esa-sceva/satcom-chunk-collection huggingface dataset:
# Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring
## 6 Conclusions  
In this article, the design and implementation of a UAV assisted long-range buoy based WSN for the monitorization of marine-coastal environments, as well as an improvement to current implementations, has been proven. The development of a LoRa mobile wireless network, from a hardware and firmware perspective, was presented, as well as the implementation of the novel LoRaNET network protocol for LoRa devices. These two developments, applied to the SIMMA end devices, provide an easy-to-use and low power solution, with an extended range of several kilometers, as well as a more cost-effective alternative with fewer infrastructure requirements. Furthermore, it serves as a less invasive method for monitoring natural protected areas and reservoirs.  
The integration of the UAV, the Davis monitoring buoys, and the LoRa communications as a single system, adds a needed flexibility to current implementations and the possibility of studying previously unreachable areas, since the buoys can be placed with a small vessel and the UAV can be flown from almost any area, given its great communications reach.  
Although this proposal was aimed at marine monitoring, this same system architecture can be used for other types of environments. For instance, the upper part of the buoy, where the slave node's electronics are housed, could be used as a portable meteorological station to temporarily monitor different areas for a given time interval.
## 2 Results and Discussion
### Phd-Doc-AbrB-Doc Composite  
The Phd-Doc family is among the least distributed TA families [50]. In the canonical system, the toxin, Doc (\"death on curing\") is associated with the antitoxin, Phd (\"prevents host death\") [50]. However, the \"mix and match\" phenomenon described above has been reported in this TA family as well, where Doc associates with antitoxins other than Phd, and Phd associates with toxins other than Doc [50].  
The Doc toxin belongs to the Fic (filamentation induced by cyclic AMP) protein superfamily [84] and has a bacteriostatic effect [84; 85]. Antitoxins harboring SpoVT-AbrB-like domains that belong to the doc-partner protein family, which shares homology with the SpoVT-AbrB superfamily as indicated by the InterPro domain analysis tool, may associate with the Doc toxin [69]. They form an AbrB-Doc composite, which belongs to the Phd-Doc TA family [69]. _Geobacillus_ strains presented here harbor this composite, as discussed below.
## 3 Results
### 3D Mapping  
For the 3D mapping of \(Q_{c}\), we followed the procedure described by [20] for the \(Q_{c}\) tomography of Mount Etna. We considered for our imaging the results gathered at \(t_{L}=30\) s. Three non-overlapping frequency bands, centered on \(f_{c}\), were selected: \(f_{c}=3\) Hz ranging within [2.1, 4.2] Hz; \(f_{c}=6\) Hz ranging inside [4.2, 8.5] Hz; \(f_{c}=12\) Hz, ranging within [8.5, 17.0] Hz.  
To make our images more reliable, we added to our results as hitherto described, as well as adding the results previously published by [23]. We then built a merged database of \(Q_{c}^{comp}\) estimates and errors, \(\Delta Q_{c}^{comp}\)(respectively, \(Q_{ci}\) and \(\sigma_{i}\) in Equations (5) and (6)). In this way, we collected a total number of source-receiver three-component estimates as follows: \(N_{s-r}=651\) for each recording station, that is, 1953 \(Q_{ci}\) values and \(\sigma_{i}\) errors. The Earth medium was subdivided in \(M_{pix}\) cubic pixels having side of 5 km. \(Q_{c}\) images at each depth are representative of a layer of 5-km thickness. For the section maps, we used an 8-km depth step. Thus, we plotted \(Q_{c}\) images at 0, 8, 16, 24, 32 km, each depth being representative of a 5-km \(j\)-th pixel. In Figure 8a-c, we plotted the \(Q_{c}\) images for \(f_{c}=3\) Hz, \(f_{c}=6\) Hz and \(f_{c}=12\) Hz, respectively. \(Q_{c}\) tomographic images show very low errors since \(\sigma\) is more than one order of magnitude less then \(Q_{c}\) in the whole area at all the considered frequencies. Therefore, our results can be considered robust and significant.