esa-sceva/satcom-chunk-collection huggingface dataset:
# SDN-Based Survivability Analysis for V2I Communications
## 3 V2I Network Survivability Definition  
According to the above analysis of the SDN-based V2I vehicle network communication models, the survivability of our proposed scheme can be defined as:  
Definition: The SDN-based V2I vehicle network survivability model (\(V\)_V_SNSM) contains a quaternion, i.e., VSNSM = \(\{E,R,P,F\}\), where the definition details of each element can be summarized as follows:  
\(E\) is the vehicle network communication application environment, the specific network communication structure is shown in Figure 2, which is based on SDN vehicle networks. The vehicle serves as a network node and communicates with each infrastructure. The inter-link switching between them controls the MPTCP through the SDN central control section.  
\(R\) is the specific parameter setting in the vehicle network. There are two types of faults in the vehicle network, which are defined as internal and external failures. For these two faults, the internal fault refers to the failure of the vehicle node itself, and the external fault refers to the V2I link fault. We set the V2I link failure rate as \(\lambda_{1}\) and the vehicle node failure rate as \(\lambda_{2}\). Their corresponding repair rates are represented by \(\mu_{1}\) and \(\mu_{2}\), respectively. It is further assumed that the number of vehicles in the vehicle network is \(N\), and the number of possible failures is \(n\) (\(n\leq N\)). Since each vehicle has three corresponding V2I links, the maximum number of possible link failures is set to be \(3N\). The details of specific parameters are concluded in Table 1.  
\(P\) denotes the series of probability distributions of parameters set in the \(R\) under certain attribute conditions, i.e., the time boundary conditions.  
\(F\) represents the survivability model as finite state machines of a continuous time Markov chain (CTMC), which is a quaternion as \(M\) = \(\{S,S_{0},L,T\}\). The parameters are described as follows:  
* \(S\) = \(\{S_{0},S_{1},S_{2},...,S_{n},...,S_{N}\}\) or \(S\) = \(\{S_{0},S_{1},S_{2},...,S_{n},...,S_{3N}\}\), where \(N\) is a finite positive integer. Two different state sets correspond to the vehicle node fault and V2I link fault, respectively. The state transition is related to the parameters in \(R\);
* \(S_{0}\) stands for the initial state of a finite state machine;
* L: \(S\) \(\rightarrow\) 2\({}^{\Lambda P}\) represents the label function of atomic propositions (\(AP\)) which are true in \(S\);
* T: \(S\times S\rightarrow\Re_{\geq 0}\) denotes state transfer matrix.
## 3 Transformation from 3 SAT-Instances to String Puzzles
### Disjunction Gadgets  
Figure 5(a) is the left and right turns of a string. Suppose that a literal \(x_{i}\) is true; namely, the gadget \(x_{i}\) has slack \(2d\). We can translate the slack \(2d\) to any place by using the gadget of Figure 5(a). When the slack \(2d\) of the blue string is smoothed out (see Figure 5(b)), the end of another string is moved down. Figure 5(c) is the simplified illustration of Figure 5(a). One of the purposes of the gadget is to use the slack from one string to extend the end of another string.  
Figure 6(a) is a disjunction gadget. The red string has slack \(2d\). This slack plays a key role in this gadget. For example, suppose there is a clause \(c_{1}=\{x_{1},x_{2},x_{3}\}\). This clause is satisfied if at least one of the three literals is true. In the figure, each of blue strings \(x_{1}\) and \(x_{2}\) has slack \(2d\), and blue string \(x_{3}\) has no slack. This is the case when the truth assignment is \((x_{1},x_{2},x_{3})=(1,1,0)\). In this case, if two slacks \(2d\) of blue strings \(x_{1}\) and \(x_{2}\) are smoothed out (see Figure 6(b)), then the corresponding two black strings obtain slacks of which the amount is \(4d\) in total. However, the amount of the slack of the red string was only \(2d\). Thus, the amount of slack which the green string obtains is \(2d\).  
Figure 5: (**a**) Left and right turn gadget; (**b**) When the slack \(2d\) of the blue string is smoothed out, the end of another string is moved down; (**c**) Simplified illustration of (**a**).  
