esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of an Improved Gravity Anchor Bold Expanded Foundation
## 4 Response of the Foundation
### Size Change Analysis  
The calculation results of the ABAQUS finite element model show that the damage to the foundation is mainly concentrated in the column of the foundation. The corbel height h1 and the ring beam width b3 were changed. Thus, five schemes of the foundation were obtained. The maximum tensile stress of each part of the foundation of five different schemes under ultimate load was further analyzed to obtain a better scheme. The corbel height h1, the ring beam width b3, and each part of the selected foundation are shown in Figure 17.  
Figure 16: (**a**) Foundation feature points and (**b**) damage evolution.  
Figure 17: Schematic illustration of the corbel height and ring beam width.  
The corbel height h1 of scheme 1, scheme 2, and scheme 3 is kept as 3000 mm. The width of the ring beam b3 increases by 250 mm in turn. The maximum tensile stress of the foundation column, the top of the bottom plate, the bottom of the bottom plate, and the cantilever of the bottom plate under the ultimate load is calculated using ABAQUS. The results are shown in Table 7.  
As shown in Figure 18, when the width of the ring beam b3 increases from 1025 mm to 1525 mm, the maximum tensile stress of the foundation column, the top of the bottom plate, and the cantilever of the bottom plate decrease to varying degrees. From the finite element analysis results and the evolution process of the foundation stress, increasing the width of the ring beam can increase the bending volume of the concrete, and the shape of the foundation tends to be uniform, which reduces the stress concentration  
As shown in Figure 19, when the corbel height h1 increases from 2500 mm to 3000 mm, the maximum tensile stress of the foundation column, the top of the bottom plate, and the cantilever of the bottom plate increases to varying degrees. This is probably because as the corbel height increases, the concrete bending moment at these positions also increases, resulting in an increase in the maximum tensile stress. The tensile stress at the top of the bottom plate increases most obviously.
# No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System
## 5 Discussion
The method considers human visual sensitive features without the presence of a reference frame which is very much required for transmitted videos. This research was conducted to implement a low-computational QA algorithm which could be used in real-time stereoscopic video transmission. To determine the influence of the binocular artifact in transmission, a series of experiments was performed, with the stereoscopic video streams individually encoded. To simulate real packet losses, the JM reference software was used. Finally, we have designed an objective no-reference quality measurement approach based on the HVS features of stereoscopic videos which combines the dissimilarity measure obtained based on the disparity index between the left and right views, and a perceptual difference measure calculated by considering the difference in the edge magnitude between temporal frames.
# A Sentence Prediction Approach Incorporating Trial Logic Based on Abductive Learning
## 4 Experiments and Results
### Chi-Squared Test  
In order to check whether there is a significant difference between the actual (\(O\)) and expected results (\(E\)), we used the mathematical statistics method of the chi-squared test to perform hypothesis testing. First, we established the test hypothesis and determined the test level.  
\[\begin{array}{l}H_{0}:O=E\\ H_{1}:O\
eq E\end{array}\]  
Meanwhile, we set the significance level \(\alpha\) to 0.05. We calculated the cardinality (\(\chi^{2}\)) by predicting the sentence and the true sentence.  
\[\chi^{2}=\sum_{i=1}^{n}\frac{(O_{i}-E_{i})^{2}}{E_{i}} \tag{13}\]  
We used the SPITL results after the 90% training set to perform the operation and obtained a \(\chi^{2}\) value of 340.8386. Since we know that its degree of freedom is the number of data minus one, i.e., (\(n-1\)), we can obtain a right-tailed probability of cardinality (\(p\)-value \(P\)) of 0.8231. Obviously,  
\[P=0.8231>\alpha=0.05\]  
so we accept hypothesis \(H_{0}\). From the above, it can be seen that the fit of the predicted values with our method to the true values is high. It also proves that the method of this paper is credible, valid, and accurate. To see more intuitively the fit between the predicted and true values, we plotted them using a graph, as shown in Figure 5, where acc is the actual value and pre is the predicted value.  
Figure 5: Line graph of actual and predicted values.
## 3 Methodology
### Accuracy Assessment  
Based on the available validation dataset at the sites, the accuracy of the results was evaluated. For a quantitative evaluation, four indicators were selected: overall accuracy, kappa coefficient, and the commission and omission of errors.  
