esa-sceva/satcom-chunk-collection huggingface dataset:
# Attenuation of Lightning-Induced Effects on Overhead Distribution Systems in Urban Areas
## 3 Results
### Induced Voltages  
In this subsection, the ratio (\(k_{V}\)) between the maximum value of the induced voltage on the line with (\(V_{max,Building}\)) and without (\(V_{max,noBuilding}\)) the building is analyzed for all three sets of simulations (A, B, and C). The line diameter was 1 cm.  
\[k_{V}=\frac{|V_{max,Building}|}{|V_{max,noBuilding}|} \tag{4}\]  
As for the EM fields' comparison, when the building was higher than the line (Figure 9), the attenuation became negligible (i.e., \(k_{V}\simeq 1\)) when the distance was more than 100 m (less than 5% deviation was observed). As a consequence, when dealing with buildings more than 100 m from the overhead line, their contribute is  
An analytical expression for \(k_{v}\) is provided in what follows fitting the numerical outcomes of the previous section with the following double exponential function:  
\[y=\alpha e^{\beta x}+\gamma e^{\beta x} \tag{5}\]  
The values of \(\alpha,\beta,\gamma\), and \(\delta\) are shown in Table 1, and \(x\) is the independent variable (\(d\) for cases A and B and \(H\) for case C).  
The fitted curves are proposed in Figures 12-14 for the sake of completeness and show a good agreement between the proposed expressions and the data obtained by means of the described simulations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & \(\alpha\) & \(\beta\) & \(\gamma\) & \(\delta\) \\ \hline A & 0.88 & 0.0006 & \(-\)0.86 & \(-\)0.085 \\ B & 1.45 & \(-\)0.39 & 0.97 & 0.0001 \\ C & 1.71 & \(-\)0.05 & \(-\)0.75 & \(-\)0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fitting coefficients of Figures 9–11.  
Figure 11: Ratio between the maximum voltage induced on the line considering or not considering the presence of a building. (C) \(d\) = 10 m, and \(H\) varies from 5 to 50 m.  
Figure 10: Ratio between the maximum voltage induced on the line considering or not considering the presence of a building. (B) \(H\) = 5 m, and \(d\) varies from 5 to 200 m.  
For the sake of completeness, in the final part of this section we analyze scenario A considering two different situations:  
1. An overhead line characterized by three conductors placed at 10 m, 1 m from each other
2. A finite ground conductivity of 1 mS/m instead of a PEC ground.  
Figure 14: Fitted curve of the attenuation coefficient of the maximum induced voltage—Case C.  
Figure 12: Fitted curve of the attenuation coefficient of the maximum induced voltage—Case A.  
Figure 13: Fitted curve of the attenuation coefficient of the maximum induced voltage—Case B.  
The first case is shown in Figure 15, while the second one is depicted in Figure 16. As observed, the overall behavior was similar to that presented in Figure 9 with the exception of a stronger attenuation for higher values of \(d\) in both cases 1 and 2.
# A Bandwidth-Efficient Dissemination Scheme of Non-Safety Information in Urban VANETs +
## 3 Design of a Scheme for Urban Dissemination  
Our starting point is the dissemination scheme that we have just explained in Section 2.2. According to our previous research [11], it achieves a redundancy level close to the optimum in dense roadway environments. However, given the different nature of urban scenarios, we will try to adapt the broadcast suppression scheme in accordance with their singularities in order to obtain good results. The store-carry-forward scheme will also have to respond to the challenges that we summarized in Section 1: the omni-directionality of the message and the difficulty of selecting a suitable relay for carrying, according to [2].  
We depict a typical situation in a city in Figure 1 to illustrate the case. In this figure, vehicle \"src\" sends a message that needs to be disseminated. If we apply our roadway scheme, the next relay will be vehicle \"B\", because it is the furthest from \"src\". However, due to the shadowing effect of buildings, vehicle \"C\" will not receive the message from \"src\", nor from \"B\". Vehicle \"A\" is the only one in the line-of-sight of \"C\". Thus, vehicle \"A\" should be the next relay, so that \"C\" can also receive the message.  
