esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of DLTS Hinges for the Assembly of the Solar Arrays of a Communication CubeSat
## 4 Results and Discussion
### Optimization Analysis--Taguchi Model
#### 4.2.1 Main Effects and Predicted Results for \(\sigma_{\rm v}\)  
An analysis was conducted on the Taguchi L9 array to observe if the design parameters could be optimized in a way that the \(\sigma_{\rm v}\) is reduced on the DLTS hinge. Table 2 and Figure 8 show the main effects of the parameters on the \(\sigma_{\rm v}\). For all nine experiments, the mean result is 1814.6 N/mm\({}^{2}\). The best quality for the purpose of this research is defined as the lowest \(\sigma_{\rm v}\) on the hinge. The experimental data show that the lowest \(\sigma_{\rm v}\) value occurred in experiment 4 (1661.3 N/mm\({}^{2}\)), and experiment 9 produced the highest \(\sigma_{\rm v}\) value (2006.7 N/mm\({}^{2}\)). For the main effect, the subtended angle is the most influential factor. As the angle is increased from 60\({}^{\circ}\) to 70\({}^{\circ}\), the \(\sigma_{\rm v}\) increases and a \(\delta\) of 315 N/mm\({}^{2}\) is observed. Curvature radius is  
Figure 7: \(\sigma_{\rm v}\) and steady-state moment variations for (**a**) radius curvatures (\(R\)), (**b**) layer thickness (\(t\)), and (**c**) subtended angle (\(\theta\)).  
the second most important factor, where an increase from 6.0 to 6.5 mm results in a \(\delta\) of 31 N/mm\({}^{2}\). The results for layer thickness show that there is no significant effect when modifying the value. Using DOE, it is possible to take the factor settings that provide an optimum result and predict the average response for this combination of control factor levels. The objective is to determine the factor where the levels minimize \(\sigma_{\text{v}}\). The results in Table 2 show that by using a radius curvature of 6.0 mm, a subtended angle of 60\({}^{\circ}\), and a layer thickness of 0.1 mm, a \(\sigma_{\text{v}}\) of 1661 N/mm\({}^{2}\) can be expected. This is an improvement of 17.2% when compared to the measured response from experiment 9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Radius Curvature [mm]** & **Subtended Angle [\({}^{\circ}\)]** & **Layer Thickness [mm]** \\ \hline
1 & 5.5 & 60 & 0.10 \\ \hline
2 & 5.5 & 65 & 0.15 \\ \hline
3 & 5.5 & 70 & 0.20 \\ \hline
4 & 6.0 & 60 & 0.10 \\ \hline
5 & 6.0 & 65 & 0.15 \\ \hline
6 & 6.0 & 70 & 0.20 \\ \hline
7 & 6.5 & 60 & 0.10 \\ \hline
8 & 6.5 & 65 & 0.15 \\ \hline
9 & 6.5 & 70 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: L9 orthogonal array of DLTS hinge design parameters.  
Figure 8: Main effects plot for \(\sigma_{\text{v}}\).
## 3 Experimental Results and Discussions
### Lateral Deformation and Strain Distribution in Cross Sections  
Figure 11 shows the lateral deformation curves and the fitting curves of the four columns. A sine function (Equation (6)) is used to fit the measured lateral deformations. The maximum discrepancy between the measured deformation and fitting result is 14%, indicating that the fitting curves provide an adequate prediction of the lateral deformation of the columns.  
\[u=u_{\rm m}\sin(\pi Z/Z_{\rm m}) \tag{6}\]  
where u is the lateral deformation; \(u_{\rm m}\) is the maximum value of u; \(Z\) is the length from the measured point to the loading point; \(Z_{\rm m}\) is the total length of the column (=1500 mm).  
Figure 10: Analysis of crack distribution: (**a**) Segmental model, (**b**) bonding stress distribution between steel bar and concrete, (**c**) bonding stress distribution between steel wire and resin concrete.  
The longitudinal strain at the mid-span position of column can be used to validate whether the plane section assumption is satisfied or not. Figure 12 shows the longitudinal strain measured through the strain gauges on the RC column and the strain gauges on the SMRC under different load grades. As can be seen from Figure 12, plane section assumption is satisfied during the deformation of specimen under different load grades.  
