esa-sceva/satcom-chunk-collection huggingface dataset:
# Phishing Attacks Survey: Types, Vectors, and Technical Approaches
## 3 Phishing Methods and Techniques
### Phishing Technical Approaches
#### 3.3.12 Tab-Napping  
Tab-napping is a type of attack performed by phishers, scammers, and hackers. The term is derived from the words \"tab\" and \"kidnapping\", where tabs are individual webpages that can be opened simultaneously in one browser window.  
In this particular method of attack, attackers make use of the fact that potential victims have unattended tabs in their browser application while they are looking at other tabs. Using this as an opportunity to attack, malicious hackers attempt to redirect the victim's unattended webpages to malicious webpages or URLs without the user noticing. From these URLs, phishing attacks can then be performed to execute scripts and extract vulnerable data and information from the user.  
Let us consider an example scenario of a tab-napping attack. In this scenario, a victim logs into their Facebook account and enters their relevant details. When the person browses their feed, he sees an interesting link for something he might be interested in; he clicks on the link which then opens in another Table While the user is looking at this second tab, a script is executed without the user's knowledge, which redirects the previous tab (i.e., the tab logged into Facebook) to a fake Facebook login page. When the user returns to the first tab, he assumes he was logged out because the session timed out, so re-enters his details into the login form. The login form simply refreshes and/or redirects the user to his previous session, and he is unaware that the malicious attacker now has access to the victim's valid credentials. Using these credentials, the attacker now has access to the victim's account and can proceed to collect vulnerable data and information about the victim.  
The only means by which a general user can protect themselves from such an attack is to check the URL in the address bar of the browser and, if applicable, that the website is using the HTTPS protocol. If the URL appears suspicious for any reason, then the user should close the tab, and open a new tab and type the desired URL.  
Most web developers make use of use of the syntax [use target = \"_blank\"] to open links to a new Table This practice is vulnerable to attacks; although it opens a link in a new tab, as desired, the syntax also allows the opened page to access the initial page and change its URL because it makes use of the JavaScript \"windows.opener\" property. Therefore, a malicious hacker can make use of this syntax to execute code that instead opens a malicious URL in the initial page when the link is clicked; this code is \"windows.opener.location.replace\" (malicious URL). To prevent this from happening, the web developer should use \"nofollow noopener norefrer\" in conjunction with the earlier syntax.
# Gated Recurrent Attention for Multi-Style Speech Synthesis
## 4 Experiments and Results
### Decaying Guided Attention  
Guided attention has been proposed to make attention alignment \(\alpha_{n,t}\) closer to a straight line by utilizing prior knowledge that attention is mostly aligned close to a straight line in speech synthesis [14]. For the input sequence with length \(N\) and the output sequence with length \(T\), the guided attention loss \(\mathcal{L}_{ga}\) is computed by  
\[\mathcal{L}_{ga}=\mathop{\mathbb{E}}_{n,t}\left[\alpha_{n,t}W_{n,t}\right] \tag{17}\]  
where \(W_{n,t}\) is given as  
\[W_{n,t}=1-\exp\left(-\frac{\left(n/N-t/T\right)^{2}}{2g^{2}}\right). \tag{18}\]  
In [14], the constant \(g\) is set to 0.2. Adding guided attention loss to the training loss helps attentions to be quickly optimized in the early stage. However, in the latter stages of training, adding guided attention loss can disrupt the reconstruction of spectrogram, and the creation of natural prosody by forcing attention alignment close to a straight line. To avoid these problems, we added guided attention loss to the reconstruction loss only up to the first 5k training iterations then removed it from the loss. Before removing the guided attention, attention was aligned very close to a straight line, then after removal, attention alignment was excessively blurred or split between 10k to 50k iterations. Based on the assumption that this problem is due to the sudden disappearance of the strong constraints that have forced the attention alignment, we added decaying guided attention loss \(\mathcal{L}_{dga}\) to the Tacotron2 loss:  
\[\mathcal{L}_{total}=\mathcal{L}_{Taco2}+\mathcal{L}_{dga}, \tag{19}\]  
\[\mathcal{L}_{dga}=\begin{cases}\mathbb{E}\left[\frac{A\alpha_{n,t}W_{n,t}}{ \sqrt{iteration+1}}\right]&\text{if }iteration<=5000\\ 0&\text{if }iteration>5000\end{cases} \tag{20}\]  
where \(A=100\) and \(g=0.4\) in this work. The attention alignment plots during the early stage of training the Tacotron2-GST with guided attention and decaying guided attention can be found in the Appendix A. It seems that decaying guided attention loss helps the attentions to be trained stably than the original guided attention.
# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 2 Data and Methods  
The methodological diagram of this paper, as shown in Figure 1, is divided into three interconnected components. Part 1 focuses on the generation of a machine learning training dataset through multi-source data fusion. Specifically, Level 2 cloud products derived from VISSR (5 km resolution) and _SI_ of AMSU-A (45 km resolution) are integrated using the pixel fusion algorithm, producing a unified training and test dataset for subsequent model training.  
Part 2 outlines the training phase of the random forest algorithm. The fused dataset undergoes preprocessing to ensure compatibility and quality, followed by iterative training  
Figure 1: The methodological diagram of the _RF_SI_ precipitation.  
to optimize the model parameters. The output of this stage is a trained RF model capable of simulating the _RF_SI_, a critical metric for precipitation prediction.  
Part 3 details the validation and inference workflow. Independent validation data, including VISSR L2 cloud products, are fed into the trained RF model to generate simulated _RF_SI_ values. A threshold-based decision mechanism is applied, where _RF_SI_ values exceeding 30 trigger a \"Precipitation\" classification, while values below this threshold result in a \"No Precipitation\" outcome.
# Topology Comparison Study of Five-Phase Wound-Field Doubly Salient Fault Tolerant Generators
###### Abstract  
To present the characteristics of pole number and pole shape of the core, the five-phase wound-field doubly salient generators (WFDSGs) with symmetric phase inductance are studied and optimised in this paper, considering the split ratio, slot fill factor and core fringing effect. Based on the principle and structure of the five-phase WFDSGs, the winding induced electro-motive force under different number of poles is theoretically analysed. The constraints for parameter optimisation design including slot fill factor, split ratio and magnetic density characteristic are given. The finite element models of 30/24-pole and 20/16-pole WFDSG are established, and the comparative simulation analysis is carried out. It is pointed out that when the inner and outer diameters of the stator and rotor, the axial length and the maximum magnetic density are constant, the induction electromotive forces of the WFDSGs with different pole numbers and same phase coil number are same. Considering the pole fringing effect, the rotor pole equivalent width is the sum of the rotor pole actual width and 4 times of the air gap. The comparison experiments between the 30/24-pole and 20/16-pole WFDSGs were carried out, which verified the correctness of the theoretical analysis and finite element analysis (FEA).  
doubly salient electromagnetic machine; generator; fault-tolerant; multi-phase +
Footnote †: journal: Applied Sciences
# Integrating Decentralized Indoor Evacuation with Information Depositories in the Field
## 5 Results
### Success Rate
#### 5.1.3 Combined Depositories  
A combined depository scenario has also been simulated. In this scenario, 40 information depositories have been deployed, twenty of which have been placed at the same locations as DepotSt, and the other twenty depositories were placed at the same locations as DepotCr. Result shows that in the cases (eleven emergency cases) where DepotSt has saved more people than NoDepot and the two emergency cases where DepotCr has saved more people than NoDepot, a combined depository scenario also saved more people than NoDepot. In the two emergency cases where DepotSt has saved fewer people than NoDepot, a combined depository scenario also has saved fewer people (Table 3).  
Compared with DepotSt, a combined depository scenario shows advantages over NoDepot in three more emergency cases, which attributes to the contribution of the additional twenty depositories near corridors.
# A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR
###### Abstract  
The requirement for effective image search, which motivates the use of Content-Based Image Retrieval (CBIR) and the search of similar multimedia contents on the basis of user query, remains an open research problem for computer vision applications. The application domains for Bag of Visual Words (BoVW) based image representations are object recognition, image classification and content-based image analysis. Interest point detectors are quantized in the feature space and the final histogram or image signature do not retain any detail about co-occurrences of features in the 2D image space. This spatial information is crucial, as it adversely affects the performance of an image classification-based model. The most notable contribution in this context is Spatial Pyramid Matching (SPM), which captures the absolute spatial distribution of visual words. However, SPM is sensitive to image transformations such as rotation, flipping and translation. When images are not well-aligned, SPM may lose its discriminative power. This paper introduces a novel approach to encoding the relative spatial information for histogram-based representation of the BoVW model. This is established by computing the global geometric relationship between pairs of identical visual words with respect to the centroid of an image. The proposed research is evaluated by using five different datasets. Comprehensive experiments demonstrate the robustness of the proposed image representation as compared to the state-of-the-art methods in terms of precision and recall values.  
