esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Proposed Transformer-GAN
By applying a multi-objective INSBBO framework, the optimization process seeks to strike a balance between maximizing detection accuracy, minimizing model complexity, and accelerating training convergence, ultimately yielding a more efficient and generalizable intrusion detection system. Moreover, the use of nonlinear migration models within the INSBBO framework introduces greater diversity and adaptive behavior into the optimization process, allowing the model to dynamically adjust to different traffic characteristics and evolving attack patterns. This adaptability is crucial in IoT environments where the nature of network traffic and attack vectors can change rapidly and unpredictably. The proposed transformer-GAN hybrid model, optimized through INSBBO, presents a highly synergistic design that combines powerful feature extraction, adversarial learning, and robust multi-objective optimization. The architecture not only addresses the challenges of learning from highly imbalanced, high-dimensional IoT traffic data but also provides a scalable, generalizable, and adaptive solution for next-generation intrusion detection systems. To assess the semantic quality and intra-class diversity of the synthetic samples generated by the GAN component, we conducted a dedicated evaluation across the seven major attack categories in the CIC-IoT-2023 dataset. For each class, 1000 samples were generated using the trained generator and subsequently evaluated using a discriminator pre-trained on real data. As shown in Table 2, the correctly classified rate ranged from 91.8% to 96.7%, indicating a high degree of realism. Furthermore, class coverage remained above 95% for all categories, suggesting that the generated samples were not only realistic but also diverse, capturing meaningful variations within each attack type. Additionally, the average confidence score of the discriminator for the generated samples remained consistently high (ranging from 0.88 to 0.94) further confirming that the GAN effectively learned the underlying distribution of each class. These metrics collectively demonstrate that the adversarial learning process did not introduce noisy or low-quality data; instead, it enhanced class representation, mitigated imbalance, and contributed to the overall robustness of the training process. This independent evaluation validates the role of GAN in supporting the hybrid Transformer-GAN architecture with synthetically enriched, high-quality training data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Attack Class**} & **Generated** & **Correctly** & **Class** & **Average** \\  & **Samples** & **Classified (\%)** & **Coverage (\%)** & **Confidence** \\ \hline DDoS & 1000 & 96.70 & 100.0 & 0.94 \\ \hline Reconnaissance & 1000 & 94.20 & 98.50 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation of GAN-generated synthetic samples per attack class.
## 3 Results  
### Gene Expression in AFB1-Producing and Non-AFB1-Producing A. flavus  
Aflatoxin production has been well documented for _Aspergillus_ sp., and the AFB1-producing genes in the AFB1 biosynthetic pathway have been identified. Therefore, this study compared gene expression levels between the AFB1-producing (the _A. flavus_ ATCC 22546 strain) and non-AFB1-producing (the _A. flavus_ ATCC 9643 strain) _A. flavus_ strains. Subsequently, the latter strain's suppressive effect was determined on the growth of the AFB1-producing strain and AFB1 production via the co-culturation of the two strains. In this regard, another non-AFB1-producing _A. flavus_ strain (_A. flavus_ ATCC 96045, known as AF36) was used as a positive control to elucidate the gene expression patterns in the _A. flavus_ ATCC 9643 strain used in our study.  
In the ATCC 9643 strain, several AFB1-producing genes, namely _affK_, _affL_, and _affR_, which are responsible for expressing versicolorin B (verB) synthase, verb desaturase, and the aflatoxin pathway regulator, respectively, were not detected in our study (Figure 1). Other AFB1-producing genes, such as _affC_, _afID_, _afIE_, _afIO_, and _afIQ_, were significantly down-regulated in the ATCC 9643 strain compared with those in the AFB1-producing strain (ATCC 22546), as shown in Figure 1. These down-regulated genes are responsible for expressing polyketide synthase, acid ketoreductase, nonsolorinic acid reductase, _O_-methyltransferase B, and oxidoreductase, respectively. Only two genes, _afG_ and _afIP_, were not differentially regulated between the ATCC 9643 and the ATCC 22546 strains. These two genes are responsible for expressing cytochrome P450 for the conversion of averantin (AVN) to hydroxyaverantin and _O_-methyltransferase A, respectively.  
