esa-sceva/satcom-chunk-collection huggingface dataset:
# Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks
## 3 Related Work
### Authentication
Additionally, during the authentication phase, the verifying vehicle must determine whether the certificate is legitimate, which increases the processing complexity of the VANET system [31]. According to [PERSON] [32], the open-medium nature of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications makes VANETs susceptible to security issues. Numerous studies have proposed security plans to address these issues. Unfortunately, many of these approaches have high computing overheads, particularly when using the batch verification method, which simultaneously checks several messages. Consequently, using ECC, a Lightweight Security Without Using Batch Verification Method (LSWBVM) technique is provided in [32]. The LSWBVM technique uses an XOR operation and a generic hash function. In an area with high traffic density, the proposed technique employs single verification rather than batch verification. According to the authors, LSWBVM accomplishes the security objectives of enabling mutual authentication between nodes and demonstrates that single verification is more effective than batch verification. [PERSON] and [PERSON] [33] proposed a secure authentication method that could withstand typical attacks. Digital signature technology is considered suitable for security authentication procedures. However, this can render the identity of the node visible. Accordingly, an anonymous signature authentication method based on group/alias has been developed to address this issue. However, there are still restrictions on authentication costs and certificate revocation [34]. [PERSON] et al. [35] proposed an authentication system with conditional privacy preservation and non-repudiation. They employed two authentication systems: ID-based Signature and ID-based Online/Offline Signature. They reported that their proposed system met all requirements and was adequate for urban vehicular communications. However, the proposed system employs public key cryptography, which may result in additional overhead. Therefore, it is necessary to verify the efficiency of the proposed system in large-scale networks [10]. In general, authentication security, which is the main concern for VANET security, remains a challenge. Moreover, creating a suitable authentication system is just one of the requirements. Another is that we must carefully consider protecting privacy when creating that system.
# Benefits of Probiotics--Biodetoxification
## 3. Probiotics--Detoxification in the Human Body
### Biotransformation of Xenobiotics  
Humans are exposed to a wide range of dangerous chemicals from food and the environment. Xenobiotics are alien compounds that enter a live creature via food, water, or inhaled air yet are not part of it. These include endobiotics when they are present in higher concentrations than their typical levels, as well as naturally occurring poisons and manmade environmental contaminants that serve as other organisms' defensive mechanisms [72; 73]. Long-term consumption of contaminated food with xenobiotics may be tied to a widespread range of human health problems like the disruption of the gut microbiome, also known as dysbiosis; reproductive tract disorders; mental health disorders; and immune system disorders. Some authors have suggested that probiotics are a promising strategy for the alleviation of their toxicity and accumulation in the human body [74; 75]. In Table 1, we present examples of probiotics' mechanisms of action in the process of xenobiotic detoxification [76; 77]. Lactic acid bacteria, the yeast Saccharomyces (_S. cerevisiae_ var. _boulardii_), and _Bifidobacterium_ spp. are the most often utilized probiotics [7; 87; 88; 89; 90; 91; 92]. Numerous advantageous characteristics of these strains can be crucial for xenobiotic detoxification, including their potent ability to bind, tolerate, or detoxify; their high tolerance to bile and acid; their strong adherence to the gut mucosa; and their potent antioxidant or immunoregulatory capabilities, which allow them to adjust to changes in the gut environment brought on by xenobiotics [8; 93; 94; 95]. Recent research has demonstrated that probiotic multiple-strain mixes are more successful, and new strains with specialized functions need to be isolated, even though the mechanisms of action of probiotics are not entirely understood [96; 97]. Detoxification processes occurring in a living organism are located mainly in the liver--the most important detoxification organ. It is the liver that is primarily responsible for the inactivation and effective elimination of both endogenous metabolites and xenobiotics. The metabolism of xenobiotics makes them more soluble in water, which facilitates their removal from the body [93]. The first pass effect is the term used to describe the process by which xenobiotics that are consumed orally travel to the upper gastrointestinal system and, if absorbed, are transferred to the liver via the hepatic portal vein. The
## 3 Case Study  
### Implementation of Statistical Distribution Models to Transformer CBM Data  
The condition parameters data that were used in the study were from 1322 oil samples (dataset) that contain 17,186 measurements data from 13 condition parameters data that were extracted from 373 distribution transformers. The data were divided into training (9425) and validation (7761) purposes. These transformers have voltage and power ratings of 33/11 kV (step-down) and 30 MVA, respectively. The age band of the data is from 1 to 25 years. Table 1 tabulates the dataset distribution for each year.  
