esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
APD Avalanche Photodiode (or -Detector)  
BERT Bit Error Ratio Tester  
BGL Background Light  
FoV Field-of-View  
InGaAs Indium Gallium Arsenide semiconductor  
IM/DD Intensity Modulation/Direct Detection  
IRT Index-of-Refraction Turbulence  
LEO Low Earth Orbit  
LP Low Pass, or reception filter (between TIA and limiter)  
OOK On/Off-Keying modulation  
PRBS Pseudo-random bit sequence  
Ppb Photons per symbol-bit  
RFE Receiver front-end  
RSSI Received signal strength indicator  
SNL Shot Noise Limited  
SNR Signal-to-noise ratio  
TIA Trans-impedance amplifier  
TNL Thermal Noise Limited  
\(B\) Bandwidth, e.g., of the RFEs reception filter  
\(c\) Speed of light in vacuum (2.998 \(\times\) 10\({}^{8}\) m/s)  
\(F_{A}\) Excess noise factor of an APD  
\(F_{n}\) Amplifier noise figure  
\(h\) Planck constant (6.626 \(\times\) 10\({}^{-34}\) Ws\({}^{2}\))  
\(i_{t}\) Thermal noise current density from amplifier  
\(I_{d}\) Dark current of a photodiode  
\(I_{dm}\) Part of dark current that will get multiplied with \(M\)  
\(I_{du}\) Part of dark current that will not get multiplied  
\(k_{A}\) Ionization coefficient ratio of electrons vs. holes  
\(k_{B}\) Boltzmann constant  
\(L_{e,\Omega,\lambda}\) Spectral irradiance (typically per nm wavelength)  
\(M\) Multiplication factor  
\(M_{opt}\) Optimum multiplication factor
## 4 Implementation
### GPSR_DA Algorithm
#### 4.1.1 Distance Calculation  
We can use Equation (1) to calculate the position information \(L_{(N_{i},D)}\), representing the distance between each node in \(N\) and the destination node \(D\). Let the coordinates of node \(N_{i}\) be \((x_{i},y_{i})\), and the coordinates of \(D\) be \((x_{d},y_{d})\).  
\[L_{(N_{i},D)}=\sqrt{(x_{i}-x_{D})^{2}-(y_{i}-y_{D})^{2}}. \tag{1}\]
# Free Vibration Analysis of Triclinic Nanobeams Based on the Differential Quadrature Method
## 4 Numerical Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Simply**} & \multicolumn{2}{c}{**Clamped-Simply**} & \multicolumn{2}{c}{**Clamped-Clamped**} & \multicolumn{2}{c}{**Clamped-Free**} \\  & & \multicolumn{2}{c}{**Supported**} & \multicolumn{2}{c}{**Supported**} & \multicolumn{2}{c}{**Clamped-Clamped**} & \multicolumn{2}{c}{**Clamped-Free**} \\ \hline \(\mu\) & **Method** & \(\omega_{1}\) & \(\omega_{2}\) & \(\omega_{1}\) & \(\omega_{2}\) & \(\omega_{1}\) & \(\omega_{2}\) & \(\omega_{1}\) & \(\omega_{2}\) \\ \hline
0 & EBBT [50] & 9.87 & 39.4849 & 15.4189 & 49.9738 & 22.3744 & 61.6847 & 3.5161 & 22.0375 \\  & DQM & 9.8679 & 39.4517 & 1541.20 & 49.9098 & 22.3578 & 61.5739 & 3.5157 & 22.0222 \\ \hline
1 & EBBT [50] & 9.4162 & 33.4301 & 14.9929 & 45.3417 & 21.1096 & 50.9844 & 3.5314 & 20.6817 \\  & DQM & 9.4143 & 33.4051 & 14.9864 & 45.2878 & 21.0946 & 50.9046 & 3.5310 & 20.6679 \\ \hline
2 & EBBT [50] & 9.0197 & 29.5117 & 14.5997 & 41.7969 & 20.033 & 44.392 & 3.547 & 19.5111 \\  & DQM & 9.0180 & 29.4911 & 14.5934 & 41.7500 & 20.0193 & 44.3275 & 3.5466 & 19.4985 \\ \hline
3 & EBBT [50] & 8.6695 & 26.7111 & 14.2353 & 38.9732 & 19.1028 & 39.822 & 3.563 & 18.4857 \\  & DQM & 8.6678 & 26.6934 & 14.2293 & 38.9312 & 19.0901 & 39.7667 & 3.5626 & 18.4740 \\ \hline
4 & EBBT [50] & 8.3571 & 24.5814 & 13.8965 & 36.656 & 18.289 & 36.4184 & 3.5795 & 17.5767 \\  & DQM & 8.3555 & 24.5657 & 13.8907 & 36.6178 & 18.2773 & 36.3694 & 3.5791 & 17.5658 \\ \hline
