esa-sceva/satcom-chunk-collection huggingface dataset:
# Minimization of _n_th Order Rate Matching in Satellite Networks with One to Many Pairings
## 1 Introduction  
The data-hungry Internet-based services make of utmost importance the synergy between the 5G and satellite networks [1, 2, 3]. Specifically, Cisco's report stated that the monthly global mobile data traffic will be 77 exabytes by 2022, and annual traffic will reach almost one zettabyte [4]. Satellites can also be useful in the domain of Internet of Things (IoT) for the connection of multiple devices, particularly for emergency scenarios and industrial applications [5]. Hence, the satellite operators are directed toward the employment of high-throughput satellite (HTS) multi-beam systems [6, 7, 8] that can offer up to Tbps capacities.  
The configuration of HTS systems includes the links of satellite-user equipment (UE) beams and the gateways (GWs)-satellite links that operate at Ka Band and Q/V, W bands, respectively. The tropospheric phenomena cause severe signal degradations in these bands that can be mitigated by the Smart Gateway Diversity (SGD) concept [6, 9, 10], similarly in this paper, exploiting the different geographical locations of GWs.
# Review of Auxetic Materials for Sports Applications: Expanding Options in Comfort and Protection
## 3 Common Auxetic Materials and Structures
### Fabricating and Characterising Auxetic Foam
Wide ranging studies, employing a variety of test protocols, have been undertaken to characterise auxetic polymeric foams for structural, mechanical, thermal, filtration and impact properties, for example. Isotropic auxetic foams with Poisson's ratio between 0 and \(-0.7\) have been reported (e.g., [3; 9; 20; 22; 23; 189; 203]). Higher magnitudes of NPR (\(<\!\!-1\)) have also been reported for anisotropic auxetic foams [187; 190]. Density measurements of whole or dissected samples are useful in assessing the extent of, and variation in, volumetric compression throughout thermo-mechanically fabricated re-entrant foam [22; 204]. Standardisation while characterising samples would help assess levels of agreement between different studies. Where possible future testing should be undertaken in accordance with, or based upon, the appropriate ASTM standard (e.g., ASTM-D412--15a [205]) for quasi-static tensile testing, which requires communication of sample dimensions, strain rate, range and measurement method and joining/contact methods between sample and test rigs.
## 3 Mapping Performances of the Four Models over the Arctic and Antarctic
### Evaluation of the Four Ionosphere TEC Models  
In this section, we compare the performances of the four models for mapping the polar ionospheric TEC. Figures 3 and 4 present the annual mapping bias errors and RMS errors of the four models for the Arctic and Antarctic regions, respectively. The statistical results were based on the GPS-TEC provided by the verification stations shown in Figure 1. It can be seen from the figures that the bias errors of the four models are less than 0.8 TECU (1 TECU = 10\({}^{16}\) electrons m\({}^{-2}\)). In the Arctic, the model bias errors were basically positive, while most of the biases were negative in the Antarctic, indicating that the models overestimate the true TEC in the Arctic and underestimate it in the Antarctic. Moreover, the GTSF model had the largest RMS errors, while the other three models had mapping RMS errors between 1.5 and 3.8 TECU. The SCH model presented the best performance, as already found by [PERSON] et al. [12] in the Arctic. Here the RMS errors of the SCH model were less than 3.0 TECU, while in the Antarctic, the RMS errors were within 1.5-3.5 TECU and the model itself had lower mapping accuracy with respect to the Arctic; this was because of the non-uniform distribution of GPS observation stations shown in Figure 1.