Figure 12: _Cont._
###### Abstract  
In the acute phase of stroke, most patients have reduced efficiency of the upper limb and in the chronic phase more than half of these patients still have a deficit in the mobility of the upper limb. The aim of this study was to investigate the effect of using the tipstim(r) device and the tested position of the body and affected upper limb on parameters of motor coordination and grip strength in patients after an ischemic stroke. A randomized, parallel crossover study was conducted in the Rehabilitation Department. The study included 29 people aged \(68\pm 9.2\) years, 5-7 weeks after ischemic stroke. Patients were randomly assigned to two parallel groups (A/B = 15 people and B/A = 14 people). In each of them, the patient received both experimental (A = tipstim(r)) and control (B = placebo effect) treatment in a specific order. The HandTutor was used to measure the parameters of motor coordination (maximum range of motion and frequency of motion). We also used an electronic dynamometer to measure the handgrip strength. The patients were examined in two positions: sitting (unstable) and lying with the trunk and affected upper limb stabilized. Results: The analysis showed smaller differences between the measurements in the A/B group than in the B/A group, both without stabilization and with stabilization (wrist Hz = \(p<0.001\); fingers 2-5 Hz = \(p<0.001\); handgrip strength = \(p<0.049\) and \(p<0.003\)). When comparing the influence of the tested position on the results of motor coordination and the handgrip strength, statistically significant differences were found in the placebo group in a stable position (Hz wrist \(p\) = 0.007, MaxROM wrist = 0.038, HzF5 = 0.039, MaxROM F4 = 0.035, HzF3 = 0.035, MaxROM F3 = 0.010, HzF2 = 0.049). Conclusions: Repeated use of the tipstim(r) device did not improve the tested parameters. A significant improvement in the results of coordination of movements and grip strength is possible in a stable position, lying down.  
typetim(r) device; hand tutor; hand; wrist; motor coordination; grip force; stabilization 2022
# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
###### Abstract  
Ontology alignment has become an important process for identifying similarities and differences between ontologies, to facilitate their integration and reuse. To this end, fuzzy string-matching algorithms have been developed for strings similarity detection and have been used in ontology alignment. However, a significant limitation of existing fuzzy string-matching algorithms is their reliance on lexical/syntactic contents of ontology only, which do not capture semantic features of ontologies. To address this limitation, this paper proposed a novel method that hybridizes fuzzy string-matching algorithms and the Deep Bidirectional Transformer (BERT) deep learning model with three machine learning regression classifiers, namely, K-Nearest Neighbor Regression (kNN), Decision Tree Regression (DTR), and Support Vector Regression (SVR), to perform the alignment of ontologies. The use of the kNN, SVR, and DTR classifiers in the proposed method resulted in the building of three similarity models (SM), encoded SM-kNN, SM-SVR, and SM-DTR, respectively. The experiments were conducted on a dataset obtained from the anatomy track in the Ontology Alignment and Evaluation Initiative 2022 (OAEI 2022). The performances of the SM-kNN, SM-SVR, and SM-DTR models were evaluated using various metrics including precision, recall, F1-score, and accuracy at thresholds 0.70, 0.80, and 0.90, as well as error rates and running times. The experimental results revealed that the SM-SVR model achieved the best recall of 1.0, while the SM-DTR model exhibited the best precision, accuracy, and F1-score of 0.98, 0.97, and 0.98, respectively. Furthermore, the results showed that the SM-kNN, SM-SVR, and SM-DTR models outperformed state-of-the-art alignment systems that participated in the OAEI 2022 challenge, indicating the superior capability of the proposed method.  