For the Ji'An site, a stratified inspection was more appropriate but it did not meet the requirements because of the small range of 21-84% probability, so it was not suitable for the stratified sampling test. In addition, it was established that there is a GF-3 image in the Ji'An site from nearly the time of the flood occurrences (access date 10 June 2019, three days apart from the Sentinel-1 data). Thus, 200 points (24 points of water and 176 points of land) in the common area of the two images for the test were randomly selected (Figure 5a). GF-3 images were also used for visual inspection.  
For the WeiFang site, 200 points were randomly selected for evaluation, enabling stratified inspections in areas from Landsat-derived cDSWE class probabilities (21-84%). Unlike the Ji'An site, there were no eligible high-resolution images. Thus, a visual inspection of the pseudo-color composite image of Sentinel-1 was performed (Figure 5b).  
Figure 4: Two types of histograms for images (Band VV). The red line is the determined threshold of the VV band and the blue line is the grayscale curve of that threshold.
## 2 Literature Review
### Social and Ethical Issues  
Seafood consumers usually attach secondary importance to social and ethical issues over other attributes. An investigation in the US that included a DCE in which social sustainability was represented by two labels: a label that promotes community and another label that certifies opportunities forishers to increase their participation in decisions, as well as fair distributions of profits that benefit coastal communities [47]. The results indicate that the WTP for social benefits was the lowest and that consumers usually have a high degree of overlap between the social benefits and those from locally sourced seafood, which suggested that there is a need for education about social problems of fisheries. In Germany, another investigation included a fictitious fairtrade claim as a separate attribute from the sustainability certification attribute in a DCE, finding that generic fair trade had a positive impact on purchasing choices, and consumers were willing to pay premiums for it, even though they were willing to pay more for certifications such as ASC and Naturland, which focus more on environmental aspects [48].  
On the contrary, in the case of coffee in the US, a study found that social/ethical benefits were valued higher than environmental benefits, as there was a higher WTP estimate for the fairtrade program (fair opportunities for producers of developing countries), followed by shade-grown (conservation of the habitat of birds and wildlife) and, finally, organic coffee (environmental benefits) [49].  
Moreover, in Belgium, a study found that although consumers rate fish sustainability and ethics as very important, it was not correlated with their patterns of consumption or attitudes towards eating fish [50]. However, consumers refused to eat wild fish because of sustainability and ethical concerns.
## 5 Security Analysis of a V2I Authentication Scheme
### Side-Channel Attacks  
If the attacker steals the information in the user's smart card through side-channel attacks and obtains the authentication information \(m_{1}\) sent by the user, then the attacker can launch identity guessing attacks and impersonation attacks and can compute the session key.  
* Identity guessing attacks/no anonymity: If an attacker knows the information \(\{ID_{TA}\), \(PID\), \(AlO\), \((VID_{i},\beta_{i}),ctr\}\) in a user's smart card through side-channel attacks, and the authentication information \(m_{1}=\big{\{}VID_{i},Q_{1},C_{1},ctr^{*}\big{\}}\) transmitted via the public channel, where \(CK_{VT}=h(ID_{v}\|\beta_{i})\), \(Q_{1}=CK_{VT}\oplus h(N_{2}\|ID_{v})\) and \(C_{1}=h(ID_{v}\|h(N_{2}\|ID_{v})\|CK_{VT}\|ctr^{*})\), then the adversary can guess \(ID_{v}\)' and find \(\beta_{i}\) based on \(VID_{i}\), compute \(CK_{VT}?=h(ID_{v}\|\beta_{i})\), \(h(N_{2}\|ID_{v})=CK_{VT}\oplus Q_{1}\), and verify whether \(C_{1}=h(ID_{v}\|h(N_{2}\|ID_{v})\|CK_{VT}\|ctr^{*})\) is correct or not. If yes, the guessed \(ID_{v}\)' is correct. Therefore, their scheme cannot achieve anonymity.
* No session key secrecy: Once the adversary can know the user's identity \(ID_{v}\) and \(CK_{VT}\) from the above analysis and can obtain \(m_{4}=\Big{\{}VID_{i}^{*},Q_{4},Q_{6},C_{4},Auth\Big{\}}\) from the public channel, then the adversary can compute \((N_{4}\oplus N_{7}\oplus ID_{r})=Q_{4}\oplus CK_{VT}\), \(\beta_{i}^{*}=Q_{6}\oplus h(CK_{VT})\) and verifies whether \(C_{4}=h\Big{(}VID_{i}^{*}\|(N_{4}\oplus N_{7}\oplus ID_{r})\|\beta_{i}^{*}\| ctr^{*}\Big{)}\) is correct or not. After successful verification, the adversary can calculate the session key \(SK=h(N_{2}\oplus N_{4}\oplus N_{7}\oplus ID_{r})\).