We have broken the problem down into two necessities:  
1. an omni-directional and circular approach in the dissemination, because targets may be moving in any direction around the source and
2. an effort to reach neighbors that may be hiding around corners.  
We propose to tackle both problems by increasing the chances of forwarding at intersections. Therefore, in order to achieve this, a vehicle that receives a new message first tries to determine if it is at an intersection. We are going to explore two different approaches for this. The first one is using a digital map together with their location coordinates. The other one is interpreting the angle between its direction and the imaginary segment that connects it to the sender. We will describe each one thoroughly below.  
Then, vehicles located in an intersection take part in a specific time contention, given by a waiting time called \(t_{j}\). The equation that determines its value is related to each of the detection techniques that we are going to use. While vehicles located at intersections use this timer, the others apply the distance-based scheme, waiting \(t_{w}\) (see Equation (1)). That is, the intersection contention only affects  
Figure 1: The shadowing problem in urban environments.  
the vehicles in the same intersection. Conversely, a vehicle located at an intersection will ignore any duplicate coming from outside it.
# DPMF: Decentralized Probabilistic Matrix Factorization for Privacy-Preserving Recommendation
## 2 Background
### Probabilistic Model for Implicit Feedback  
Conventional MF methods are tailored to the need for explicit feedback and cannot be directly used for implicit feedback as they have no numeric ratings to build the loss function. To solve this problem, [PERSON] et al. [15] provide a PMF model using implicit feedback to implement item recommendation task. Specifically, \(M_{ik}\) denotes two types of implicit feedback including positive and negative feedback. [PERSON] et al. assume \(M_{ik}\) is from a Bernoulli distribution that equals 1 when user \(i\) and item \(k\) have implicit interactions and equals 0 otherwise. The uniform weighting strategy [12] is adopted to sample negative implicit feedback. By calculating the product between the user latent factor vector and the item latent factor vector, the probability distribution of \(M_{ik}\) is obtained as:  
\[\begin{split} p(M_{ik}=1|U_{i},Z_{k})=g(U_{i}^{T}Z_{k}),\\ p(M_{ik}=1|U_{i},Z_{k})=1-W_{ik}g(U_{i}^{T}Z_{k}),\end{split} \tag{9}\]  
where \(W_{ik}\) denotes the weight of the negative samples. Similar to Equation (2), it defines the conditional distribution of \(M\) as:  
\[p(M|U,Z)=\prod_{i=1}^{m}\prod_{k=1}^{n}p(M_{ik}|U_{i},Z_{k})=\prod_{(ijk)\in P}g (U_{i}^{T}Z_{k})\cdot\prod_{(ijk)\in N}(1-W_{ik}g(U_{i}^{T}Z_{k})) \tag{10}\]  
where \((i,k)\in P\) and \((i,k)\in N\) means that the interaction between user \(i\) and item \(j\) are positive and negative implicit feedback, respectively. As we discuss in Section 2.1, it places zero-mean spherical Gaussian priors on user latent matrix \(U\) and the item latent matrix \(Z\):  
\[p(U|\sigma_{U}^{2})=\prod_{i=1}^{m}\mathcal{N}(U_{i}|0,\sigma_{U}^{2}I) \tag{11}\]  
\[p(Z|\sigma_{Z}^{2})=\prod_{k=1}^{n}\mathcal{N}(Z_{k}|0,\sigma_{Z}^{2}I) \tag{12}\]
# Automated Detection of Misinformation: A Hybrid Approach for Fake News Detection
## 5 Experiments and Results
### Comparing ML Models across Feature Extraction Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Extract** & **Model** & **ACC** & **Precision** & **Recall** & **F-Score** & **Time** \\ \hline TF-IDF & LR & 0.97 & 0.98 & 0.96 & 0.97 & 3.95 \\ TF-IDF & DT & 0.99 & 0.99 & 0.99 & 5.95 \\ TF-IDF & KNN & 0.83 & 0.88 & 0.77 & 0.83 & 5.28 \\ TF-IDF & GBC & 0.99 & 1 & 0.99 & 0.99 & 71.7 \\ TF-IDF & BNB & 0.94 & 0.95 & 0.92 & 0.94 & 2.9 \\ TF-IDF & MNB & 0.93 & 0.93 & 0.93 & 0.93 & 2.88 \\ \hline Empath & LR & 