be input to the CSWin stem. Similarly, the feature map produced by CSWin is filtered by the Demodulate Bridge to eliminate redundant data. The feature maps produced by CSWin and MaxViT are then merged and decoded in the multi-Scale Sampling Decoder. Following the MERIT approach, the output of the multi-Scale Sampling Decoder is upsampled in the output head to generate the prediction map. The public announcement of MINTFormer is presented below.  
Figure 1: MINTFormer framework diagram.  
\[\text{\emph{MINTFormer}}\begin{cases}\text{\emph{Setx}}_{1:i}=SSD(DB(Encoder_{M}(x)))\\ x^{{}^{\prime}}=OC-S(x_{1:A})\\ x^{{}^{\prime\prime}}=DB\Big{(}Encoder_{C}\Big{(}x^{{}^{\prime}}\Big{)}\\ \text{\emph{Setx}}_{2:i}=SSD\Big{(}x^{{}^{\prime\prime}},\text{\emph{Setx}}_{1 :i}\Big{)}\\ \text{\emph{Sety}}_{i}=[UP_{to256}(\text{\emph{Setx}}_{2:i})]\end{cases} \tag{1}\]  
In Equation (1), \(\text{\emph{Set}}\) denotes the output set of the feature map and \(i\in[1,4]\), \(x\) represents the feature map, and \(y\) represents the final output of MINTFormer. \(Encoder_{M}\) and \(Encoder_{C}\) denote the MaxViT and CSWin components of the Heterogeneous encoder, respectively. \(SSD()\) represents the multi-Scale Sampling Decoder. \(DB()\) denotes the Demodulate Bridge, and \(UP_{to256}\) signifies upsampling the feature map to \(256\times 256\) using bilinear interpolation.
## 4 Impact of Oxygen Transmittance Compensation on Different SIF Retrieval Strategies  
In order to quantify the expected improvement achieved by each of the proposed compensation techniques in Section 3, we now present a collection of tests that were developed using a set of simulated radiance spectra at varying sensor levels (from 3 m to 20 m above TOC) following Equation (3). In the radiance simulation process at sensor level, total atmospheric upward transmittance (including aerosols) and at-surface solar irradiance were simulated using the atmospheric RTM MODTRAN. In order to disentangle errors derived for SIF due to the O\({}_{2}\) effects from those due to the retrieval technique, four tests were performed: (1) a high spectral resolution test assuming the knowledge of surface reflectance (Section 4.1); (2) a test using the O\({}_{2}\) compensated 3 FLD formulation (Section 4.2); (3) a test using the O\({}_{2}\) compensated SFM formulation (Section 4.3); and (4) a final test adapting an airborne atmospheric correction strategy by coupling the O\({}_{2}\) transmittance with the compensation of the ISRF convolution on the SFM (Section 4.4).
## 2 Leakage Model of Shield Joints Based on the Persson Model  
A mathematical model to describe the leakage of shield joints must contain parameters that at least can reflect the influencing factors, including the joint gap, the joint offset, the contact surface roughness, and the service time. The method explored here is adequate. This section initially describes the influencing factors of shield joints and then introduces the process of calculating the average interfacial separation using the Persson model. It then defines the critical leakage state and the self-sealing effect, as well as deduces the solution of critical leakage pressure and the self-sealing stress. Finally, the size of the percolation channel is calculated through the above process, and further the leakage is obtained by Navier-Stokes equations (N-S equations).
## 2 Numerical Simulation Method  
### Three-Dimensional Geometry Model  
In this study, commercial software Creo was used to model the three-dimensional (3D) geometry structure of full tubular pump device as shown in Figure 1. The pump device consists of an inlet passage, front guide vane, impeller, motor rotor, post guide vane, and outlet passage. Four kinds of backflow clearance radius \(R_{\text{b}}\) are were used: 0, 0.5, 1, and 2 mm. The calculated results under \(R_{\text{b}}\) = 2 mm were validated by test data. Table 1 shows the main geometry parameters and design parameters of this full tubular pump device. The pump head \(H\) and efficiency \(\eta\) can be calculated by Equations (1) and (2) [23]:  
\[H=\frac{p_{\text{out}}-p_{\text{in}}}{\rho g} \tag{1}\]  
\[\eta=\frac{\rho gHQ}{P_{\text{s}}} \tag{2}\]  
where \(p_{\text{out}}\) and \(p_{\text{in}}\) are the total pressure of the outlet and inlet for the full tubular pump device, respectively. \(\rho\) is the water density and \(g\) is the acceleration of gravity. \(Q\) presents the volume flow rate, and \(P_{\text{s}}\) is shaft power.
