esa-sceva/satcom-chunk-collection huggingface dataset:
# Extended Geometry and Probability Model for GNSS+ Constellation Performance Evaluation
## Abbreviations  
3D Three-dimensional  
ADOP Ambiguity Dilution of Precision  
BDS-3 third-generation BeiDou navigation satellite system  
GDOP geometric DOP  
CEA cutoff elevation angle  
DOP dilution of precision  
GDOP (geometric DOP), HDOP (horizontal DOP), PDOP (position DOP),  
TDOP (time DOP), and VDOP (vertical DOP)  
ECEF earth centered earth fixed  
EDOP east DOP  
EGAPM extended geometry and probability model  
GAPM geometry and probability model  
GEO geostationary earth orbit  
GFZ GeoForschungsZentrum Potsdam  
GNSS Global Navigation Satellite System  
GPS Global PositioningSystem  
HDOP horizontal DOP  
HEO highly eccentric orbits  
IGSO inclined geosynchronous orbit  
LEO low earth orbit  
MEO medium earth orbit  
MGAPM modified GAPM  
NDOP north DOP  
PDOP position DOP  
PNT positioning, navigation and timing  
PP Precise Point Positioning  
QZO Quasi-Zenith orbit  
QZSS Quasi-Zenith Satellite System  
RMS root mean squares  
SBAS satellites-based augment system  
SPP Standard Point Positioning  
TDOP time DOP  
VDOP vertical DOP  
WAAS Wide Area Augmentation System
# Advancements and Applications of Rim-Driven Fans in Aerial Vehicles: A Comprehensive Review
## 5 Design
As the primary flow traverses the rotating blades, a pressure increase compels the flow within the gap to move in an upstream direction. Utilizing axial-flux motors for rim drives comes with certain limitations that require consideration. Unlike their radial-flux counterparts, axial-flux motors experience increased radius and tip speed when the stack length is augmented. This leads to larger machine radii, necessitating larger shells or housings around the propulsion system. This, in turn, contributes to heightened drag and system mass. Achieving optimal performance in axial-flux rim-driven fans requires a delicate balance among fan diameter, tip speed, and motor stack length. The manuscript presents a proof-of-concept iteration of an electric propulsion system design code [67], employing low fidelity models of aircraft, fan, and motor to optimize the range of a specific aircraft. The presented case study demonstrates a potential range enhancement of 4 km for the X-57, contingent upon the implementation of the considered propulsion system topology. [PERSON] et al. present an optimization algorithm for designing efficient rim-driven machines that consider multiple constraints [68]. A rim-driven pump is designed and assessed using FEM, showing a 95% conformity rate for the output of the algorithm. Despite the constraints not being entirely satisfied, the algorithm is expected to contribute to the efficient design of high-performance rim-driven systems and achieve cost savings and higher output efficiency.
# Surface-Modified Compounds Improve the Detection Sensitivity of Terahertz Metasurface Biosensors
## 3 Results
### Detection of Cancer Markers after WS\({}_{2}\) Modification  
The first compound used for modification is WS\({}_{2}\), which is a gray fine crystal or powder with metallic luster, belongs to the hexagonal crystal family, and has semiconductivity and diamagnetism. The concentration of WS\({}_{2}\) dispersions at 10 mg/mL was prepared and sonicated to fully disperse. After that, 10 \(\upmu\)L of WS\({}_{2}\) dispersion was added to the sensor surface and left for 30 min. The microscope image (20\(\times\)) of the sensor has modified by WS\({}_{2}\) is shown in Figure 9a, from which, it can be seen that many black dots of WS\({}_{2}\) cover the surface of sensor. The Figure 9b shown the frequency shift caused by WS\({}_{2}\), after calculated, the Q-factor of sensor is 21.56, and the Q-factor of WS\({}_{2}\)-modified sensor is 11.5. And then, the experiments procedure was similar to that in Section 3.1. The experimental results are shown in Figure 10.  