Therefore, if at least one of the three blue strings has slack \(2d\), then the green string obtains slack \(2d\). It should be noted that the amount of slack the green string obtains is at most \(2d\). In Figure 6(a), we can suppose that the value of \(q\) is negligibly small, since the diameter of any string and the size of the holes of Figure 5(a) are assumed to be negligibly small.  
We construct a gadget of Figure 6(a) for every clause \(c_{1},c_{2},\ldots,c_{m}\). If a literal \(x_{i}\) appears in \(k\) clauses, then the blue string \(x_{i}\) is connected to \(k\) black strings.  
The number of clauses \(c_{1},c_{2},\ldots,c_{m}\) is bounded by a polynomial in \(n\). By an observation similar to variable gadgets, one can see that the transformation from the set of \(m\) clauses to the sequence representing \(m\) disjunction gadgets can be computed in polynomial time.
## 4 Results and Discussion
### Attenuation Behavior of Organic Materials
Wood behaved similarly to polymers in terms of frequency dependence, but some showed quadratic type or Mason-McSkimin relations, which cannot be explained by existing literature. Strong anisotropic attenuation behavior was present in all wood cases, caused by the tree growth process.
# Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints
## 1 Introduction
For example, [PERSON] [33] proposed a shear strength model based on a stacked ensemble of the support vector machine (SVM), M5P model tree, and random forest (RF) algorithms. [PERSON] and [PERSON] [34] employed a search algorithm-enhanced support vector regression model to predict joint shear strength. They noted that, compared to the Barton model, this model shows significant superiority in predicting joint shear strength. Recently, based on tree-based models and convolutional neural networks, [PERSON] et al. [35] developed a new ML model for predicting joint shear strength. Recently, artificial neural networks, particularly the multilayer perceptron (MLP), have gained attention due to their powerful ability to model nonlinear relationships between input features and output responses. MLP is composed of interconnected layers of neurons, where each neuron applies an activation function to a weighted sum of its inputs. The network iteratively adjusts its weights through the backpropagation algorithm to minimize the prediction error. This flexible structure enables MLP to capture complex patterns and interactions that are often difficult to represent using traditional statistical models. For a comprehensive background of the mathematical and computational principles of MLP, readers are referred to [PERSON] et al. [36], [PERSON] et al. [37], and [PERSON] et al. [38]. Despite these advancements, existing ML models still face limitations, including suboptimal accuracy and limited generalization capabilities, highlighting the need for further research. Leveraging the robust nonlinear modeling capabilities of MLP and the global optimization strengths of the slime mold algorithm (SMA), this study proposes a novel hybrid machine learning model, termed the SMA-MLP model. A dataset for joint shear strength consisting of five independent variables was established. The robustness and predictive performance of the proposed model were comprehensively evaluated using four different performance evaluation indicators. Furthermore, a comparative analysis was conducted to benchmark the predictive performance of the SMA-MLP model against other machine learning models.
## 3 Results
### Concentrations of Heavy Metals in Farmland Soil Dominated by Husbandry  
#### 3.2.1 Concentrations of Heavy Metals in Soil Planted with Potato  
Table 1 presents the statistics of heavy metals in soil planted with potato. The pH of the soil ranged from 6.9 to 8.9; 78.6% of the soil samples had a pH greater than 7.5, and the soil was weakly alkaline.  
The mean concentrations of Cd, Cu, Pb, Zn, Cr, As, Hg, and Ni were 0.2, 28.9, 15.4, 69.4, 78.4, 8.4, 0.02, and 53.1, respectively. The average concentrations did not surpass the screening values, showing that there was no pollution risk in the soil. The coefficient of variation (CV) denotes the variation degree between sampling points [40]. It is generally believed that CV \(\leq\) 0.10 was weak variability, 0.10 < CV \(\leq\) 0.90 was medium variability, and CV \(>\) 0.90 was strong variability [53]. The greater the coefficient of variation, the more uneven the content of elements in the soil. The results of CV indicated that the Hg had the largest CV, and fell into the category of strong variability, and Cd, Pb, As, Cr, Ni, Cu, and Zn belonged to the medium variability. Such high variation of Hg is probably influenced by human activities, suggesting that there are point sources of pollution [54]. This point, however, requires further investigation
# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 4 Evaluation Framework
### Datasets
#### 4.1.1 Cityscapes  
Cityscapes [55] is one of the most popular datasets in the field of semantic segmentation and autonomous driving. At first, it was recorded as a video, thus the images are especially selected frames captured from 50 different cities The selection was based upon the need for a great number of objects, variety of scenes and variety of backgrounds. Totally, 30 individual classes are provided grouped into 8 categories. The Cityscapes dataset contains around 5000 images of fine annotation and 20,000 images of coarse annotation. The contained urban street scenes were captured over several months of spring, summer and fall during daytime with good weather conditions.
