esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results and Discussion  
### Simulation Environment and Performance Metrics  
In this paper, we use Python 3.6 and the Simpy module to simulate the underlying communication of UANETs. We assume that each UAV is flying at the same altitude. A total of 40\(\sim\)100 UAVs were randomly distributed in a 2000 m \(\times\) 2000 m area. Nodes move 10 m/s in the training phase and 1\(\sim\)20 m/s in the test phase for the DSEGR protocol. The communication radius is 350 m; the hello packet interval is 0.5 s; the DDQN contains four layers corresponding to \(5\times 16\times 4\times 1\) neurons, and each layer uses SELU as the activation function. Considering that the dimensions of each feature are not the same, the different features are processed by using the Min-Max normalization method, which can speed up the convergence speed of DSEGR. Adam is used as the optimizer. The learning rate \(\alpha\) is set to 0.001. The small-batch gradient descent method is used to update parameters, and the batch size is 32. The initial energy of UAVs is 1000 J. The simulation parameter settings of the DSEGR protocol are listed in Table 2.  
In this paper, we compare the performance of QNGPSR, DSEGR, FEQSAI, and GPSR protocols in the training and testing phase. To verify the validity of our proposed routing protocol, we analyze the convergence rate of different routing protocols in the training phase, and take the average end-to-end (E2E) delay, packet delivery ratio (PDR), and the time of the first dead node as routing performance evaluation indicators, where average E2E delay can reflect timeliness, PDR demonstrates the reliability of routing algorithms, and the time of the first dead node indicates whether nodes' energy levels are balanced in a UANET.
## 2 Materials and Methods
### Module 2: Choice of the First Reference Radial  
A good reference radial is one which does not include aliased information, as the following dealiasing depended on the already adjusted radial velocity gates. The most likely position occurred where the wind direction was almost orthogonal to the direction in which the antenna was pointing. Furthermore, the average value of the absolute value of that radial's Doppler velocity was at a minimum.  
To be a first reference radial, four criteria must be met. First, they are minima in the curve of the normalized average of the absolute values of the measured velocities at all the valid gates along each radial. Second, these two initial reference radials should be separated by approximately 180\({}^{\circ}\)[18]. The reference radial was chosen with the larger average velocity and more valid gates in all of the radials. Because of the smaller Nyquist interval of the C-band radar, more than two radials may meet the first criterion in C-band radial velocity data when observing the same weather situation, and only a couple of radials were separated by nearly 180\({}^{\circ}\) (unless there were multiple folds). The third criterion is that the reference radial must have the biggest difference between minima in the curve and the adjacent maxima. The fourth criterion is that the number of data points for the radial with the minimum sum must contain at least two-thirds of the average number of valid gates in all the radials in each azimuths.  
The above-elaborated strategy works fine and can determine the correct first reference radial in most conditions, but still easily fails when multiple-folded radial velocity gates show up in tropical cyclones. To remove the ambiguous result from the multiple-folded radar radial velocity gates, a standard which is the same as in the ADTH scheme [19] due to the prevailing real environmental wind direction was added in this module. The wind direction was calculated by the gradient velocity-azimuth display (G-VAD) scheme proposed in [PERSON] et al. [3]. The velocity-azimuth display (VAD) scheme [21] can predict the areal vertical-profile distribution of the horizontal-wind information from one Doppler weather radar. However, the VAD scheme suffers from troubles when the observed radial wind field has aliased radial velocity gates or has a wide region of missing gates. The G-VAD scheme does not need prior dealiasing or other measurement information and can calculate the real environmental wind direction and speed from the velocity gradient information of radial velocity observations. The radial has more valid radial velocity gates and the wind direction perpendicular to that of the real environmental will be chosen as the first reference radial (more details can be referred to in HE2019).
# Utilizing Soft Computing Techniques to Estimate the Axial Permanent Deformation of Asphalt Concrete
## 2 Permanent Deformation Influential Factors  
Due to the combined impact of traffic loading, environmental conditions, and internal properties of asphalt mixtures, asphalt pavements tend to undergo permanent deformation, referred to as rutting [16]. Moreover, the magnitude and number of wheel passes and the change in ambient temperature are considered the main external factors influencing the rutting severity [17]. A high traffic volume induces significant shear stress within individual pavement layers and at the interfaces between layers, leading to plastic deformation that accumulates over time and manifests as surface ruts [1]. Higher temperatures also make pavements more susceptible to rutting since such temperatures reduce the viscosity of bitumen, which increases the pavement's deformation and decreases its resistance to loading deformation [18].  
