esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Bulk Crystal Characteristics  
### Crystal Structure  
The ScBO\({}_{3}\) crystal remains a trigonal crystal system, a 3m1 point group, and a R\(\bar{3}\)c space group. As exhibited in Figure 1a, the Sc atoms and O atoms are coordinated to form ScO\({}_{6}\) octahedra, which are connected to each other to form a pore structure. The B atoms are located in the pores and coordinate with the O atoms to form BO\({}_{3}\) tetrahedra. In addition, the effective ionic radii of Yb\({}^{3+}\) and Sc\({}^{3+}\) are 0.868 and 0.745 A [35], respectively, which is beneficial, even for the Yb\({}^{3+}\)-dropped and lattice pattern distortion in Yb\({}^{3+}\):ScBO\({}_{3}\) crystals.  
The diffraction indices of ScBO\({}_{3}\) crystal, measured by X-ray powder diffraction, matched well with standard PDF#79-0097, as exhibited in Figure 1b. After calculations, the cell parameters of ScBO\({}_{3}\) were obtained: a = b = 4.776 A and c = 15.405 A. When doped with the Yb\({}^{3+}\) ion, the cell parameters and crystal structure apparently do not adjust compared with undoped ScBO\({}_{3}\) crystal [36]. Here, the buoyancy method was applied for measuring crystal density, and the result was 3.787 g/cm\({}^{3}\), which corresponded with the theoretical density of 3.812 g/cm\({}^{3}\).  
Figure 1: **(a)** Structural diagram of ScBO\({}_{3}\) and **(b)** comparison between X-ray powder diffraction pattern of standard PDF#79-0097 and Yb\({}^{3+}\):ScBO\({}_{3}\) crystal [34].
# Dropped Object Impact Analysis Considering Frequency and Consequence for LNG-FPSO Topside Module
## 5 Numerical Simulation and Test Evaluation
### Drop Impact Experiment for a Cone-Type Object  
In this study, the impact energy of the suction drum bundle selected as the QRA results needs to be more precisely calculated through drop impact simulation and reflected in the evaluation. To verify this, the following basic experiment was conducted and the drop impact simulation of cone-type object verified by comparing the experimental results to the analytical results. A drop impact simulation was then performed on the suction drum bundle. Figure 11 shows the drop impact experimental facility used in this study. The facility's capacity is described in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Max. Value** & **Unit** \\ \hline Dropped object weight & 1000 & kg \\ \hline Drop height & 5 & m \\ \hline Impact energy & 51 & kJ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental facility capacity.  
Figure 10: Impact energy exceedance curve by impact frequency.  
The drop impact experiment shows that cone-type objects, such as in Figure 12, perpendicularly free fall to the steel plate at a height of 3 and 5 m, respectively. The maximum vertical displacement of the steel plate is observed along the A-A\({}^{\prime}\) and B-B\({}^{\prime}\) lines, totaling 25 measuring points as shown in Figure 13.  
As a result, the maximum vertical displacement at the drop height of 3 m was 79.6 mm and the maximum vertical displacement at the drop height of 5 m was 98.4 mm as shown in Figures 14 and 15. This means that as the drop height increases, the maximum vertical displacement also increases with the increase in the impact amount; thus, the experiment was effectively conducted.  
Figure 11: Drop impact experimental facility.  
Figure 12: Cone-type dropped object shape.  
Figure 16 and 17 show the strain changes over time immediately after impact (0 s) at the drop height of 3 and 5 m, respectively. In both cases, it can be seen that the maximum strain at the 150 mm spot is greater than eight times the maximum strain at the 500 mm location. In addition, it is possible to verify that the strain remains after 0.06 s at the 150 mm spot, but at the 500 mm location, the strain converges to a value that is nearly zero. This means that the test was effectively conducted and can be seen as a relatively good implementation of local variations caused by a drop impact near the collision point.  
Figure 14: Vertical displacement of the drop impact experiment (drop height of 3 m).  