## 4 Precise Point Positioning Results
### Commercial Service Comparison  
Two comparisons have been performed by companies. The first one is from the Hemisphere company, which uses a proprietary software. Three configurations were tested, summarized in Table 11. A combination of L1/L2/L5, E1/E5a/E6, and B1I/B2I/B3I signals has been applied for GPS, Galileo, and BeiDou, respectively. The following Figure 16a-c shows typical convergence patterns for a selected station (ALBY) and is representative of what can be achieved for each test case:  
1. This is the typical convergence in float mode. The process takes about 30 min to attain full convergence.  
Figure 15: NRCan PPP with instantaneous ambiguity resolution. Courtesy: NRCan.  
2. The results are similar to those presented in [30]. With partial ambiguity fixing, 10 cm of accuracy is achieved instantaneously, and fully ambiguity fixing at the centimeter level is obtained after 2 min of convergence.
3. With the addition of Beidou signals, full ambiguity resolution is instantaneous. Note that a better noise combination can be chosen for Beidou (namely B1c/B2a/B3) but has not been used here.  
Table 11 presents as well the performance results obtained with the three proposed test cases.  
The second commercial comparison was conducted by U-Blox. They performed several single epoch tests with a selected set of stations in a three constellations and three bands configuration, and they computed the percentage of epochs where the resulting position was below 5 cm. The results are presented in Figure 17 and show that this criterion is fulfilled a large majority of the time.  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Test Case** & **1** & **2** & **3** \\ \hline GNSS Measurements & GPS + GAL + BDS & GPS + GAL + BDS & GPS + GAL + BDS \\ \hline Biases applied & Only code biases were applied for the three systems & GPS: code and phase biases, GAL: code and phase biases, BDS: code biases only & Code and phase biases were applied for the three systems \\ \hline Hor. mean [cm] & 5.8 & 1.3 & 1.0 \\ Hor. RMS [cm] & 6.7 & 1.8 & 1.1 \\ Hor 95\% [cm] & 13.6 & 3.2 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Hemisphere PPP-AR processing strategy.  
Figure 16: Positionning results obtained by the Hemisphere company on the ALBY station on 17 June 2021. Courtesy: Hemisphere. (**a**) Hemisphere PPP (float mode). (**b**) Hemisphere PPP (partial AR). (**c**) Hemisphere PPP (full AR).  
Figure 17: Instantaneous convergence below 5 cm. Courtesy: U-Blox.
# Effective Uptake of Cadmium and Chromium from Wastewater Using Carbon-Based _Capsicum annuum_
## 4 Results
### Adsorption Data  
#### 4.3.1 Initial Concentration Effect of Activated Carbon  
The capacity of activated carbon for the uptake of Cd(II) and Cr(VI) ions was assessed based on the sorption rate at different concentrations. The sorption of Cd(II) and Cr(VI) at different initial concentrations is displayed in Figure 6. The diagrams show that the adsorption capacity increases as the concentration of the Cd(II) and Cr(VI) in the solution increases. As the concentration of the two pollutants increased, the sorption capacity of the adsorbent increased due to the driving force between the liquid and the solid phases [52; 53]. Activated carbon from _C. annuum_ presented potential for the sorption efficiency of Cd(II) and Cr(VI) ions. Despite this, when the concentration increased above 60 mg/L, the adsorption sites on the carbon became saturated and a decrease in adsorption was observed.  
