esa-sceva/satcom-chunk-collection huggingface dataset:
# Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study
###### Abstract  
Phishing detection with high-performance accuracy and low computational complexity has always been a topic of great interest. New technologies have been developed to improve the phishing detection rate and reduce computational constraints in recent years. However, one solution is insufficient to address all problems caused by attackers in cyberspace. Therefore, the primary objective of this paper is to analyze the performance of various deep learning algorithms in detecting phishing activities. This analysis will help organizations or individuals select and adopt the proper solution according to their technological needs and specific applications' requirements to fight against phishing attacks. In this regard, an empirical study was conducted using four different deep learning algorithms, including deep neural network (DNN), convolutional neural network (CNN), Long Short-Term Memory (LSTM), and gated recurrent unit (GRU). To analyze the behaviors of these deep learning architectures, extensive experiments were carried out to examine the impact of parameter tuning on the performance accuracy of the deep learning models. In addition, various performance metrics were measured to evaluate the effectiveness and feasibility of DL models in detecting phishing activities. The results obtained from the experiments showed that no single DL algorithm achieved the best measures across all performance metrics. The empirical findings from this paper also manifest several issues and suggest future research directions related to deep learning in the phishing detection domain.  
Keywords:phishing detection; deep learning (DL); deep neural network (DNN); convolutional neural network (CNN); long short-term memory (LSTM); gated recurrent unit (GRU) +
Footnote †: journal: Applied Sciences
# Bridging User Perception and Stickiness in Business Microblog Contacts: A Moderated Mediation Model
## 5 Conclusions  
### Discussion  
This study presented a moderated mediation model to discuss the relationship between user perception and customer stickiness. First, this paper argues that emotional connection plays an important role in mediating the link between them. Second, this study proposes that adaptivity can moderate the mediating effect of emotional connection. The results showed that the relationship between perceived usefulness and customer stickiness can be mediated by emotional connection, but that mediating effect cannot be moderated by adaptivity. That is, when customers perceived more usefulness of the microblog, it can increase the feeling of emotional connection with other members, thereby resulting in increasing the customer stickiness of the microblog. However, adaptivity does not impact the mediating process. One possible reason for this result is that community members sense the usefulness of microblog for access of information that allows members to satisfy their needs, and that microblog offers a platform to develop their social connection, therefore strengthening their commitment to stay in the virtual community. Thus, the sense of community effect is aroused without adaptive systems to fit their needs [4]. Specifically, during this psychological mechanism, they could only devote little cognitive effort in absorbing what members receive from the microblog, and then they fall into the state of flow [29; 64]. As such, adaptivity is not necessarily applicable for those members who build the sense of community.  
Furthermore, the results showed that the relationship between perceived ease of use and customer stickiness is not mediated by emotional connection; however, after considering the moderating effect, the findings indicated that moderated mediation exists. That is, the mediating effect of emotional connection was contingent on adaptivity. Specifically, corporations determined that the ease of use of the system would not develop an emotional connection with online users, and consequently, would not attract users to stay longer. Due to time and capability limitations, large amounts of messages may cause information overload, make users confused, and lead to poor decisions. Adaptivity provides the solution to resolve these problems. Under high levels of adaptivity, users can access more precise and reliable information. In such a situation, when users sense an easy platform to operate, they may increase their involvement and interactions with other people, thereby developing an emotional connection that, in turn, strengthens their willingness to stay. Therefore, firms should notice that the ease of use of a microblog is a necessary, but not sufficient, condition. After incorporating the sufficient condition of adaptivity into the system, firms will attract users to stay longer and gain a competitive advantage.