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Region** & \(Q_{0}\) & \(\alpha\) & \(t_{L}\)**(s)** \\ \hline Gargano (Italy) [this study] & 63 & 1 & 40 \\ Gargano (Italy) [23] & 50 & 0.7 & 40 \\ Mts. Peloritani (Italy) [44] & 50 & 0.7 & 40 \\ Messina Strait (Italy) [45] & 76 & 0.5 & 40 \\ Southeastern Sicily (Italy) [46] & 38 & 1.4 & 40 \\ Central Appennines (Italy) [39] & 88 & 0.6 & 40 \\ Granada Zone (Southern Spain) [3] & 86 & 0.9 & 40 \\ Almeria Basin (Iberian Peninsula) [47] & 63 & 0.9 & 40 \\ Pannonian Basin (Croazia) [48] & 108 & 0.7 & 40 \\ Northern Morocco [49] & 140 & 0.9 & 40 \\ Racha Region (Georgia) [50] & 79 & 1 & 60 \\ Mainland Gujarat (India) [51] & 112 & 0.9 & 40 \\ Nepal Himalaya [52] & 143 & 1 & 40 \\ Northwestern Himalaya [53] & 150 & 1 & 30 \\ Sikkin Himalaya [54] & 91 & 1 & 40 \\ Taiwan [55] & 93 & 0.8 & 30 \\ Western Nagao, Japan [56] & 112 & 0.7 & 30 \\ Andaman Islands [57] & 122 & 0.8 & 40 \\ Parcels Basin (Brazil) [58] & 98 & 1.2 & 40 \\ Eastern Cuba [59] & 64 & 0.8 & 20 \\ North Tanzania [60] & 146 & 1 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of \(Q_{0}\) and \(\alpha\) of other regions with active tectonics in Italy and worldwide, for approximately the same \(t_{L}\).
## 3 Results and Discussion
### The EYB Eyebrow Image Results  
#### 3.2.1 Curvature-Based Method Results  
In this section, an evaluation is performed on the eyebrow images still with the EYB dataset. Table 7 displays the performance results for the eyebrow images using the same experimental conditions for the eye images from Table 3. In contrast to the results from the eye images, from Table 7, we can see that the results from the eyebrow images displayed high accuracy. Almost all combinations with curvatures produced higher results compared with the eye images. The _SVM-1_ demonstrated superior performance to the original images with the highest accuracy of 92.63% and 91.24% from an extracted min principal curvature (\(N\)).
## 3 Results and Discussion
### Oxide Surface Characterization
#### 3.1.4 Oxide Surface Compositions  
Table 2 compiles the EDS surface compositions for each oxide group. The corresponding average Ca/P ratios for each oxide group are provided in Figure 4. The alpha- and beta-phase-stabilizing alloying elements Al, V, and Mo within each titanium alloy substrate were found to be largely absent from the oxides in these EDS surface analyses. The resulting Ca/P surface ratios for each oxide group were approximately 2.0, which is close to the range observed in bone tissue (1.5-1.7) [28,55]. Recent studies in our laboratory added P oxide dopants through anodization and subsequently added Ca onto the anodized surfaces using hydrothermal soaking treatments [56]. The two-step oxides exhibited surface Ca/P ratios similar to those found in hard tissues like bone and were shown to significantly improve the Ca/P ratio. The results of the two-step oxides exhibited surface Ca/P ratios are shown in Figure 4.  
prove osteoblast mineralization compared to non-anodized CPTi substrates [56]. Titanium oxide surfaces containing Ca and P dopants have been shown to improve bone-implant interface bone formation by increasing the local mineral content, and these coatings are thus considered osteoinductive [57; 58].  
For magnesium, the TiMo \(\beta\) and TiMo \(\alpha+\beta\) oxides exhibited higher uptake than the CPTi and TAV oxides, as shown in Table 2. Previously, Mg-hydroxyapatite combination sol-gel dip coatings on titanium surfaces showed improved interfacial coating adhesion strengths and push-out forces compared to the hydroxyapatite-only counterparts [59]. Furthermore, micro-CT analyses of these Mg-hydroxyapatite combination coatings showed improved trabecular bone formation and osseointegration abilities. In a rabbit tibia model study, Mg-doped titanium anodization coatings showed rapid integration into the bone and improved removal torque values compared to non-anodized implants [60].
