esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Delay Inter-Packet Coding in Vehicular Networks
## 3 Packet Recovering Codes
### Nonbinary Convolutional Codes
#### 3.2.1 Code Description and Error-Correcting Properties
Now, consider erasure patterns of weight four whose erasures are located in different subblocks. According to the listed above code properties, only combination \(\mathbf{v}=(2,2,0,0...)\) requires an additional analysis. The corresponding equations with respect to the unknowns \((z_{1},z_{2},z_{3},z_{4})\) are determined by the parity-check matrix  
\[ \begin{pmatrix}1&1&0&0\\ \alpha^{i}&\alpha^{i\prime}&1&1\\ \alpha^{2i}&\alpha^{2i\prime}&\alpha^{j}&\alpha^{i\prime}\\ 0&0&\alpha^{2i}&\alpha^{2j\prime}\\ \end{pmatrix}  \tag{13}\]  
The determinant of Equation (13) is  
\[\Delta=(\alpha^{i}+\alpha^{i\prime})(\alpha^{j}+\alpha^{i\prime})(\alpha^{i}+ \alpha^{i\prime}+\alpha^{j+j\prime})\]  
The equality \(\Delta=0\) is equivalent to the equality  
\[\alpha^{i}+\alpha^{i\prime}+\alpha^{j+j\prime}=0, \tag{14}\]  
and never happens if \(c>2n\) since \(\alpha^{j+j\prime},j+j<2n<c\) is a basis field element and, consequently, it has binary representation of Hamming weight 1. Then, the Hamming weight of the binary representation of the LHS of Equation (14) is equal to either 1 or 3. Therefore, determinant of the system is nonzero and there are no nontrivial solutions of weight less or equal to four. More detailed analysis for the case \(n=4\) allows computing the generating function of weights of unrecoverable erasure patterns as a function of formal variable \(D\)  
\[f(D)=\frac{a(D)}{b(D)},\]where \(a(D)\) and \(b(D)\) are polynomials of degree 20 and 16, respectively. Probability of packet loss after decoding can be expressed via \(f(D)\) as follows  
\[P_{e}=\frac{1}{n}f(D)|_{D=\epsilon}, \tag{15}\]  
where \(\epsilon\) is the channel erasure probability. The series expansion of Equation (15) is  
\[P_{e}=\frac{1}{n}\sum_{i=d_{\text{free}}}^{\infty}f_{i}\epsilon^{i}\, \tag{16}\]  
where \(f_{i}\) denotes the \(i\)th coefficient in series expansion of \(f(D)\), that is, the number of uncorrectable erasure patterns of weight \(i\). It was found numerically that for \(\epsilon\leq 1/4\) the first eight coefficients are enough to compute \(P_{e}\) with high precision. The first coefficients \(f_{i}\) in Equation (16) are given in Table 2.  
In what follows, Equation (16), which does not take into account the decoding delay and computed with coefficients from Table 2, is interpreted as the lower bound on the packet loss probability for the RS convolutional codes on the BEC.
# Multimodel Phishing URL Detection Using LSTM, Bidirectional LSTM, and GRU Models
## 3 Proposed Models
### Proposed Model II: Bidirectional Long Short-Term Memory (Bi-LSTM)  
In a Bi-LSTM network, the information flows in two directions through the backward and forward layers [30], whereas in regular long-short-term memory, there is only one possible way of information flow, either using a backward or forward layer. In the Bi-LSTM model, the output layer can obtain information from the past and future states simultaneously. The general architecture of the Bi-LSTM is shown in Figure 3[32; 33]. See Algorithm 2.  
\[A_{t}^{f}\ =\ \tanh\left(W_{xA}^{f}x_{t}+W_{AA}^{f}A_{t-1}^{f}+b_{A}^{f}\right) \tag{8}\]  
\[A_{t}^{b}\ =\ \tanh\left(W_{xA}^{b}x_{t}+W_{AA}^{b}A_{t+1}^{b}+b_{A}^{b}\right) \tag{9}\]\[y_{t}=W_{dy}^{f}A_{t}^{f}+W_{Ay}^{b}A_{t}^{b}+b_{y} \tag{10}\]  
where \(A_{t}^{f}=\) forward-layer output sequence, \(A_{t}^{b}=\) backward-layer output sequence, \(y_{t}=\) output vector, \(\sigma=\) activation function used to merge \(A_{t}^{f}\) and \(A_{t}^{b}\), \(W_{f}=\) weight matrix, and \(b_{f}=\) bias.  