# An Experimental Study of the Leakage Mechanism in Static Seals
## 2 Review the Leak Rate Prediction Model
### Leakage Process  
The leak rate of seal interface (equivalent to a one-layer porous medium) depends on the porosity, i.e., the ratio of the volume of percolation channels to total volume of interface. In this model, the percolation channels within the seal-contact interface are formed by pores. The porosity \(\epsilon\) of contact interface is calculated by geometric method is given by:  
\[\epsilon=\frac{L_{x}L_{y}d-V_{t1}+V_{t2}}{L_{x}L_{y}(d+l_{max,v})}, \tag{8}\]  
where \(V_{t1}\) and \(V_{t2}\) are the integrated volume of asperities and pores are obtained by integrating the volume and distribution function (Equation (3)) of the single asperity and pore, respectively. \(L_{x}L_{y}(d+l_{max,v})\) is the volume of the seal-contact interface, equalling the product of length \(L_{x}\), width \(L_{y}\) and height \((d+l_{max,v})\). In the same way, the volume between the reference plane and contact plane is given by \(L_{x}L_{y}d\).  
The pores-forming percolation channels within the seal-contact interface are considered to be a combination of filled tortuous capillaries with their diameter \(\lambda\) satisfying the fractal power law, based on the fractal theory of fluidic transport in porous media developed by [PERSON] [27; 28; 29; 30]. The fractal dimension \(D_{f}\) of tortuous capillaries given by the porosity \(\epsilon\). In addition, the permeability \(K_{v}\) is defined as:  
\[K_{v}=\mu V\left(-\frac{dx}{dp}\right)=\frac{\pi D_{f}\lambda_{max}^{4}}{128 \tau(4-D_{f})S}. \tag{9}\]  
The leak rate of ring gasket can be obtained by [18]:  
\[Q_{g}=\frac{2\pi K_{v}h(p_{0}-p_{t})}{\mu\ln\frac{\tau_{L}}{\tau_{0}}}, \tag{10}\]  
where \(p_{0}-p_{t}\) represent the fluid pressure difference.
# Simulation of Test Arch Based on Concrete Damage Plasticity Model and Damage Evolution Analysis
###### Abstract  
In light of the limited research on latent damages during the construction of large-span suspension arches, this study introduced a method to simulate structural damage utilizing random porosity. Initially, based on data from real-world engineering projects, the most susceptible areas within the arch structure were pinpointed. Subsequently, multiple test arch simulation models were constructed. Employing Python, commands for random porosity were implemented within ABAQUS and distinct mesh modules were devised to depict structures under varying degrees of damage. The current investigation delved into the structural responses of these susceptible areas under different damage rates, shedding light on damage progression patterns. Notably, our findings demonstrated that concealed damages on the top plate of the arch foot profoundly influenced structural integrity, whereas damages at the arch hance were comparatively minimal and predominantly manifest at the arch base. The pronounced localized damage at both the arch base and hance initiated and intensified at sectional corners, necessitating enhanced anti-crack measures in these regions. Moreover, depending on the stresses of the arch structure, diverse reinforcement strategies could be employed, optimizing the balance between load-bearing efficiency and cost considerations.  
Keywords:reinforced concrete arch bridge; concrete damage plasticity model; initial damage; damage evolution; structural failure +
Footnote †: journal: Applied Sciences
# Advanced Medical Image Segmentation Enhancement:
## 2 Related Works
### Optimization-Based Approaches  
Optimization-based methods have gained significant attention for advanced medical image segmentation enhancement [54; 55]. These approaches are designed to fine-tune and optimize segmentation algorithms [22], which aligns with the core objectives of our study. By leveraging optimization strategies, these methods hold the promise of enhancing the accuracy and efficiency of medical image segmentation [56; 57], which aligns with the central theme of our research: \"Advanced Medical Image Segmentation Enhancement (AMISE): The PSO-Based HE Approach\".  
Through a comprehensive exploration of optimization-based approaches conducted by various researchers, their relevance for enhancing the AMISE approach becomes evident. This review encompasses a broad spectrum of optimization strategies, including traditional optimization algorithms, metaheuristic approaches, and machine-learning-based optimization [58; 59; 60], all of which resonate with the innovative and advanced nature of our proposed method. These techniques aim to address the intricate challenges of medical image segmentation and to ultimately optimize algorithm parameters to improve the segmentation outcomes, and, by extension [24; 33], to advance the core concept of image enhancement that underpins our research.  