Figure 13: Test specimen (AH36, 1200 \(\times\) 1200 \(\times\) 10 mm).  
Figure 15: Vertical displacement of the drop impact experiment (drop height of 5 m).  
From the aforementioned results, it was confirmed that the experiment was effectively conducted and the resultant values of vertical displacement and strain were used as a reference to verify the validity of the simulation method by comparing it to the results of the drop impact analysis later introduced.
# Joint Trajectory and Communication Design for Buffer-Aided Multi-UAV Relaying Networks
## 2 System Model and Problem Formulation
### Problem Formulation  
Let \(A=\left\{a_{k,m}\left[n\right],m\in\left\{S,D\right\},\forall k,n\right\}\), \(P=\left\{p_{k}\left[n\right],\forall k,n\right\}\), and \(Q=\left\{q_{k}\left[n\right],\forall k,n\right\}\), which corresponds to the relay selection, UAV transmit power, and UAV trajectory design, respectively. It is assumed that the locations of the stationary ground nodes are known, and our goal is to maximize the average throughput of the network by jointly optimizing \(A\), \(P\), and \(Q\). We formulate the average throughput maximization problem as:  
\[\max_{A,P,Q}\frac{1}{N}\sum_{n=2}^{N}\sum_{k=1}^{K}a_{k,D}\left[n\right]R_{k,D} ^{{}^{\prime}}\left[n\right] \tag{17a}\]\[s.t. \sum_{i=2}^{n}R_{k,D}^{\prime}\left[n\right]\leq\sum_{i=1}^{n}R_{k,S} \left[n\right],\forall k,n=2,\ldots,N\] \[\left(1\right)-\left(3\right),\left(6\right)-\left(10\right)\]  
Inequation (17b) is the information causality, which implies that the forwarded data by UAV must be already stored in its buffer. Inequations (1)-(3) are constraints on the UAV's mobility, including the UAV's initial and final location, the maximum speed of UAV and collision avoidance. Inequations (6)-(8) are relay selection constraints, and Inequations (9) and (10) are the average power and peak power constraints of transmit power. Since all UAVs can only transmit the data that have been previously stored from \(S\), according to the Shannon formula, Problem (17) can be rewritten as:  
\[\max_{A,P,Q}\frac{1}{N}\sum_{n=2}^{N}\sum_{k=1}^{K}a_{k,D}\left[n \right]\log_{2}\left(1+\frac{p_{k}\left[n\right]\left|h_{k,D}\left[n\right] \right|^{2}}{\sigma^{2}}\right) \tag{18a}\] \[s.t. \sum_{i=2}^{n}a_{k,D}\left[n\right]\log_{2}\left(1+\frac{p_{k} \left[n\right]\left|h_{k,D}\left[n\right]\right|^{2}}{\sigma^{2}}\right)\] \[\leq\sum_{i=1}^{n}a_{k,S}\left[n\right]\log_{2}\left(1+\frac{p_{S }\left|h_{k,S}\left[n\right]\right|^{2}}{\sigma^{2}}\right),\forall k,n=2, \ldots,N\] \[\left(1\right)-\left(3\right),\left(6\right)-\left(10\right)\]  
Since the objective function is non-concave and the constraints are non-convex, Problem (18) is non-convex and difficult to solve directly. First, since the optimization variables \(A\) for relay selection are binary, the constraints in (6)-(8) involve integer constraints. Second, Constraint (3) is non-convex with regard to UAV trajectory variables \(Q\). Finally, the information causality constraint in (18b) is non-convex because of the coupling of three optimization variable. In the next section, an efficient algorithm is proposed to make the problem mathematically tractable.