```
Input : URL \(\{x_{1},x_{2},x_{3}\dots\ x_{L}\}\), \(L=\) length of URL Output : Phishing or legitimate (\(\gamma\)) Step 1 : Begin Step 2 : For \(t=\) I to \(L\) do  Compute forward layer output sequence \(A_{t}^{f}\) Equation (8) Step 3 : End for Step 4 : For \(t=\) L to 1 do  Compute backward layer output sequence \(A_{t}^{b}\) Equation (9) Step 5 : End for Step 6 : Obtain Y by merging \(A_{t}^{f}\) and \(W_{t}^{b}\) using sigmoid activation function Step 7 : End
```  
**Algorithm 2** Bi-LSTM for phishing URL detection
# The AI-Powered Evolution of Big Data
## 4 Materials and Methods
### Big D -- The Big Data Analyzer
Big D interprets diverse user inputs better than other models by adopting a more adaptable query structure and incorporating advanced text preprocessing techniques (including tokenization, stop word removal, and lemmatization). These improvements ensure more precise responses, enabling the bot to manage complex and varied queries effectively. Three-dimensional visualizations of tokenization are created by generating vector embeddings for each token and reducing them to three dimensions for the purpose of plotting. Such a zoomable plot enhances the analytical depth of Big D by providing a three-dimensional perspective on token relationships within text data. Utilizing PCA for dimensionality reduction, Big D visualizes token embeddings, helping to uncover patterns and clusters in the data. Such insights are invaluable for recognizing trends and themes not immediately apparent in the raw text. The visualization discussed effectively demonstrates word structures and relationships, showing how terms are grouped by usage and context. A series of advanced text preprocessing steps were implemented to enhance Big D's ability to analyze and understand text data. These steps refine the input data to ensure that subsequent analysis is accurate and meaningful. The new text preprocessing pipeline includes several key improvements:

All text was converted to lowercase for consistency. Punctuation marks were removed to eliminate noise. The text was tokenized, i.e., broken down into words or tokens. Tokenization is a fundamental aspect of natural language processing (NLP), and the quality of this process as well as prediction results significantly vary based on the methods used for it. We followed the commonly used practice of removing stop words\(-\)the most frequently words used in every English text, like \"the\" and \"and\"\(-\)that do not contribute to the meaning. Words are reduced to their base or root form through the process of lemmatization, which is also common. It helps to normalize the text by converting different forms of a
## 5 Conclusions  
The method presented in this paper offers a simplified and transparent approach for producing future land cover scenario maps. Our process contains important stakeholder involvement that was integral to identifying and prioritizing the factors that drove new urban growth in the model. Our main objective was to develop a straightforward process that was easy to communicate. We make several observations describing the degree of success we obtained in our study.  
1. Although simple, our land cover projection requires the use of a quantitative GIS process to actually produce the maps, so it still faces the same issues of other scenario modeling efforts when translating qualitative data to quantitative.
2. Keeping the stakeholder consultation limited is advantageous, because it allows researchers to model future land cover under manageable time and effort constraints with widely available GIS data. Our land cover scenarios represent a gradient of potential realities based on the same storyline, and researchers still needed to make assumptions and set some of the parameters themselves.
3. The stakeholder consultation led to place specific analysis (e.g., different growth rates for urban _vs._ rural areas). The land use regulatory system is unique to Oregon, and the spatial data based on it encapsulates a great deal of external analysis that made our process faster. This highlights the potential difficulty in generalizing scenario development frameworks that facilitate reproducibility [15].  