In another non-AFB1-producing strain, _A. flavus_ ATCC 96045, five genes, namely _affE_, _afIK_, _afIL_, _afIO_, and _afIQ_, were undetectable compared with those in ATCC 22546 (Figure 1). Among them, compared with those in ATCC 9643, two genes, _afIE_ and _afIQ_, were significantly down-regulated in ATCC 96045. However, these two genes were also down-regulated in ATCC 9643, with a negligible difference in expression levels. In ATCC 96045, two genes, _afIP_ and _afIS_, were not differentially expressed. As mentioned above, _afIP_ is a _O_-methyltransferase A-expressing gene, whereas _affS_ is responsible for expressing a gene that regulates aflatoxin production. Therefore, the non-AFB1-producing _A. flavus_ strains exhibited significant down-regulation of AFB biosynthesis.
# Recent Trends of Authentication Methods in Extended Reality: A Survey
## 5 XR for Usability Studies & Device Pairing  
### Cyber-Security Usability Studies Using VR  
Evaluating the usability of novel authentication schemes when the system is assessed within the context of its intended use can be challenging. It often necessitates inviting participants in user studies into the lab. Nonetheless, participant recruitment typically occurs solely within the local region, and corresponding evaluations frequently lack reality. Many works provide some evidence that VR is a promising substitute technology for empirical usability evaluations. They claimed that VR has the potential to address the limitations of lab-based authentication studies and advance towards more practical authentication experiments, especially in situations where evaluations are expensive (requiring special hardware), difficult to realize due to safety, or limited because of moral and legal restrictions. VR systems' replicas save researchers time and money by avoiding the need to construct real-life models. They also enable researchers to increase diversity by engaging study participants from several global locations. Table 10 represents a summary of the methods that established the use of VR to conduct usability studies. [PERSON] et al. [189] broke ground for the suitability of using VR as a testbed for real-world authentication systems. In their work, the authors replicated the authentication mechanism CueAuth in VR. CueAuth explored three cue-based authentication methods (eye gaze, mid-air, and touch) on situated displays. The lab-based VR usability study revealed that some usability studies, namely entry accuracy, perceived workload, and security perception, can be translated from VR to the real world. However, variations in terms of results between the VR usability study and the real-world study are significantly noticeable. Authentication in VR using the gaze-based approach took less time than the touch-based approach. In addition, the security studies carried out in both VR and real-world environments resulted in both similar and different results, depending on the instances. For example, shoulder surfing attacks' rates for a human subject and a VR avatar were similar but scored differently in terms of accuracy. [PERSON] et al. [190] investigated the concept of foot-based user authentication for public displays. They implemented FeetAuth, a realistic-looking VR subway scene where users authenticate on a public display to buy public transportation tickets. They conducted a VR-powered user study on the virtual prototype, which is difficult to recreate in the
# RNA-Peptide Conjugation through an Efficient Covalent Bond Formation
## 3 Results
### Contribution of the RNA-Peptide Interaction for the Reaction Yield  
RNA sequence dependency of the reaction was next evaluated to address the proximity effect of reactive groups. The specific interaction between the Rev peptide and RRE RNA of the RNP scaffold was expected to have an influence on the efficiency of covalent linkage formation. A sequence-scrambled scrAn16 RNA that contained the same number of each nucleotide to An16 RNA was prepared (Figure 3A). The reaction of the oxidized scrAn16 with Ac-Rev-(GGS)\({}_{3}\)G-HZ was performed at pH 5. The MALDI TOF MS analysis using the reaction solution after removing the unreacted peptide was performed to characterize the products. The production of covalently linked RNP was confirmed by observing respective MS peaks (Figures S2 and S3). In the five trials, scrAn16 showed a similar averaged reaction yield (92 \(\pm\) 5%) to that of the parent An16 (92 \(\pm\) 5%) when conjugated with the hydrazide group of Ac-Rev-(GGS)\({}_{3}\)G-HZ (Figure 3B and Figure S1B,D). The result showed that the RNA sequence did not affect the efficiency of reaction. The non-specific electrostatic interaction between the RNA and the cationic Rev peptide would relate to the reaction efficiency. To verify this notion, the reaction for the formation of covalently linked RNP was carried out by using a truncated peptide of Ac-Rev-(GGS)\({}_{3}\)G-HZ. A truncated peptide possessing only the GGS peptide linker moiety, Ac-GGSGSGSGS-HZ, was designed by deleting the Rev peptide sequence from Ac-Rev-(GGS)\({}_{3}\)G-HZ. The truncated peptide showed a significant decrease in the yield to 57 \(\pm\) 1%, even after a prolonged reaction time for 14 h (Figure 4 and Figure S1C). This result supported the notion that the electrostatic interaction between RNA and the peptide mainly contributed to increasing the reaction yield. Thus, the RNP scaffold was useful for the efficient production of RNA-peptide conjugates.  