Next, the condition parameters data were sorted, scaled logarithmically, and then plotted on the x-axis. The y-axis represents either Weibull or normal distribution quantiles, converted into probability values while using CDF Equations (2) and (4), respectively. Figures 2 and 3 show the probability plots that were obtained for dielectric breakdown voltage and acidity from year 1 to 15, fitted by normal and Weibull distributions, respectively. Based on Figure 2a,b, the dielectric breakdown voltage data for year 1 to 2 and year 4 to 6 are close to the normal distribution fitting line. For year 3, the apparent deviation of the dielectric breakdown voltage data from the fitting line occurs at probabilities higher than 95% and between 10% and 40%. Significant deviations are also observed, particularly at probabilities less than 10% and above that 90%, as shown in Figure 2c-e for year 7 to 15.  
Figure 3a-e plot the Weibull distribution fittings for acidity data. Apparent deviations can be observed at lower and upper tails of the Weibull distribution. The Weibull distribution could not represent the acidity data for year 1 quite well due to the large data variation within this period, as shown in Figure 3a. There are slight deviations of the acidity data from the Weibull distribution fitting at a probability less than 10% for year 2 to 3. The acidity data for year 4 to 6 could be represented quite well by the Weibull distribution, as shown in Figure 3b. The patterns of Weibull distribution fittings for year 7 to 9 and year 10 to 12 are quite similar, whereby apparent deviations of the acidity data occur at probabilities between 10% and 40%, as shown in Figure 3c,d. The acidity data for year 13 to 15 deviate from the Weibull distribution fittings at probability less than 40%, as shown in Figure 3e.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline
**Year** & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 \\
**Dataset** & 9 & 22 & 26 & 30 & 32 & 37 & 33 & 49 & 52 & 52 & 56 & 77 & 76 & 61 \\ \hline
**Year** & 15 & 16 & 17 & 18 & 19 & 20 & 21 & 22 & 23 & 24 & 25 & & **Total** \\
**Dataset** & 113 & 116 & 86 & 103 & 87 & 65 & 55 & 37 & 31 & 10 & 7 & & **1322** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset distribution for each year.  
Figure 2: Normal distribution probability plot for dielectric breakdown voltage from year (**a**) 1 to 3; (**b**) 4 to 6; (**c**) 7 to 9; (**d**) 10 to 12; and, (**e**) 13 to 15.
## 4 Experimental Set-Up  
### Datasets  
The presented methodology is tested over three corpora. The first two, CorpusCine and PaperReviews, configure a controlled experiment because, for both corpora, we have access to the ground truth (the actual semantic orientations). Conversely, a third corpus related to the Colombian conflict is employed, representing a more challenging scenario because such a dataset has not been labeled.
## 1 Introduction
A comparison of the pros and cons of static and dynamic analysis methods is shown in Table 1.  