0.76 & 0.76 & 0.75 & 0.76 & 60.63 \\ Empath & DT & 0.75 & 0.75 & 0.76 & 0.75 & 61.33 \\ Empath & KNN & 0.76 & 0.78 & 0.73 & 0.75 & 60.93 \\ Empath & GBC & 0.82 & 0.84 & 0.8 & 0.82 & 73.98 \\ Empath & BNB & 0.72 & 0.73 & 0.69 & 0.71 & 60.6 \\ Empath & MNB & 0.76 & 0.8 & 0.69 & 0.75 & 60.57 \\ \hline Hybrid & LR & 0.98 & 0.98 & 0.99 & 0.98 & 127.78 \\ Hybrid & DT & 0.99 & 1 & 1 & 0.99 & 127.6 \\ Hybrid & KNN & 0.89 & 0.88 & 0.83 & 0.87 & 128.59 \\ Hybrid & GBC & 0.99 & 1 & 0.99 & 0.99 & 394.79 \\ Hybrid & BNB & 0.97 & 0.97 & 0.94 & 0.95 & 126.57 \\ Hybrid & MNB & 0.94 & 0.95 & 0.92 & 0.94 & 126.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of testing six ML models using different feature extraction methods on Merged dataset. Another point worth noting here is that the MNB model achieved 100% recall with TF-IDF and Empath, meaning that the model recognizes all the news articles as fake. The same happened with LR using Empath. This can also be attributed to the lack of data. Overall Analysis  
Overall, it is evident that logistic regression consistently achieves the highest accuracy across all three datasets, positioning it as the most reliable model for prediction. When time efficiency is a priority, the multinomial naive Bayes (MNB) and Bernoulli naive Bayes (BNB) models stand out, clocking in the shortest runtime. In terms of feature extraction methods, the hybrid approach invariably enhances all models' performance. However, this comes with the trade-off of a considerably longer runtime. The TF-IDF method, on the other hand, provides a favorable balance between performance and speed, yielding respectable accuracy in a relatively short time. An interesting dynamic is seen between MNB and BNB models. MNB shows superior performance when dealing with larger datasets, while BNB takes the lead in the context of smaller data volumes; ref. [33] demonstrates the importance of choosing the right model based on the specific characteristics of the dataset.
## 3 Results and Discussion
### Electrochemical Analysis  
#### 3.3.1 Open Circuit Potential  
OCP refers to the potential difference between a working electrode and a reference electrode when no load is applied. It can be used to assess the corrosion of metals and provide fundamental data for other electrochemical tests. Figure 7 shows how the OCP of N80 steel changed in various environments with a 10 MPa CO\({}_{2}\) partial pressure. The results demonstrate the distinct behavior of the OCP curve at different immersion times. During the initial stage of corrosion (0 to 3.5 days), ions such as Cl\({}^{-}\) and SO\({}_{4}\)\({}^{-}\) in the solution penetrated the double layer at the electrode interface due to their strong penetration ability. These ions adsorbed onto the electrode, leading to a decrease in the OCP of the N80 steel sample. As the corrosion time continued (3.5 to 7 days), the surface Fe of the electrode transformed into Fe\({}^{2+}\), causing changes in the charge distribution of the double layer and a positive shift in the double layer potential, resulting in an increase in the OCP. Additionally, the formation of corrosion products on the electrode surface led to resistance polarization, effectively preventing the adsorption of active ions and further contributing to the increase in the OCP. After 7 days, the continued generation and stacking of corrosion products were accompanied by detachment and shedding, influencing the compactness of the surface corrosion products and indirectly affecting the contact between active ions and the N80 steel sample, ultimately causing fluctuations in the OCP. Within the temperature range of 40 \({}^{\circ}\)C to 120 \({}^{\circ}\)C, as the temperature increased, the movement frequency of active ions accelerated, leading to an increased frequency of contact with the electrode surface. This resulted in a gradual decrease in the OCP of the N80 steel sample.  