Figure 3: (**A**) Nucleotide and peptide sequence of An16 RNA, scrAn16 RNA, Ac-Rev-(GGS)\({}_{3}\)G-HZ peptide, and Ac-(GGS)\({}_{3}\)G-HZ peptide; (**B**) a denaturing PAGE (8 M Urea) analysis of the conjugation reaction products of c-RNPs constructed from An16 or scrAn16 RNA with Ac-Rev-(GGS)\({}_{3}\)G-HZ. Lane1: An16 RNA; Lane 2: An16 and Ac-Rev-(GGS)\({}_{3}\)G-HZ reacted for 3 h; Lane 3: scrAn16 RNA; Lane 4: scrAn16 and Ac-Rev-(GGS)\({}_{3}\)G-HZ reacted for 3 h; Lane 5: scrAn16 and Ac-Rev-(GGS)\({}_{3}\)G-HZ reacted for 14 h.
## 4 Methodology  
### Principal Components Analysis  
The slope instability shows typical fluctuation and multi-scale characteristics. Due to the influence of random factors such as noise and interference, it is difficult to fully extract useful information from the data by directly modeling the original data, and the prediction accuracy is low. More importantly, there is a significant correlation between various types of data, which is easy to cause repetition of sample information input, increases the complexity of model training, and reduces the generalization performance. The principal component analysis (PCA) method is a classical data dimensionality reduction and denoising method, which maps high-dimensional data into a few principal components in low-dimensional space by matrix transformation. The original data are compressed to facilitate calculation and improve the overfitting problem, and these principal components contain most of the useful information in the original data [77].  
The application of the PCA method in the slope field was proved to be feasible and effective [78; 79]. PCA is used to eliminate the correlation between the six key variables in the dataset established in Section 3.1, and the new dataset is established. The steps are as follows:  
The original dataset \(X_{mxn}\) can be represented as a matrix with \(m\) rows and \(n\) columns, where \(m\) is the number of samples and \(n\) is the number of variables:  
\[X_{m\times n}=\begin{pmatrix}x_{11}&\ldots&x_{1n}\\ \vdots&\ddots&\vdots\\ x_{m1}&\ldots&x_{mn}\end{pmatrix}. \tag{4}\]  
The dimension difference between each input variable may be too large to affect the prediction results. Therefore, to better use the PCA method, the \(X_{mxn}\) was standardized and transformed into a standardized matrix \(X^{*}\) according to Equation (5).  
\[{x_{i,j}}^{*}=\frac{x_{i,j}-\mu_{j}}{\sqrt{\sigma_{i}}},i=1,2,\cdots,m;j=1,2, \cdots,n \tag{5}\]  
where \(x_{i,j}^{*}\) denotes the normalized value; \(\sigma_{i}\) and \(\mu_{j}\) denote the variance value and mean value of original data, respectively.  
The correlation matrix \(R\) is calculated from the \(X^{*}\) as shown in Equation (6).  
\[R_{m\times n}={X^{*}}^Calculate the eigenvalue of each principal component according to \(\left|\lambda-R\right|=0\) to find the eigenvalues \(\lambda_{i}\). Finally, the contribution rate of each principal component \(\eta_{i}\) is obtained, and the equation is as follows:  
\[\eta_{i}=\frac{\lambda_{i}}{\sum\limits_{i}^{n}\lambda_{i}}. \tag{7}\]
## 7 System Performance Optimization
### CSI-Based Localization for Wireless Transmitters  
There are many methods to locate public wireless transmitters. For example, [PERSON] et al. [42] proposed an accurate localization method by using received signal strength (RSS). However, RSS is related to the transmit power of the public APs, and the attacker may change the transmit power to confuse the user to get the position of the public APs in the public place. Thus, the RSS-based localization method is not feasible in the public place, and the CSI-based localization method is used to locate the public APs in this paper.  
As mentioned above, a kind of finger motion can generate a unique pattern in time series CSI measurements. In addition, different positions can also generate different patterns in CSI measurements; thus, CSI can also be used to locate public APs. As shown in Figure 5, the CSI measurements of the same position are always the same at different periods of time, and the CSI amplitude values in Figure 4(a) are from 8-11. We can see from Figure 4(b) that the CSI measurements of different positions are different.  
