esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
One of the advantages of studying the 3'UTR region is to determine the translation rate of mRNA into proteins, which further influences the binding of regulatory factors, controlling the initiation or elongation of translation and further modulating gene expression [74; 75; 76; 77]. In our study, the G20210A SNP was present in the 3'UTR region, and 7% of FI women were found to have GA/AA genotypes, while a couple of control women had developed the GA genotype. Furthermore, the SNPs present in exons can modulate gene transcription and translation, and the SNPs present in introns can attack RNA splicing, genomic imprinting and lncRNAs, while the SNPs present at 5'UTR can promote translation [77]. In this study, the G1691A and C677T SNPs were present in Exon-10 and Exon-4 (Table 1); 5% of the SNPs were the GA and AA genotypes in the FI cases, and a single GA genotype was present in the control subjects; while in the C677T SNP, 26% of the CT and TT genotypes existed in the FI cases, and 28% of the CT and TT genotypes appeared in the control women. The CT and TT genotype frequencies were found to be high in the controls in comparison to the cases. A previous study in Saudi Arabia investigated the relationship between the C677T SNP and FI, and the prevalence of the CC, CT and TT genotypes was found to be 70.7%, 23.3% and 6% in the cases and 82%, 15.3% and 2.7% in the control women. We studied the C677T SNP as a trio combination for the thrombophilic variants, showing consistency with previous studies [78]. We found negative associations in all of the studied SNPs, and this was supported by previous studies of thrombophilia in the global population [41; 79; 80; 81; 82]. Overall, the negative association results indicate that there is no risk of the studied SNPs in human diseases [83; 84]. In general, a relationship was already established between thermophobia and obesity through endogenous factors [85]. In our study, thrombophilic SNPs did not affect women with obesity in either the FI cases or the control population. However, in a recent case-report study, a 48-year-old Saudi obese patient was diagnosed with a heterogenous mutation in the G20210A SNP [86]. Additionally, this patient was diagnosed with diabetes and dyslipidemia. In our study, none of the women were diagnosed with diabetes or dyslipidemia. The FI cases involved in this study were confirmed only with obesity and infertility. In this study, we selected a single SNP from a single gene for the 13 thrombophilic genes and we did not perform validation for our results, which can be considered the final limitations of this study. One of the advantages of this study is that we have recruited all the Saudi women and a clinician confirmed FI based on regular check-up visits.
## 3 Design, Implementation and Test of the DXN Engine
### DXN Tests Scenarios and Experiments  
The following three test scenarios are designed to test the following three algorithms we implemented inside DXN: (a) Network Load-Based Optimisation Algorithm, (b) Network Activity-Based Optimisation Algorithm and (c) Nodes Transmission Scheduling Optimisation Algorithm. Furthermore, for our testing, we used the following hardware equipment: a single GW, MultiConnect Conduit gateway (MTCDT-LEU1), and SNs, MultiConnect mDots (MTDOT-868) (Figure 4). LoRaWAN 1.1 was the LoRa protocol version used in all of our tests. The maximum number of nodes used in our latest test was 13 SNs as they were enough to be deployed as regular LoRa SNs, ENs, and MNs. Each node was set to send a packet at 5 min interval, and the bridging algorithm will take effect when a node fails to reach the GW after three attempts. The time intervals are chosen to reduce the months run time of the actual scenario to obtain the results within a short time. For implementing the AI models, we used BigML, an online AI platform [17]. The DNN model was implemented with the following configurations: three hidden layers (64 neurons each), Adam algorithm, ReLU activation function and the weights were set automatically by BigML. Finally, for the Time Series model, a separated dataset was used that is arranged sequentially. BigML models time series data as a combination of components: level, trend, seasonality and error. For each component, it will be modelled additively, multiplicatively or not included at all. The training time was set to 6 h (eligible to end earlier if no further improvements can be achieved).