On the other hand, internal factors mainly concerning the mixed design and material properties are equally important contributors to permanent deformation. The characteristics of the used aggregates, which include the particle size distribution, the shape of the particles, and the texture, are responsible for the degree of interlock and load transfer in the mixture [16]. Using angular and crushed aggregate leads to better internal friction and, therefore, greater interlocking, hence greater resistance of the mixture to shear deformation [17]. Bitumen percentage and air voids are also essential factors; high bitumen content or low air voids result in creating a mixture with low stiffness and low strength, thus making it more vulnerable to plastic deformation [1]. Inversely, low bitumen content or a higher percentage of air voids can reduce the cohesion of the mixture and increase the formation of premature cracking, which ultimately contributes to rutting [18]. Therefore, adequate compaction of the mixture during placement and using modifiers such as polymers can improve the ability of the mix to resist stress due to traffic and temperature [16]. For this reason, understanding the causes of rutting helps design asphalt mixtures that resist permanent deformation. Moreover, using predictive tools, such as machine learning models, offers additional opportunities to quantify the impact of these factors and refine design methodologies for modern traffic and environmental conditions.
# Randomized Feature and Bootstrapped Naive Bayes Classification
## 2 Related Theory  
Naive Bayes is a probabilistic classifier that leverages [PERSON] theorem to assign a class label to an instance based on observed features. It assumes the conditional independence of features given the class label, which simplifies computation and enables efficient handling of high-dimensional data. Let \(X=(X_{1},X_{2},\ldots,X_{n})\) represent the feature vector with \(n\) features and denote the feature vector, and let \(Y\in\{y_{1},y_{2},\ldots,y_{K}\}\) denote the target class variable with \(K\) possible classes. Given a dataset of \(N\) total observations, the naive Bayes classifier is trained using \(N_{tr}\) observations and is tested on \(N_{te}\) observations, where \(N=N_{tr}+N_{te}\).  
According to [PERSON] theorem, the posterior probability of a class \(Y=y_{k}\) given the feature vector \(X\) is expressed as  
\[P(Y=y_{k}|X)=\frac{P(X|Y=y_{k})P(Y=y_{k})}{P(X)}, \tag{1}\]  
where \(P(Y=y_{k})\) is the prior probability of class \(y_{k}\), \(P(X|Y=y_{k})\) is the likelihood of observing \(X\) given \(Y=y_{k}\), and \(P(X)\) is the marginal likelihood of \(X\). Since \(P(X)\) is constant for all classes during classification, it can be omitted.  
To simplify computation, naive [PERSON] assumes that all features \(X_{i}\) are conditionally independent given the class \(Y\). Thus, the likelihood \(P(X|Y=y_{k})\) can be factorized as  
\[P(X|Y=y_{k})=\prod_{i=1}^{n}P(X_{i}|Y=y_{k})\,. \tag{2}\]  
This results in the posterior probability [7; 28] are  
\[P(Y=y_{k}|X)\propto P(Y=y_{k})\prod_{i=1}^{n}P(X_{i}|Y=y_{k})\,. \tag{3}\]  
The naive [PERSON] decision rule selects the class \(y_{k}\) that maximizes the posterior probability:  
\[Y=\underset{y_{k}}{\text{argmax}}P(Y=y_{k})\prod_{i=1}^{n}P(X_{i}|Y=y_{k})\,. \tag{4}\]In practice, logarithms are used to avoid numerical underflow when computing the product of probabilities:  
\[\log P(Y=y_{k}|X)=\log P(Y=y_{k})+\sum_{i=1}^{n}\log P(X_{i}|Y=y_{k})\,. \tag{5}\]
## 4 Test and Result
### Adaptive Strategy Test
In general, degradation exists in most cases; the calculation load increased by 6.54% in the other 15 cases, and 5 of them take 10% more time. Only three cases achieved more than a 10% improvement, and the remaining six cases averagely improved by 5.41%. The superiority of the adaptive KBW strategy is the most obvious in STNY-NEWH and BONE-STNY. Thus, the proposed AMC-KF and adaptive KBW strategy can generally improve filtering efficiency. The RMS improvement of the proposed AMC-KF compared to the original fixed KBW is shown in Figure 8. Here, the negative values mean a positioning-accuracy degradation compared to the adaptive KBW strategy. Figure 6: Time consumption with different KBW strategies on NEWH-BONE, QCLF-ANGS, STNY-NEWH. Figure 7: Time efficiency improvement for different KBW strategies. The RMS increases significantly at least on one direction component while KBW = 1 and 5. Especially, the RMS on U deteriorated by almost six times (\(-584.261\%\)) compared to the adaptive KBW. However, no significant performance fluctuation appears when the fixed KBW = 25 and 30, except for the \(-43.13\%\) degradation on U (KBW = 25) and the \(23.3\%\) improvement on E (KBW = 30), which both occur on QCLF-ANGS. Although the large KBW seems better, the improvement is hardly permitted as the increase in KBW amplifies the time consumption and positioning errors. In conclusion, the proposed AMC-KF method takes both efficiency and accuracy into account and is more progressive than the traditional methods with KBW fixed.