5 & EBBT [50] & 8.0762 & 22.8914 & 13.5803 & 34.7103 & 17.5696 & 33.7581 & 3.5963 & 16.7629 \\  & DQM & 8.0747 & 22.8771 & 13.5748 & 34.6750 & 18.5586 & 33.7137 & 3.5960 & 16.7527 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sensitivity of the size-dependent natural frequencies for different nonlocal parameters and boundary conditions. Thus, the same systematic study is repeated for a clamped-simply nanobeam, whose results are listed in Table 5 and are depicted in Figure 3, with the aim of understanding the role of the nonlocal parameter, the strain gradient parameter, and the nondimensional geometrical ratio \(L/h\), in its vibration response. Based on a comparative evaluation of the response between the present case (clamped-simply supports) and the simply-supported case, a general increase in frequency is observed with respect to the previous example, due to the clamped boundary condition enforced on one side, and the general increase in stiffness of the structures. Moreover, an increasing nonlocality \(\mu\) yields a decreasing structural stiffness, together with a general decrease in the fundamental frequencies. At the same time, an increase in the strain gradient \(l\) enables an increase in frequency, independently of the length-to-thickness ratio. Similar considerations can be repeated for the whole vibration modes here analyzed. Figure 2: Variation of the first four natural frequencies for different \(L/h\) and small-scale parameters (simply-simply supported nanobeam).
## 4 Intelligent Agent Algorithms
### Directional Source Packets for Multi-Agent Routing Planning
#### 4.2.3 Sector Angle Designation  
In SG-MIP, radius is an important parameter in source node grouping operation. In D-MIP, it is solved that how to determine the sector midline. However, how to choose the right angle \(\theta\) is still a challenge. The size of the sector is also determined by sector angle \(\theta\). Firstly, \(\theta\) can be selected based on WSN node density. Meanwhile, \(\theta\) should be changed dynamically according to different routes. As shown in Figure 10, some sectors need a large angle such as VCL\({}_{1}\) to contain all source nodes in the corresponding direction. Finally, after several rounds of route planning, fine-tuning can be used to handle isolated nodes. For example, simple grouping can be used to merge the remaining isolated source nodes into the last route. The algorithm should be able to add source nodes one by one, and plan the route according to the shortest path to minimize the added cost.
## 3 Methodology
### Feature Extraction
#### 3.2.1 Systolic and Diastolic Features  
The direct pressure wave travelling from the left ventricle to the periphery of the body represents the systolic peak. On the other hand, the reflections of the pressure wave by arteries of the lower body represent the diastolic peak. Each cycle of the PPG signal contains one systolic and one diastolic peak as seen in Figure 7a. These peak locations were extracted as features of the PPG signal.  
Figure 6: Raw PPG signal (**top**) was first preprocessed with ALS (**middle**) and then cleaned with a Gaussian filter (**bottom**). Both high-frequency noise and baseline variations had been removed.  
DeIT refers to the time separation between systolic and diastolic peaks. It was computed from each pair of the systolic-diastolic peaks of the signal. DeIT found from the time difference between the systolic and diastolic peak in each cycle of the preprocessed PPG signal were stored in an array. The systolic, diastolic and DeIT features are presented in a sample PPG signal in Figure 7a.