* Impersonation attacks: According to the above analysis, the adversary can know \(\{ID_{TA},PID,AlO,(VID_{i},\beta_{i}),ctr\}\) stored in smart card and can obtain user's identity \(ID_{v}\) and \(CK_{VT}\), then they can launch impersonation attacks. The adversary randomly selects \((VID_{i},\beta_{i})\), selects two fresh random numbers \(N_{2}\) and \(N_{3}\), and computes \(CK_{VT}=h(ID_{v}\|\beta_{i})\), \(Q_{1}=CK_{VT}\oplus h(N_{2}\|ID_{v})\) and the verification parameter \(C_{1}=h(ID_{v}\|h(N_{2}\|ID_{v})\|CK_{VT}\|ctr^{*})\), where \(ctr^{*}=ctr+N_{3}\). Finally, the adversary transmits the service access request \(m_{1}=\{VID_{i},Q_{1},C_{1},ctr^{*}\}\) to \(RSU\). Obviously, it can pass through the authentication of TA and can establish the session key. So, their scheme is not resistant to impersonation attacks.  
**Remark 1**: _The reasons why [PERSON] et al.'s scheme suffers from these attacks are that once the attacker can obtain information stored in the smart card through side channel attacks, the attacker can guess the user's identity, and the authentication request depends on the unencrypted information stored in the smart card and the user's identity. On the other hand, the generation of session key in their scheme does not use Diffie-Hellman value._
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
## 2 Review of Spectra of 2D Plane Waves Received by Uniform Linear Arrays
In this case, the spectral ROS of the required 2-D digital filter should consist of \(K\) narrow-angle double-trapezoidal-shaped passbands, as shown in Figure 4. In our proposed system, we use 2-D sparse FIR filters with trapezoidal passbands, as shown in Figure 4. A single trapezoidal passband is specified by the temporal bandwidth of the broadband bandpass plane wave, as well as the angle \(\alpha_{k}\); \(k=1,2,\ldots,K\) which depends on the DOD and the half-angle \(\delta_{k}\). The operation of the multibeam transmit beamformer is explained in detail in the next section.
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 3 Methodology
### Machine Learning Models  
A variety of supervised machine learning algorithms were applied to classify Roman Urdu hate speech. The models were trained and evaluated to determine their effectiveness:  
* Logistic Regression (LR): A simple yet effective model for binary classification that assigns probabilities to class labels [37].
* Support Vector Machines (SVM): Uses a hyperplane-based approach to separate hate speech and non-hate speech text [38].
* Random Forest (RF): A machine learning technique in which several decision trees are generated and aggregated to produce reliable results [39].
* Naive Bayes (NB): A probabilistic model that assumes word independence, making it efficient for text classification tasks [40].
* k-Nearest Neighbors (KNN): A distance-based model that classifies text based on similarity to labeled examples [41].
* Gradient Boosting Machines (GBM): A boosting technique that sequentially improves weak models to create a strong classifier [42].  
Each model was evaluated to compare accuracy, recall, and precision in detecting hate speech.
## 5 Example Analysis  
To verify the applicability of the bending stiffness calculation for joints connecting porcelain bushings and flanges in Equations (27) and (29), two examples of UHV electrical equipment in the literature [18; 22] were considered, as shown in Figure 9. The basic parameters are listed in Table 5.  
Figure 8: Comparison of bending stiffness results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Arrester 1** & **Arrester 2** \\ \hline Outside diameter, \(d_{c}\) (mm) & 510 & 600 \\ Inside diameter, \(d_{b}\) (mm) & 400 & 500 \\ Bushing length (mm) & 2115 & 2350 \\ Cement height, \(h_{c}\) (mm) & 200 & 200 \\ Cement clearance, \(t_{c}\) (mm) & 10 & 20 \\ Outside diameter, \(D_{f}\) (mm) & 590 & 700 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Geometric parameters of typical ultra-high voltage electrical equipment.  
Figure 9: Ultra-high voltage lightning arresters.  
The modulus of elasticity, \(E_{g}\), of the cement bond is 24 GPa; the modulus of elasticity, \(E_{c}\), of the porcelain bushing is 110 GPa; and the modulus of elasticity, \(E_{f}\), of the flange is 150 GPa.  
According to the experimental results for the two arresters, [PERSON] et al. [18; 22] found that the bending stiffness given by Equation (30) was more accurate. Thus, the results calculated using Equation (30) are considered as the reference values, and the bending stiffness is calculated using Equations (1) and (27); the results are presented in Table 6 and Figure 10.  