The significance of optimization-based approaches aligns with the primary objective of our study, which is to refine and advance medical image segmentation through the AMISE approach. By optimizing segmentation algorithms, these techniques contribute to the precise delineation of regions of interest within medical images [61]. Thus, they directly pertain to our research's fundamental goal of advancing medical image segmentation by integrating particle swarm optimization and histogram equalization and offer a valuable perspective into the strategies used to achieve this enhancement.
## 2 Methods
### Dataset  
This study employed the EyePACS dataset, encompassing 35,155 images, each classified according to the severity of diabetic retinopathy (DR), from level 0 (no retinopathy) to level 4 (proliferative retinopathy). For this research, a numerical system, 0, 1, 2, 3, 4, was utilized to denote the severity of DR, as reflected in the accompanying image data. Each grade represented a distinct stage of DR, allowing for an organized and efficient analysis of the disorder's progression.  
Level 0: No apparent retinopathy, 25,849 images.  
Level 1: Mild non-proliferative retinopathy, 2438 images: In the earliest stage of diabetic retinopathy, the walls of the blood vessels in the retina weaken. Tiny bulges protrude from the vessel walls, sometimes leaking fluid and blood into the retina. Nerve fibers in the retina may begin to swell, and central vision may be affected.  
Figure 1: An example of LeNet-1.  
Figure 2: An example of LeNet-5 [30].  
Level 2: Moderate non-proliferative retinopathy, 5288 images: As the disease progresses, blood vessels that nourish the retina may swell and distort. They may also lose their ability to transport blood. Both conditions cause characteristic changes to the appearance of the retina and may contribute to DME (diabetic macular edema).  
Level 3: Severe non-proliferative retinopathy, 872 images: Many more blood vessels are blocked, depriving blood supply to areas of the retina. These areas secrete growth factors that signal the retina to grow new blood vessels.  
Level 4: Proliferative retinopathy, 708 images: At this advanced stage, the signals sent by the retina trigger the growth of new blood vessels. This process is called neovascularization. However, these new blood vessels are abnormal and fragile. They grow along the retina and the surface of the clear gel that fills the inside of the eye. By themselves, these blood vessels do not cause symptoms or vision loss. However, they have thin, fragile walls. If they leak blood, severe vision loss and even blindness can result.
## 2 Basic Principles
### Pulse Radar  
While CW-based radar and its subclasses rely on moving targets to create micro-Doppler signatures, pulse radar is able to gather a range of information on non-moving targets, e.g., human postures, by applying short electromagnetic pulses. A modification that combines the principles of both CW and pulse radar is _pulse-Doppler radar_.  
In pulse radar, the RF signal is generated by turning on the emitter for a short period of time, switching to the receiver after turning off the emitter, and listening to the reflection. The measuring principle is based on the determination of the round-trip time of the RF signal, which has to meet specific requirements with regard to the maximum range and range resolution. These are determined by the _pulse repetition frequency (PRF)_, or alternatively, the interpulse period (_IPP_), and the _pulse width_ (\(\tau_{\rm p}\)), respectively. A variant of pulse radar is _Ultra-Wideband_ (UWB) radar, which is characterized by low-powered signals and very short pulse widths, which leads to a more precise range determination, although it has a drawback with regard to the _Signal-to-Noise Ratio_ (SNR).  
The reflected RF signals contain intercorrelated information about the target and its components, i.e., human limbs, as well as the surrounding environments, through scattering effects in conjunction with multipath propagation. Due to its high resolution, small changes in human postures create different measurable changes in the shape of the reflected signal. Using sequences of preprocessed pulse signatures, specific activities can be distinguished from each other and used as features in the setup of classification models.  