Ultimately, we acquired an answer to our pressing research question of where will new urban growth be placed and how much of it will there be. We conclude that developed land will consume portions of the metropolitan fringe, and its amount will be determined by how much population will be present in the area by 2050. The case study points out that a relatively simple GIS-based modeling process is possible given available data, but this also leads to sacrificing some complex dynamic processes of land cover change. Therefore, this effort represents an initial step in modeling land cover in our study area, and further modifications and refinements to the participatory framework and to the model itself are warranted.
## 5 Conclusions  
In this study, vibration measurements were conducted on buildings adjacent to and on the track to validate the developed finite element model. The model was subsequently employed to predict vehicle-induced vibration responses under various operational conditions, thereby elucidating vibration variations during different construction stages and discussing relevant foundation effects and building effects induced by soil-structure interaction. The following main conclusions were drawn:  
1. Soil vibrations induced by train operations exhibit greater magnitudes during the site preparation stage in the free field compared to other construction phases, suggesting that existing structures have a mitigating effect on soil vibrations.
2. Pile foundation construction can effectively mitigate soil vibration to a significant extent; the transfer function of the soil and pile is minimally affected by the construction of pile foundations in comparison to the construction of platforms and buildings.
3. In the range of 50-80 Hz, higher soil-structure transfer coefficients in the far field compared to those near the vibration source indicate buildings attenuate high-frequency vibrations.  
Figure 15: Soil-column coupling transfer function. (**a**) Platform construction; (**b**) building construction.  
During platform construction, there is a more pronounced resonance effect between the buildings and soil at 10 Hz. The coupling loss of vibration energy from the platform construction is higher than that from the building construction above 20 Hz. These findings contribute to a deeper comprehension of the influence exerted by foundations and building structures on the propagation of ground vibrations, thereby offering valuable insights for designers in determining the optimal location and layout of building structures, as well as serving as a reference for implementing measures aimed at reducing vibrations.  
Conceptualization, C.Z.; methodology, J.H. and C.Z.; validation, J.H., C.Z. and Y.C.; investigation, J.H., Y.C., L.H., J.W., C.L. and Z.L.; resources, L.H., J.W., C.L. and Z.L.; data curation, J.H., C.Z. and Y.C.; writing--original draft preparation, J.H.; writing--review and editing, C.Z., Y.C. and Z.T.; visualization, L.H.; project administration, C.Z. and Z.T.; funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51908139.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors would like to give thanks to [PERSON] and [PERSON] for collaborating on the vibration measurements.  
Author [PERSON] was employed by the company China Academy of Railway Science (Shenzhen) Research and Design Institute Co., Ltd. Author [PERSON] was employed by the company Guangzhou Urban Planning & Design Survey Research Institute Co., Ltd. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 6 The Proposed ECPSO-MTP for Image Segmentation  
In this section, comparison experiments with accuracy that were conducted on some test images are introduced to further investigate the clustering performance of the proposed ECPSO-MTP. Eight images from the public image segmentation dataset of BSDS500 are employed as test images for clustering approaches, and three compared approaches, including classic PSO, K-means, and spectral clustering (SC), are adopted to demonstrate the effectiveness of the proposed ECPSO-MTP.