# Food and Drinking Water as Sources of Pathogenic Protozoans: An Update
## 8 Blastocystis hominis
hominis_ STs 1-3 alter cognitive function and prefrontal cortex gene expression, thus suggesting further detrimental effects of _B. hominis_ on well-being [230]. The global estimated pooled prevalence of _B. hominis_ infection in immunocompromised people was 10%--specifically, 21% in Australia, 12% in America, 11% in Europe, 10% in Asia, and 7% in Africa [211]. A cross-sectional study involved patients immunocompromised for different reasons and found that _B. hominis_ infection was significantly higher in immunocompromised patients and not statistically different for immunodeficiency of distinct origin [231]. Contrasting results were obtained in Turkey where _B. hominis_ infection was more frequent in immunocompetent patients (21.8%) compared to immunocompromised patients (12.7%) [39]. Recent symptomatic cases that could be of food origin involved a previously healthy 13-year-old boy who had fever, headache gastrointestinal symptoms for two days after consumption of food in a restaurant [232] and a 3-year-old boy with HIV at stage three and under treatment for pulmonary tuberculosis, who suffered from gastrointestinal symptoms for about seven days and presented sunken eyes, decreased skin turgor, and a rash on the extremities [233]. After the detection of _B. hominis_ in feces, patients were treated with TMP-STX or metronidazoleople and recovered [232; 233]. B. hominis was detected in fresh vegetables [85], and contaminated handmade food eaten on the way home was possibly a source of _B. hominis_ infection in schoolchildren aged 3 to 15 years old from a rural community in Mexico who showed 44% prevalence of the parasite, represented by subtypes ST1, ST2, and ST3 [234]. B. hominis was detected in tap water and drinking water treatment facilities worldwide, and it is not destroyed by chlorine treatment at the adopted concentrations of 0.2-5.0 ppm. Indeed, isolates exposed to chlorine recovered growth capacity after 24 h of ceased exposure, suggesting that resistant forms, i.e., cysts, were formed during exposure. Chlorine concentrations ranging between 178 and 2857 ppm were required for complete inhibition and subtypes ST1 and ST7 were the most resistant. All subtypes were also resistant to hydrogen peroxide, with ST8 and ST9 showing the highest resistance. Concentrations ranging from 103 ppm to 3338 ppm were necessary to impede recovery after 24 without exposure. Therefore, hydrogen peroxide at concentrations used for disinfection, i.e., 5000 ppm, is adequate to eliminate the organism from surfaces and objects [13].
# Studying the Impact of a UDP DoS Attack on the Parameters of VoIP Voice and Video Streams
## 6 Discussion
### Summary of Results  
The presented results confirm the results from the previous study obtained when carrying out the same research with the difference that the Asterisk FreePBX was attacked with different TCP DoS attacks. It was then found that the different TCP DoS attacks did not affect the parameters of the voice and video streams. Asterisk was not blocked. The same results were obtained in this study. Again, as in the previous study, Asterisk's built-in firewall was enabled. These two different studies show that Asterisk is designed to resist different DoS attacks that do not affect the parameters of the different streams and do not cause the server to be blocked. This is confirmed by the result in Figure 42. Figure 42 represents what the result of running the \"vuln\" script in Nmap was. Through this script, Nmap checks the device under test for the most common vulnerabilities. As can be seen from the result, the Asterisk FreePBX is only slightly vulnerable to some DoS attacks that could possibly lead to a denial of service.  
Figure 41: Asterisk’s memory load for the whole video call study period.  
Figure 40: Asterisk’s CPU load for the whole video call study period.  
From the carried-out study, it can be argued that the Asterisk FreePBX is sufficiently secured from the impact of various TCP DoS and UDP DoS attacks. This VoIP platform is suitable for small to medium-sized institutions that cannot afford large investments in additional cyber protection measures. The only way to detect either of the two attacks (TCP DoS or UDP DoS) is to monitor the \"Live network Usage\" tab from the dashboard menu of the Asterisk FreePBX or configure alarms to be sent when there is an increase in traffic above a certain value. As seen from the carried-out study, this is the only way to detect the presence of either of the two attacks because these attacks do not affect the quality of voice and video streams--calls do not drop, no delays occur, and there is no packet loss. Regardless of the available built-in security of the Asterisk FreePBX, it is recommended to add other additional security measures such as those discussed in [35; 36; 37; 38; 39; 40].