# A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages
## 2 Materials and Methods
### Rmse  
For vibration signals, RMS value indicates the change in instantaneous signal amplitude within their sampling period, which can reflect their respective vibration energies. Meanwhile, information entropy represents the system complexity resulting from multiple uncertain factors [35]. The RMSE \(E_{RMS}\) is obtained by integrating the RMS into the information entropy, which combines the advantages of the two. Different fault types can be represented with different RMSE values [36].  
The computational procedure for the RMS entropy is as follows:  
Step 1: The RMS values of various _IMF_ components are computed following the sound pressure level conversion:  
\[R_{i}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}IMF_{i}^{2}(n)} \tag{5}\]  
where \(R_{i}\) denotes the RMS value of the \(i\)-th component and \(N\) is the number of sample points.  
Step2: The RMS values are constituted into an eigenvector A:  
\[A=[R_{1},R_{2},\cdots,R_{k}] \tag{6}\]  
Step3: The RMS values are homogenized:  
\[P=R_{1}+R_{2}+\cdots+R_{k},P_{i}=\frac{R_{i}}{P},\sum_{i=1}^{K}P_{i}=1 \tag{7}\]Step4: The RMSE can be derived from the definition of information entropy as:  
\[E_{RMS}=-\sum_{i=1}^{K}P_{i}\log_{2}P_{i} \tag{8}\]  
where \(K\) represents the number of IMF components; and \(E_{RMS}\) denotes the RMS entropy.
## 3 Results and Discussion
### Liquid Oxygen Compatibility of EP/BCI/BSEA Composites
#### 3.1.3 Section Morphology Analysis after the 98] Lot  
The SEM section morphology of samples and surface morphology with no reaction and incompatible reactions after liquid oxygen compatibility tests are presented in Figure 7. As exhibited in Figure 7a-c, the section morphology of EP/BCI/BSEA-2 and EP/BCI/BSEA-5 was obviously rougher than the section morphology of pure EP, indicating that the comprehensive mechanical properties of BSEA-modified EP composites were enhanced, and introducing BSEA could improve the mechanical properties of the EP matrix, which was helpful for inhibiting the occurrence of a sensitive reaction in 98] LOT. As exhibited in Figure 7d-f, the surface topography of pure EP and EP/BCI/BSEA-2 exhibited a fault  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Sample** & \multicolumn{4}{c}{**O1s**} & \multicolumn{4}{c}{**P2p**} \\ \cline{3-8}  & & & **C-O-C** & **C=O** & **PO\({}_{3}\)/P\({}_{2}\)O\({}_{5}\)** & **P=O** & **P-O-C** \\ \hline EP/BCI/BSEA-2 & Before & BE (eV) & 531.95 & 531.20 & — & — & — \\  & & Area (\%) & 41.56 & 58.44 & — & — & — \\  & After burning & BE (eV) & 532.25 & 531.50 & 134.60 & 133.00 & 132.00 \\  & & Area (\%) & 33.83 & 66.17 & 24.76 & 57.34 & 17.90 \\ EP/BCI/BSEA-5 & Before & BE (eV) & 531.80 & 531.25 & — & — & — \\  & & Area (\%) & 49.03 & 50.97 & — & — & — \\  & After no reaction & BE (eV) & 532.10 & 531.45 & 134.50 & 133.00 & 132.01 \\  & & Area (\%) & 35.08 & 64.92 & 24.46 & 44.43 & 31.11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: O1s and P2p peak fitting results of pure EP and BSEA-modified EP.  
Figure 6: Liquid oxygen compatibility mechanism of the BSEA-modified EP.  
zone with some crack propagation, but the fault zone in EP/BCI/BSEA-2 was narrower than that of pure EP. Moreover, the surface morphology of the EP/BCI/BSEA-5 sample presented an irregular collapse. The results indicated that EP/BCI/BSEA-2 had excellent impact resistance compared with pure EP and EP/BCI/BSEA-5 samples. Figure 7g shows that there were large amounts of ultrafine particles on the surface of the pure EP sample with the explosion reaction. These ultrafine particles were formed by the deposition of gaseous volatiles released or the incomplete combustion of carbon during the explosion. Figure 7h shows the flash sample of pure EP, which locally presented a similar morphology to the explosion sample. The burning sample [Figure 7i] of EP/BCI/BSEA-2 after the liquid oxygen compatibility impact test showed not only ultrafine particles, but some micropores on the external face of the EP/BCI/BSEA-2 sample. This is mostly due to the mechanism wherein BSEA could generate P-containing high-energy free radicals (PO, PO2- or HPO2- radicals) and could be decomposed to a non-flammable gas, such as CO2, N2, NO2 or NH3, during the process of burning [37]. These volatile gases break through the carbon layer and exert a flame-retardant effect to form pores, thus preventing further combustion of the EP matrix.