image analysis; image retrieval; spatial information; image classification; computer vision Article
# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
## 5 Results
### Experimental Results  
We present the results for our proposed model based on the conducted experiments that differed in terms of the training approach, as explained in Section 4.3. In Table 7, we summarize the results obtained from the experiments using the four-performance metrics: accuracy, F1-score, recall, and precision [39]. We provide the obtained results of the five-fold cross-validation experiment. Typically, in this type of experiment, the overall performance of the model is evaluated by utilizing the resulting average for each of the performance metrics across the five folds. Moreover, we show the results of the three experiments that used the train/test split approach.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters.** & **Optimal Value** \\ \hline Number of neurons in BiLSTM & 32 \\ Dropout rate & 0.5 \\ Optimizer & ADAM \\ Learning rate & 0.0001 \\ Loss function & Binary\_crossentropy \\ Activation function & Sigmoid \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimal values of the parameters.  
As shown in Table 8, the proposed model obtained good accuracy values in all experiments, ranging from 84% to 86%. We note that the highest accuracy, which equaled 86%, occurred in the train/test split experiment with 80% for the training set and 20% for the testing set. In the five-fold cross-validation experiment, the result of the F1-score measure was 83%, while in the other three experiments, it maintained a value equal to 84%. The resulting values of the precision measure were either 83% or 84% in all four experiments. Furthermore, the resulting values of the recall measure were 82% in the five-fold cross-validation experiment. However, in the three train/test split experiments, the recall measure values were 86%, 85%, and 87%.
# Can AI Automatically Assess Scan Quality of Hip Ultrasound?
## 2 Materials and Methods
### Model Development  
We used a 3D CNN consisting of convolutional and fully connected layers to predict scan quality. The optimal architecture for the network (as shown in Figure 2) was dynamically determined based on accuracy of classification on the validation set. Like the C3D model, we used a homogenous kernel size of 3 \(\times\) 3 \(\times\) 3 in all layers. To minimize the memory requirement in training, we resized the original 800 \(\times\) 600 \(\times\) 256 image to a 32 \(\times\) 32 \(\times\) 32 tensor. Since the input tensor is already much smaller compared to the original image, we applied max pooling only in the slice dimension, i.e., a pooling ratio of 1 \(\times\) 1 \(\times\) 2. Hence, the height and width of each slice was maintained at 32 \(\times\) 32 pixels. We used a stride size of 1 in all convolutional layers and used 16, 32 and 64 kernels in the convolutional layers conv1, conv2 and conv3, respectively. The model was trained for 100 epochs to minimize a categorical cross entropy loss optimized using rmsprop at learning rate lr = 0.001. To prevent the model from overfitting, we used 20% dropout in the fully connected layers.  
Labels for training the network were obtained from the manual scoring on 1548 other 3 DUS scans excluded from the test set by one reader ([PERSON] with 4 years of experience in hip ultrasound image analysis). Factors such as the straightness of the ilium, visibility of labrum, os ischium and femoral head and presence of movement artifacts were considered while scoring the images. These labeled images were divided into the training (70%, 1083 images, 651 high-quality images, 432 low-quality images) and validation set (30%, 465 images, 300 high-quality images, 165 low-quality images).  
In our dataset, we had a relatively low number of poor-quality images. This is typical of ultrasound scanning taking place in research settings where scans are acquired by well-trained sonographers. We tried to address this issue by assigning a higher weight (0.65 vs. 0.45) for misclassified low-quality scans.