Figure 6: Ca (**a**) and Mg (**b**) distribution at the steel surface determined by means of EDS surface scanning of N80 steel after immersion tests for 7d (Ca wt.% and Mg wt.% represent the content of Ca and Mg elements in the corrosion products, respectively).  
Figure 7: OCP variation for N80 steel in CO\({}_{2}\)-saturated formation water at various immersion times (**a**) and temperatures (**b**).
## 3 Performance Analysis
* 44:\(Psmall=w^{\prime}[j]\times(1-e^{\prime}[j])\times(Pf\_s+rho\_s\times Psmall)+(1-w^{ \prime}[j])+e^{\prime}[j]\times Pe\);
* 45:\(SumPsmall+=Psmall\);
* 46:end
* 47:// Calculate power consumption due to state switching using the Equation (13). * 48:\(Pswitching+=f[j]\times(1-s[j])\times Pon\_off+(1-f[j])\times s[j]\times Pon\_sleep\)
* 49:end
* 50:// Calculate total power consumption using the Equation (1). * 51:\(Ptotal=Pmacro+SumPsmall+Pswitching\);
* 52:// Set \(Pb\) and \(Pbest\) if the fitness value is lower than the \(Pbest\) and update \(v\). * 53:\(if\)\(Ptotal<Pbest\)
* 54:\(Pb[p\_index,:]=p[p\_index,:]\);
* 55:\(Pbest[p\_index]=Ptotal\);
* 56:\(v[p\_index,:]=v^{\prime}\);
* 57:end
* 58:// Choose the \(Pb\) with the best \(Pbest\) of all the particles as the \(Gbest\). * 59:// Update BS state information. * 60:\(if\)\(Pbest<Gbest\)
* 61:\(Gb=Pb[p\_index]\);
* 62:\(Gbest=Pbest[p\_index]\);
* 63:\(BSstate=[w^{\prime},e^{\prime}]\);
* 64:end
* 65:end
* 66:end
* 67:\([w,e]=BSstate\);
* 68:\(a=Gb\); ```
**Algorithm 2**Find Particle
```
1:\(FindParticle(v^{\prime},d,r)\)
2:Initialize\(w,p\)
3:// Apply the Equations (16)-(18). 4:for\(i<n\)do
5:// Set association if a UE demands low rate data service. 6:if\(d[i]=1\)and\(r[i]==0\)
7:\(p[i,m]=1\);
8:\(w[m]=1\);
9:continue;
10:end
11:for\(j<m+1\)do
12:// Set association if a UE demands high rate data service. 13:if\(d[i]=1\)and\(r[i]==1\)
14:// Extract the \(k\) of the best index of BS using the Equation (29). * 15: extract\(k\)
* 16: end
* 17: end
* 18: // Serve UE's demand using MBS. * 19: if\(k\) does not exist or does not meet the condition of the Equation (14). * 20: \(p[i,m]=1\);
* 21: \(w[m]=1\);
* 22: // Serve UE's demand using SBS. * 23: else
* 24: \(p[i,k]=1\);
* 25: \(w[k]=1\);
* 26: end
* 27: end
* 28: return[\(p\), \(w\)]; ```
**Algorithm 3**Find State Information of BS
```
1:FindBSStatateInformation(\(w\), \(e\), \(w\)')
2:Initialize\(e\), \(f\), \(s\)
3: // Apply the Equations (19) and (20). 4:for\(j<m\)do
5: // \(u\_r\) is the number of UEs that request high rate data traffic. 6: // \(u\_l\) is the number of UEs that exist in the overlapping areas commonly covered
7: // by the BS and the neighbor BSs. 8:if\(u\_r[j]>\)\(Phr\)and\(u\_l[j]>\)\(Ilhr\)and\(w^{\prime}[j]\)!= 1
9: \(c[j]=1\);
10: end
11: // Find state switching of BS. 12:if\(w[j]\)!=\(w^{\prime}[j]\)and\(c[j]==c[j]\)
13: \(f[j]=1\);
14: continue;
15: end
16:if\(w[j]==w^{\prime}[j]\)and\(c[j]\)!=\(c[j]\)
17: \(s[j]=1\);
18: continue;
19: end
20:end
21:return[\(e\), \(f\), \(s\)]; ```  
**Algorithm 4**Find State Information of BS
## 4 Results and Discussion  
### Preprocessing of Data  
(a) IP Addresses and Port Numbers Removal--IP addresses and port numbers are removed from source and destination hosts because IP address and port numbers in the original dataset may lead to overtraining of neural networks and classifiers.  