The sorption isotherm analyses show the correlation between the pollutant concentrations in the water and the amount of adsorbate on the adsorbent when both phases are in equilibrium [54]. Two sorption isotherms were used to estimate the interaction of Cd(II) and Cr(VI) ions with the activated carbon and are displayed in Table 1. The data show that the adsorption process obeys the Freundlich isotherm model. The Freundlich model suggests that a multilayer deposition on the sorbent surface is the cause of the sorption process [55]. The value of K\({}_{\text{f}}\) also suggests that Cd(II) and Cr(VI) ions are coordinated with the adsorption sites of the activated carbon [39]. Freundlich sorption constants were calculated from the isotherm with a coefficient of R\({}^{2}\) of 0.972 for Cd(II) and 0.985 for Cr(VI). The sorption capacities of the adsorbent were 15.24 for Cr(VI) and 34.34 mg/g for Cd(II). [PERSON]'s non-linear regression did not fit well with the experimental data. This could be because the Langmuir equation does not provide a mechanism for understanding sorption phenomena; it is usually used to estimate the maximum uptake from experimental data [56]. The MPSD and RMSE results for Cd(II) and Cr(VI) on activated carbon indicate that the reduced error values are obtained in the Freundlich model.  
Figure 5: pH at the point zero charge by activated carbon.
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 4 Enhanced Multi-Objective Salp Swarm Algorithm (EMSSA)
### Enhanced Multi-Objective Salp Swarm Algorithm (EMSSA)
#### 4.2.3 Follower Updating Strategy  
In the original formula for follower position update, the movement of followers has a certain degree of randomness and is only related to the position of the previous individual. It lacks the ability to exchange information with other individuals, which can lead to the algorithm getting stuck in local optima. To address these shortcomings, this paper introduces a new formula for follower position update, which is expressed as follows  
\[L=1-\frac{t}{T} \tag{19}\]  
where \(t\) represents the current iteration count, \(T\) represents the maximum number of iterations, and the value of \(L\) decreases from 1 to 0 as the iteration \(t\) progresses.  
\[X_{d}^{i}=\left\{\begin{array}{ll}(X_{d}^{i-1}+LX_{d}^{i})/2&\quad f(X_{d}^ {i-1})\leq f(X_{d}^{i})\\ X_{d}^{i-1}-\sin(X_{d}^{i})&\quad f(X_{d}^{i-1})>f(X_{d}^{i})\end{array}\right. \tag{20}\]  
where \(L\) is added in front of the individual in the better position to reduce the influence of individuals in poorer positions. Conversely, a sine mechanism is used to control the range of individual movement. This approach significantly reduces the randomness of followers, enhances information exchange between individuals, and ensures population diversity.
## 2 Methods
### GO Method  
The GO method defines 17 standard operators, the specific information of which is shown in Table 1. These operators contain information such as specific types, data, or arithmetic rules.  
Figure 1: Flowchart of SMA-DE hybrid algorithm.  
Analyzing the reliability of a system using the GO method requires a quantitative calculation of the operators [42]. This process calculates the state probability of each operator according to the direction of the signal flow starting from the input and calculates the cumulative state probability of the system and of the output signal. In this process, if there is a common signal in the signal flow, it needs to be corrected to ensure the accuracy of the calculation results [43]. The arithmetic rules are presented below.  
1. For each operator, the state probabilities of its output signals are calculated according to its arithmetic rules, and these state probabilities become the input signals of the next relevant operator. The cumulative state probabilities of the system and the state probabilities of the output signals are calculated by continuously following the direction of the signal flow.
2. When there is a common signal in the signal stream, it needs to be corrected. When the output signal stream contains a common signal, the state accumulation probability of the output signal is to be divided by the common signal state accumulation probability.