When CSI values are used to do localization for public APs, first we need to remove the noise from the obtained CSI measurements, then PCA is used to reduce the dimension of CSI measurements, and the details of CSI-based localization are described in the following sections.
## 3 Results
### Validation of Mean Diurnal Radiation  
Figure 3 illustrates the range of R\({}^{2}\) values, spanning from 0.54 to 0.99, across all locations. Specifically, the R\({}^{2}\) values for both EU locations were markedly different from each other. While the Cabauw (CAB) location demonstrated a fairly good agreement between satellite-derived and ground-based GHI estimates, with an R\({}^{2}\) value of 0.81 (Figure 3A), the Cener (CNR) location exhibited a poor comparison result, with an R\({}^{2}\) value of 0.51 (Figure 3B). The R\({}^{2}\) value for Japanese sites employing Himawari-8 images [Abashiri (ABS) and Tateno (TAT)] stands at 0.91, as indicated in Figure 3C,D. Conversely, Heliosat-2 estimates of GHI from MSG images exhibit a mean R\({}^{2}\) value of 0.95 for African locales [Gobabeb (GOB) and USAid Venda (VUW)], as depicted in Figure 3E,F. Notably, the Saudi West Coast (KSA) location in Saudi Arabia boasts the highest R\({}^{2}\) value, of 0.99, as evidenced in Figure 3G. The R\({}^{2}\) values for the Ashok Nagar (ASO) and Honnali (HON) locations in India were identical and show a fairly good correlation (0.86) between the model and the ground GHI, as evident in Figure 3H,I respectively. Across all locations, more than 92.4% of data points fell within a range of \(\pm\)2 standard deviations (SD).  
Regarding predicted GHI estimates, two EU locations show an overestimation in terms of MB, ranging from +9.7 to +20.60 W\(\cdot\)m\({}^{-2}\) (Figure 3A,B). Similarly, overestimations were also found for the two Japanese locations, with MB ranging from \(\sim\)3.0 to 11.9 W\(\cdot\)m\({}^{-2}\) (Figure 3C,D). Conversely, African sites in GOB and the Saudi Arabian location of KSA exhibit underestimations ranging from \(-\)5.6 to\(-\)\(-\)29.0 W\(\cdot\)m\({}^{-2}\). RMSE values range from a minimum of 13.8 W\(\cdot\)m\({}^{-2}\) in the KSA location to a maximum of approximately 46.0 W\(\cdot\)m\({}^{-2}\) in GOB. In India, the satellite GHI estimates show a minor overestimation of +7.5 W\(\cdot\)m\({}^{-2}\), while a nominal underestimation was observed in HON, with an MB of \(-\)2.3 W\(\cdot\)m\({}^{-2}\). The RMSE values across all the locations varies from a minimum of 13.8 W\(\cdot\)m\({}^{-2}\) in KSA to a maximum of 71.2 in CNR.
# Image Forensics Using Non-Reducing Convolutional Neural Network for Consecutive Dual Operators
## 2 Detection of Image Processing Operator Sequence
### Problem Formulation  
Assuming that there are two operators, \(\alpha\) and \(\beta\), in the image operation sequence, the detection of a dual operator sequence can be understood as a multiclass classification problem. The five classes according to processing history can be formulated:  
\(\Omega 0\): An image is not operated by any operator;  
\(\Omega 1\): An image is operated by \(\alpha\) operator;  
\(\Omega 2\): An image is operated by \(\beta\) operator;  
\(\Omega 3\): The first image is operated by \(\alpha\) then operated by \(\beta\);  
\(\Omega 4\): The first image is operated by \(\beta\) then operated by \(\alpha\).  
Image histograms can be visualized to recognize the detection complexity of dual operator sequence. Image histogram provides the summary of image pixels according to their intensity. The changes in the pixel intensity are unavoidable when applying any type of operator. A single image is considered to understand changes in the image after applying some operator. In Figure 1, a pristine image (ORI) with 128 \(\times\) 128 pixels in BOSSbase [30] and a corresponding histogram are shown.  