# Hybrid Approach with Improved Genetic Algorithm and Simulated Annealing for Thesis Sampling
## 2 Background
### Genetic Algorithm  
The genetic algorithm (GA) [7] is an evolutionary algorithm inspired by the principle process of natural selection and survival of the fittest. GA is commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operations such as mutation, crossover and selection [8]. As shown in Figure 1.  
GA mainly borrows the principle of the survival of the fittest in biological evolution. First, the solution to the optimization problem is encoded. Here, the encoded solution is called a chromosome, and the elements that make up the encoding are called genes. Then, according to the objective function of the optimization problem, the corresponding fitness function is constructed, and the chromosome is selected by the probability distribution determined by the size of the fitness function. The survived chromosomes constitute a population, and at the same time, the parents are randomly generated according to the probability distribution. Then, the parents create offspring through mating between the codes, and consequently, the offspring mutate with a certain probability to form a new population. At this time, the decoding turns back to the solution to the optimization  
Figure 1: Schematic diagram of the genetic algorithm.  
problem. Finally, the termination rule is applied. Through the above cycle, the solution of the optimization problem is obtained.  
Common iterative methods tend to fall into a local minimum trap, and thus, a dead loop phenomenon emerges, which makes the iteration process unable to continue. To overcome this shortcoming, GA adopts a global optimization approach [8]. Compared with traditional optimization methods (like enumeration, heuristics, etc.), GA has good convergence due to the adaptation of the biological evolution model [9]. GA also has several advantages as follows.  
* It has no bias for specific problem areas.
* It has the capacity to perform a fast and random search.
* It uses a simple search process with the evaluation function.
* It features randomness as the probabilistic mechanism used for the iteration.
* It is extensible and easy to integrate with other algorithms.  
Many variants of GA and its applications were thoroughly studied by [10]. Recently, GA has been one of the widely-used algorithms in solving optimization problems such as for image processing and communications. In this paper, we use it to make a sampling inspection of 40,000 masters' theses according to the three rules for thesis sampling in Shanghai mentioned in Section 2.2.
# Preparation Techniques of TiO\({}_{2}\) Nanofluids and Challenges: A Review
## 2 Preparation Methods
### Single Step Method
#### 2.1.3 Submerged Arc Method  
In this method, a pure titanium rod is submerged into the base fluid in the vacuum chamber. After that, the process parameters such as pulse duration, electric current breakdown voltage, off-time duration, the pressure of the vacuum chamber and base fluid temperature are established, an electric arc generator generates a high pulsed arc in the temperature range of 5000-20,000 K that evaporates the deployed titanium rod along with some base fluid. The vaporized metal particles are cooled and condensed in the vacuum chamber with the help of isolated liquid. The nanofluids prepared by using this method are stabilized and well dispersed in the base fluid.  
[PERSON] and [PERSON] [43] analyzed the process parameters of the arc spray nanofluid synthesis system and prepared deionized water-based TiO\({}_{2}\) nanofluid. Nanoparticles with an average extent of less than 20 nm, high distribution, improved roundness and dispersion in the base fluid were successfully achieved by using this technique. Figure 5 shows the schematic diagram of the [PERSON] and [PERSON] experimental apparatus. [PERSON] and [PERSON] [44] used the improved submerged arc method to fabricate TiO\({}_{2}\) nanofluid. In this method, electric energy was converted to heat energy with temperatures exceeding 100,000 \({}^{\circ}\)C, which melts the metal more rapidly. By using this improved method, they produced finer, stabilized and dispersed nanoparticles as shown in Figure 6.  
Figure 5: Schematic diagram of submerged arc method [43].  
[PERSON] et al. [45] fabricated TiO\({}_{2}\) nanofluid by using the submerged arc nanoparticles synthesis system and investigated the ultraviolet characteristics of the prepared nanocomposite. They observed that the nanocomposite of TiO\({}_{2}\) with UV irradiation can be more effective for the decomposition of Toluene.  