(b) Spaces Removal--Some tags in the dataset contain spaces that have no meaning in the actual data representation, but return different results in the data classification process, resulting in different classification of the packets. Thus, these spaces are removed.  
(c) Label Encoding--The label of each piece of data is encoded. The label of each piece of data in the dataset contains the type of attack corresponding to that data, with different attack types corresponding to different specific strings. Encoding the strings into a specific value simplifies the learning process for the classifier. In the machine learning module, the classifier can learn the category values for each array.  
(d) Data Normalisation--As the range of values taken from the data in the dataset does not meet the requirements of the classifier, the data range and format needs to be normalised to specify a minimum value for each data attribute. The normalisation and standardisation of the data provides a consistent value for the classifier, improving the correlation between the data and reducing the variability between the data features and improves the efficiency of the classifier.
## 3 Results and Discussion
### Effects of BGLSP on Flavor Characteristics of CSB  
GC-MS data of major volatile compounds isolated from CSBs using HS-SPME are summarized in Figure 5 and Table 6. A total of 115 volatile compounds from nine different chemical classes were identified in CSBs. These compounds included 7 ketones, 10 acids, 23 esters, 9 aldehydes, 27 alkanes, 20 alcohols, 1 nitrogen containing compound, 9 olefins, and 9 benzenoids. It is commonly reported that the pathway of flavor compound formation in CSB mainly includes lipid oxidation, heat treatment, and microbial metabolism [10]. Alcohols and benzenoids, which were predominant in the extracts of the control sample (75.36% and 10.11% of the total volatiles, respectively), were the most prominent compounds. The fruity and grassy fragrances detected in the control sample were primarily attributed to the presence of alcohols, which have been previously identified as key odorants in control CSB [28]. As the proportion of BGLSP increased, the content of alcohols decreased, while the content of ketones and aldehydes increased significantly, and both groups reached a maximum when the addition proportion of BGLSP was 10%. Aldehydes and ketones are associated with lipid oxidation, which contributes to the development of fatty, green, nutty, or buttery odors [16]. Monitoring the oxidative degradation of unsaturated fatty acids is essential for ensuring CSB quality and producing healthier, safer, and more appealing foods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Addition Amount (\%)** & **Hardness (g)** & **Chewiness (mJ)** & **Gumminess (g)** & **Springiness (mm)** & **Cohesiveness** \\ \hline
0 & 1980.30 \(\pm\) 37.33 c & 1890.05 \(\pm\) 41.90 b & 1809.13 \(\pm\) 42.70 b & 7.34 \(\pm\) 0.07 a & 1.04 \(\pm\) 0.01 a \\
0.5 & 1947.67 \(\pm\) 36.81 c & 1877.12 \(\pm\) 86.38 b & 1833.70 \(\pm\) 88.06 b & 7.57 \(\pm\) 0.08 a & 0.95 \(\pm\) 0.04 ab \\
1 & 1966.19 \(\pm\) 26.39 c & 1836.59 \(\pm\) 92.79 b & 1858.61 \(\pm\) 63.57 b & 7.30 \(\pm\) 0.01 a & 0.95 \(\pm\) 0.06 ab \\
2 & 1985.22 \(\pm\) 17.91 c & 1869.43 \(\pm\) 36.65 b & 1834.95 \(\pm\) 57.37 b & 7.46 \(\pm\) 0.04 a & 0.88 \(\pm\) 0.01 ab \\
5 & 2289.90 \(\pm\) 121.39 b & 1853.89 \(\pm\) 57.17 b & 1933.25 \(\pm\) 33.04 b & 7.24 \(\pm\) 0.05 ab & 0.85 \(\pm\) 0.02 b \\
7 & 2371.52 \(\pm\) 90.08 b & 1908.32 \(\pm\) 72.37 b & 1964.54 \(\pm\) 48.60 b & 6.85 \(\pm\) 0.27 bc & 0.79 \(\pm\) 0.09 b \\
10 & 2911.38 \(\pm\) 89.78 a & 2294.41 \(\pm\) 30.16 a & 2381.66 \(\pm\) 43.73 a & 6.63 \(\pm\) 0.11 c & 0.79 \(\pm\) 0.05 b \\ \hline \hline \end{tabular}  
* Results are means \(\pm\) SD of triplicate determinations. Column values with different lowercased letters are significantly different (\(p<0.05\)). Abbreviations: BGLSP, broken \(G\). _luclium_ spore powder; CSB, Chinese steamed bread.  