ontology alignment; ontology matching fuzzy string matching; machine learning; lexical alignment; semantic alignment; natural language processing Article
## 3 Optimal Power Allocation (PA), Cooperative Relaying and Channel Models in PD-NOMA
### Cooperative Relaying and Channel Models in PD-NOMA
Figure 10: Comparisons of BER performance for 2 users, in DBPA scheme [18] and the proposed FIS based model. channel has more line-of-sight (LOS) paths (K-factor) than the Rayleigh channel, which has no LOS paths and more delays. In the DBPA scheme, the far-user is allocated higher power and the near-user with lower power, but this is the case when power is distributed among users based on distance separation from the gNodeB. However, the proposed FIS system decides the PA based on multi-input parameters. Given the foregoing, it is possible that, despite being close to the gNodeB, a near-user (U\({}_{1}\)) has poor channel condition due to low input SNR and high foliage depth, as compared to a far-user (U\({}_{2}\)) having high input SNR and low foliage depth. In this case, the proposed FIS system assigns maximum power to U\({}_{1}\) and minimum power to U\({}_{2}\). The simulation is performed for a scenario in which U\({}_{1}\) has (\(d_{1}\) = 90 m, \(SNR_{1}\) = 15 dB, and \(df_{1}\) = 25 m) and U\({}_{2}\) has (\(d_{2}\) = 110 m, \(SNR_{2}\) = 25 dB, and \(df_{2}\) = 8 m) from the gNodeB. The FIS-based system assigns optimal power factors of \(P_{1}\) = 0.64 and \(P_{2}\) = 0.36 to U\({}_{1}\) and U\({}_{2}\), respectively. In this case, U\({}_{1}\) will not be used as a relay node to assist the far-user U\({}_{2}\). The reason is simple: U\({}_{1}\) suffers from greater signal losses due to low input SNR and high foliage depth, and hence its contribution as a relay node is negligible. It is observed that U\({}_{1}\), which is closer to gNodeB and has higher power, directly decodes its signal from the combined signal, whereas U\({}_{2}\), which is further away and has a lower power factor, uses the SIC technique to negate the effect of the U\({}_{1}\) signal from the combined signal to retrieve its U\({}_{2}\) signal. As shown in Figure 14, the BER threshold of \(10^{-3}\) is obtained at an SNR of 6 dB and 8 dB by U\({}_{1}\) and U\({}_{2}\), respectively. The degraded U\({}_{2}\) performance is caused by  
Figure 12: BER performance for two users, in the proposed FIS based model in Rician (SUI-3) Channel Model. Figure 13: BER performance for two users, in the proposed FIS based model in Rayleigh (SUI-6) Channel Model. using the SIC technique on the U\({}_{1}\) signal. Table 3 contains a detailed description of the parameters and the results obtained in various attempts. The complexity of the FIS system increases with the number of inputs and outputs, the number of MFs used for the universe of discourse, and the selection of different rule base sets.
## 7 LPWAN Topologies and Architecture
### LPWAN Topologies  
Prominent topologies are star and mesh. Generally, star or star-on-star topology is preferred for LPWAN over mesh networks for preserving battery power and increasing the communication range. LPWAN's long range connectivity allows such single-hop networks access to a large number of nodes, thus reducing the cost. From a coverage viewpoint, traditional wireless sensor technologies such as ZigBee, Bluetooth, and Wi-Fi are not designed for wide coverage, and hence are not directly applicable as LPWAN technologies.  
The simplest form of wireless network topology is a point-to-point network in which nodes communicate directly with a central node. It is often used for remote monitoring applications and can be useful in hazardous environments where running wires is difficult or dangerous. Such LPWAN technologies support a star topology, as shown in Figure 3a. A star network consists of one gateway node to which all other nodes connect. Nodes can only communicate with each other via the gateway. Node messages are relayed to a central server via gateways. Each end node transmits the messages to one or multiple gateways. The gateway forwards the messages to the network server, where redundancy, errors, and security checks are performed. Star networks are fast and reliable because of their single hop feature. Faulty nodes can also be easily identified and isolated. However, if the gateway fails, all the nodes connected to it become unreachable. As the end node sends messages to multiple gateways, there is no need for gateway-to-gateway communication. This simplifies the design as compared with networks where the end nodes are mobile. Mesh topology network consists of a gateway node, sensor nodes, and sensor-cum-routing nodes connected, as shown in Figure 3b.  