For SCC, Figure 10a,b show the experiment results of WS\({}_{2}\) modified sensor to detect SCC. The relationship between the concentration of SCC and the frequency shift is F = 0.85 \(\times\) C\({}_{\rm SCC}\) + 3.2, R\({}^{2}\) = 0.9912, and the sensitivity S\({}_{\rm SCC}\) = 0.85 GHz/lg(pg\(\cdot\)mL\({}^{-1}\)). For CEA, Figure 10c,d show the experiment results of CEA detection by WS\({}_{2}\) modified sensor. The relationship between CEA concentration and frequency shift is: F = 0.775 \(\times\) C\({}_{\rm CEA}\) + 2.65, R\({}^{2}\) = 0.9428, and the sensitivity S\({}_{\rm CEA}\) = 0.775 GHz/lg(pg\(\cdot\)mL\({}^{-1}\)). Figure 10e,f show the experiment results of NSE detection by WS\({}_{2}\) modified sensor. The relationship between the concentration of NSE and the frequency shift is F = 0.7286 \(\times\) C\({}_{\rm NSE}\) + 2.9286, R\({}^{2}\) = 0.9868, and the sensitivity S\({}_{\rm NSE}\) = 0.7286 GHz/lg(pg\(\cdot\)mL\({}^{-1}\)).  
The experimental results indicate that, compared with the unmodified sensor, the sensitivity of WS\({}_{2}\) modified sensor to these three markers were significantly improved. At the some time, the relationship between the shift of resonance frequency and the concentration of marker is more obvious.  
Figure 9: (**a**)Microscope image of the sensor has modified by WS\({}_{2}\). (**b**) The frequency shift caused by WS\({}_{2}\).
## 3 Results and Discussion
### Wind Power Production  
Strong winds can be complicated to handle for wind turbines, as the power curve typically drops rapidly from maximum to zero production as wind increases beyond the turbine cut-off wind speed \(w_{cutoff}\) as discussed in Equation (2). In addition to the incoming mean wind speed, turbulence and rapid changes of wind direction introduce further challenges for wind turbine power production. In Figure 12, the total power production from the 18 wind turbines at Fakken is shown in the black line. During the start of the Ylva event, the measured wind is above the rated wind speed for the turbine and the production is around the installed power of 54 MW. During the morning of 23 November, the power production first dropped to around 50 MW before dropping rapidly to zero during the storm event. As the wind speed decreased on 24 November, the power production started up again and stabilised around 50 MW before a slight drop at the end of the high-wind-speed event. Note that the turbine control system is not connected to the wind speed at the measurement mast but instead uses measurements at the nacelles of each wind turbine in addition to possible other external sources.  
Using the publicly available power curve of the Vestas V90 3 MW turbine, we have extracted the wind speeds from the WRF models at the location of each wind turbine, as shown in Figure 1c, and calculated the corresponding wind power production at Fakken wind park. The power production results of the different models are shown in Figure 12 together with their RMSE. As expected, the D01 and D02 models show very low power production during the start and end of the event, but also full production during the strongest part of the Ylva storm. This is a direct result of the underestimation of wind speed, as discussed earlier. The power productions using the D03 and D04 models are higher during the start and end of the storm event, and, in addition, they are also able to output zero production at times during the main storm event. Finally, the power production using WRF LES result shows clearly higher production during the start and the end of the event, and this method provides a longer period of zero production during the storm, although not as long as the measured production. The RMSE values summarise the overall model production, with LESs giving the lowest error, which was also seen for the RMSE of wind speeds in Figure 8.  
Figure 12: Measured and modelled power production during the Ylva storm together with their corresponding RMSE.
###### Abstract  
Numerical simulation can provide quantitative information on ultrasonic beam propagation and plays an important role in analyzing its detection ability and in optimizing the corresponding parameters of the phased array ultrasonic testing (PAUT). In this paper, a fast finite element simulation method of PAUT is developed using an improved explicit integration algorithm and a non-zero element compressed storage method. The new method is applied for the simulation of PAUT of a type-B sleeve weld, and compared with the commercial finite element software and experiment results. We found that the computation time and memory consumption of the new method is only about a 15 th and a 40 th of the commercial finite element software, respectively.  
p +
Footnote †: journal: Applied Sciences  
line method, the finite difference time domain method (FDTD) calculated using MATLAB has a higher accuracy [16]. This method obtains the approximate solution of the differential equation by configuring the discrete grid nodes in a continuous region in a certain way, and directly expanding the differential operator with truncated Taylor series on the grid nodes [17]. However, the calculation accuracy of this method is still not high enough, and it is difficult to deal with the problems of the free interface and complex structure [18]. Compared with the wave line method and FDTD, the finite element method (FEM) and the commercial software based on it, such as COMSOL [19] and Abaqus [20,21], have a higher calculation accuracy and adaptability [22,23]. However, in the process of ultrasonic finite element numerical simulation, the element size is usually less than a 10 th of the wavelength, resulting in a large amount of calculations and storage, which limits its practical application of the present ultrasonic wave simulation method based on FEM.  