[PERSON] and [PERSON] [46] used the arc spray nanoparticle synthesis system (ASNSS) to prepare TiO\({}_{2}\) nanofluid to be used as engine lubrication oil. A focused electric arc of temperature in the range of 6000 \({}^{\circ}\)C to 12,000 \({}^{\circ}\)C was used to evaporate the titanium in the vacuum chamber. The vaporized titanium was transformed into nanocrystalline powder which collected in the cooling chamber and dispersed into the ethylene glycol to acquire the final solution.  
By using this technique, we can easily achieve a temperature of 10,000 \({}^{\circ}\)C that is more than enough to melt and vaporize the titanium rod. By employing this technique, nanofluids that are generated are highly stable with improved particle roundness and dispersion.
# The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin
###### Abstract  
Quantifying the effects of alpine GMB (Glacier Mass Balance) on river runoff is an important content of climate change. Uncertainty exists in GMB monitoring when applying remote-sensing technology. There are several reasons for these uncertainties, such as terrain deviation co-registration among different topographic data, the mismatch between GSE (Glacier Surface Elevation) from satellite monitoring and the GMB that comprises the physical glacier properties, the driving factors of GMB, and the response patterns of the runoff within the basin. This paper proposed a method based on the ridge line co-registration of DEMs (Digital Elevation Models), and the Tailan River basin, which is a typical glacier melt runoff recharge basin located in the southern Tianshan Mountains, was selected. Abnormal values in GSE changes were removed using ice thickness data, and the GSE results were optimized based on the regularity of the GSE change with altitude to estimate the GMB. The driving factors of the GMB and the response characteristics of the runoff in the basin were also explored. The results showed that the accuracy of the optimized GSE results across different periods has improved by more than 25%. The mean annual thinning value of GSE in the basin from 2000 to 202 was \(-\)0.25 \(\pm\) 0.02 m\(\cdot\)a\({}^{-1}\), corresponding to a GMB value of \(-\)0.30 \(\pm\) 0.02 m w.e.a\({}^{-1}\), indicating a consistent GMB loss state. Combined with climate data, the glaciers in the basin were impacted by rising temperatures, and the smallest increase in annual precipitation in the basin was insufficient to compensate for the GMB loss. Moreover, in the past 22 years, glacier meltwater accounts for 46.15% of the total runoff in the Tailan River basin.  
Tailan River basin; alpine glacier; InSAR; glacier surface elevation; glacier mass balance; glacier runoff +
Footnote †: journal: Materials Research Letters  
The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin  
[1]B. Yang  
1 School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China; [EMAIL_ADDRESS] (B.Y.); [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (A.B.); [EMAIL_ADDRESS] (S.W.); [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (X.G.); [EMAIL_ADDRESS] (X.C.); [EMAIL_ADDRESS] (X.C.); [EMAIL_ADDRESS] (X.C.); [EMAIL_ADDRESS] (X.C.)
# Educational Escape Rooms as an Active Learning Tool for Teaching Telecommunications Engineering
## 2 Methodology
### Escape Room 2
#### 2.2.3 Radio Propagation  
The final puzzle for this escape room involves computation and reasoning around Free Space Path Loss (FSPL). _FSPL_ is a measure of attenuation of radio energy between a transmitter and receiver assuming an obstacle-free line-of-sight path [33]. Assuming non-directional antennas, _FSPL_ can be computed in dB based on antenna gain, distance, transmit frequency and the speed of light as described in (1).  
\[FSPL=20\log_{10}(d)+20\log_{10}(f)+\log_{10}(\frac{4\pi}{c})-G_{Tx}-G_{Rx} \tag{1}\]  
where, \(d\) is the distance in km,\(f\) is the frequency in MHz, \(c\) is the speed of light and \(G_{Tx}\) and \(G_{Rx}\) are the gains for the \(Tx\) and \(Rx\) antennas, respectively.  
In this puzzle students are given a map (Figure 3) and a table which has parameters for antenna gain and frequency for each of the transmitters (1-6). Two frequencies which are commonly used within LoRa communications (433 MHz and 915 MHz) were chosen with a range of different antenna gains to demonstrate the significant effect of frequency on path loss. To solve this puzzle, students need to compute the Euclidean distance (using the scale) for each transmitter and then calculate the FSPL for each transmitter. Once all the FSPL's have been computed the solution is ordered from smallest to largest in terms of FSPL.  