## 2 Materials and Methods
### Data Analysis
#### 2.4.3 Modelling the Phenology of Sap Flow  
[PERSON] et al. [39] described the seasonal dynamics of sap flow in temperate deciduous tree species using the following empirical function:  
\[\text{SFD}=\text{FLAI-}(a\text{-}R_{\text{s}}+b\text{-}\text{VPD}) \tag{5}\]  
where FLAI is the normalized leaf area index (LAI), \(R_{\text{s}}\) is the solar radiation (MJ m\({}^{-2}\) day\({}^{-1}\)), VPD is the vapor pressure deficit (hPa), and \(a\) and \(b\) are coefficients. To evaluate the potential of the leaf phenological metrics for predicting sap flow phenology, we replaced FLAI of Equation (5) with normalized leaf phenological metrics (PM\({}_{\text{norm}}\)), and adapted the hierarchical Bayesian framework as follows:  
\[\text{SFD}_{\text{day}\text{t}}=\text{PM}_{\text{norm}\text{t}}\text{-}(a_{ \text{t}}\text{-}R_{\text{s}\text{,day}}+b_{\text{t}}\text{-}\text{VPD}_{ \text{day}}) \tag{6}\]  
where \(R_{\text{s}\text{,day}}\) and VPD\({}_{\text{day}}\) are the daily total \(R_{\text{s}}\) and the daytime mean VPD, respectively. The subscript of each variable and coefficient, i.e., t, refers to an individual tree. Additionally, to determine the phenological transition dates of sap flow, we estimated the seasonal dynamics of PM\({}_{\text{norm}}\) using the double logistic model given by [PERSON] et al. [40]:  
\[\text{PM}_{\text{norm}\text{t}}=c_{1\text{t}}/[1+\exp\left\{-(T_{\text{t}}-T_{ \text{1t}})/d_{1\text{t}}]\right\}^{\text{e}1\text{t}}-c_{2\text{t}}/[1+\exp \left\{-(T_{\text{t}}-T_{\text{2t}})/d_{2\text{t}}]\right\}^{2\text{t}} \tag{7}\]  
where \(T\) is the day of the year, and \(c_{1}\), \(c_{2}\), \(d_{1}\), \(d_{2}\), \(e_{1}\), \(e_{2}\), \(T_{1}\), and \(T_{2}\) are coefficients. Because PM\({}_{\text{norm}}\) is estimated from the measured SFD\({}_{\text{day}}\) and environmental factors using Equations (6) and (7), we referred to it as optimized SFD\({}_{\text{opt}}\)). The SFD\({}_{\text{day}}\) values estimated by SFD\({}_{\text{opt}}\) were closely correlated with the measured values across all individuals (\(r^{2}\) = 0.89 and root-mean-square error, RMSE = 0.093 for all pooled data, data not shown).  
All coefficients were interconnected in this model framework and obtained as probability distributions (i.e., posterior distributions) at the individual tree level. Sampling from the posterior distributions of all parameters was performed using the Markov chain Monte Carlo (MCMC) method. We ran 20,000 iterations for each of the three independent MCMC chains after a warm-up of 12,000 iterations, with the chains thinned every five iterations to yield a posterior sample size of 4800. The convergence of the Markov chains for each parameter was checked using R-hat values (<1.1) by comparing the variance within and among chains. The Bayesian framework and related analyses were run in R version 4.1.2 using the 'rstan' package [41].
# A Delay-Aware and Reliable Data Aggregation for Cyber-Physical Sensing
## 3 The System Model and Problem Statement
### The Data Aggregation Model  
In the data collection process, an intermediate or collecting node can collect information from other sensor nodes. Then, it aggregates them into a unit of information, i.e., a packet, and then forwards the computed and aggregated packet to the next hop or to the sink. In this paper, data aggregation refers to the situation in which several data packets meet at a node in the routing procedure and they are aggregated into one new data packet. The new data packet has the same size with the original data packet.  
The lossless step-by-step multi-hop aggregation model is adopted to do data aggregation [2]. In the data aggregation model, the aggregation of node \(i\) with its \(\chi\) multiple inputs is implemented in an orderly way, that is, receiving data is aggregated with current data according to the arrival order. Let \(v_{i}\) represent the source data packet of node \(i\), and \(\theta(i,j)\) represents the intermediate temporary aggregation result of node \(i\) and node \(j\). For simplification, \(\theta_{i}\) denotes the current temporary aggregation result of node \(i\). We apply \(\psi_{i}\) to denote the final aggregation result of node \(i\) with all receiving nodes' data and its own data.  