# Anti-Multipath Performance Improvement of an M-ary Position Phase Shift Keying Modulation System
## Appendix A  
This proof is based on mathematical induction. If the receiver has a larger signal-to-noise ratio using the normalized symbol joint decision scheme than using the symbol joint decision scheme, the comparison result is given by  
\[\gamma_{N}(x_{1},x_{2},\cdots,x_{n})>\gamma_{S}(x_{1},x_{2},\cdots,x_{n}) \tag{1}\]among them,  
\[\begin{split}\gamma_{S}(x_{1},x_{2},\cdots,x_{n})&= \frac{E_{s}}{N_{0}}\frac{(\sum\limits_{i=1}^{n}a_{i})^{2}}{\sum\limits_{i=1}^{n} 1^{2}}\\ &=\frac{E_{s}}{N_{0}}\frac{(\sum\limits_{i=1}^{n}a_{i})^{2}}{n} \end{split} \tag{10}\]  
\[\begin{split}\gamma_{N}(x_{1},x_{2},\cdots,x_{n})&= \frac{E_{s}}{N_{0}}\frac{(\sum\limits_{i=1}^{n}\frac{a_{i}^{2}}{a_{1}})^{2}}{ \sum\limits_{i=1}^{n}(\frac{a_{i}}{a_{1}})^{2}}\\ &=\frac{E_{s}}{N_{0}}\frac{(\sum\limits_{i=1}^{n}a_{i}^{2})^{2}( \frac{1}{a_{1}})^{2}}{\sum\limits_{i=1}^{n}(a_{i})^{2}(\frac{1}{a_{1}})^{2}}\\ &=\frac{E_{s}}{N_{0}}\frac{(\sum\limits_{i=1}^{n}a_{i}^{2})^{2}}{ \sum\limits_{i=1}^{n}(a_{i})^{2}}\\ &=\frac{E_{s}}{N_{0}}\sum\limits_{i=1}^{n}(a_{i})^{2}\end{split} \tag{11}\]  
Since \(a_{i}\) is the \(i\)-th path gain, \(1\geq a_{1}>a_{2}>\cdots\cdots>a_{n}>0\) exists.  
\[\therefore\gamma_{N}(x_{1},x_{2},\cdots,x_{n}) >\gamma_{S}(x_{1},x_{2},\cdots,x_{n}) \tag{12}\] \[\therefore\frac{E_{s}}{N_{0}}\sum\limits_{i=1}^{n}(a_{i})^{2} >\frac{E_{s}}{N_{0}}\frac{(\sum\limits_{i=1}^{n}a_{i})^{2}}{n} \tag{13}\]  
Since \(\frac{E_{s}}{N_{0}}\) is greater than 0, the formula to be proved is given by  
\[n\sum\limits_{i=1}^{n}(a_{i})^{2}>(\sum\limits_{i=1}^{n}a_{i})^{2} \tag{14}\]  
when \(n=2\),  
\[\therefore(a_{1}+a_{2})^{2} >0 \tag{15}\] \[\therefore a_{1}^{2}+a_{2}^{2} >2a_{1}a_{2}\] (16) \[\therefore 2(a_{1}^{2}+a_{2}^{2}) >a_{1}^{2}+a_{2}^{2}+2a_{1}a_{2}\] (17) \[\therefore 2(a_{1}^{2}+a_{2}^{2}) >(a_{1}+a_{2})^{2} \tag{18}\]  
When \(n=m\), the inequality is workable.  
\[m(a_{1}^{2}+a_{2}^{2}+\cdots+a_{m}^{2})>(a_{1}+a_{2}+\cdots+a_{m})^{2} \tag{19}\]when \(n=m+1\),  
\[(a_{1}+a_{2}+\cdots+a_{m}+a_{m+1})^{2} =(a_{1}+a_{2}+\cdots+a_{m})^{2}+2(a_{1}+a_{2}+\cdots \tag{112}\] \[+a_{m})a_{m+1}+a_{m+1}^{2}\] \[\therefore m(a_{1}^{2}+a_{2}^{2}+\cdots+a_{m}^{2})>(a_{1}+a_{2}+ \cdots+a_{m})^{2}\] (113) \[\therefore(a_{1}+a_{2}+\cdots+a_{m})^{2}+2(a_{1}+a_{2}+\cdots+a_{m}) a_{m+1}+a_{m+1}^{2}<m(a_{1}^{2}+\] (114) \[a_{2}^{2}+\cdots+a_{m}^{2})+a_{m+1}^{2}+2a_{1}a_{m+1}+\cdots+2a_{ m}a_{m+1}\] \[\therefore m(a_{1}^{2}+a_{2}^{2}+\cdots+a_{m}^{2})+a_{m+1}^{2}+2a_{1 }a_{m+1}+\cdots+2a_{m}a_{m+1}<m(a_{1}^{2}+\] (115) \[a_{2}^{2}+\cdots+a_{m}^{2})+a_{m+1}^{2}+a_{1}^{2}+a_{2}^{2}+ \cdots+a_{m}^{2}+ma_{m+1}^{2}=(m\] (116) \[+1)(a_{1}^{2}+a_{2}^{2}+\cdots+a_{m}^{2}+a_{m+1}^{2})>(a_{1}+a_{2 }+\cdots+a_{m}+a_{m+1})^{2} \tag{117}\]  
To sum up, the following formula is true.  