Figure 2: UART Puzzle which spells out “Nine”.
## 2 Materials and Methods
### RCCA Cross-Correlation Matrix  
The \(\mathbf{X}\) and \(\mathbf{Y}\) variables are projected onto a low-dimensional space. Let \(s=2\) for the chosen dimension to adequately account for the data correlation. The projection is done on the equiangular vector \(W_{k}\) between the canonical variates \(\mathbf{U_{k}}\) and \(\mathbf{V_{k}}\) (\(k=1,2\)) represented by \(\mathbf{W_{k}}=(\mathbf{U_{k}}+\mathbf{V_{k}})/2\). In CCA, the variables \(X\) and \(Y\) are symmetrically analyzed, and it is clear that \(\mathbf{W}\) are the closest (most correlated) variable to \(\mathbf{X}\) and \(\mathbf{Y}\)[26]. To identify and grouping each pair of strongly correlated variables, we use a standard measure to quantify the most important cross-correlations, which is based on the scalar product of the \(\mathbf{X}\) and \(\mathbf{Y}\) coordinates on the canonical axes \(\mathbf{W_{k}}\) (\(k=1,2\)).  
Let \(\mathbf{x}\) and \(\mathbf{y}\) be the coordinates of the variables \(\mathbf{X}\) and \(\mathbf{Y}\), respectively, on the axes \(W_{1}\) and \(W_{2}\), which are the correlations between the variables \(\mathbf{X}\) (or \(\mathbf{Y}\)) and \(\mathbf{W_{k}}\): \(\mathbf{x}=(\mathbf{cor}(\mathbf{X},\mathbf{W_{1}});\mathbf{cor}(\mathbf{X},\mathbf{W_{2}}))\) and \(\mathbf{y}=(\mathbf{cor}(\mathbf{Y},\mathbf{W_{1}});\mathbf{cor}(\mathbf{Y},\mathbf{W_{2}}))\). The Matrix \(Z=\mathbf{xy^{\prime}}\) (\(0\leq Z\leq 1\)) represents an approximation of the cross-correlation score of \(\mathbf{X}\) and \(\mathbf{Y}\), using the scalar product between the variables vectors \(\mathbf{x}\) and \(\mathbf{y}\)Indeed, the scalar product is defined as the product of the two vectors lengths and their cosine angle: \(Z=\parallel\mathbf{x}\parallel\parallel\mathbf{y}\parallel\ cos\theta(\mathbf{x},\mathbf{y})\), where \(\theta(\mathbf{x},\mathbf{y})\) is the angle between \(\mathbf{x}\) and \(\mathbf{y}\). Therefore, the cross-correlation value should be stronger or weaker depending on the lengths and the angles between the variable vectors; the higher the norm of the projected vectors, the stronger their relationship. Furthermore, the variables \(\mathbf{X}\) and \(\mathbf{Y}\) have a strong relationship if they are projected in the same (or opposite) direction, so the cosine is close to 1 (resp. of \(-1\)).