# 5G-MEC Testbeds for V2X Applications
## 2 Background
### Fundamentals of ETSI-MEC
#### 2.2.2 5G and MEC Integration  
The MEC system interacts with the 5G SBA as shown in Figure 4. The MEP is treated as an application function (AF) to access the 5G core network [21]. The UPF configuration and MEC deployment scenario depend on the network and MEC provider. UPF integration into the MEC system is the primary component, which can have a distributed and customizable data plane from the perspective of the MEC system. The traffic configuration of the data plane is managed through the NEF-PCF-SMF route. The MEH elements can be deployed in the data network of the 5G system, whereas the NEF is deployed in the MEO [22].  
Figure 4: MEC deployment in 5G reference architecture.
## 5 Results
### Ablation Study
#### 5.2.1 Efficacy of MSFE  
To demonstrate the efficacy of the MSFE, it was integrated with the baseline network to analyze the improvements. According to Table 4, for the DRIVE dataset, the baseline with the MSFE achieved significantly superior outcomes across the four assessment criteria, reaching up to 0.9680, 0.9810, 0.9807, and 0.8213. Compared with the baseline, accuracy, AUC, specificity, and sensitivity improved by 0.0054, 0.0059, 0.0036, and 0.0161, respectively, with sensitivity exhibiting the most noticeable enhancement. Sensitivity, which measures the proportion of correctly identified vessel pixels over all vessel pixels, is a key assessment indicator. A higher sensitivity indicates a better ability to correctly classify the vessel pixels. In our network, the purpose of the MSFE is to enhance the network's ability to capture multi-scale information and improve the accuracy of vessel segmentation. Therefore, by introducing the MSFE, more vessel pixels are correctly classified and thus the accuracy and sensitivity are improved, which is consistent with our experimental results. For the CHASE_DB1 dataset, the experimental outcomes in Table 5 illustrate the efficiency of the MSFE. The accuracy, AUC, specificity, and sensitivity values achieved by the baseline were 0.9743, 0.9723, 0.9836, and 0.8090, while the evaluation indicators of the baseline with the MSFE were 0.9754, 0.9752, 0.9849, and 0.8254. Similar to the results obtained for the DRIVE dataset, each performance metric improved, with sensitivity exhibiting the largest increase of 0.0164. For the STARE dataset, the baseline with the MSFE improved compared to the baseline in three indicators, including accuracy, AUC, and sensitivity, reaching 0.9765, 0.9774, and 0.8198. On all three datasets, the baseline with the MSFE achieved higher AUC and accuracy values, which measure the network's overall ability to correctly identify pixels. This demonstrates that the MSFE comprehensively enhances the network's segmentation performance while capturing multi-scale information.
## 4 Channel Selection Algorithm Based on Multi-Armed-Bandit Algorithm
### Learning Rules of the Multi-Armed-Bandit Algorithms
#### 4.2.1 \(\epsilon\)-Greedy Based Algorithm  
The \(\epsilon\)-greedy algorithm is a simple method that determines the ratio of exploration to exploitation using the variable \(\epsilon\). Therefore, it is widely used as an algorithm for solving MAB problems. In each trial \(t=1,2,\ldots\), among the \(K\) channels, the channel that seems to have the highest reward probability from past experience is selected with a probability  
Figure 4: MAB problem vs. channel selection problem.  