## 2 Preparation and SEY Characterization Methods of MgO Coatings  
### Preparation of MgO Coatings by ALD  
ALD technique was used to prepare MgO films. Heavy-doped silicon sheets with low resistance were used as the substrates (n-type Si, crystal direction <100>, resistivity less than 0.3 \(\Omega\)-cm). Chemical reaction diagram of MgO film prepared by ALD is shown in Figure 1. In the preparation experiments, high-purity nitrogen (99.99% purity) was used as carrier gas, and the precursors of ALD reaction contain Mg(C\({}_{5}\)H\({}_{5}\))\({}_{2}\) and water vapor. During the experiment, the reaction temperature in the chamber was 200 \({}^{\circ}\)C, the gas flow of the carrier gas was 20 sccm, and the pressure of the reaction chamber was maintained at 0.1 Pa. The process of preparing MgO using ALD process can be briefly described as follows: Step 1, introduce gaseous Mg(C\({}_{5}\)H\({}_{5}\))\({}_{2}\) for 2 s to react with the -OH surface. Step 2, after the reaction in step 1, the byproduct C\({}_{5}\)H\({}_{6}\) is born and a -Mg(C\({}_{5}\)H\({}_{5}\)) surface is acquired, then introduce carrier gas N\({}_{2}\) for 10 s to remove the byproduct C\({}_{5}\)H\({}_{6}\). Step 3, inject water vapor into the chamber for 0.02 s to react with the -Mg(C\({}_{5}\)H\({}_{5}\)) surface. Step 4, after the reaction, the by-product C\({}_{5}\)H\({}_{6}\) is born again and a -OH surface is obtained, then introduce N\({}_{2}\) for 10 s to remove the byproduct C\({}_{5}\)H\({}_{6}\). When the above four steps have been carried out, one ALD cycle is completed and a single layer of MgO is formed. The film with a specific thickness can be prepared by repeating the ALD cycle for a certain time. For our experiment conditions, the increment of film thickness after one ALD cycle is about 0.1 nm, and the experiment results reveal that the more cycles were carried out, the more stable the deposition rate is. In the research of this section, the count of ALD cycle is set as 10, 30, 70, 120, 200 and 300 respectively, the expected thickness values of these films are 1, 3, 7, 12, 20 and 30 nm.
# Multi-Constrained Optimization Method of Line Segment Extraction Based on Multi-Scale Image Space
## 2 Methods
### Overall Procedure of Optimization  
When the \(k\)th line segment in the _SumLineVec_ line feature set was processed (\(k<\text{dim}\)), the previous \(k\)-1 line segments were used as reference lines for judgment. In this article, vertical distance constraint, angle constraint, and grayscale constraint thresholds were set respectively by the empirical method decision above.  
According to the constraint relationships between line segments as described above, the specific procedure of line segment optimization and purification was as follows:  
(1) The line feature vector space \(R^{1}\) and \(R^{2}\) were opened for storing line segments that were optimized by geometric and grayscale constraints. The initial state was \(R^{1}=\{\}\) and \(R^{2}=\{\}\).  
(2) Line segments extracted from the original image in _SumLineVec_ were taken as reference lines and directly taken into the optimized set \(R^{1}\).  
Figure 6: The grayscale constraint relationship is based on NCC measurement. Two arbitrary line segments with the same direction information are merged into a virtual line segment and \(3\times 3\) matching windows \(G_{1}\), \(G_{2}\), and \(G_{3}\) are opened grayscale points at the central positions of AB, BC, CD segments on a virtual line segment. NCC is the grayscale normalized cross-correlation index between line segments.  
(3) Line segments extracted from other scale image space in _SumLineVec_ were judged as determined lines with reference lines in the optimized set based on the geometric distance and angle constraint relationships. If the determined line satisfied the optimization and purification situation, \(R^{1}\) would be updated. Otherwise, the determined line would be deleted.  
(4) When all lines in _SumLineVec_ were processed, an optimized set \(R^{1}\) of line segments was obtained.  
(5) For any two lines in \(R^{1}\), virtual line segments were constructed by directional characteristics, where the grayscale constraint relationship was used for judgement. If two lines satisfied the grayscale constraint optimization situation, \(R^{2}\) was optimized. Otherwise, those lines that do not need to be optimized were directly stored to \(R^{2}\).  
(6) The optimization set \(R^{2}\) was purified by geometric constraint as a purification criterion. When geometric distance between line segments satisfies the culling condition, it was considered that there was a redundant line segment and we deleted the error. Also, when the angle distance between lines satisfied the culling condition, it was considered that there was a pseudo-line, which we deleted. Finally, we achieved the resulted line segment feature set \(R^{2^{*}}\).