Figure 3: (**a**) Star topology, (**b**) mesh network topology.  
All the nodes can connect directly to each other in a full mesh topology. In a partial mesh topology, some nodes are connected to some of the others, but others are only connected to those with which they exchange the most messages.  
Mesh networks have several advantages, such as availability of multiple routes for reachability, simultaneous up/downstream transmissions, easy scalability of the network, and capability of self-healing. These networks have some disadvantages, including complexity owing to redundant nodes, added latency because of multi-hop communication, and an increase in cost. The redundancy of nodes also compromises the energy efficiency of the network.
## 4 Experimental Setup  
Images of sizes \(512\times 768\) and \(768\times 512\) from the Kodak database [40], which consists of 24 images, were used for experimentation. These images are labeled as Kodak-n, where \(n\) is the image identifier. Some of these images are shown in Figure 3.  
The performance criteria to evaluate the quality of watermarked and recovered images obtained by the proposed framework are: PSNR, SSIM and PSNR-HVS-M [35]. The PSNR metric is defined as follows:  
\[PSNR=10\ log_{10}\frac{MAX^{2}}{MSE}, \tag{11}\]  
\[MSE=\frac{1}{XY}\sum_{i=0}^{X-1}\sum_{j=0}^{Y-1}{[I(i,j)-I^{\prime}(i,j)]}^{2}, \tag{12}\]  
where \(I\) represents the original image, \(I\prime\) corresponds to the modified image, the arguments \(i,j\) are used for the pixel position and \(X,\ Y\) are the number of rows and columns, correspondingly. The SSIM metric is computed using the following equation:  
\[SSIM(x,y)=\frac{\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2\sigma_{xy}+C_{2} \right)}{\left(\mu_{x}{}^{2}+\mu_{y}{}^{2}+C_{1}\right)\left(\sigma_{x}{}^{2} +\sigma_{y}{}^{2}+C_{2}\right)}, \tag{13}\]  
where \(\mu\) and \(\sigma\) denote the mean and variance of images \(x\) and \(y\), \(\sigma_{xy}\) is the covariance between \(x\) and \(y\), constants \(C_{1}\), \(C_{2}\) are: \(C_{1}=\left(0.01L\right)^{2},\ C_{2}=\left(0.03L\right)^{2}\), and \(L=255\)[41].  
Furthermore, _Precision_ and _Recall_ metrics were utilized to measure the alteration detection performance of the proposed method. These metrics are based on the numbers of true positives (\(TP\)), false positives (\(FP\)) and false negatives (\(FN\)) among all pixels:  
\[Precision=\frac{TP}{TP+FP}, \tag{14}\]  
\[Recall=\frac{TP}{TP+FN}. \tag{15}\]
## 2 Test Specimens
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Specimen** & & **Pure Mode I** & **Mixed Mode I/II** & **Pure Mode II** & **Pure Mode III** & **Mixed Mode I/III** \\ \hline \multirow{4}{*}{SCB} & \multirow{2}{*}{Asymmetric SCB} & Test condition & \(\rm S_{1}/R=0.8\) & \(\rm S_{1}/R=0.8\) & \(\rm S_{1}/R=0.8\) & - & - \\  & & \(\rm S_{2}/R=0.8\) & \(\rm S_{2}/R=0.18\) & \(\rm S_{2}/R=0.083\) & - & - \\ \cline{2-7}  & \multirow{2}{*}{Accentric SCB} & \multirow{2}{*}{Coenetry factor} & \(\rm Y_{1}=14.5\) & \(\rm Y_{1}=14.5\) & \(\rm Y_{1}=21.37\) & - & - \\ \cline{2-7}  & & & & & & \\ \cline{2-7}  & \multirow{2}{*}{Inclined cracked SCB} & Test condition & - & \(\rm S/R=0.65\) & \(\rm S/R=0.65\) & - & - \\ \cline{2-7}  & & & \(\rm B=37^{\circ}\) & \(\rm B=55^{\circ}\) & - & - \\ \cline{2-7}  & \multirow{2}{*}{Hecinned cracked SCB} & \multirow{2}{*}{Geometry factor} & - & \(\rm Y_{1}=1.2\) & \(\rm Y_{1}=1.28\) & - & - \\ \hline \multirow{2}{*}{ENDB} & \multirow{2}{*}{Test condition} & \(\rm S_{1}/R=0.9\) & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \(\rm