In order to solve the above problem, a fast FEM-based simulation method of ultrasonic testing is developed in this paper. Using a new explicit integration algorithm and non-zero element one-dimensional compressed storage of the coefficient matrixes, the amount of calculation and storage of the new fast FEM-based simulation method can be significantly reduced. To verify the calculation efficiency and accuracy of the new method, it is applied for the simulation of PAUT of the type-B sleeve weld and compared with the commercial FEM software and experiment results. These studies are shown in detail in the following three sections: Fast Finite Element Simulation Method for PAUT, Simulation Results and Comparison, and Conclusion.
## 3 Results and Discussion
### Raman Spectra  
Raman spectroscopy is an effective characterization method for the identification of carbon materials. Two obvious characteristic peaks were observed within 800-2000 cm\({}^{-1}\) (Figure 6) for each curve. The band at about 1338 cm\({}^{-1}\) is correlated with the structural defect of graphene lattice and identified as a typical disordered carbon structure, which is noted as band D. The band at about 1568 cm\({}^{-1}\) is associated with the C-C stretching of sp\({}^{2}\) carbon atoms and identified as an ordered carbon structure, which is noted as band G. Therefore, the intensity ratio of D to G (I\({}_{\text{D}}\)/I\({}_{\text{G}}\)) is generally considered to judge the defect degree of carbon materials [16]. The I\({}_{\text{D}}\)/I\({}_{\text{G}}\) values of raw CF and ao-pDA/ACF are 2.74 and 2.88, respectively. However, the ht-pDA/ACF showed the highest value at 3.81 among the three cathodes, which indicates that more defect sites formed on the surface of ht-pDA/ACF than that of CF and ao-pDA/ACF. Such results prove that the modification of pDA by the hydrothermal process introduced numbers of defects to the CF. Moreover, the spherical structure of ht-pDA/ACF could expose more defects compared to ao-pDA/ACF. It has been reported that the defects of the carbon materials could be the active site to produce H\({}_{2}\)O\({}_{2}\) via the 2e\({}^{-}\) ORR [22]. The ht-pDA/ACF with more defects could show higher activity and selectivity for H\({}_{2}\)O\({}_{2}\) production. Furthermore, the I\({}_{\text{D}}\)/I\({}_{\text{G}}\) of ht-pDA/ACF after electrolysis (Figure S6) remained at 2.94, which is still higher than CF and ao-pDA/ACF, indicating that the ht-pDA/ACF had good performance after electrolysis.
# Current Challenges Supporting School-Aged Children with Vision Problems: A Rapid Review
## 3 Overall Context of Vision Screening
### Computerized Vision Screening Programs  
Computerized vision screening programs offer a broad range of test batteries for vision screening, including visual acuity, color vision, contrast vision, stereovision, visual efficiency skills, visual field, test, and oculomotor behavior of the eyes ([PERSON], 2019; [PERSON], 2019). For example, Visual Efficiency Rating (VERA) is a computerized software developed for school nurses to screen visual acuity, saccades, and the accommodative and vergence facility in children ([PERSON], 2019). VERA simulates reading by placing numbers or text sequentially in a pattern while performing the vision test on children ([PERSON], 2019). However, the disadvantage of VERA is the low range of sensitivity, which could miss children with vision problems. [PERSON] et al. (2019) observed that the sensitivity of VERA ranges from 45% to 64% on school children with visual acuity of 20/25 or better.  
In recent years, there has been an increasing interest in using eye-tracking computer programs to screen oculomotor behaviors of the eyes by reading or looking at the animated objects on a computer screen. RightEye ([PERSON], 2019) and C&Look ([PERSON], 2019) are the latest developments in the quest to create a new generation of software that can detect the signs and patterns of oculomotor problems (see Figure 2c).  
As mentioned earlier, the accuracy of visual field testing can be lowered due to several factors, especially alertness and the inappropriate responses of children in the perimeter. [PERSON] et al. (2019) developed a computerized solution for pediatric visual field tests using computer games and an organic light-emitting diode (OLED) to address such challenges. This approach was adopted to provide an enjoyable test experience, and results indicated that this solution is feasible for children.