\[n\sum_{i=1}^{n}(a_{i})^{2}>(\sum_{i=1}^{n}a_{i})^{2} \tag{118}\]  
Hence,  
\[\gamma_{N}(x_{1},x_{2},\cdots,x_{n})>\gamma_{S}(x_{1},x_{2},\cdots,x_{n}) \tag{119}\]
## 3 Results and Discussion
### Water Uptake  
The water uptake parameter of the membrane corresponds to the hydrophilicity characteristic of the membrane [40]. The water uptake of the membranes was calculated (Supplementary Section S9, Equation (S3)). The percentages of water uptake and porosities of CA and CTG-based membranes were calculated and presented in Table 2.  
Figure 5: Contact angle micrographs of (**a**) CA, (**b**) CTG—100, (**c**) CTG—300 and (**d**) CTG—500 membranes recorded after every 20 s.  
The water uptake for pristine CA was found to be 72.00% while those of the modified membranes, i.e., CTG-100, CTG-300, and CTG-500 membranes were 76.93, 78.06 and 80.53%, respectively. These results are consistent with the materials' affinity for water as it was shown in Figure 5, i.e., because CA is more hydrophobic than the modified membranes, it had the lowest water uptake. Additionally, the water uptake efficiency increased with the increasing concentration of the hydrophilic photocatalyst loaded onto the membrane.
# Fabry-Perot Interferometer Based on Suspended Core Fiber for Detection of Gaseous Ethanol
## 3 Results  
As the sensor is subjected to atmospheres with different concentrations of ethanol, a shift towards longer wavelengths (red shift) is observed in the spectral response. This is due to an increase in the refractive index of the atmosphere with the increase in ethanol concentration. The wavelength shift of one peak is shown in Figure 5. By applying a linear fitting to the entire dynamic range, a sensitivity of (3.9 \(\pm\) 0.2) pm/wt.% (R\({}^{2}\) > 0.983) is attained. As the refractive index in the hollow regions increases, there is also a change in the effective refractive index of the mode propagating in the SCF, due to the evanescent field [17]. This leads to the red shift observed in Figure 5.  
Notice that, although the changes in the ethanol concentration resulted in a variation in the atmosphere refractive index, with the current experimental setup, it was not possible to estimate this variation. Further studies should be carried out with an adequate experimental procedure to estimate the sensor sensitivity towards refractive index and to better compare with other sensors presented in the literature. To assess the sensor reproducibility, the same experiment was carried out using other sensing heads. The behavior was similar, although the sensitivity attained was slightly affected by the sensor length. Therefore, in the context of a real application, a previous calibration should be done to each  
Figure 4: Reflection spectrum of the developed sensor.  
Figure 5: Sensor wavelength shift for different concentrations of ethanol. The blue circles represent the experimental data, and the dashed blue line represents the applied linear fit.
# Analysis of Wind Speed Characteristics Along a High-Speed Railway
## 5 Detailed Analysis of Predictability Performance and Model Validation
### Evaluation Indicators  
Evaluation indicators effectively quantify the prediction accuracy of forecasting models. Commonly used metrics include the root mean square error (\(RMSE\)) and the normalized mean absolute error (\(nMAE\)) [46]. The corresponding calculation results are presented below. In these equations, \(y_{t}\) represents the actual wind speed, while \(\hat{y}_{t}\) denotes the predicted wind speed.  