## 3 Results and Discussion
### Optimization Process Results
The most promising result can be seen in the soot vs. HC comparison (bottom-right plot in Figure 12), where both pollutant emissions were drastically reduced from the oxy-fuel baseline case, even reaching values much lower than the initial diesel reference case, with plenty of solutions still under the constraints. Based on the results, the optimized geometry profile was compared against the baseline geometry, which is the same of the original diesel engine but adapted to the CR 28. The comparison of both geometries is shown in Figure 13. The baseline step-bowl profile changed to a new geometry without the step, where the re-entrant configuration has also disappeared. Initially, one objective of the step-bowl was to split the the fuel spray in two parts, with one direction toward the cylinder head, and the other part into the bowl. This helped the spray to avoid reaching the squish zone, resulting in less soot generated close to the cylinder walls, decreasing the soot-in-oil production in the engine [46; 47]. The new bath-tube geometry is a result of the optimization process that reduces the soot emissions, and at the same time improves the efficiency, because this new geometry is focused on the area reduction, which reduces the heat transfer losses and improves the engine efficiency [48]. Additionally, the spray angle changes in order to match the new geometry. The spray angle in the baseline case conduces the injected fuel within the piston bowl, promoting spray-wall impingement; meanwhile in the new configuration the angle is wider. Therefore, the distribution of the spray in the chamber volume is more uniform, increasing the interaction between the injected fuel with the gas mixture available inside the chamber. Figure 12: Pareto front of max. pressure of the engine vs. efficiency, CO emissions, HC. and soot evolution vs. efficiency. The pollutant emissions are normalized by the reference diesel case value. Finally, Table 8 summarizes the parameters that describe the combustion system for the baseline and the optimized cases. The oxy-fuel combustion concept requires an injector with lower orifice number (that is higher diameter in order to keep the same nozzle area). A wider spray angle is obtained, matching the new piston bowl geometry and avoiding excessive fuel impingement with the walls. Moreover, the optimal number of holes depends on the engine operating condition and compression ratio, but in general a low number of holes results in good efficiencies because it promotes the highest rate of heat release during the mixing combustion phase [47,49]. The swirl number was reduced following the number of injector nozzle behavior. The injection pressure is higher, improving the spray momentum, the atomization, and the mixing rate. The last parameter is the SOI and it was delayed until an instant where it is possible to control the peak of in-cylinder pressure within the limit of the engine operation.
# Prediction Interval Adjustment for Load-Forecasting using Machine Learning
## 4 Experiments and Results  
To measure the efficiency of the prediction intervals (Normal and Adjusted), we propose the use of the Prediction Interval Coverage Probability (PICP) [31] and the Dawid-Sebastiani Score (DSS) [32]. We also measure probabilistic and point-forecast MAE and RMSE per horizon \(h\) and the PICP per period \(p\) for a better understanding of the prediction interval efficiency. To evaluate the quality of the Adjusted prediction interval, three experiments were conducted: All days, Weekdays and Weekends in dataset \(D_{Test}\). For every experiment, normal prediction intervals (Normal) and Adjusted prediction interval (Adjusted) are evaluated. The process to implement these evaluations consists of three steps:  
1. Calculate Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) for the point forecasts for each horizon.  
Figure 10: The support value distribution per period of the dataset to extract the rules.  
2. Calculate the Dawid-Sebastiani score (DSS) and Prediction Interval Coverage Probability (PIPC) along with Probabilistic RMSE and MAE per horizon.
3. Estimate PIPC and probabilistic RMSE and MAE of the Adjusted prediction interval per period. In the following section, the implementation of the mentioned metrics is described.