# An Accurate Platform for Investigating TCP Performance in Wi-Fi Networks
## 4 Evaluation
### CCA Evaluation with Different Wi-Fi Settings  
In this section, we use our platform to evaluate CCAs with different Wi-Fi settings. We want to see how CCAs behave when the Wi-Fi link becomes the bottleneck for client-server communication. Hence, we set the bandwidth link between S1 and S2 (as in Figure 3)  
Figure 7: CDF of the RTT ratio in two-client scenario.  
to 1000 Mbps. Moreover, the queue size on S1 is configured at 100 packets. In the first experiment, we changed the Wi-Fi standard used in the evaluation to IEEE 802.11n and IEEE 802.11 ax. In the second experiment, we compare the CCA performance in the Wi-Fi network using IEEE 802.11g with varying signal strength (i.e., the value of RSSI between client-AP are \(-45\) dBm and \(-70\) dBm).  
The evaluation results in the first experiment with IEEE 802.11n are shown in Figure 8, in which Figure 8a and Figure 8b present the throughput, RTT values, respectively. Unlike the previous results, we can see that the throughput values with all CCAs reach around 48 Mbps. That is because the Wi-Fi link becomes the bottleneck and operates with IEEE 802.11n. Regarding the RTT values, there are no extremely high values; hence, the bufferbloat event did not happen in this setting. Among all CCAs, Westwood has the lowest RTT, and scalable has the second lowest one. We have a similar conclusion about Westwood's RTT, when observing the evaluation results with IEEE 802.11 ax shown in Figure 9b. However, the throughput of Westwood is the lowest among all CCAs'. Note that we configured the 5 GHz band in our IEEE 802.11 ax evaluation. From Figure 9a, except for Reno and Westwood, all other CCAs can reach the total capacity of the 802.11 ax wireless link (i.e., the S1-S2 link bandwidth is 1 Gbps). Among them, BBR achieves the lowest RTT performance. We can conclude from the two sets of evaluation results that our platform can be used for CCA evaluation with different Wi-Fi standards.  
The second experiment's throughput and RTT results are shown in Figure 10. As shown in Figure 10a, with all CCAs, the throughput values have been reduced when the RSSI value becomes smaller. However, the CCAs can occupy all the available bandwidth in all cases. In the case of RTT, as shown in Figure 10b, the values are varied depending on the CCAs and signal strengths. With \(-70\) dBm RSSI, all the RTTs are within an acceptable range (less than 30 ms). That is because the queue of S1 has not been full; hence, there was no extra additional delay. With the stronger signal, the queue may be sometimes full, causing the bigger RTTs. Similar to the previous experiment, with the Wi-Fi bottleneck link, Westwood has the best RTT performance. The results indicate our platform's usability in the evaluations with different Wi-Fi settings.  
Figure 10: Experiments on changing RSSI in IEEE 802.11g.  
Figure 9: CCA evaluation with IEEE 802.11 ax.
# Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator
## 3 Kinematics Modeling and Control Methodology
### Inverse Kinematics  
In general, inverse kinematics is much more complicated than forward kinematics especially for CHSM due to its high level of redundant DOF which means numerous analytical solutions. Otherwise, the numerical method suffers severe penalty from solving the pseudo-inverse of the Jacobian matrix which costs increasing rapidly computation time. Under these circumstances, an efficient, simplified, and accessible solution could be taken advantage instead of the numerical method.  
Two assumptions are introduced to simplify the joints' motion. First, the last tubular unit keeps horizontal direction paralleling to the Y-axis of the world coordinate system. This restriction improves stability for the visual sampling and keeps away from image distortion. Furthermore, the \(N\)-th joint angle \([\alpha_{N},\beta_{N}]\) could be easily obtained from \([\alpha_{N},\beta_{N}]+\sum_{i=0}^{N-1}[\alpha_{i},\beta_{i}]=0\) if the other ones are definite. Second, adjust each joint as slightly as possible and primarily use the last couple of joints during moving the end-effector to the desired position. That means if the desired position is contained in the working space of the last three tubular units, the last three joints should be adjusted, and the others should keep remaining unchanged. When the desired position is beyond working space, a requirement of adding some new left-neighbor tubular unit arises. Under this condition, we simplify the model into a Three-Connecting-Rod Mechanism by forcing the middle tubular units to form a straight line which means their joint angles are set to zero.  