# Improving Transaction Speed and Scalability of Blockchain Systems via Parallel Proof of Work +
## 4 Results of Experimental Evaluation
### Evaluation in Local Environment
#### 4.1.2 Results  
For difficulty levels 1, 2, 3 and 4, there is no significant difference between solo mining and parallel mining. However, for difficulty levels 5, 6 and 7, there is improvement in parallel mining, and, as Figures 6 and 7 depict, this improvement becomes significant with the increase in the difficulty level and the number of miners. In solo mining, the average time depends only on the level of difficulty, but, in parallel mining, the average time depends on both the difficulty level and the number of peers. If the level of difficulty increases, the average time required increases. Again, if the number of peers increases, the average time decreases because the miners are working in parallel and no two miners perform the same work. Another important aspect to notice is that the average time taken for one peer in parallel mining is almost the same as that in solo mining regardless of the number of peers. This is because, when there is only one miner in parallel mining, no parallel work is taking place. The improvement reaches 34% for five miners compared to one miner. It should be noted that the results may vary based on the processing power allocated to the miners.
# On the Accuracy of Turbulence Model Simulations of the Exhaust Manifold
## 1 Introduction
The study found that a horizontal pipe with a twisted tape insert significantly improved the heat transfer rate in both laminar and turbulent flow regimes. The CFD simulations were in good agreement with the experimental data, validating the accuracy of the model. [PERSON] et al. [12] presented a theoretical analysis that evaluated three distinct computer-aided engineering (CAE) methodologies for simulating fluid flow and thermal distribution in the exhaust manifold of a heavy-duty diesel engine. The fluid flow pulsations in the engine exhaust manifold represented a significant level of complexity. Consequently, experimental measurements of the manifold temperatures were conducted to validate the results obtained from each CAE approach. The predicted metal temperature was subsequently utilized to carry out a thermo-structural durability analysis. It is noteworthy, however, that assessing the exhaust manifold design of the HD diesel engine extends beyond the parameters of the present study. [PERSON] et al. [13] compared k-epsilon turbulence models for simulating gas release and dispersion using the FLACS CFD code. The study identified the most appropriate k-epsilon model for simulating gaseous release in complex environments. The three models analyzed were the standard, RNG, and realizable k-epsilon models. The realizable k-epsilon model provided the best results, while the standard k-epsilon model was the most computationally efficient. The RNG k-epsilon model predicted the turbulence intensity most accurately. [PERSON] et al. [14] analyzed the exhaust manifold of a four-stroke, four-cylinder, spark-ignition engine using three different fuels: gasoline, methane, and methanol. The focus was on assessing flow characteristics and backpressure at 1000 rpm. Methane exhibited the lowest exit pressure and demonstrated superior exhaust manifold performance. [PERSON] et al. [15] evaluated the performance of the exhaust manifold in a four-cylinder gasoline engine using three fuels--gasoline, alcohol, and LPG. The study aimed to analyze the flow characteristics and thermal behavior and minimize the backpressure. The results showed that LPG fuel produced the least backpressure and was a viable alternative to gasoline. The main goal of this study is to use the CFD approach to analyze and simulate the behavior of exhaust gases as they flow through an exhaust manifold. This analysis will furnish comprehensive details regarding different factors, including velocity, pressure, temperature, and turbulence, which are usually difficult or impossible to acquire through experimentation. To achieve this goal, the study will use multiple turbulence models to compare them and determine the most appropriate one.
## 3 The Relay Experiment and Parameters Analysis
### The Relay Lifetime Prediction Based on Multi-Dimensional GM  
The GM (1, 5) model is established to predict relay life. The GM (1, 5) model includes the parameters of the final lifetime, the temperature, the dynamic contact voltage drop peak, the static contact voltage drop, the duration of the dynamic contact resistance, and the contact bounce time. Among them, the final lifetime is the dependent variate and others are the independent variates.  