## 3 Results and Discussion
### Analysis of Tyrosine Inhibition Activity of GV Extracts  
Melanin is a dark brown pigment that protects the skin from UV radiation [77]. However, excessive melanin accumulation can lead to hyperpigmentation, which manifests as melasama lesions and freckles [78]. Tyrosine catalyzes the biosynthesis of melanin from melanocytes and is an effective agent for skin-whitening effects. Therefore, this study analyzed tyrosinase inhibition activity to investigate the skin-whitening effects of GV extracts (Table 4). The tyrosinase inhibitory activity of GV extracts was in the range of 6.0-38.0%, with the highest activity observed in GVM, followed by GVW and GVE. Although GVM showed significantly higher tyrosinase inhibitory activity (37.9 \(\pm\) 1.4%) than GVW and GVE, it demonstrated lower activity than ascorbic acid (50.2 \(\pm\) 2.7%), which was used as the positive control.  
[PERSON] et al. [79] examined the tyrosinase inhibition activity of 40 seaweeds extracted at 20 \({}^{\circ}\)C and 70 \({}^{\circ}\)C and reported high activity in aqueous extracts of red seaweed _Schizophrenia dubyi_, brown seaweed _Endarachme binghamiae_, _Ecklonia cava_, and _Sargassum siliqustrum_. These results show a difference in tyrosinase inhibitory activity depending on both the extraction temperature and species. [PERSON] et al. [80] reported differences in the tyrosinase inhibitory activity of extracts depending on the substrate used (such as 3,4-dihydroxy-L-phenyl-alanine and L-tyrosine). Therefore, further studies are required to evaluate the tyrosinase inhibitory activity of GV extracts under different conditions, including different extraction methods and substrates.
# Cheating Detection in Online Exams Using Deep Learning and Machine Learning
## 2 Materials and Methods
### Classification Metrics
When used with Recall, it provides more detailed information about the overall performance of the model. It is calculated by Formula (5). \[Specificity=\frac{TN}{TN+FP} \tag{5}\]  
**Matthews correlation coefficient (MCC):** It measures how well the model can distinguish between positive and negative classes and takes into account all classification errors. The value of MCC ranges from \(-1\) to \(1\), with \(1\) indicating perfect classification, 0 indicating random prediction, and \(-1\) indicating a completely opposite classification. It is calculated by Formula (9). \[MCC=\frac{(TP*TN)-(FP*FN)}{\sqrt{(TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)}} \tag{6}\]  
The dataset used in this study was obtained within the scope of the study titled Development of a New Model for Detecting Undesirable Behavior in Online Exams,' approved by the Ethics Principles and Ethics Committee of Bitlis Eren University (Bitlis, Turkey) with decision number 2022/15-19 and E.3165 (The Ethics Committee decision is provided in Appendix A). The dataset consists of supervised and unsupervised exam data from 221 students, using both real and simulated exams conducted through the Academic Learning Management System (ALMS), the distance education system of Bitlis Eren University, for the Basic Information Technologies course. The data, transferred by the researcher into the Python/Pandas library in Excel format, include the participants' departments, grades, their responses to the questions (A1, A2,..., A15), the number of correct and incorrect answers, the total time spent by students in the system, the time spent per question, the total time spent on solving questions, and the predicted variable (R_per) defined by the researcher.
# A Generic Automated Surface Defect Detection Based on a Bilinear Model
## 2 Methodology
### Defect Localization
#### 2.2.1 Grad-CAM  
Although CNN has significant effects on image processing tasks for a long time, it has been a controversial method due to the poor interpretability of the CNN internal feature extraction, thus a new field, which is called the interpretable research of deep learning, appeared. Apart from that, Grad-CAM is a visualized method of the convolutional neural network, which can be used to visualize network category location results in the last level of the network's convolutional layer.  