In the simple model, the tubular units and universal joints are substituted by lines (with a length of \(L=H+2D\)) and dots respectively where \(A_{Nd}\) represents the \(N\)-th joint's position. The subscripts \(d\) and \(o\) denote the desired position and the initial position. After the tracking target's moving trajectory is captured by the tracking component, \(A_{Nd}\) is definite and the number \(m\) of demanding joints to be adjusted can be inferred according to Equation (17). Equation (17) means the desired position is beyond the working space of the last \(N-m-1\) joints but is contained in the working space of the last \(N-m\) ones.  
\[\left\{\begin{aligned} \left\|A_{Nd}-A_{(m+1)o}\right\|& \geq(N-m-1)\times L\\ \left\|A_{Nd}-A_{m0}\right\|&\leq(N-m)\times L\end{aligned}\right. \tag{17}\]Based on the simplified Three-Connecting-Rod Mechanism the \(A_{md}\), \(A_{(m+1)d}\) and \(A_{Nd}\) satisfies Equation (18).  
\[\begin{cases}\left\|A_{Nd}-A_{(m+1)d}\right\|=(N-m-1)\times L\\ \left\|A_{(m+1)d}-A_{md}\right\|=L\end{cases} \tag{18}\]  
\(A_{(m+1)d}\) is located on an intersecting circle between a sphere (center at \(A_{md}\), radius of \(L\)) and another (center at \(A_{Nd}\), radius of \((N-m-1)L\)). Following the second assumption the \(A_{(m+1)d}\) is accessible and the other \(A_{id},i=m,m+2,\ldots,N-1\) could be inferenced via the coordinate transformation introduced earlier.
## 2 Related Works  
### Bloom Filter Theory  
A Bloom filter (BF) [31] is a bit-vector-based data structure used to represent the membership information for set \(S=\{x_{1},x_{2},\ldots,x_{n}\}\) consisting of \(n\) elements in universe set \(U\). We use _key_ and _element_ interchangeably to denote \(x_{p}\in S\) for \(1\leq p\leq n\). A Bloom filter is an array of \(m\) bits, which is initialized to zero. As each cell of a Bloom filter \(c[j]\), for \(0\leq j<m\), is composed of a single bit, the value of each cell can have only 0 or 1. A Bloom filter uses \(k\) independent hash functions \(\{h_{1},h_{2},\ldots,h_{k}\}\) in programming and querying. The optimal number of hash functions is as follows [32; 33].  
\[k=\frac{m}{n}ln2 \tag{1}\]  
For element \(x_{p}\in S\), \(h_{i}(x_{p})\) can have any value in range \(\{0,1,2,\ldots,m-1\}\) for \(1\leq i\leq k\), and \(h_{1}(x_{p})\), \(h_{2}(x_{p})\), \(\ldots\), and \(h_{k}(x_{p})\) are cell indexes for \(x_{p}\). In programming \(x_{p}\), the \(c[h_{i}(x_{p})]\)s are set to 1 for \(1\leq i\leq k\). In querying a given input \(y\) to check its membership, the Bloom filter verifies whether all of the \(c[h_{i}(y)]\)s are set to 1 using the same \(k\) hash functions as programming. If at least one of \(c[h_{i}(y)]\)s is 0, it is obviously \(y\
otin S\), which is termed a _negative_. If all \(c[h_{i}(y)]\)s are 1, it would be \(y\in S\), which is termed a _positive_. However, even in case of \(y\
otin S\), all \(c[h_{i}(y)]\)s may be set to 1 because of hash collision, and it is termed a _false positive_. The false positive rate can be reduced by increasing the size of the Bloom filter, but cannot be completely eliminated. However, negative results are always true. The false positive rate of the BF is obtained as follows [32; 33].  
\[f=\left(1-(1-\frac{1}{m})^{kn}\right)^{k}\approx\left(1-e^{-\frac{kn}{m}} \right)^{k} \tag{2}\]  
A Bloom filter and its variant structures have been popularly used for many applications due to their simple and space-efficient nature identifying non-members of a given set [34; 35; 36; 37; 38; 39; 40].