The verification results for engineering examples indicate that the equivalent bending stiffness equation established in this study is relatively accurate and applicable for the calculation of the bending stiffness of joints connecting porcelain bushings and flanges in UHV electrical equipment.
## 3 Experiment Setup and Evaluation
### Tools  
MATLAB (r) version R2022a was used to train and test the models on a laptop with the following specifications: Intel-Core i7-11800H CPU @ 64-bit 2.30 GHz, 16 GB RAM, NVIDIA GeForce RTX 3060, and CUDA v11.7.
## 5 Discussion  
In this study, EFFs were generated by integrating both raw and post-processed EPFs, i.e., ECMWF and NCEP, into calibrated hydrological models, i.e., XAJ and GR4J. The flood forecasts were produced from four 'NWP + HM' schemes, i.e., (A) ECMWF + XAJ, (B) GE + XAJ, (C) ECMWF + MHM, and (D) GE + MHM. The performance of both the raw and post-processed flood forecasts was evaluated using deterministic indicators such as \(R\) and _RME_, alongside probabilistic indicators like _CRPSS_ and quantile histograms, as well as three uncertainty evaluation indicators--_ARIL_, _PCI_, and _Pucci_.  
The analysis reveals that forecasts driven by raw EPFs yield satisfactory correlation and relative error, but are limited in probabilistic metrics such as _CRPSS_ and quantile histograms. Results demonstrate that after GPP correction, all schemes show marked improvements in both deterministic and probabilistic terms, with high deterministic skills and moderate probabilistic skills over 1-7 lead days. This enhancement also mitigated the degradation of forecast quality over lead days, validating the effectiveness of the GPP method, consistent with the findings of [PERSON], [PERSON] [46]. These findings underscore the necessity of employing post-processing methods to enhance the reliability of flood forecasts.  
A comparative analysis between schemes (A) and (B) shows that using a single hydrological model, flood forecasts based on GE do not show a significant improvement over those using a single EPF. Comparisons between schemes (A) and (C) reveal that when using a single EPF, forecasts based on MHM offer a broader ensemble range, a smaller ensemble mean error, and an increased accuracy of the ensemble mean. This improvement in flood forecast quality is credited to the integration of hydrological model uncertainty. Comparing scheme (A) with scheme (D) shows that although using multiple forecasting models and hydrological models yields forecasts with higher dispersion, they exhibit lower correlation, larger biases, and lower _CRPSS_. This suggests that while it is reasonable to use GE to quantify input uncertainty, compared to a single EPS, GE may not necessarily enhance flood forecasting performance, aligning with the findings of [PERSON], [PERSON] [31]. Therefore, it is recommended to combine a single EPF with MHM (i.e., scheme (C)) to achieve reliable flood forecasting. Moreover, results indicate that compared to input uncertainty, the uncertainty in hydrological models can more effectively enhance the predictability of flood forecasts. Therefore, focusing on hydrological model uncertainty rather than input data uncertainty could decrease computational time and burden in flood forecasting.
## 2 Materials and Methods
### Experimental Protocol  
In this research study, _Saccharomyces cerevisiae_ yeast cells BY-4741, MATA were employed as a model organism due to their eukaryotic nature, resembling human body cells in composition, including a nucleus and other organelles, and sharing 23% homologous genes. They offer additional advantages such as rapid reproduction, a short life cycle, cost-effectiveness in cultivation and storage, and suitability for acidic conditions with high sugar content that inhibits bacterial growth and prevents contamination.  
The experimental protocol was established on the basis of the one specified in our previous studies [44; 48]. The manipulation and preparation of experimental yeast samples were exclusively carried out within a laminar box Streamline SCR-2A1 Cabinet (manufactured by Esco Micro Pte. Ltd., Singapore). This cabinet is equipped with a light source, a UV lamp, and a specialized HEPA air filter that ensures the sterilization of the environment. The experimental solution itself consisted of pre-cultured yeast (cultured 24 h at room temperature prior to each experiment) diluted into YPD substrate comprising 1% (\(w/v\)) yeast extract, 2% (\(w/v\)) peptone, 2% (\(w/v\)) D-glucose and 95% distilled water. Ten samples were prepared in this manner, divided into 5 exposed and 5 control samples. Following homogenization of the solution on a shaker, a sample was extracted from each flask using a micropipette to determine the cell count.  