Figure 5: Flowchart of the channel selection.  
of \(1-\epsilon\). Subsequently, another slot is randomly selected with a probability of \(\epsilon\). In the \(\epsilon\)-greedy algorithm, \(\epsilon\) must be set or adjusted correctly. If the value of \(\epsilon\) is too low, it will be difficult to find the optimal behavior. On the contrary, the behavior will be close to random and the rewards that can be obtained will be unstable if the \(\epsilon\) is too high. The estimated reward probability of machine \(k\) is expressed as follows:  
\[p_{k}(t)=\frac{R_{k}(t)}{N_{k}(t)}, \tag{1}\]  
\[k^{*}=\begin{cases}\arg\max_{k\in K}p_{k}(t)&\text{if }1-\epsilon,\\ \text{Randomly selected}&\text{otherwise},\end{cases} \tag{2}\]  
where \(N_{k}(t)\) is the number of transmissions by channel \(k\) until time \(t\), that is, the number of times channel \(k\) is selected. \(R_{k}(t)\) is the number of rewards gained until time \(t\), that is, the number of successful transmissions and ACKs received. \(k^{*}\) is an index of the channel to be selected at the next time \(t+1\), with the highest reward probability at time \(t\). Algorithm 1 describes the \(\epsilon\)-greedy based channel selection process.  
```
1:Initialize \(R_{k}(0),N_{k}(0)\)
2:Set \(\epsilon\in[0,1]\)
3:while wake time \(t=1,2,\ldots\), is not expired do
4: Generate a normal random number \(x\in[0,1]\)
5:if\(x>\epsilon\)then
6: Select channel \(k^{*}=\arg\max p_{k}(t)(\forall k\in K)\)
7: Transmit the data frame
8: Update \(N_{k}(t)+1\)
9:if the data frame is transmitted and the corresponding ACK frame is received then
10: Data transmission succeed
11: Update \(R_{k}(t)+1\)
12:else
13: Data transmission failed
14: Update \(p_{k}(t)\) given by Equation (1)
15:endif
16:else
17: Select channel \(k^{*}\) from \(K\)
18:endif
19:\(t=t+1\)
20:endwhile
```  
**Algorithm 1**\(\epsilon\)-greedy based channel selection algorithm.
## 4 Simulation Settings and Results
### Testing the SC and SCL Decoders  
To verify the correct operation of the SC and SCL decoders, we first reproduce the decoding results of [60]. That system employs a \((1024,512)\) polar code constructed using frozen bit indices, as suggested for 5G [61]. The verification procedure is conducted over AWGN channels with quadrature phase shift keying (QPSK) modulation. Each QPSK symbol \(s\) carries two information bits. The additive noise samples are uncorrelated zero  
Figure 4: Receiver model of the terminals with the hard threshold.  
mean CSCG random variables with variance \(\sigma_{n}^{2}\). The two information bits correspond to the symbols \(b_{1}\) and \(b_{2}\). We define sets \(\mathcal{S}_{i}(0)\) and \(\mathcal{S}_{i}(1)\), \(i=1\) or \(2\) of QPSK symbols as corresponding to information bits \(b_{i}=0\) or \(b_{i}=1\), respectively. The channel LLR for \(b_{i}\) is calculated as in [62]:  
\[L(b_{i})=\ln\frac{p(y|b_{i}=0)}{p(y|b_{i}=1)}=\ln\frac{\sum_{s\in\mathcal{S}_{i }(0)}e^{-\frac{|y-s|^{2}}{\sigma_{n}^{2}}}}{\sum_{s\in\mathcal{S}_{i}(1)}e^{- \frac{|y-s|^{2}}{\sigma_{n}^{2}}}},i=1,2 \tag{49}\]  
In Figure 5, we present the frame error rate (FER) performance of the \((1024,512)\) polar code with SC and SCL decoding. Compared with the corresponding results from [60], which we also include in Figure 5 for convenience, we see a good match. Our simulation results are consistent with the results in [60], indicating that our implementations for the SC and SCL decoders operate correctly.
## 2 Basics of Gust Load Alleviation  
### Gust Load Definition  
Gust, being a complicated phenomenon, is often referred to as atmospheric turbulence. The following two idealized categories of gusts are generally considered in industry for aircraft design, namely [33]: (1) Discrete gusts: the instantaneous gust velocity profile is usually defined by a deterministic form, such as 'one-minus-cosine' and'sharp-edged' shapes and (2) Continuous turbulence: the gust velocity varies randomly.