When data \(\theta_{j}\) from node \(j\) is transmitted to node \(i\), node \(i\) aggregates \(\vartheta_{j}\) with its own data (may be the origin data \(v_{i}\) or the intermediate temporary data \(\theta_{i}\)). If current data packet of node \(i\) is the origin data \(v_{i}\), and data from \(j\) is also origin data \(\vartheta_{j}=v_{j}\), which means that the data to be aggregated is both origin source data, the aggregation formula is:  
\[\vartheta(i,j)=\epsilon\max(v_{i},v_{j})+(1-\epsilon)\min(v_{i},v_{j}) \tag{1}\]  
where, min and max denote the class of general minimum and maximum computation functions. In Formula (1), \(\epsilon\) is the correlation coefficient and the value is between 0 and 1. When \(\epsilon=1\) and \(\epsilon=0\), it respectively represents the class of general maximum and minimum computation functions. When doing aggregation, if any one of to be aggregated data is not origin source data, the aggregation formula is as the follows:  
\[\vartheta(\vartheta_{i},\psi_{j})=\mu\max(\vartheta_{i},\psi_{j})+(1-\mu) \min(\vartheta_{i},\psi_{j}) \tag{2}\]  
In Formula (2), \(\mu\) denotes the impact factor and it is a decimal not bigger than 1 (e.g., \(\mu=0.8\)). Min and max denote the class of general minimum and maximum computation functions. \(\vartheta_{i}\) and \(\psi_{j}\) respectively refer to the intermediate temporary aggregation result of node \(i\) and final aggregation result of child node \(j\). There is at least one non-origin data packet in \(\vartheta_{i}\) and \(\psi_{j}\).
## 3 Methodology
### Quality of Sensing of Device--\(M_{qs}\)  
\(M_{qs}\) represents the quality of sensing in each device. The \(M_{qs}\) can be calculated as follows.  
\[M_{qs}=N_{sd}\;*\;Q_{s} \tag{5}\]  
where \(N_{sd}\) is the number of sensing devices and \(Q_{s}\) is the quality of each sensing element.  
Function 1 explains the measurement of Quality of Information (QOI) based on the different threshold values of parameters such as \(R_{bd}\), \(T_{ri}\), \(T_{rt}\), and \(M_{qs}\). Here, the QOI acquires three different possible values, i.e., high, medium, and low. Function 2 categorizes the mobile users who participated in crowd sensing based on the QOI. Additionally, the QOI is categorize into three category such as \(M_{u1}\), \(M_{u2}\), and \(M_{u3}\) and assigned as high, medium, and low, respectively.  
```
Inputs:\(R_{bd}\), \(T_{ri}\), \(T_{rt}\) and \(M_{qs}\) fornode \(i\) to \(n\)do  calculate \(R_{bd}\),  calculate \(T_{ri}\),  calculate \(T_{rt}\)  calculate \(M_{qs}\) endfor fornode \(i\) to \(n\)do if(\(R_{bd}\) > 1 && \(T_{ri}\) > 1 && \(T_{rt}\) > 1 && \(M_{qs}\) > 1) \(\text{QOI}\) = high; elseif(\(R_{bd}\) == 1 && \(T_{ri}\) == 1 && \(T_{rt}\) == 1 && \(M_{qs}\) == 1) \(\text{QOI}\) = medium; elseif(\(R_{bd}\) < 1 && \(T_{ri}\) < 1 && \(T_{rt}\) < 1 && \(M_{qs}\) < 1) \(\text{QOI}\) = low; endif endif endif endfor
```  
**Function 2:**_Category_Mobile_users 0_  
```
Inputs:QOI for each node \(i\) to \(n\)do if(\(QOI\) > high)  Insert \(i\) to \(\{M_{u1}\}\) elseif(\(QOI\) == medium)  Insert \(i\) to \(\{M_{u2}\}\) elseif(\(QOI\) < low)  Insert \(i\) to \(\{M_{u3}\}\) endif endif endif endfor
```  
**Function 3:**_Category_Mobile_users 0_  
In Algorithm 1, the QACM algorithm describes the entire process of crowd sensing that explains the deployment of the sensing nodes involved in crowd sensing. Once the deployment is over, the QOI measurement function decides the quality of information based on different parameters (mentioned in Function 1). Then, categorization is done using the Categorization function (mentioned in Algorithm 1). There are three categories: \(M_{u1}\), \(M_{u2}\), and \(M_{u3}\) assigned to high-, medium-, and low-range sensitivity requirement applications.  
```
Input: Mobile users participate in crowd sensing no-N nodes Step 1: Random Node Deployment Step 2: Sensing the random events by deployed Nodes N Step 3: QOI_Measurement () Step 4: Category_Mobile_users () Step 5: (Assign Different categories of mobile sensing group based on data sensitivity level requirements of applications.) for Category \(M_{ui}\)\(i\) to 3 do if(\(i=1\))  Assign Category \(M_{ui}\) for high Sensitive Applications: else if(\(i=2\))  Assign Category \(M_{ui}\) for Medium Sensitive range Applications: else if(\(i=3\))  Assign Category \(M_{ui}\) for Low range Sensitive Applications: endif endif endif endfor
```  
**Algorithm 1**_QACM Algorithm_