\[RMSE=\sqrt{\frac{1}{N}\sum\limits_{t=1}^{N}(\hat{y}_{t}-y_{t})}, \tag{8}\]  
\[nMAE=\frac{1}{N}\sum\limits_{t=1}^{N}\left|\frac{\hat{y}_{t}-y_{t}}{y_{\max}} \right|, \tag{9}\]  
In addition, we combine wind speed predictability with the concept of predictability rate (\(PR\)) to dynamically assess the model's dynamic characteristics. The formula for model predictability rate is as follows:  
\[PR=\frac{B}{\sqrt{\frac{1}{m}\sum\limits_{t=1}^{m}(\hat{y}_{t}-y_{t})}}, \tag{10}\]  
where \(B\) represents [PERSON]'s test statistic, with \(m=12\). This indicator is determined by the predictability of the input data and the associated prediction error. The Bartlett test statistic quantifies predictability; a higher value of \(B\) corresponds to a lower prediction error, a higher predictability rate, and improved wind speed prediction accuracy.
# Networked Roadside Perception Units for Autonomous Driving
###### Abstract  
Vehicle-to-Everything (V2X) communication enhances the capability of autonomous driving through better safety, efficiency, and comfort. In particular, sensor data sharing, known as cooperative perception, is a crucial technique to accommodate vulnerable road users in a cooperative intelligent transport system (ITS). In this paper, we describe a roadside perception unit (RSPU) that combines sensors and roadside units (RSUs) for infrastructure-based cooperative perception. We propose a software called AutoC2X that we designed to realize cooperative perception for RSPUs and vehicles. We also propose the concept of networked RSPUs, which is the inter-connection of RSPUs along a road over a wired network, and helps realize broader cooperative perception. We evaluated the RSPU system and the networked RSPUs through a field test, numerical analysis, and simulation experiments. Field evaluation showed that, even in the worst case, our RSPU system can deliver messages to an autonomous vehicle within 100 ms. The simulation result shows that the proposed priority algorithm achieves a wide perception range with a high delivery ratio and low latency, especially under heavy road traffic conditions.  
Keywords:cooperative ITS; autonomous vehicle; cooperative automated vehicles (CAV); V2X; cooperative perception; open-source software +
Footnote †: journal: Sensors
## 3 Cell Base Structure and Frequency Response Analysis
### Frequency Response Analysis of Traditional Vibration Isolation Base  
Based on the traditional cellular structure of the honeycomb base, the long side is 14 cm and the short side is 10 cm. The structure is solid structure. The geometric diagram of the honeycomb base is given in the Figure 6c, and the vertical direction is laid with 6 layers.  
The vertical coordinate of the frequency response curve graph represents the logarithm of the displacement ratio between the initial position and the position of the end point. The smaller the value is, the better the vibration isolation effect is. The larger the range is, the wider the vibration isolation bandwidth of the modified mechanism to external excitation is. As can be seen from Figure 6d, the traditional honeycomb base with solid steel structure does not have isolation effect on external excitation in any frequency band.
# GNSS and LiDAR Integrated Navigation Method in Orchards with Intermitt GNSS Dropout
## 5 Conclusions  
To mitigate the issue of signal blockage by dense orchard canopies causing significant drops in satellite navigation system accuracy, this study introduces a loosely coupled laser odometer and GNSS/INS system for robotic vehicles in orchards. The approach includes creating the vehicle's kinematic model, developing a PID-based path-tracking algorithm, planning paths tailored to orchard terrain, and executing navigation trials. Results indicate that with the robotic vehicle moving at 0.35 m/s, the peak tracking error stays below 0.35 m, the mean absolute error remains under 0.1 m, with a standard deviation close to 0.078, and an RMSE under 0.017 m. The autonomous navigation and control system adopted by the robotic vehicle meets the requirements of autonomous navigation of orchard robots. It improves the navigation accuracy in the partially obstructed orchard environment and improves the performance of the navigation system. When satellite signals are obstructed, laser odometry becomes the standard for navigation and positioning. Compared to the GNSS/INS method, this approach reduces the maximum and average positioning deviations by 1.6 m and 1.1 m, respectively. This effectively enhances navigation stability and positioning accuracy under conditions of blocked satellite signals, overcoming the challenges of self-positioning in environments where satellite signal interruptions occur. The disadvantage is that the positioning error of the laser odometer fluctuates greatly, which still needs to be further optimized and improved.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We wish to thank the above foundations for their support.  