## 3 Particle Swarm Optimization and Fuzzing
### The Relationship between Fuzzing and Particle Swarm Optimization  
Fuzzing is widely used due to its high automatization and efficiency. The number of generated high-quality samples is the key to the fuzzing efficiency. High-quality samples in different types of fuzzing are different. In coverage-oriented fuzzing, high-quality samples refer to those samples that can execute to new edges. In target-oriented fuzzing, high-quality samples refer to those samples that can be executed to suspected vulnerable points. Different fuzzing techniques use different guidance methods for the test process to increase the number of high-quality samples. We discuss the relationship between PSO and fuzzing using different guidance methods.  
Non-oriented fuzzing focuses only on whether the program crashes in the test process; it does not interfere with the mutation according to the real-time internal state of the program. Therefore, the generation of test samples is conducted in a blind state that does not account for relevant information. In contrast, the PSO algorithm accounts for the other relevant information of the sample; as a result, the PSO algorithm cannot be used in this way.  
The goal of coverage-oriented fuzzing is to improve the coverage of the sample execution. The coverage is the attribute of many samples, not the attribute of a single sample. However, the evaluation of particles in the PSO algorithm is based on a single sample. That the coverage of a single sample is high does not mean that the sample can improve the coverage rate, and whether a single sample can improve the coverage rate in different test processes is inconsistent. Therefore, the indicators to improve the coverage rate here cannot be used as indicators to measure a single sample, and the PSO algorithm is not suitable for generating samples directly in coverage-oriented fuzzing.  
The goal of target-oriented fuzzing is to make the sample execute the designated target point; thus, a sample that can execute the target point is a high-quality sample. The evaluation of the current sample can be measured as the sample distance from the target: the closer the sample is to the target, the higher the evaluation value. The sample distance can be specified as the shortest distance of sample execution blocks from the target code block. The sample distance is an attribute of a single sample, which is consistent with the evaluation of a single particle in the particle swarm. Therefore, we attempt to introduce the PSO algorithm into target-oriented fuzzing, and we use PSO's powerful ability to quickly approach the optimal solution to generate many test samples that can reach the target point to improve the probability of triggering a vulnerability.
# Non-Destructive pXRF on Prehistoric Obsidian Artifacts from the Central Mediterranean
## 5 Applications of pXRF on Obsidian Artifacts from Central Mediterranean Sites
### Sardinian Obsidian in Continental Italy  
The transportation and trade of obsidian from Monte Arci in Sardinia to Corsica and beyond was realized in the early study by [PERSON] et al. [7], which identified it at several sites in Southern France and Northern Italy. Since then, Monte Arci obsidian has been identified at many sites in Northern Italy, which also is a great distance from the other central Mediterranean sources, with changes over time in proportion to Lipari obsidian [55; 56]. In addition, social network analysis supports hypotheses of different obsidian pathways, including open-water north-bound from Corsica to Southern France [57]. With the very large number of non-destructive analyses conducted in this millennium, we now see that Sardinian obsidian also reached central Italy as a significant percentage of assemblages and made its way to southernmost Italy and even Sicily in very small numbers, supporting an interpretation of a down-the-line type exchange during the Neolithic [3]. A total of just 23 artifacts of Sardinian obsidian (out of nearly 1300 obsidian artifacts analyzed) were identified at sites south of Rome, indicating that travel routes were not directly across the Tyrrhenian, but from Sardinia to Corsica, then through the Tuscan archipelago and southward, on or along the Italian peninsula (Figure 11). Undoubtedly, other materials were exchanged and traveled in opposite directions, including domesticated animals (sheep, goat, cattle, pig), produce (from wheat, barley, other plants), clothing, tools, wood, etc. [58].  
Figure 9: Pantelleria is separated by Balata dei Turchi (BdT), and both Lago di Venere (LdV) 1 and 2.  
Figure 10: Map of central Mediterranean showing archaeological sites with \(\geq\)10 source analyses of obsidian artifacts. Sites in red analyzed by the author.
## 6 Result and Discussions
### Evaluation Metrics
#### 6.3.1 Perplexity Score  
The perplexity values of the [PERSON] model for next-word prediction that have been provided indicate that the language model has performed satisfactorily on both the training and testing sets. A slightly lower testing perplexity of 1.6189 indicates good generalization to unseen data, while a training perplexity of 1.6443 indicates effective learning of the language patterns. According to these findings, the model may be able to predict words in a sequence with reasonable accuracy, which makes it a useful tool for a variety of natural language processing applications. For character-based LSTM, the perplexity is 3.5598.