# Generation of High Resolution Vegetation Productivity from a Downscaling Method
## 2 Materials and Methods
### Methods
#### 2.3.3 Accuracy Assessment  
To assess the performance of downscaling method proposed in this paper, the estimated GPP from the MusyQ GPP algorithm was validated with the ground observed GPP. Noting that the mean GPP values in a 3 pixels \(\times\) 3 pixels window around each observation site were used to compare with the ground observed GPP to decrease the co-registration errors between images and observation sites. Besides, the estimated GPP and NPP were validated with high resolution GPP and NPP pixel by pixel. The Determination Coefficient (R\({}^{2}\)), Root Mean Square Error (RMSE) and Mean Relative Error (MRE) were used to quantify the downscaled GPP and NPP.
# Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks
## 5 Power Allocation Solution
\begin{subarray}{c}i\
eq u_{c}\\ i\
eq u_{c}\end{subarray} }^{U}q_{i}|\mathbf{h}_{u_{c}}^{T}\mathbf{w}_{i}|^{2} +1\right)}f_{1}+\frac{1}{\left(\beta_{u_{c}}\sum_{ \begin{subarray}{c}i\
eq u_{ c}\\ i\
eq u_{c}\end{subarray} }^{U}q_{i}|\mathbf{h}_{u_{c}}^{T}\mathbf{w}_{i}|^{2}+1 \right)}f_{2}, \tag{26}\]  
where \(f_{1}(u_{c})\), \(f_{1}(u_{ce})\), and \(f_{2}(u_{e})\) equal zero  
\[f_{1}(i)=\frac{B\beta_{u_{c}}|\mathbf{h}_{u_{c}}^{T}\mathbf{w}_{i}|^{2}}{ln2} i\
eq u_{c}\text{ and }i\
eq u_{ce}, \tag{27}\]  
\[f_{2}(i)=\frac{B\beta_{u_{c}}|\mathbf{h}_{u_{c}}^{T}\mathbf{w}_{i}|^{2}}{ln2} i\
eq u_{e}. \tag{28}\]  
A new variable \(\eth\) is introduced to smooth the optimization function. Then, we formulate the \(\eth\) optimization problem for the NOMA-based scheme as follows:  
\[\underset{\mathbf{q}\geq 0}{maximize} \eth\] (29) Subject to \[\mathrm{C}_{1},\mathrm{C}_{2}^{new},\mathrm{C}_{3}^{new}\] \[\mathrm{C}_{4}:X(\mathbf{q})-Z\Big{(}\mathbf{q}^{(k-1)}\Big{)}- \bigtriangledown Z^{T}\Big{(}\mathbf{q}^{(k-1)}\Big{)}\Big{(}\mathbf{q}- \mathbf{q}^{(k-1)}\Big{)}\geqslant\eth.\]  
The final objective function in (29) can be effectively solved to obtain the objective sub-optimal power allocation solution. To obtain the optimal \(\eth_{optimum}\) that gives the optimal power allocation, we apply the bisection-based iterative algorithm shown in Algorithm 3, where the search domain is halved in each iteration to guarantee fast convergence. ```
1:Input:\(q_{max}\), tolerance \(\epsilon>0\)
2:output:\(q_{u}\) for \(u\) = 1,...,U. 3:Initialization: set \(\mathfrak{I}^{lower}=0\) and \(\mathfrak{I}^{upper}=min\Big{(}\frac{\mathbb{R}}{7}\Big{)}\). 4:Calculate:\(\mathfrak{I}=\frac{upper_{+}\mathfrak{I}^{lower}}{2}\). 5:Solve: (29) using the sequential convex approximation for \(\mathfrak{I}\) and evaluate \(q_{u}\). 6:If feasible: then \(\mathfrak{I}^{lower}\leftarrow\mathfrak{I}\) and \(q_{u}=q_{optimum}\) for \(u\) = 1,...,U. 7:Else \(\mathfrak{I}^{upper}\leftarrow\mathfrak{I}\)
8:while: \(\mathfrak{I}^{upper}-\mathfrak{I}^{lower}>\epsilon\)
```  
**Algorithm 3** Bisection algorithm for solving the max-min fairness power allocation problem. The procedures of finding the iterative power allocation using convex software packages for a given \(\mathfrak{I}\) is given in Algorithm 4. ```
1:Initialize:\(k=0\) and \(\mathbf{q}^{(0)}\)
2:Repeat:
3:Solve: the feasibility problem in (29)
4:Subject to \(C_{1},C_{2}^{new},C_{3}^{new},C_{4}\)