# Deep Learning-Based Estimation of Muckpile Fragmentation Using Simulated 3D Point Cloud Data
## 5 Verification of Particle Size Distribution Estimation
### Particle Size Distribution Estimation Using CG Data  
Using the training data created in Section 4, the particle size distribution estimation network was trained. For the training, 1000 sets of data consisting of 3D point cloud data and corresponding particle size distribution data as labels were used. Out of the 1000 sets, 800 were used for training, 100 for validation, and 100 for testing. The computer used was the supercomputer Cygnus [38] at the University of Tsukuba's Center for Computational Sciences, equipped with a CPU: Intel Xeon Gold 6126 Processor and four GPU: NVIDIA Tesla V100 32 GiB. The batch size for training was 16, the initial learning rate was \(1\times 10^{-5}\), the parameter update was done using the [PERSON] method [39], and the training was conducted for 2000 epochs. At this time, the radius \(r\) of the sphere when generating groups in the Local module was set to 0.15 so that the largest rock in the simulation would fit. During training, the point cloud data was randomly and slightly rotated and the points were slightly shifted for data augmentation.  
The results of performing group division using the FPS method with the input 3D point cloud XYZ coordinates are shown in Figure 15. Figure 15a,b show the results of group division in the Local module. The blue point cloud in Figure 15a represents the input XYZ coordinates of the muckpile fragmentation, and the red points indicate the selected centroids. Figure 15b shows the result of generating spheres with radius \(r\) centered on the selected centroids. From these results, it was confirmed that each centroid was spatially sampled evenly. In addition, Figure 15c shows the result of sampling in the Global module. The sampled point cloud retains the overall shape information of the muckpile fragmentation rather than the fine shape information. From these results, it was confirmed that the sampling in the Global module was also performed appropriately.  
The results of adding the features learned in the Global module to the particle size distribution scores obtained in the Local module and performing the training are shown in Figure 16. As shown in Figure 16a, the learning curve depicts an accuracy of 96.3% during the training phase. Figure 16b illustrates the transition of the loss function, which evaluates the magnitude of the difference between the estimated values and the correct values; the value of the loss function was recorded as 0.1237. It is essential to note that the stated 96.3% accuracy pertains specifically to the training data. We will further provide the exact estimation accuracy in subsequent sections to offer a comprehensive view of the model's overall performance. The results underline that the muckpile fragmentation particle size distribution estimation network utilized in this study demonstrates efficacy with the simulation-generated training data.  
Figure 15: Results of group partitioning using Farthest Point Sampling.
# A Rectangular Notch-Band UWB Antenna with Controllable Notched Bandwidth and Centre Frequency
## 2 Antenna Design
### Design of The UIWB Antenna with A Conventional Notch  
The notch characteristics are realized in UWB antennas to reject signals from unwanted bands using EBG structures. To achieve the notch characteristics in the UWB antenna (i.e., Antenna-2), a rectangular EBG was introduced on the back of the substrate, which is connected to the patch by a shorting pin, as shown in Figure 5 and designated as Antenna-3. Figure 6 shows a comparison of the \(|\text{S}_{11}|\) characteristics of Antenna-2 and Antenna-3. The antenna with the one optimized EBG achieved a conventional notch at the WLAN band (5.15-5.9 GHz) without degrading the overall bandwidth of the UWB antenna. Thus, a notch was successfully realized.  
Figure 4: \(|\text{S}_{11}|\) characteristics of Antenna-1 and Antenna-2.  