The contributions of the proposed study can be summarised as follows: \begin{table}
\begin{tabular}{c c c} \hline \hline
**Analysis Method** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{Static} & Fast scanning and control of malicious applications [27]. & Static analysis is ineffective as many malicious applications use a range of deformation technologies such as bytecode encryption, reflection, and local code execution [27]. \\  & No need to tune execution environments and relatively low computational load for static analysis [28]. & The signature database is limited and unable to detect zero-day malware [29]. \\ \cline{2-3}  & Complete code coverage [30]. & \\  & Ability to reveal errors that do not manifest themselves [31]. & \\ \hline \multirow{4}{*}{Dynamic} & Detect malicious applications that use some obfuscation techniques such as code encryption or packaging [28]. & It requires a lot of effort to build the environment and the code coverage may be insufficient to safely analyse malware [30] \\ \cline{2-3}  & Monitor the behaviour of malware in real time and identify malware types more accurately [32] & It requires high automation and real time and requires more time and memory resources [33]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages and disadvantages of static and dynamic analysis methods. * Presenting a CNN model and image-based approach for Android malware analysis based on the values determined with hyperparameter optimisation in the classification model. * Performing classification and learning operations on the current dataset. * Using BERT as a text classification algorithm for numerical representation of textual features to generate images. * Using the image of each feature side by side as a single image file to effectively detect Android malware. This study has five sections. The remainder of this paper is structured as follows: The second section provides an overview of studies that leverage deep learning and machine learning-based artificial intelligence approaches for Android malware analysis, with a focus on the generation and presentation of the images of features in these studies. In the third section, we delve into the materials and methods employed in the process of analysing Android malware, elaborating on the techniques used and the performance metrics selected to gauge the effectiveness of these methods. The fourth section presents the experimental results obtained from the various models used in our analysis, accompanied by a comprehensive discussion of these findings. Finally, in the last section of this paper, we offer general evaluations and insights derived from the results presented, highlighting the implications and potential future directions in the field of Android malware analysis.
## 4 Performance Evaluation Results and Discussion
### Considering Normalization of Components in User Influence, User Activity
The analysis results from Figures 9 and 10 show that only normalizing and giving the same weight to the components may have unexpected results due to the data characteristics. Therefore, the data characteristics should be carefully understood, and the parameters need to be configured appropriately. In addition to normalizing the components, the weights of the components may be adjusted to reflect the emphasized part of the user influence and user activity. The correlations with user influence and the correlations with user activity, with the normalized and equal weight components, are shown in Table 5. The correlations between user influence, user activity, number of elites, number of fans, number of friends, number of reviews, and recent review date were analyzed. The user influence and user activity no longer show a strong correlation with any of the other variables. This could be because now that the components are normalized, a user that has an average value for each component could become better than users with very high values in only one of the components and low values in other components. The correlation among the user influence, user activity, and Yelp variables could become weak in these cases. As an observation of our whole experiments, in the initial setting where no normalization was done and the same weight for all components were used, the emphasis naturally shifted to the component that had a large range value. The analysis results using this approach was considerably good. This provides insight into which component weight should have a larger weight assigned. In our second setting, with the normalization and equivalent weights for all components, this kind of characteristic was ignored, resulting in unexpected results. The weights of the components could be additionally adjusted to enhance the usefulness of the user influence and user activity concepts.
## 3 SHM of Bridge, AI and Recent Technologies
### SHM of Bridge and AI and 3D Printing  
Researchers and engineers have been pursuing improvements in automated pavement crack sealing platforms from the early 1990s in some universities such as Carnegie Mellon University (CMU), the University of Texas, Austin (UT Austin) and University of California, Davis (UC Davis) [138; 139; 140]. With recent developments in additive manufacturing and great interest in automation of the structural maintenance, the use of 3D printing as a promising technology in bridge SHM systems has attracted the attention of researchers. Pavement maintenance has a special role as an indicator of bridge traffic in the SHM system process of bridges. As one of the automated maintenance methods, automated pavement crack sealing was developed using 3D printing technology [141]. Development of automated 3D printers has been based on two main models of platforms: a Cartesian table-based platform and a robot arm-based platform. The development of these two platforms has continued until today. Figure 6 is a simplified representation of the 3D printers used by DTU Wind and Energy Systems, which includes both a Cartesian table-based platform (Figure 6a) and a robot arm-based platform (Figure 6b).  
After detecting damage on the pavement with a scanner or a camera, a 3D printer based on fused deposition modeling (FDM) is used to repair damages with asphalt sealant [138].  