The original data are transformed to eliminate the errors caused by different magnitude between the variates. The transformation equation is expressed as follows:  
\[x^{\prime}(k)=x(k)/x(1) \tag{26}\]  
The parameter identification array \(p_{n}\) of the GM is obtained by calculation:  
\[p_{n}=[0.0129\ 0.0387\ 1.0017\ 1.3569\ -1.3172\ 0.0383]^{T} \tag{27}\]  
The final prediction equation is expressed as follows:  
\[\begin{array}{ll}\dot{x}_{1}^{(0)}(k)=&0.0387x_{2}^{(1)}(k)+1.0017x_{3}^{(1) }(k)+1.3569x_{4}^{(1)}(k)\\ &-1.3172x_{5}^{(1)}(k)+0.0383x_{6}^{(1)}(k)-0.0129x_{1}^{(1)}(k)\end{array} \tag{28}\]  
The prediction result of GM (1, 5) model is shown in Figure 5.  
Figure 5: The comparison of prediction value using GM (1, 5) with actual value.  
Figure 5 indicates the average error between the prediction value and the actual value of each relay is \(\overline{e}=\sum\limits_{i=1}^{15}e(i)=3.01\%\). In sum, the lifetime value predicted by the multi-dimensional GM and the actual relay lifetime still has a larger error.
# A Decentralized Blockchain-Based Trust Management Framework for Vehicular Ad Hoc Networks
## 3 Simulation Results and Discussion
### Simulation Setup  
An extensive set of simulations has been conducted using the vehicular network simulator Veins [25], conjointly with the road traffic simulator SUMO [26]. Both the proposed framework and EDTCP protocol [15] used in the trust metrics evaluation phase are implemented in Veins. The considered network map is shown in Figure 3.  
The vehicles enter the network from one entry point with a rate of 2 vehicles/s. All vehicles speed up to a maximum of 50 km/h. In the considered scenario, the vehicles will encounter congestion events on the road, in this case, they slow down or/and stop for a while till the road becomes unblocked. These congestions are reported in alerts broadcast periodically (every 1 s) in the network. Table 1. summarizes the simulation parameters.
# Statistical Properties of SIS Processes with Heterogeneous Nodal Recovery Rates in Networks
## 4 Results  
### Time Evolution and Steady State  
We developed a simulator based on the \(\varepsilon\)-SIS model, depicted in detail in Section 3.4. The time evolution of the SIS process with heterogeneous recovery rates for one specific parameter set is demonstrated in Figure 3. At the beginning of the simulation instance, a small number of nodes were selected to be infected, while the others were susceptible. The fraction \(y(t)\) of infected nodes was recorded as the simulation proceeded step by step, shown as the dotted curve. The steady-state fraction of infected nodes was estimated using (14), shown as the straight line. Additionally, the standard deviation was calculated using the root of the proposed approximation (17) of the variance.  
The steady-state fraction (14) of infected nodes as a function of the infection rate for several different mixed proportions of nodes--\(p=0.2\), \(p=0.5\), and \(p=0.8\)--with a recovery rate of \(\delta_{a}\) was verified via the simulations, as shown in Figure 4. The markers and curves represent the simulation and theoretical results, respectively. For any marker, the corresponding data were obtained by running a single simulation instance for the associated parameter setup and calculating the time-averaged value of the recorded number of infected nodes.  
Figure 3: The fraction \(y(t)\) of infected nodes versus the simulation time for a single run of the SIS process in a complete graph with \(N=40\) nodes. The blue dotted curve represents the simulation data obtained by running the \(\varepsilon\)-SIS model. The thick red line and the dashed green lines represent the expected fraction (14) of infected nodes and the corresponding standard deviations calculated based on the variance (17), respectively.
# New Approach of Blind Adaptive Equalizer Based on Genetic Algorithms
## 3 Methods
### Genetic Algorithm Setting  
#### 3.5.1 Objective Function  
The optimization problem with stochastic linear restrictions to be treated by the genetic algorithm is to minimize \(M\tau_{1}+M\tau_{2}\), as defined in Equation (23), subject to the inequality constraints defined by Equations (24)-(27) and to the equality constraint defined by Equation (28).
## 7 Ground-Segment Optimization Aspects
### Applicable Optimization Methods for Ground Segment  
Looking back at the state-of-art methods from Table 2 and the example studies from the previous subsection, which mostly utilize traditional methods for simple analysis, we can identify the main applicable optimization methods for the ground-segment optimization as NSGA-II, LSTM, and Iterative Ground Station Deployment based on Marginal Revenue Maximization (IGSD-MRM). In this subsection, we will provide an overview for these methods.