# Optimization of Distribution Network Current Protection for Inverter-Based Distributed Power Access
## 5 Current Protection Optimization Enhancement Program  
The setting value \(I_{set-c}\) of the conventional current interruption protection is set according to the current flowing \(I_{\text{Fmax}}\) through the protection during a three-phase short circuit at the end of the line in the case of avoidance of the maximum mode of operation, i.e., the following equation:  
\[I_{set-c}=K_{rel}I_{\text{Fmax}}=\frac{K_{k}E}{Z_{\text{Smin}}+\alpha Z_{L}} \tag{13}\]  
where E is the phase electromotive force of the equivalent power supply of the system; \(Z_{\text{Smin}}\) is the minimum impedance between the protection installation to the equivalent power supply of the system; \(Z_{L}\) is the impedance of the protected line; and \(K_{rel}\) is the reliability coefficient, which is generally taken as 1.2-1.3. Set in the line \(\alpha\)L at the occurrence of a short circuit fault (\(0\leq\alpha\leq 1\), L is the length of the line), the short circuit current is as follows:  
\[I_{F}=\frac{K_{k}E}{Z_{S}+\alpha Z_{L}} \tag{14}\]  
where \(Z_{S}\) is the actual impedance between the protection installation and the equivalent power supply of the system, and \(K_{k}\) is the short circuit type coefficient, which is taken as 1 in the case of a three-phase short circuit and \(3/2\) in the case of a two-phase short circuit. The actual protection range \(\alpha\) can be obtained as follows:  
\[\alpha=\frac{K_{k}(Z_{\text{Smin}}+Z_{L})-K_{rel}Z_{S}}{K_{rel}Z_{L}} \tag{15}\]  
\[\alpha_{\text{max}}=\frac{1}{K_{rel}}-\frac{(K_{rel}-1)Z_{\text{Smin}}}{K_{rel} Z_{L}}<1 \tag{16}\]  
Further observation of the formula reveals that the level of protection sensitivity is insufficient to safeguard the entire length of the line. It is therefore evident that more sophisticated methodologies must be put forth to enhance the sensitivity.  
In order to adjust the action setting value in real time and enhance the protection sensitivity, the method proposed in this paper employs the continuous monitoring of real-time load current fluctuations, identifies the RMS value of the fundamental wave of Protection 1, and incorporates an adaptive threshold value. This protection method has the potential to significantly enhance protection sensitivity by enabling real-time adjustments to the action setting value.  
The sole parameter to be acquired in real time is the root mean square (RMS) value of the fundamental wave of the line current at the current protection, with the remaining parameters predetermined. The following section outlines the methodology for enhancing the sensitivity of adaptive current protection for distribution grids with distributed power access [16].
# Experimental Evaluation of Display Field Communication Based on Machine Learning and Modern Design
## 5 Experimental Evaluation
### Performance According to Distance, Camera Angle, and Image Rotation
Therefore, we can conclude that capturing the display as a whole is an important factor in DFC. As a result, enough standoff distance, which makes the camera acquire the whole display area, is required.
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 2 Study Area and Data
### Leaf Area Index Satellite Products
#### 2.2.6 GLASS LAI  
The GLASS LAI product at 8-day and 500 m spatial resolution is produced from MOD09A1 8-day surface reflectance data [32]. The GLASS LAI is estimated using a general regression neural network (GRNN). Firstly, the effective CYCLOPES LAI is converted to true LAI using the clumping index [52]. The true LAI is then combined with the MODIS LAI through a weighted linear combination according to their uncertainties determined from the ground-measured true LAI. The original MODIS reflectance data (MOD09A1) are reprocessed to remove cloud contaminated pixels and fill the missing data to obtain continuous and smooth reflectances using the algorithm proposed by [PERSON] et al. [53]. GRNN is trained using the combined LAI and the reprocessed MODIS reflectance data for each biome type over the BELMANIP sites [54]. Finally, the trained GRNN is applied to retrieve LAI from the yearly MOD09A1 surface reflectance product.
# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
## 5 Result and Analysis
### Storage Cost  
As the generated packets are low in size, they take less storage and ultimately reduce storage costs. The lower storage cost in the scheme is because EE-FDAS has generated more compacted data packets. For instance, if 15 packets are transmitted in 60 min, its rate will be 1 packet per 4 min. If the data is transmitted as 1 packet per 3 min, 20 packets are transmitted in 60 min. Similarly, if the rate is 1 packet per 2 min, then 30 packets are sent in 60 min. Similarly, for 1 packet per minute, the transmission rate is 60 packets per 60 min. In case of emergency when the packet is transmitted after every 30 s, it shows 120 packets per hour. For calculating the data size as per the number of parameters sent, it is checked that how many values are 16-bit and how many are 32-bit, Packet Size = sum + header size then their sum and data packets header size provides packet size. The X-axis shows the number of packets per unit of time while the y-axis represents the storage cost in bytes. In Figure 7, the storage cost is presented when number of packets per unit time is 20, the FDAS, DICA, DICA_EXTENSION, and EHDA takes 27 bytes, 167 bytes, 145 bytes, and 22 bytes. Apart from this, the proposed scheme EE-FDAS has the lowest communicating cost of 15 bytes because 50% to 60% of values are in the normal range so they are replaced with the Boolean digit zero which significantly shrinks the data packet size.