By preprocessing the damage image, the shapes of pavement damage are specified. A 3D model of the repair is generated according to the shape and severity of the damage. A combination of AI methods such as machine learning and damage detection methods such as ultrasonic techniques, X-rays, etc. can be used to detect the shape, size, and depth of the damage. Furthermore, AI methods can be used to specify the type of repair based on the severity and shape of the damage.  
Through 3D slicing software, the 3D model of the repair is converted to G-codes, which are utilized to control the movement of the nozzle heads and deposition of the materials. Based on the depth and shape of the damage, different types of materials, e.g., asphalt sealant with tiny sand and outer asphalt sealant with larger sand, can be used by the 3D printer to repair the damage. The selection of the material based on the type ofdamage can be automated using AI methods to accelerate and optimize the repair process.  
Figure 6: Schematic view of DTU Wind and Energy Systems 3D printers: (**a**) Cartesian table-based platform and (**b**) robot arm-based platform.
## 3 Results and Discussion
### Weight Loss  
Table 2 shows the results of mass loss, acidity, pH, and SSC of the product at harvest (Fresh) and after 30 days of storage in different packaging conditions. The mass loss had a very different trend due to the packaging system used (\(p\) = 0.000), whereas there is no effect of the _A. pullulans_ treatment on its evolution. As expected, the unpackaged product (OP) met the greatest mass loss (5.75-6.23%), resulting in excessive drying of the peduncle and rachis (Figure 3). The lowest values were detected in sealed box (SP) ranging between 0.04 and 0.06%, whereas in BD, it was ten times higher (0.12-0.14%). In our experiment, the relative humidity in the cold room was set at around 80%; therefore, the higher weight loss in OP was expected, whilst in the other packaging tested it was negligible. This confirms  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & & \multicolumn{2}{c}{_p-Value_} \\
**Parameter** & **Packaging (P)** & **Treatment (T)** & **PxT** \\ \hline Mass Loss & 0.000 & 0.256 & 0.297 \\ Acidity & 0.858 & 0.092 & 0.979 \\ pH & 0.000 & 0.691 & 0.037 \\ SSC & 0.025 & 0.000 & 0.012 \\ L* & 0.017 & 0.000 & 0.194 \\ a* & 0.000 & 0.994 & 0.966 \\ b* & 0.000 & 0.237 & 0.861 \\ CIRG & 0.000 & 0.000 & 0.048 \\ Detachment & 0.536 & 0.269 & 0.936 \\ Firmness & 0.927 & 0.907 & 0.558 \\ TBC & 0.000 & 0.014 & 0.000 \\ TMC & 0.083 & 0.116 & 0.019 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The \(p\)-value calculated for each variability source by two-way ANOVA.  
that LDPE film is an effective water vapour barrier [37] and that it can be used to store table grapes without causing significant weight loss. Broadly, weight loss occurs during cold storage as a natural process depending upon the temperature, packaging material, relative humidity of the storage environment, and grape variety. In a previous study [38], weight loss of _Red Globe_ table grapes packaged in PP or PET film with a modified atmosphere ranged between 5 and 12% after 21 days of storage at 5 \({}^{\circ}\)C.  
Mass loss lower than 1% was also reported by [PERSON] et al. [22] in _[PERSON]_ table grapes packaged using various modified atmospheres and wrapped with oriented polypropylene (OPP) of 20 \(\upmu\)m thickness. In contrast, breathable films ease mass loss as, if not properly designed, they do not represent a hindrance against water evaporation during storage. [PERSON] et al. [23] found mass loss of up to 1.5 and 4% after 18 days of storage at 1 \({}^{\circ}\)C in _Flame Seedless_ packaged using, respectively, perforated and non-perforated OPP film. Five cultivars of organic table grapes packaged under passive MAP with sealed bags experienced up to 2% weight loss after 30 days of storage at 0.5 \({}^{\circ}\)C, where the lowest values (0.5%) were found in red grape _Vitis vinifera_ L. cv. 'Cesme Pembesi' [33].