The quantification of cell numbers was performed using hemocytometry, a method that involves the use of a microscope, a calibration slide with a grid--a Burker chamber, and a device for photographic recording. Subsequently, the samples were placed within the coil cavities and introduced into the incubator. Following exposure, the samples underwent another quantification assessment. The growth coefficient \(X\) was employed to assess the response of the biological system, calculated as the relative ratio between the partial growth coefficients of the exposed and control samples at a particular position (E-A) of the coils:  
\[X=\frac{E}{C}, \tag{9}\]  
where \(E\) represents the partial growth coefficient of the exposed sample:  
\[E=\frac{\sum_{i=1}^{N}E_{\text{after}(i)}}{\sum_{i=1}^{N}E_{\text{before}(i)}}\, \tag{10}\]  
where \(E_{\text{after}}\) stands for the number of cells after the experiment, while \(E_{\text{before}}\) denotes the number of yeast cells before the experiment for the exposed sample. \(C\) represents the partial growth coefficient for the control sample:  
\[C=\frac{\sum_{i=1}^{N}C_{\text{after }(i)}}{\sum_{i=1}^{N}C_{\text{before }(i)}} \tag{11}\]  
The count of cells for the control sample after and before the experiment is indicated by parameters \(C_{\text{after}}\) and \(C_{\text{before}}\). The parameter \(N\) signifies the quantity of Burker chamber squares, which, in this instance, amounted to 6.
# Modeling and Performance of the IEEE 802.11p Broadcasting for Intra-Platoon Communication
## 3 Platoon System
### Vehicle Control Model  
The vehicle dynamics are highly nonlinear. However, under certain assumptions [32] and with appropriate feedback, vehicle dynamics can be linearized. We apply a simple vehicle dynamics model for a vehicle's longitudinal motion as in [33], which can be described as a function. The spacing error is defined as  
\[\epsilon_{i}=p_{i-1}-p_{i}-l_{i-1}-g_{i-des} \tag{1}\]  
where \(p_{i}\) represents the position of the \(i^{th}\) vehicle; \(p_{i-1}\) and \(l_{i-1}\) denote position and length of its preceding vehicle, respectively; and \(g_{i-des}\) is the desired gap between \(i^{th}\) and \((i-1)^{th}\) vehicle. Starting at \(t=0\) with initial condition \(\epsilon_{i}(0)\), the goal of consensus control is to achieve convergence  
\[\epsilon_{i}(t)\to 0,\text{ as }t\rightarrow\infty \tag{2}\]  
assuming that \(\epsilon_{i}=0\), we can obtain the desired position of the \(i^{th}\) vehicle  
\[p_{i-des}=p_{i-1}-l_{i-1}-g_{i-des} \tag{3}\]  
Considering feedback messages includes relative acceleration, speed, and position of both preceding vehicles and the leader vehicle, vehicles' desired acceleration is expressed as  
\[u_{i-des}=(1-q_{1})a_{i-1}+q_{1}a_{l}-q_{2}(v_{i}-v_{i-1})-q_{3}(v_{i}-v_{l})- q_{4}\epsilon_{i} \tag{4}\]  
where \(q_{1}\), \(q_{2}\), \(q_{3}\) and \(q_{4}\) are design parameters. Here, variables with symbol \"\(l\)\" refer to leader vehicles. A first-order filter is used to model the actuator lag and signal processing delay.  
\[u_{i-des}=(1+\mu s)u_{i} \tag{5}\]  
Figure 1: Platoon structure model.  
where \(\mu\) is the total delay including control delay, sensor detection and processing delays, and actuator delays, which is treated as a constant.
###### Abstract  
This research paper presents an assessment of geohazards on Lefkas Island, Greece, using Geographic Information System (GIS) technology to map risk and enhance public awareness through an interactive web application. Natural hazards such as landslides, floods, wildfires, and desertification threaten both the safety of residents and the island's tourism-dependent economy, particularly due to its seismic activity and Mediterranean climate. By combining the Sendai Framework for Disaster Risk Reduction with GIS capabilities, we created detailed hazard maps that visually represent areas of susceptibility and provide critical insights for local authorities and the public. The web application developed serves as a user-friendly platform for disseminating hazard information and educational resources, thus promoting community preparedness and resilience. The findings highlight the necessity for proactive land management strategies and community engagement in disaster risk reduction efforts. This study underscores GIS's pivotal role in fostering informed decision making and enhancing the safety of Lefkas Island's inhabitants and visitors in the face of environmental challenges.  
natural hazards; GIS analysis; GIS web app; public awareness; interactive mapping Article