# Bearing Severity Fault Evaluation Using Contour Maps--Case Study
## 3 Results
### Failure Characterisation  
The study of the influence of localised defects on the vibration signature of a bearing is a frequently addressed topic [57]. In the presence of a localised fault, the modulation phenomenon is triggered, produced by periodic impacts that excite the bearing resonance frequencies. [PERSON] and [PERSON] [54] have described the vibration modes of a bearing, under localised failure in each component, using envelope analysis. For defects in the outer race, the fault frequency will have side bands modulated to the frequency of rotation of the shaft in its first harmonics. In the case of failure in rolling elements, although the envelope spectrum should present harmonics with side bands modulated at the cage frequency, it presents impulsive content. Another characteristic considered in the vibration mode of rolling elements is the presence of harmonics of the cage frequency [16].  
However, a problem arises when diagnosing a combined defect, due to the interference that can occur between the vibration modes of the affected components. As detailed in Section 2.2.2, in this study, we analysed a combined outer race and rolling element fault. Figure 14 shows a vibration spectrum at 350 rpm. For both failure cases, there were changes in the failure mode. The Outer Track Fault Frequency (BPFO) harmonics no longer have sidebands spaced at the shaft turning frequency. In contrast, for rolling elements, harmonics corresponding to the frequency of the cage were not observed, and only the impulsive content of the lateral bands of the rolling elements failure frequency (BSF) was evident.  
On the other hand, it is important to highlight that although both components presented the same level of severity, the amplitude of BPFO was predominant, in comparison with BSF. This is why this study focused on the diagnosis of failure in the outer race, considering the diagnosis of failure in rolling elements in future works.
## 3 Channel Model  
The UWOC channel losses include those caused by absorption, scattering, turbulence, and pointing errors. The channel attenuation caused by absorption and scattering can be given as \(h_{\mathrm{c}}=\exp[-c(\lambda)z]\)[20], where \(c(\lambda)\) is the extinction coefficient, including the absorption coefficient and the scattering coefficient, and \(z\) is the communication link length. The geometric loss can be expressed as \(h_{\mathrm{I}}=D_{\mathrm{rx}}^{2}/(z\theta)^{2}\), where \(D_{\mathrm{rx}}\) symbolizes the received aperture diameter and \(\theta\) indicates the transmitter divergence angle [20]. From this, the channel attenuation for \(h_{\mathrm{c}}\) and the geometric loss for \(h_{\mathrm{I}}\) can be considered constant and invariant during the symbol transmission duration interval. Therefore, the UWOC channel fading can be expressed as \(h(t)=h_{\mathrm{t}}(t)\cdot h_{\mathrm{p}}(t)\), where \(h_{\mathrm{t}}(t)\) and \(h_{\mathrm{p}}(t)\) denote the attenuation caused by ocean turbulence and pointing errors, respectively.
# QoS-Aware Scheduling Algorithm Enabling Video Services in LTE Networks
## 5 Result and Discussion  
In this part, the performance of PrOEXPRule, our proposed algorithm, is measured and assessed for video traffic. It is evaluated with respect to QoS parameters including system delay, throughput, fairness, and packet loss ratio. In order to assess our proposed LTE DL technique, it was compared to prevalent LTE scheduling techniques, i.e., EXP/PF, PF, M-LWDF, and EXPRule. The simulation scenarios were tailored based on a single-cell strategy and particular parameters as explained in Section 3.1. The results of the simulation of the proposed LTE DL technique PrOEXPRule are shown in Figures 2-5; these results illustrate the exact values that were gained from the simulator results, and these outcomes show that the proposed technique attained better performance with respect to QoS requirements and fulfilled the needs of video traffic streaming for the users of the network.