S/R=0.95\) & \(\rm S/R=0.95\) \\  & & \(\rm S_{2}/R=0.9\) & & & & \(\rm\beta=65^{\circ}\) & \(\rm\beta=53^{\circ}\) \\ \cline{2-7}  & \multirow{2}{*}{Geometry factor} & \(\rm Y_{1}=1.25\) & - & - & \(\rm Y_{\rm III}=0.071\) & \(\rm Y_{\rm III}=0.065\) \\ \cline{2-7}  & & & & & & \\ \hline \multirow{2}{*}{BD} & Test condition & \(\rm\beta=0^{\circ}\) & \(\rm\beta=15^{\circ}\) & \(\rm\beta=28^{\circ}\) & - & - \\ \cline{2-7}  & \multirow{2}{*}{Geometry factor} & \(\rm Y_{1}=1.1\) & \(\rm Y_{1}=1.1\) & \(\rm Y_{1}=0.8\) & \(\rm Y_{\rm III}=1.8\) & - & - \\ \cline{2-7}  & & & \(\rm S_{2}/L=0.7\) & \(\rm S/L=0.7\) & - & - & - \\ \cline{2-7}  & \multirow{2}{*}{Geometry factor} & \(\rm Y_{1}=4.1\) & \(\rm Y_{1}=3.2\) & - & - & - \\ \cline{2-7}  & & & & & & \\ \end{tabular}
\end{table} 
Table 1: Corresponding values of geometry factors (\(\rm Y_{1}\), \(\rm Y_{1}\) and \(\rm Y_{\rm III}\)) for the investigated test specimens under different mode misties.
## 3 POD Results
### Improvement on POD Using Regional Stations
The orbit of GEO satellite C59, located at longitude 140 deg, shows similar performance as IGSO satellites due to the global network selected. For the ground-only POD results, the along-track error generated from 10 regional stations is worse than that generated from 10 global stations. This may be attributed to the fact that the selected regional stations are mainly concentrated in two narrow regions: China in the northern hemisphere and Australia in the southern hemisphere. The GEO satellite fixed above the equator is stationary relative to the ground. In this case, this tracking station distribution is not sensitive to the change of the orbits in the along-track direction. The addition of ISL effectively reduces the along-track error of the regional station POD orbit from 87.4 to 19.3 cm, a significant decrease of 78%. The 3D accuracy is therefore improved by about 73.9%. It follows that ISL can significantly improve the accuracy of the orbits computed with observations from regional stations.
## 2 Preliminaries of State Estimation Methods Using Kalman Filter
### Kalman Filter (KF)  
In linear systems, the KF primarily estimates the variables or performs state estimation. It works on dynamic models which have the state (1) and observation (2) equations, where the state equation is a linear combination of the process noise (\(w_{k}\)), inputs (\(u_{k-1}\)) and prior states (\(x_{k-1}\)), and the latter is combination of states (\(x_{k}\)) and measurement noise (\(v_{k}\)) ([PERSON], 2017). Therefore, the state equation of the dynamic model is derived as follows:  
\[x_{k}=Ax_{k-1}+Bu_{k-1}+w_{k}. \tag{1}\]  
Similarly, the dynamic model's observation equation is derived as  
\[y_{k}=Hx_{k}+v_{k}, \tag{2}\]  
where the state and observation vectors are represented as (\(x_{k}\), \(y_{k}\)), and the state transition matrix is represented by \(A\), the observation matrix is represented by \(H\) and the input matrix is represented as \(B\). The process and measurement noise vectors (3), which are represented by \(w_{k}\) and \(v_{k}\), respectively, are assumed to be normally distributed and positive definite:  
\[q(w)\sim N(0,Q),\ \ \ q(v)\sim N(0,R). \tag{3}\]  
The states obtained during estimation at moments \(k-1\) and \(k\) are called the prior state estimation (\(\hat{\xi}_{k}^{-}\)) and posterior state estimation (\(x_{k}\)) ([PERSON], 2017). Based on these prior and posterior states, we derived prior and posterior estimation errors (4) and the covariance equations of the priori and posterior states (5).  