Figure 5: Design procedure to achieve conventional notched UWB patch antenna.  
Figure 6: \(|\text{S}_{11}|\) characteristics of Antenna-2 and Antenna-3.  
The optimized parameters of the Antenna-3: \(A=16\) mm, \(L=24\) mm, \(h=1.52\) mm, \(g=1\) mm, \(Lg=7\) mm, \(Lg=12\) mm, \(Sw=3.2\) mm, \(Wd=0.6\) mm, \(r=0.3\) mm, \(l_{1}=16\) mm, \(w_{1}=0.9\) mm, \(s=1.1\) mm, and \(p=0.2\) mm.
# Characterization of Pterygomaxillary Suture Morphology: A CBCT Study
## 2 Materials and Methods
### Measurement Method  
The data were stored in the Digital Image Communications in Medicine (DICOM) templates. The thickness of the slice used was 1 mm. The measurements were performed using Randi Ant Dicom Viewer software version 5.5 (Medixant, Poznan, Poland). The measurements were taken directly from the CBCT scans and included one angular and two linear parameters that characterize PMS morphology. All of the measurements were established on the axial sections of the maxilla separately for the right and left side of the patient. These measurements included the following:  
1. PMS thickness (mm): The maximal distance measured between the innermost points on the lateral concave and medial convex surfaces of the suture (Figure 3). The axial cross section in which the PMS thickness was greatest was evaluated separately for each side since the height of this section could vary slightly between the right and left sides of the same patient, depending on the anatomical differences between the sides.
2. PMS angularation (\({}^{\circ}\)): The angle created between the PMS thickness line and the midsagittal plane (Figure 3).
3. PMS height (mm): The suture height was calculated based on the number of axial sections counted from the suture initiation (the section where the maxillary lump connects to the pterygoid hamulus) and the suture termination (the last section before the suture is opened into a pterygomaxillary fissure) multiplied by the slice thickness (Figure 4).  
Figure 3: The method for measuring PMS thickness and angulation. The blue line denotes the midsagittal plane, the white line denotes PMS thickness measured from the most convex point on the lateral surface of the suture and the most medial point of the suture, and the \(\alpha\) angle represents the PMS angulation, measured between the midsagittal plane and the continuation of the PMS thickness line (the yellow dotted line).
## 3 Tax Fraud Modeling with Neural Networks
### Dimension Adjustment: Reduction of the Dimension According to the Main Components
#### 3.3.1 Multilayer Perceptron Network Model Estimation and Diagnosis Phase  
For this research, feedforward artificial neural networks have been utilized, with \(r\) inputs (the most significant variables or income tax return items which are liable to be targeted for fiscal fraud), a sole hidden layer composed of processing elements \(q\), and an output unit (fraud variable for indicating whether an individual has avoided tax or not). The network will make tax fraud modeling possible in the case of personal income tax returns and also permit assessment of an individual's propensity to fraudulent practices. Estimation of the Multilayer Perceptron model, as depicted in model (1), was carried out with the 11 main components obtained in the previous section as the input variables, while the mark variable served as the output variable. There are several advantages to using the main components as input variables in model (1). Firstly, the effect of the atypical values is eliminated since the component values have a far lower range than the initial variables. Given that the components are linear combinations of the initial variables, the effects of the atypical values are reduced. Secondly, data confidentiality increases since it is very difficult to identify individuals from the component values. Thirdly, multicollinearity problems in the model are eliminated because the components are uncorrelated. Fourthly, normality is induced in the model's variables due to the main components asymptotically normal behavior. Lastly, the predictive models with independent variables derived from a dimension reduction always adjust well, with a very favorable diagnosis. In relation to the results obtained during the learning phase, 70% of the data was allocated to the training phase, while the other 30% were used during the testing phase. As to the number of rows in the database, there were approximately 2,000,000 in total, out of which 1,350,974 went towards training and the remainder for testing. There was no missing data in the database. The hyperbolic tangent function was used as the activation function in the hidden layers. The activation function in the resulting layer is the Softmax function. Finally, a sole hidden layer was utilized in the network, with a prediction percentage of only 15.8 percent for incorrect predictions during the training phase, and the same percentage in the testing phase. Table 3 details the estimation results of the network's synaptic weights. The first seven columns of the said table estimate the synaptic weights of the input layer neurons regarding the hidden one (\(y_{j}\)), while the last two columns estimate the synaptic weights of the hidden layer neurons in relation to the output layer (\(\beta\)).