In Figure 2, histograms of the pristine image (Figure 1a) are displayed after applying a single operator and dual operator sequence. Four operators--Gaussian blurring with standard deviation 1.0 (GAU_1.0), median filtering with filter size 5 \(\times\) 5 (MF5), un-sharp masking sharpening with radius 3.0 (SH_3.0), up-sampling with factor 1.5 (UP_1.5) and their two operator sequence from GAU_1.0 MF5 to UP_1.5 GAU_1.0 are considered. As discussed above, five classes are possible while considering two operators--GAU_1.0 and MF5 for example. There is a slight difference in the histograms of \(\Omega\)1 (GAU_1.0), \(\Omega\)2 (MF5), \(\Omega\)3 (GAU_1.0 MF5), and \(\Omega\)4 (MF5 GAU_1.0) as shown in Figure 2. A similar pattern is also followed by other operators. The histogram of the median filtered image is very similar to the pristine image histogram. It is due to the nonlinear behavior of the median filter. Further, the internal statistical information becomes limited while operated by dual operators.  
One attempt [23] is made to detect the image resizing and Gaussian blurring operations pair. The image features are visualized in the frequency domain. However, the results are  
Figure 1: Pristine image and histogram.  
Figure 2: Image histogram for a single operator and dual operators.  
not encouraging for the operator sequence. The \(\Omega\Omega\)3 and \(\Omega\Omega\)4 operators sequence are not detectable. In spite that the strength of each operator varies, one operator artifacts in dual operators sequence can be suppressed by another operator.
## 2 Data and Methods
### Methods
#### 2.3.2 Water Surface Evaporation Estimation  
Since the PT-JPL model is mainly used for estimating land surface evaporation, it is not suitable for estimating water surface evaporation. Therefore, for water bodies in the watershed, the FAO-24 Penman formula [37], which is widely used and has high calculation accuracy, was used to calculate the water surface evaporation:  
\[\mathrm{ET_{W}=\frac{1}{\lambda}\bigg{(}\frac{\Delta}{\Delta+\gamma}(R_{n}-G)+6.4 3\frac{\gamma}{\Delta+\gamma}(1+0.862u_{2})D\bigg{)}} \tag{2}\]  
\[D=e_{s}-e_{a} \tag{3}\]  
where \(R_{n}\) is the surface net radiation (MJ/(m\({}^{2*}\)d)); G is the soil heat flux; \(u_{2}\) is the wind speed at a height of 2 m (m/s); D is the saturated water pressure difference (kPa); \(\Delta\) is the slope of the saturated water pressure curve (kPa/\({}^{\circ}\)C); \(\gamma\) is the surface hygrometer constant (kPa/\({}^{\circ}\)C), taken as 0.066; D is the water pressure difference (kPa); \(e_{s}\) is the saturated water pressure (kPa); \(e_{a}\) is the actual water pressure (kPa); and \(\lambda\) is the latent heat of evaporation, taken as 2.45 MJ/kg.
_Applied_  
_sciences_  
_Article_  
An Analysis of Angular Indexing Error of a Gear Measuring Machine  
[PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 1,2+\({}^{\text{(2)}}\)  
Received: 1 November 2017; Accepted: 22 January 2018; Published: 25 January 2018  
**Featured Application: The method introduced in the paper can be applied to, and can increase angular indexing accuracy of, all machines equipped with a mandrel to rotate the workpiece, such as gear measuring machines (GMMs), gear grinding machines, polygon grinding machines, cam grinding, and measuring machines.**  
**Abstract:** In gear measuring machines (GMMs), the tested gear is mounted on a mandrel, which is pivoted at both ends by two centers in a vertical arrangement. The upper center is fixed and the lower center is driven by the spindle of rotation. The coaxiality error between the central line of the mandrel and the spindle average line of a GMM always exists in terms of the offset and angle measured in one plane. Such a coaxiality error would cause an angular indexing error of tested gear resulting in measurement error. This phenomenon has rarely been investigated. In this paper, a GMM is taken as an example and its coaxiality error of the mandrel and spindle error of the rotary stage are measured. The difference of rotated angles between the mandrel and spindle is theoretically analyzed by derived formulae. Calibrated by a precision polygon and an autocollimator, the predicted angular index error of the mandrel was consistent with experimental results. Through the experimental verification, it was found that, when the coaxial deviation between the two centers was 10 \(\upmu\)m and the lower center tip's radial motion error was 1.6 \(\upmu\)m, the angular indexing deviation of the mandrel was \(\pm 5^{\prime\prime}\). If the errors were compensated according to the analyzed model, the residual error was reduced to \(\pm 2^{\prime\prime}\). A significant improvement in the angular positioning accuracy of the GMM can be achieved.  
**Keywords:** angular indexing error; gear measurement; coaxiality error; positioning of centers; error compensation