## 2 Description of Prototype Seismically Isolated Buildings
### Design of Seismically Isolated Buildings
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**(a) Building at High Seismicity Region**} \\ \hline  & \multicolumn{10}{c}{**Under Strong Windy Load**} & \multicolumn{10}{c}{**Under Weak Windy Load**} \\ \cline{2-13}  & \multicolumn{3}{c}{**3-Story**} & \multicolumn{3}{c}{**5-Story**} & \multicolumn{3}{c}{**10-Story**} & \multicolumn{3}{c}{**3-Story**} & \multicolumn{3}{c}{**5-Story**} & \multicolumn{3}{c}{**10-Story**} \\ \cline{2-13} \multicolumn{1}{c}{-} & \multicolumn{3}{c}{**3S\({}_{\text{E}}\)40C**} & \multicolumn{3}{c}{**5S\({}_{\text{E}}\)40C**} & \multicolumn{3}{c}{**10S\({}_{\text{E}}\)40C**} & \multicolumn{3}{c}{**3S\({}_{\text{E}}\)30B**} & \multicolumn{3}{c}{**5S\({}_{\text{E}}\)30B**} & \multicolumn{3}{c}{**10S\({}_{\text{E}}\)30B**} \\ \cline{2-13}  & **LB** & **M** & **UB** & **LB** & **M** & **UB** & **LB** & **M** & **UB** & **LB** & **M** & **UB** & **LB** & **M** & **UB** & **LB** & **M** & **UB** \\ \hline \(k_{e}\) & 68 & 86 & 86 & 140 & 140 & 140 & 186 & 186 & 186 & 48 & 48 & 48 & 76 & 76 & 76 & 128 & 128 & 128 \\ \(k_{d}\) & 4.7 & 6.6 & 8.9 & 8.7 & 11.0 & 14.9 & 11.6 & 14.4 & 19.9 & 2.8 & 3.6 & 4.7 & 4.3 & 5.4 & 6.9 & 7.6 & 9.6 & 12.7 \\ \(k_{eff}\) & 5.5 & 8.1 & 11.3 & 10.4 & 13.3 & 18.7 & 15.5 & 20.1 & 29.5 & 3.4 & 4.3 & 6.0 & 5.3 & 6.7 & 9.1 & 9.2 & 11.7 & 16.2 \\ \(\mathcal{Q}_{y}\) & 498 & 622 & 933 & 917 & 1146 & 1719 & 2177 & 2722 & 4083 & 298 & 373 & 559 & 399 & 499 & 748 & 1036 & 1295 & 1942 \\ \(D_{M}\) & 489 & 402 & 314 & 483 & 417 & 333 & 506 & 435 & 339 & 632 & 551 & 441 & 666 & 577 & 451 & 744 & 644 & 514 \\ \(\Delta F_{R,req}\) & 328 & 328 & 328 & 571 & 571 & 571 & 1271 & 1271 & 1271 & 328 & 328 & 328 & 568 & 568 & 568 & 1271 & 1271 & 1271 \\ \(\Delta F_{R}\) & 1150 & 1948 & 2056 & 2103 & 2292 & 2476 & 2931 & 3126 & 3375 & 895 & 983 & 1041 & 1427 & 1546 & 1551 & 2837 & 3086 & 3267 \\ \hline \multicolumn{13}{c}{**(b) Building at Low-to-Moderate Seismicity Region**} \\ \hline  & \multicolumn{10}{c}{**Under Strong Windy Load**} & \multicolumn{10}{c}{**Under Weak Windy Load**} \\ \cline{2-13}  & \multicolumn{3}{c}{**3-Story**} & \multicolumn{3}{c}{**5-Story**} & \multicolumn{3}{c}{**10-Story**} & \multicolumn{3}{c}{**3-Story**} & \multicolumn{3}{c}{**5-Story**} & \multicolumn{3}{c}{**10-Story**} \\ \cline{2-13} \multicolumn{1}{c}{-} & \multicolumn{3}{c}{**3L\({}_{\text{E}}\)40C**} & \multicolumn{3}{c}{**5L\({}_{\text{E}}\)40C**} & \multicolumn{3}{c}{**10L\({}_{\text{E}}\)40C**} & \multicolumn{3}{c}{**3L\({}_{\text{E}}\)30B**} & \multicolumn{3}{c}{**5L\({}_{\text{E}}\)30B**} & \multicolumn{3}{c}{**10L\({}_{\text{E}}\)30B**} \\ \cline{2-13}  & **LB** & **M** & **UB** & **LB** & **M** & **UB** & **LB** & **M** & **UB** & **LB** & **M** & **UB** & **LB** & **M** & **UB** \\ \hline \(k_{e}\) & 70 & 89 & 89 & 122 & 122 & 123 & 199 & 200 & 201 & 59 & 59 & 59 & 78 & 79 & 79 & 135 & 136 & 139 \\ \(k_{d}\) & 5.1 & 9.4 & 12.9 & 10.4 & 12.8 & 14.5 & 24.0 & 3