## 4 Proposed Sensing Results Separation
### Sensing Results Separation without Duplication of Sensing Results
#### 4.2.1 Spectrum Detection and Estimating CSI and CFO  
Since each sensor sends the subcarrier with different frequency to FC, at most one main lobe of subcarrier exists in each DFT point. Without loss of generality, we consider FC detects the subcarrier sent by \(k\)th sensor. From Equation (3), the \(v\)th subcarrier component is given as follows.  
\[\begin{split}\gamma_{k}(v)=\gamma_{k}\sum_{m=1}^{N}\exp\left(j2 \pi\frac{n_{k}m+m\varepsilon_{k}-mv}{N}\right).\end{split} \tag{5}\]  
For simple explanation, the noise component is ignored. The proposed technique evaluates the phase rotation speed for estimating the frequency offset [25]. FC detects the signal during the window size of DFT for two or more times in a row. For simple explanation, this paper considers twice detections.  
We consider the subcarrier components detected by the first DFT are given by Equation (5). After first DFT detection, the \(v\)th subcarrier component in the second DFT detection is given as follows.  
\[\begin{split}\gamma_{k}^{\prime}(v)=\gamma_{k}\sum_{m=1}^{N} \exp\left(j2\pi\frac{n_{k}(m+N)+(m+N)\varepsilon_{k}-(m+N)v}{N}\right),\end{split} \tag{6}\]  
where \(\gamma_{k}\) and \(\varepsilon_{k}\) are constant during two times detections of DFT. Since \(n_{k}\) and \(v\) are integer, the following equation is set.  
\[\begin{split} Y_{k}^{\prime}(v)=\gamma_{k}\sum_{m=1}^{N}\exp \left(j2\pi\frac{n_{k}m+(m+N)\varepsilon_{k}-mv}{N}\right).\end{split} \tag{7}\]  
Note that \(\gamma_{k}\) does not depend on the subcarrier number \(v\) but it is constant for the frequency domain. Therefore, as far as \(|\varepsilon|<0.5\) is set, the spectrum selected in terms of maximal spectrum power is equal to the main lobe of the subcarrier. As a result, the \(v=n_{k}\) spectrum components in first and second DFT detections are given as follows.  
\[\begin{split}\gamma_{k}(v=n_{k})&=\sum_{m=1}^{N} \exp\left(j2\pi\frac{m\varepsilon_{k}}{N}\right)\gamma_{k}\\ \gamma_{k}^{\prime}(v=n_{k})&=\sum_{m=1}^{N}\exp \left(j2\pi\frac{(m+N)\varepsilon_{k}}{N}\right)\gamma_{k}\\ &=\exp\left(j2\pi\varepsilon_{k}\right)Y(v=n_{k}).\end{split} \tag{8}\]Finally, the CFO is estimated as  
\[\varepsilon_{k}=\log\left(\frac{Y_{k}^{\prime}(v=n_{k})}{Y_{k}(v=n_{k})}\right) \frac{1}{j2\pi}. \tag{9}\]  
In addition, the CSI is estimated as  
\[\gamma_{k}=\frac{Y_{k}(v=n_{k})}{\sum_{m=1}^{N}\exp\left(j2\pi\frac{m_{k}}{N} \right)}. \tag{10}\]
# Analyzing Zone-Based Registration under 2-Step Paging in Mobile Communication Network
## 4 Numerical Results
In this case, if the same paging method is adopted, then the total cost of having a large \(q\) is high because location registration occurs more frequently due to an increase in the probability of returning to the previous cell. In conclusion, it can be noted that if 2-step paging is adopted, then when \(n=4\), the total signal cost is minimized and the paging cost is significantly reduced compared to simultaneous paging. Moreover, the total signal cost is significantly reduced. Figure 13: Total signaling cost for various \(q\) (\(n=4\), \(\lambda_{c}=1\)).