In order to obtain a class activation map \(L^{n}_{Grad-CAM}\), the score gradient \(\frac{\partial y^{n}}{\partial A^{k}}\) of the class \(n\) is firstly calculated, in which \(A^{k}\) represents the weight of the class \(n\) of the first \(k\) feature map, and \(y^{k}\) represents the score of the category before the soft-max. Then the gradient of the global average pooling layer is used to obtain the importance \(\alpha^{n}_{k}\) of the first \(k\) feature map for the category \(n\).  
\[\alpha^{n}_{k}=\frac{1}{Z}\sum_{i}\sum_{j}\frac{\partial y^{n}}{\partial A^{k }_{ij}} \tag{4}\]  
where \(Z\) represents the size of the feature map and \(A^{k}_{ij}\) represents the activation value of the position in \((i,j)\) the \(k\) first feature map. Finally, the weighted sum of the forward activation features is performed according to Formula (4), and a Grad-CAM of a given class can be obtained using a rectified linear unit (ReLU).  
\[L^{n}_{Grad-CAM}=ReLU(\sum_{k}\alpha^{n}_{k}A^{k})=\max(0,\sum_{k}\alpha^{n}_{ k}A^{k}) \tag{5}\]  
Grad-CAM can explain the feature extraction results of the network and enhance the trust of the network performance, which is particularly important to the training network of small samples, because the insufficient number of training samples may lead to an inadequately trained network, thus causing a problem that the judgments of the network for a particular class may not be based on the real discriminant region in the image, and this results in serious over-fitting. In addition, Grad-CAM is used in the defect detection network, which can automatically locate the defects of input images in the prediction stage only by image-level annotation in the training stage.  
In this paper, the Grad-CAM of defective images are generated. As shown in Figure 1, the Grad-CAM highlights the defect regions.  
Figure 3: Data stream of the bilinear model.
## 3 Results and Discussion
### FESEM Analysis  
The results of FESEM characterization for the pure TiO\({}_{2}\) substrate and dye-soaked TiO\({}_{2}\) (DN-F05 and DN-F05-Ag) can be seen in Figure 6a-c. Each image was enlarged 10,000\(\times\) to determine the changes in the layer structure. Figure 6a shows pure TiO\({}_{2}\) morphology, where there are uneven spherical lumps, while Figure 6b shows the TiO\({}_{2}\) substrate structure soaked in dye DN-F05. There are small balls attached to the FTO coated by TiO\({}_{2}\), which proves the existence of a TiO\({}_{2}\) layer on the FTO substrate.  
Figure 6c shows the characterization results for the TiO\({}_{2}\) substrate that was immersed in dye DN-F05 mixed with the Ag solution. It can be seen that there has been a significant  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Pos. (2 Th.)** & **Int. (\%)** & **FWHM Radian (\$)** & **\(\mathbf{\beta}\)Ccos \(\mathbf{\theta}\)** & **D = k\(\mathbf{\lambda}\)/B/Ccos \(\mathbf{\theta}\)** & **D (nm)** \\ \hline TiO\({}_{2}\) & 25.1563 & 100 & 0.004 & 0.004 & 37.144 & 37.178 \\  & 25.3778 & 96.21 & 0.004 & 0.004 & 37.211 & \\ \hline TiO\({}_{2}\):DN-F05 & 25.2763 & 100 & 0.010 & 0.009 & 15.938 & 22.142 \\  & 33.7241 & 65.15 & 0.006 & 0.005 & 28.345 & \\ \hline TiO\({}_{2}\):DN-F05:Ag & 33.770 & 100 & 0.007 & 0.006 & 24.271 & 52.667 \\  & 51.509 & 91.710 & 0.003 & 0.02 & 81.062 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Crystallite size calculation table for TiO\({}_{2}\), TiO\({}_{2}\):DN-F05, and TiO\({}_{2}\):DN-F05:Ag samples.  
structural change due to the addition of Ag, which induces structural damage. This is highly related to the nature of the semiconductor material used, as soaking in a large concentration of Ag will cause changes in certain properties and thereby increase conductivity. This is because too many Ag particles will attempt to enter the semiconductor layer. If the concentration of Ag is high, this will lead to particle aggregation on the TiO\({}_{2}\) surface, which will clog the coating's pores and hinder the absorption process [29].