\[e_{k}^{-}=x_{k}-\hat{x}_{k}^{-},\ \ \ e_{k}=x_{k}-\hat{x}_{k}. \tag{4}\]  
\[P_{k}^{-}=E\Big{[}e_{k}^{-}e_{k}^{-T}\Big{]},\ \ \ P_{k}=E\Big{[}e_{k}e_{k}^{T}\Big{]}. \tag{5}\]  
The Kalman Filter equations require \(x_{k}\) such that the posterior state estimation can be calculated based on a priori estimates and weighted differences between real and estimated measurement values, leading to a need for update and prediction equations. Therefore, the formulated prediction equations are given by (6) and (7):  
\[\hat{x}_{k}^{-}=A\hat{x}_{k-1}+Bu_{k-1}, \tag{6}\]  
\[P_{k}^{-}=AP_{k-1}A^{T}+Q, \tag{7}\]  
and, following are the update equations:  
\[K_{k}=\frac{P_{k}^{-}H^{T}}{HP_{k}^{-}H^{T}+R}, \tag{8}\]  
\[\hat{x}_{k}=\hat{x}_{k}^{-}+K_{k}(z_{k}-H\hat{x}_{k}^{-}), \tag{9}\]  
\[P_{k}=(I-K_{k}H)P_{k}^{-}. \tag{10}\]The Kalman gain matrix of (8), (9) and (10) is represented by \(K_{k}\), and \((x_{k},P_{k},I)\) are the optimum state filter vector \(x_{k}\), process covariance matrix \(P_{k}\) and identity matrix \(I\), respectively. There are certain limitations of KF: it is helpful in linear systems only, and the noise should be Gaussian. Since nearly all the real-time systems behave in a nonlinear way, the KF has limited application practically [42].
# 5G-MEC Testbeds for V2X Applications
## 3 Existing Testbeds and Implementations
### 5G V2X Testbeds
#### 3.2.3 5G V2X Testbed for Cooperative Automated Driving  
This testbed is a 5G V2X wireless testbed built on an adjustable and reconfigured SDR that is designed for cooperative autonomous driving [44]. The key novelty of this testbed is the Radio-Frequency (RF) front end, which is reconfigurable. In particular, the testbed allows testing new OFDM waveforms built on parameters such as pulse-shaping, a dynamic and self-contained structure layout, GNSS-assisted hybrid synchronization, and scheduled multiple access for reducing latency. The radio interface, which includes modulation, coding, a frame structure, and multi-antenna systems, is freely designed and modified during the tests. The radio subsystem is simple to connect with the OBU inside the vehicle, which is a general-purpose computer-based system. The radio module can perform real-time analysis that enables live transmission, testing, data collection, and offline analysis for channel and automotive prediction. The radio module has a small form factor and power dissipation, making it ideal for integration into automobiles with limited performance and power supply. For the RF Unit (RFU) setup, the radio subsystem construction components are NI/Ettus-based USRP X310 SDR platforms. A high-performance Intel hardware-based computer running a lightweight Linux operating system is utilized for the Baseband Unit (BBU). The RF external (RFE) subsystem is self-built and integrated with the power amplifier and switches based on Time Division Duplex (TDD) to increase transmitting power and reception sensitivity.  
Two types of use cases are considered in the testbed: Type 1 use cases exhibit predictable behavior and are limited to three phases: planning, initialization, and execution; and Type 2 usage scenarios are based on emergency braking and cooperative emergency maneuvers. This testbed describes short-distance platooning and lane merging as the main Type 1 usage scenarios. Conversely, Type 2 usage scenarios are distinguished by quick response times and the inability to plan ahead of time, which typically occur in emergency circumstances. The lab measurements and field tests carried out using this testbed show that the emergency brake triggered with a latency of less than a millisecond [45].