In [70], the authors developed and investigated a time-modulated UWB radar system to detect adult humans inside a building for security purposes. In contrast to static detection, Ref. [44] used bistatic UWB radar to collect data on eight coarse-grained activities for human activity classification. The data were collected at a center frequency of 4.7 GHz with a _resolution bandwidth_ (RBW) of 3.2 GHz and an RBF of 9.6 MHz, which were reduced in dimensionality by Principal Component Analysis (this is discussed in the next subsection) and used within a classification task based on a _Support Vector Machine_ (SVM) after a manual feature extraction using the histogram of principal components for a short time window.
## 2 Materials and Methods
### Finite Element Analysis
#### 2.2.3 Discussions  
Effect of Graphite on Sealing Ability  
On the basis of the previous study in relation with the cryogenic triple-offset butterfly valve with the laminated seal, which is installed in liquefied natural gas (LNG) marine engine, to control the flow  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Comparison Model**} & \multirow{2}{*}{**Graphite Thickness (mm)**} & \multicolumn{2}{c}{**Reduction of Peak Contact Stress**} \\ \cline{3-4}  & & **Slope Angle of 66.1\({}^{\circ}\)** & **Slope Angle of 86.1\({}^{\circ}\)** \\ \hline
1-layer & - & 65.6\%\(\downarrow\) & 66.5\%\(\downarrow\) \\ \hline \multirow{3}{*}{3-layer} & 0.6 & 42.6\%\(\downarrow\) & 48.6\%\(\downarrow\) \\  & 0.8 & 37.1\%\(\downarrow\) & 48.1\%\(\downarrow\) \\  & 1 & 22.0\%\(\downarrow\) & 42.2\%\(\downarrow\) \\  & 1.2 & 15.8\%\(\downarrow\) & 29.9\%\(\downarrow\) \\ \hline \multirow{3}{*}{5-layer} & 0.6 & 18.3\%\(\downarrow\) & 35.8\%\(\downarrow\) \\  & 0.8 & 16.7\%\(\downarrow\) & 22.0\%\(\downarrow\) \\ \cline{1-1}  & 1 & 14.4\%\(\downarrow\) & 16.0\%\(\downarrow\) \\ \cline{1-1}  & 1.2 & 7.3\%\(\downarrow\) & 12.7\%\(\downarrow\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of peak contact stress at the new seal with those at the 1-layer, 3-layer, and 5-layer models (350 \({}^{\circ}\)C).  
Figure 16: Contact stress distribution at stainless steel of the 7-layer model with graphite thickness of 0.8 mm.  
Figure 17: Contact stress distribution at graphite of the 7-layer model with graphite thickness of 0.8 mm.  
of liquid nitrogen (\(-196\)\({}^{\circ}\)C) to liquefy natural gas [25], the contact stress behaviors under the three different temperatures (\(-196\)\({}^{\circ}\)C, \(25\)\({}^{\circ}\)C, and \(350\)\({}^{\circ}\)C) were analyzed to discover the graphite effect at each temperature. In the stainless steel layer, the contact stress was increased at \(350\)\({}^{\circ}\)C as a result of undergoing thermal expansion, while in the graphite layer, the larger contact stresses were observed at \(350\)\({}^{\circ}\)C and \(-196\)\({}^{\circ}\)C rather than \(25\)\({}^{\circ}\)C, which indicates that the graphite is a favorable for sealing property at severe temperature. As more laminated layers of the graphite, the contact stresses at both graphite and stainless steel decrease at \(350\)\({}^{\circ}\)C, but opposite behavior was observed at \(-196\)\({}^{\circ}\)C. It is worth noticing that the graphite contributes improving driving performance of the disc at the high temperature and sealing ability at the cryogenic temperature, as shown in Table 8.
# Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance
## 4 Results and Discussion
### Performance of Dual-Frequency PPP  
Table 6 shows the parameter estimation method in dual-frequency quad-constellation PPP models. The first order of the ionospheric effect has been eliminated in the IF model by the observation combination. However, the combined ambiguities are not integers any more, and the combined observations have higher standard deviations. Similarly, the IF and UC approaches are equivalent as the variance-covariance matrix follows the law of covariance propagation and the ionosphere constrained approaches also leads to different results. Figure 6 provides an intuitive comparison of b1/b2 dual-frequency PPP with different schemes for GPS, GLONASS and GPS/Beidou/GLONASS/Galileo solutions at station SEYG on 3 September 2018. The value errors of the respective solutions were shifted by 0.4 m in order to avoid overlapping. The convergence criterion is defined when the component of the error is less than 0.1 m and stays within 0.1 m in the subsequent epochs. During the period 00:00 to 02:00 UTC, approximately 8-10 and 6-8 satellites can be seen at SEYG in each epoch for GPS-only and GLONASS-only PPP and the corresponding variation position dilution of precision (PDOP) is 1.5-2.6 and 1.7-3.0, respectively. The observed satellite number of quad-constellation PPP are between 27 and 32 and the PDOP values are below 1.0 and very stable. It is clear that the IF and UC approaches have almost the same performance at SEYG. The convergence time of ionosphere constraint GPS-only PPP is less than the UC approach thanks to the external ionospheric information at SEYG. The convergence time of ionosphere constrained GLONASS-only PPP is little longer compared with the UC solution, which is mainly caused by the existing IFBs in GLONASS though the pseudo-range observations are assigned a relatively lower weighting. Similarly, the IFBs of GLONASS can also explain why the convergence \begin{table}
\begin{tabular}{c c c c} \hline  & **IF** & **UC** & **Ionosphere Constraint** \\ \hline Observed quantity & 2m+2n+2p+2q & 4m+4n+4p+4q & 5m+5m+5p+5q \\ Parameter number & m+n+p+q+8 & 3m+3n+3p+3q+8 & 3m+3n+3p+3q+12 \\ Redundancy & m+n+p+q–8 & m+n+p+q–8 & 2m+2n+2p+2q–12 \\ Type of the parameters & Position, receiver clock, ZWD, ambiguity & Position, receiver clock, ZWD, slant ionospheric delay, ambiguity & Position, receiver clock, ZWD, DCB, slant \\  & & & delay, ambiguity & ionospheric delay, ambiguity \\ \hline \end{tabular}
\end{table} 
Table 6: Parameter estimation method in dual-frequency quad-constellation PPP schemes.
# Single Image Super-Resolution Method Based on an Improved Adversarial Generation Network
## 4 Experiments
### Implementation Details  
#### 4.2.1 Laplacian Pyramid Performance  
Decreasing by a factor of 1/2 from the original size to 1/4 size in the Laplacian pyramid framework, we obtain the LR images by using a down-sample operator on the HR images. A multiscale Gaussian pyramid G(\(l\)) = [\(l_{1}\), \(l_{2}\), \(l_{3}\)] has been built. As shown in Figure 4, we use a bicubic kernel (\(3\times 3\)) with an up-sampling operation to match the size of the upper layer. Specifically, we first up-sample the 1/4 size image by a factor of 2, and then connect it to the 1/2 size image. Then, we up-sample the 1/2 size with a \(\times 2\) factor to match the original-sized image. The up-sample operator on the small images (1/4 size, 1/2 size) are from Equation (2). All path inputs that have the same size are fused into the Laplacian pyramid image.  
As shown in Table 1, we design different multipath inputs as contrast experiments. We utilize the same parameter configuration to test the representative datasets on B100 and Urban100. The quantitative results have shown that the more input paths, the higher the PSNR. The extra size inputs can provide potential information for the Laplacian pyramid image. Therefore, our pre-trained module not only exploits down-top levels of robust information, but it also produces high-frequency samples for the generative model.  
Figure 4: The Laplacian pyramid framework for our pre-trained module, which contains three convolutional layers with \(3\times 3\) filter kernels, and two up-sampled operators by a factor of 2.