## 3 The Proposed System
### Energy Model  
The network requires energy for a variety of purposes, including sensing, receiving, aggregation, and transmission. The sensor node can be changed during the data transmission or receiving at any moment. A sensor node consumes energy when transmitting or receiving data. The node energy in the network has an initial value of P0 and also the energy of the node is not rechargeable [KK16]. The energy squandering PTk whilst transferring K bits/packet, d distance from regular node \(\mathrm{a_{th}}\) to CH \(\mathrm{b_{th}}\) follows [YM22] delineated as per Equation (1). If the connection distance d for the transmission model is small (free space model) d2 else the distance is multi-path d4, they are based totally on the distance between the senders as well as the receivers. The received energy, PRk, required for receiving k bits/packet at a distance d follows delineated as per Equation (2). Pel, the electronic energy consumed per bit, represents several modules, such as spreading. Pmp denotes the multi-paths energy model while \(\mathrm{P_{fs}}\) provides the free space energy model. d0 is the threshold distance determined as shown in Equation (3).  
\[P_{TX}(K.d)=\begin{cases}K\Big{(}P_{el}+P_{fs}*d^{2}\Big{)}&if\ d<d_{0}\\ K\Big{(}P_{el}+P_{mp}*d^{4}\Big{)}&if\ d\geq d_{0}\end{cases} \tag{1}\]  
\[P_{TX}(K.d)=K*P_{el} \tag{2}\]  
\[d_{0}=\sqrt{P_{fs}/P_{mp}} \tag{3}\]  
The suggested protocol is designed to increase the lifetime of the network, energy efficiency, stability, and load balance. The energy dissipation during transmission is calculated using the energy model given above. Data reception and collecting: The proposed protocol operates in two phases, the setup phase, and the stationary phase. Cluster construction, CH selection, and data transfer from CH to BS are all performed. The WSN that is being utilized is essentially heterogeneous. It is made up of ordinary and advanced nodes. Due to network non-uniformity, advanced nodes with higher initial energy improve network performance. Advanced nodes are more energy-efficient than regular nodes and fewer than standard nodes. Inspired by nature, meta-heuristics are optimization strategies that simulate nature to solve optimization issues. Striking results increase the potential and viability of nature-inspired algorithms and also their research in various fields. Stable and energy-efficient clustering algorithms are offered for WSNs in this section, similar to LEACH, as traditional protocols, GA, COY, AO, and HHO. We compared the algorithms to obtain convergence on the optimal solution.
## 3 Approximate DFT
### Optimization Problem  
Systematically, a way to obtain such low-complexity DFT estimators is by means of matrix approximation theory. In matrix formalism, the DFT is described by the following operation:  
\[\mathbf{X}=\mathbf{F}_{N}\cdot\mathbf{x}, \tag{2}\]  
where the \(N\)-point DFT matrix, denoted by \(\mathbf{F}_{N}\), is the \(N\times N\) matrix with elements given by \(f_{k,n}=\omega_{N}^{-nk}\), \(k,n=0,1,\ldots,N-1\).  
In the context of approximations for discrete transforms, qualitatively, an approximate discrete transform, \(\hat{\mathbf{F}}_{N}\), is a transform matrix such that \(\hat{\mathbf{X}}=\hat{\mathbf{F}}_{N}\cdot\mathbf{x}\approx\mathbf{X}= \mathbf{F}_{N}\cdot\mathbf{x}\). In other words, the exact and the approximate transform-domain signals are \"close\" in some relevant sense to be quantitatively defined. Generally, an approximation transform must satisfy the following conditions: (i) it can largely preserve meaningful properties of the exact transform; (ii) there is mathematical proximity between the exact and approximate transform matrix according to a contextually relevant metric function--usually a performance figure of merit; and (iii) it possesses a significantly lower computational cost, compared to the exact computation by FFTs. In [95], an overview of the theory is made available.  
Usually, in order to preserve the physical interpretation of the approximate spectrum, approximate transformation matrices are sought to be close in the Euclidean sense to the associate exact transformation matrix. This can be accomplished through the minimization of the Frobenius norm of the difference between the exact and candidate matrices for approximation, subject to the restriction of low complexity. Therefore, a low-complexity approximation can be obtained by restricting its elements to numerical sets of trivial multiplicands, such as \(\{0,\pm 1\}\) or \(\{0,\pm 1,\pm 2\}\).  