# Targeted Universal Adversarial Examples for Remote Sensing
## 1 Introduction
On the other hand, universal adversarial perturbations are not able to reveal the vulnerabilities of victim models completely since misleading the victim models is quite basic. Studying the class-wise vulnerabilities of models might also be helpful for understanding deep learning. To this end, we propose targeted universal adversarial examples (TUAE, see Figure 1a) to fool the victim models with prefixed targets, which is evidently more challenging and promising to give us more insights into deep neural networks. Compared to universal adversarial examples, we are able to mislead the victim models with targeted universal adversarial examples to make predictions as we expect. Moreover, the attackers might only be interested in some classes for specific reasons. Targeted universal adversarial examples, however, would affect the input images from all classes, which would raise the suspicions of people [22], Thus, we further propose source-targeted universal adversarial examples (STUAE, see Figure 1b), where we compromise the victim models to misclassify the images from the source class as a different target class. In the meanwhile, the victim models would classify other images, except source classes, correctly. The main contributions of this paper are summarized as follows. 1. To the best of our knowledge, we are the first to study targeted universal adversarial examples in remote sensing data, without accessing the victim models in evaluation. Compared to universal adversarial examples, our research helps reveal and understand the vulnerabilities of deep learning models in remote sensing applications at a fine-grained level. 2. We propose two variants of universal adversarial examples: targeted universal adversarial example(s) (TUAE) for all images and source-targeted universal adversarial example(s) (STUAE) for limited source images. 3. Extensive experiments with various deep learning models on three benchmark datasets of remote sensing showed strong attackability of our methods in the scene classification task. The rest of this paper is organized as follows. Section 2 reviews works related to this study. Section 3 elaborates on the proposed targeted universal adversarial attack methods with details. Section 4 presents the dataset information, experimental settings, implementation details, and summarizes the experimental results. Conclusions and other discussions are summarized in Section 5.
## 3 Methods
### Spiking-Softmax Method
#### 3.3.3 Loss of the Spiking-Softmax Method in Different Time Steps  
The time step K required by the SI-exp neuron not only affects the inference speed of the Spiking-Softmax method in SNNs but also correlates with the approximation quality of Spiking-Softmax. Since the internal parameters H(t), d(t), and T(t) of the SI-exp neuron are related to the time step K and need to be pretrained, a selection of 10,000 numbers as independent variables, evenly spaced in the interval [\(-\)1, 1], was chosen as training data, with their corresponding exponential values used as training labels. Then, by setting different time steps K, we trained the internal parameters of the SI-exp neuron for different K values, verifying the relationship between the time step K and the approximation quality of Spiking-Softmax.  
The essence of the trade-off between the size of K and the approximation quality of Spiking-Softmax is illustrated in Figure 12, with the specific effect reflected in the values of mean square error. By analyzing the trade-off between the delay caused by spiking neurons and accuracy, we found that the results were optimal when the time step K = 12, at which point Spiking-Softmax achieved the lowest MSE. Since the training range of the internal parameters of SI-exp neurons is [\(-\)1, 1], we computed the mean square error using random values in the range [\(-\)1, 1], independent of any specific dataset. Therefore, our Spiking-Softmax method exhibits extremely strong generalization. Typically, rate-based conversions can only simulate simple activation functions, such as ReLU. Additionally, a large number of time steps are usually required to achieve a sufficiently accurate approximation, leading to high power consumption. However, our proposed SIT-conversion based on the Spiking-Softmax method only requires 12 time steps to simulate the Softmax activation function, with an MSE of only 0.013991, making the error almost negligible.