5:Set:\(k=k+1\). 6:Until:\(\mathbf{q}^{(k)}\) converges. ```  
**Algorithm 4** Iterative SCP power allocation for obtaining \(q\).
## 3 Simulation
### MPP of Axial Cracks of Different Diameters  
The simulation results of the \(\varphi\)50 mm \(\times\) 8 mm steel pipe under different excitation currents are shown in Figure 6. Figure 6a-e show the cloud of the permeability distribution above the defect, and Figure 6g-k reflect the current density variation below the probe. When the magnetizing current is small, the MPP caused by the axial defect in the internal wall is weak, which has a small impact on the eddy current distribution. When the excitation current is strong enough, the MPP causes an effective eddy current perturbation in the surface layer of the steel pipe.  
\begin{table}
\begin{tabular}{c c} \hline
**Parts** & **Parameter** \\ \hline Excitation coil & Excitation frequency = 50 kHz \\  & Voltage = 5 V \\ Excitation coil & Number of turns = 100 turns \\  & Inner diameter = 4 mm \\  & Outside diameter = 8 mm \\ \hline Magnetized coils & Magnetizing current = 5.5 A \\  & N = 3200 \\ \hline Detection coil & Turn Ratio = 100 turns \\  & Inner diameter = 4 mm \\  & Outside diameter = 8 mm \\  & R = 4 \(\Omega\); L = 90 \(\mu\)H \\ \hline Steel pipe material & Material = No. 45 \\  & Maximum permeability \(\mu_{r}\) = 675 \\ \hline Defects in the inner wall & Width = 1 mm \\  & Depth = 1 mm, 1.5 mm \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameter settings.  
Figure 5: Steel magnetization curve.  
The maximum induced voltage of the probe is obtained every time the defect passes directly through the probe, as shown in Figure 7a. The changes in MPP under different magnetization currents while other parameter conditions remain unchanged are shown in Figure 7b. The voltage signal gradually increases with the enhancement of the excitation current at different excitation currents, and the voltage signal peaks around 4.5A and then gradually decreases with the enhancement of the excitation current. \(\Delta Vpp\) is the peak-to-valley value of the signal.
## 4 Discussions
### Computing Resource Consumption  
Although the computational complexity of the algorithms is not very high, the consumption of computational resources (e.g., memory and processor resources) is still a challenge when dealing with extremely large-scale point cloud data. Especially in real-time applications, computational resource consumption may become a bottleneck. To solve this problem, parallel computing techniques can be utilized to improve the computational efficiency by distributing computational tasks to be executed on multi-core processors. Parallelization is achieved using techniques such as OpenMP ([[https://www.openmp.org/](https://www.openmp.org/)]([https://www.openmp.org/](https://www.openmp.org/))) or CUDA ([[https://developer.nvidia.com/cuda-toolkit](https://developer.nvidia.com/cuda-toolkit)]([https://developer.nvidia.com/cuda-toolkit](https://developer.nvidia.com/cuda-toolkit)), accessed on 4 July 2024), as well as the use of distributed computing frameworks (e.g., Apache Spark ([[https://spark.apache.org/](https://spark.apache.org/)]([https://spark.apache.org/](https://spark.apache.org/))) or Hadoop ([[https://hadoop.apache.org/](https://hadoop.apache.org/)]([https://hadoop.apache.org/](https://hadoop.apache.org/)))) for the distributed processing of large-scale point cloud data on multiple machines to further increase the processing power.