In symbols, a possible formulation for the problem of deriving a DFT approximation is given by [95]  
\[\min_{\hat{f}_{k,n}} \|\mathbf{F}_{N}-\mathbf{S}\cdot\hat{\mathbf{F}}_{N}\|_{\mathrm{F}}\] (3) subject to \[\hat{f}_{k,n}\in\{0,\pm 1\}^{2}, \tag{4}\]  
where \(\hat{f}_{k,n}\), where \(k,n=0,1,\ldots,N-1\), denotes the entries of the approximate matrix \(\hat{\mathbf{F}}_{N}\), \(\|\cdot\|_{\mathrm{F}}\) denotes the matrix Frobenius norm, and \(\mathbf{S}\) is a normalization matrix used to ensure that the basis vectors of \(\mathbf{S}\cdot\hat{\mathbf{F}}_{N}\) have energy equal or near to one.
## 5 Discussion
### Key Observations  
* Effective malfunction identification: The model reliably detected faulty sensors--such as those producing constant or zero values over time--by leveraging the reconstruction error profile from the Transformer Autoencoder and confirming with Isolation Forest. This prevents the propagation of corrupted data across the network.
* Improved intrusion discrimination: By integrating Isolation Forest with XGBoost, the system was able to distinguish between sensor malfunctions and external intrusions more accurately. This dual-stage classification significantly reduced the confusion between hardware failures and malicious behavior.
* Benefit of adaptive thresholding: The use of dynamic, data-driven thresholding (based on rolling mean and standard deviation) provided robust anomaly sensitivity while minimizing false positives. This approach is particularly effective in handling the inherent noise and variability in WSN data streams.
# OQPSK Synchronization Parameter Estimation Based on Burst Signal Detection
## 1 Introduction
### Related Work  
The improved energy method given in [4] achieves a high detection rate, but it uses the center of the energy window as the starting point of the burst signal, so there is a certain deviation from the true starting point. Cross-correlation is then used in the earliest burst signal detection based on the Data-Aided (DA) method; however, its performance is poor in the presence of frequency offset [5]. The double correlation [6] can effectively eliminate the influence brought by frequency offset. Unfortunately, it contains many conjugate multiplications, resulting in high computational complexity. In [7], a segment correlation algorithm was introduced to greatly reduce the complexity at the expense of part of the anti-frequency offset ability. The burst signal detection adopted in this article is further simplified on the basis of segment correlation.  
Once the burst signal detection is done, we need to estimate the synchronization parameters. Synchronization includes carrier synchronization and timing synchronization, in which carrier synchronization includes frequency offset synchronization and phase offset synchronization. We should note that for burst signals, the synchronization parameter estimation often adopts the feedforward algorithm, so the method based on the phase-locked loop [8] is not applicable here. A code-assisted carrier synchronization algorithm was proposed in [9], but this method requires the use of channel coding, and the iterative concept makes its implementation more complicated. In [10], a low-complexity carrier synchronization method was designed under the assumption of a special frame structure. The L&W, KAY, L&R, and Fitz [11; 12; 13; 14] algorithms are commonly used to feedforward estimate the frequency offset. However, the accuracy and estimation range of these algorithms cannot be well coordinated. More specifically, L&W and KAY have a wide estimation range, but low accuracy, while L&R and Fitz have a high accuracy, but a narrow estimation range, and their complexities increase with the improvement of the estimation accuracy. The phase offset estimation is usually performed after the frequency offset is estimated. Finally, the received signal needs to be compensated for the frequency offset based on the above results, and then, the Maximum-Likelihood (ML) principle is applied to estimate the phase offset.  
For timing error estimation, the commonly used feedforward algorithms are the Lee, AVN, and Zhu [15; 16; 17] algorithms; nevertheless, their performances rely heavily on the roll-off factor, and they cannot be directly applied to OQPSK signals. In [18] (in the simulation, we call the algorithm Ye), a DA-based feedforward timing error estimation algorithm using trigonometric interpolation was proposed, but this algorithm is disabled for anti-frequency offset.