\end{table}
Table 5: Effects of BGLSP on textural properties of CSB.
# Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder +
## 5 Experiments
### Parameter Sensitivity  
The influence of hyper-parameters is investigated in this section, which includes \(\lambda\), \(\alpha\) and \(\beta\) in (3) and (7), respectively. In the experiments, when one parameter is changed, the values of the other parameters are fixed. \(\alpha\) is sampled from {1E-04, 5E-04, 0.01, 0.05, 0.1, 0.5, 1}, \(\beta\) is sampled from {1E-04, 5E-04, 1E-03, 5E-03, 0.01, 0.05, 0.1, 0.5, 1}, and \(\lambda\) is sampled from {1E-04, 1E-03, 0.01, 0.1, 1, 10}, respectively. All the results for the ImageNet datasets are reported in Figure 5-7. According to the observations, we set \(\alpha=0.01\), \(\beta=0.005\) and \(\lambda=0.01\) to obtain the best and most stable results.  
Figure 4: \(\mathcal{A}\)-distance on Corel, ImageNet and Corel datasets. The x-axis and y-axis represent the \(\mathcal{A}\)-distance on the raw data and learned features space.  
Figure 5: Parameter Influence on \(\alpha\) of SCSA on ImageNet dataset. The y-axis represents the classification accuracy of the target domain; the x-axis represents the value range of \(\alpha\).
## 2 Structural Topology Optimization and Heuristic Methods
The effectiveness of the CA approach in the generation of structural topologies and its versatility can be observed in various engineering problems starting from compliance minimization problems (e.g., [19]), including problems with stress and displacement constraints (e.g., [20]), through reliability-based topology optimization (e.g., [21]) or dynamic-static coupling topology optimization ([22]) to the topology optimization of energy absorbers ([23; 24]) or the optimization of multi-materials structures ([25] or [26]), composites ([27]), and design of materials (e.g., [28; 29]). The mentioned papers may serve only as examples of numerous applications of the Cellular Automata method. Additionally, a new trend in developing this method can be observed, namely, a hybridization of CA with other methods, pertaining to both classical methods ([19; 30; 31] for size optimization) and heuristics. Among hybridization CA with heuristics applied to topology optimization, the following can be mentioned: a Cellular Automaton mimicking colliding bodies [32], a hybrid evolutionary bi-directional Cellular Automaton [33], and the cuttlefish algorithm (CFA) for truss topology optimization utilizing the Cellular Automata approach [34]. The survey of combinations of CA with heuristics in broad fields of engineering computations is presented in [35; 36], where, among others, the cellular grey wolf optimizer (CGWO) is proposed. In that paper, utilizing the hunting mechanism of grey wolves, the analysis steps need to be performed for each individual at each iteration, which may cause a significant increase in computation time for large engineering problems. It is worth stressing that in the present paper, which also refers to GWO, the idea is different--the analysis is performed once for each iteration and the derived rules of Cellular Automata govern the behavior of the system. In general, the effectiveness of the population-based algorithms strongly depends on the population size. An extensive discussion of this aspect of topology optimization problems can be found, e.g., in [37]. In the optimization process at each iteration the fitness function must be calculated for each individual. As a result, the population-based methods usually require a larger number of functional calls than other non-population-based heuristics. When considering the topology optimization problems using Cellular Automata, it is necessary to mention the valuable and groundbreaking papers of [PERSON] et. al, e.g., [38], where the hybrid Cellular Automata (HCA) is introduced. It must be underlined, however, that in those papers, hybridization refers to the interaction of analyses based on finite elements with the Cellular Automaton approach.