## 5 Examples of Forward-Inverse Modeling
### Atmospheric Input & Output
#### Retrieval Surface Roughness and Aerosol Parameters
The dip in the measured reflectance in the lower left panel is due to aircraft shadowing. [Reproduced from Figures 1a and 2a of [80].] Since the surface roughness parameters (\(\sigma_{\rm c}^{2}\), \(\sigma_{\rm u}^{2}\), \(\sigma^{2}\), and wind direction) inferred from the channel at 1036 nm are independent of wavelength, this information can be applied at shorter wavelengths (472, 672, and 870 nm) to improve the ocean color retrieval in the sun-glint area. The aerosol and ocean parameters can be retrieved from radiances outside the glint region (viewing angles 20-60 degrees and relative azimuth angles 120-240 degrees) using multi-angle reflectances in three CAR channels at 472, 682, and 870 nm. For this purpose, [PERSON] et al. [80] used AccuRT, a RTM for the coupled atmosphere-ocean system (described in Sections 2-4) combined with an optimization technique [see Equation (154)] to retrieve aerosol and water constituent parameters simultaneously [70,107]. The [PERSON] et al. aerosol model [30] and the CCRR bio-optical model [58] were used in AccuRT for this retrieval. In this case, the retrieved aerosol optical depth at 870 nm was 0.086, the Angstrom coefficient (472 to 870 nm) was 1.463, the chlorophyll concentration was 0.67 mg\(\cdot\)m\({}^{-3}\), the colored dissolved organic matter (CDOM) absorption coefficient at 443 nm was 0.07 m\({}^{-1}\), and the mineral particle concentration (MIN) was 0.009 g\(\cdot\)m\({}^{-3}\). When these retrieval results were applied to the BRDF simulation, a very good match to the CAR measurements were obtained, especially in the glint area [80]. Hence, the glint information retrieved from the channel at 1.036 nm can be used in ocean color remote sensing to estimate the glint contribution at visible and NIR ocean color channels.
## 3 Results and Discussion
### SMC Attachment and Proliferation  
Suppression of vascular smooth muscle cells (SMCs) from excessive proliferation is an effective method for reducing the in-stent restenosis and hyperplasia, which is the most serious clinical problem in the treatment of cardiovascular diseases [46]. Thus, SMCs' attachment and proliferation test _in vitro_ can be performed to investigate the anti-hyperplasia property of biomaterials. The fluorescence images of the SMCs on all samples after one day and three days of culturing are presented in Figure 6. It seemed that there were more SMCs on no-donor samples surfaces compared to the donor surface, and the more Cu doped in the TiO\({}_{2}\) films, the less SMCs that were adherent on the samples, suggesting Cu ions may have some influence on the attached number of SMCs. It also could be seen that the SMCs on the no-donor surfaces showed a synthetic phenotype (polygon morphology), while the SMCs on the donor surfaces presented a contractile phenotype (spindle morphology). This result indicated that the Cu-doped surface could promote the SMCs' phenotypic contraction and inhibit SMCs' excessive proliferation by catalyzing donor release NO.  
Figure 7 displays the amounts of SMCs on the Cu0, Cu1, Cu2, Cu3 and Cu4 surfaces by CCK-8 assay after one day and three day cultures, respectively. The results showed that increasing doped amounts of Cu in the TiO\({}_{2}\) films (Cu1, Cu2, Cu3 and Cu4) could reduce the attachment of SMCs (attached SMC numbers in one day: Cu4 \(<\) Cu3 \(<\) Cu2 \(<\) Cu1 \(<\) Cu0), but could not effectively inhibit the SMCs' proliferation because there were no significant differences between all the no-donor samples and the number of SMCs after three days of culture. Notably, the donor samples showed a significantly lower number of SMC compared with the no-donor samples, and the number of SMCs reduced as the amount  
Figure 6: The fluorescence staining of SMC on the Cu0, Cu1, Cu2, Cu3 and Cu4 surfaces after one day and three days of culturing, respectively.  
of doped Cu increased. This result was consistent with the NO release result and the SMC staining result, indicating the amount of doped Cu was positively correlated with the released RSNO and was negatively correlated with the SMCs' attachment and proliferation. The results above demonstrated that the Cu-doped TiO\({}_{2}\) films could significantly induce SNAP to release NO and further inhibit SMCs' attachment and proliferation.
# Characterization of Inter-System Biases in GPS + BDS Precise Point Positioning
After the analysis of ISB characterization, we implement combined GPS and BDS PPP with modeling the ISB as time constant, random walk process and white noise. The comparison of positioning performances will provide the guidance in proposing the optimal modeling strategy of ISB. The remainder of this work is organized as follows: In the subsequent section, we review the undifferenced GPS/BDS PPP model and present the processing strategy. Following this, we designed four experiments to analyze, respectively, the influences of satellite clock datum, receiver type, antenna type and ambient environment upon ISB characterization. We also conducted a GPS/BDS PPP experiment with modeling ISB in different manners to find the optimal strategy. Finally, the last section presents summaries and conclusions.