The authors declare that this study received funding from Science and Technology Cooperation Project of Xinjiang Production and Construction Corps. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
## 3 Thermal-Induced Structural Dynamic Analysis of Antenna
### Thermal Analysis
For the beams with solar radiation angles, \(\beta=30^{\circ}\) and \(\beta=15^{\circ}\), the trends for the responses are the same as the horizontal beam. The less the solar radiation angle is, the larger the steady-state average temperature is. This phenomenon is quite understandable since a lower value \(\beta\) on the right side of Equation (17) can result in a lower value of \(\cos\beta\). For the right inclined beam with solar radiation angle \(\beta=75^{\circ}\), the trend for the average temperature response is quite opposite to the response of the horizontal beam due to the heat flux absorbed being less than that emitted from the surface radiation in this condition. The simulation results for the right inclined beam agree well with Figure 10a in reference [3] for a single boom. Figure 8: Average temperature response. Figure 9: Perturbation temperature responses. The dynamic responses for the farthest nodes 9 and 25 are shown in Figures 11 and 12, respectively. In Figure 11, it is found that similar to node 5, the thermal deformations in six dimensions are still more severe than vibrations, and the vibration in \(z\)-direction and \(\theta_{y}\)-direction are the most significant. In addition, the deformations in \(x\)-direction, \(y\)-direction, \(\theta_{x}\)-direction, and \(\theta_{w}\)-direction are almost the same as those for node 5, while the deformations in the z-direction and \(\theta_{y}\)-direction grow significantly. The steady deformations are about \(-0.027\) m, \(-0.044\) m, \(-0.3\) m, \(-0.0038\) deg, \(-0.05\) deg, and \(0.015\) deg. The farther the node is from the fixed rod, the greater the displacements can be. The simulations in this subsection provide basic information for thermal-induced vibration responses, which will be used as a comparison for the coupling condition in Section 4.
## 6 Simulation Verification
### Delay and Energy Model Verification  
\(t\) is set to 0.01 and five paths are set. Table 3 is the actual simulation results, \(T_{ete}\) is the end-to-end delay, \(L_{e}\) is the path length, \(H_{ete}\) is the actual hop number, \(T_{ete}/H_{ete}\) is the actual hop delay, \(L_{e}/H_{ete}\) is the actual hop length. The expansion angle increases from 40 to 120 degrees. With the increase, \(T_{ete}\) and \(L_{e}\) increased correspondingly.  
Figure 8: Selection of the source sending angle range.  
The average delay per hop is 0.0026 s, which is consistent with the theoretical calculation. It is noted that the actual distance per hop is 53 m, which is smaller than the 60 m set. Since the actual distance per hop is smaller than the corresponding virtual distance per hop. In order to verify the impact on energy consumption and time delay, the simulation is set from 25 m to 60 m, and the results are shown in Figure 9. Energy consumption and time delay show a compromise. The smaller \(R_{k}\) is, the larger the delay and the lower the energy consumption are. Under the condition of meeting the demand of time delay, the smaller \(R_{k}\) is, the lower energy consumption is.
## 2 Materials and Methods
### Hybrid Single-Particle Lagrangian Integrated Trajectory  
The backward trajectory was calculated using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model developed by the National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory (ARL) to determine the origin of air masses [33]. The model is based on a hybrid calculation between the Lagrangian methodology, using a moving frame of reference for the advection and diffusion calculations as air parcel trajectories extend from their initial locations, and the Eulerian approach, employing a fixed three-dimensional grid as a frame of reference for computing pollutant air concentrations. The model can be executed on the web through the Real-time Environmental Applications and Display sYstem (READY) of NOAA (www.ready.noaa.gov/HYSPLIT_traj.php). In this study, the HYSPLIT model was applied using the initial data from the Global Data Analysis System (GDAS) and the modeled vertical motion calculation method for 72 h, at a mean sea level of 0 km. The vertical motion option for analysis was selected as the vertical speed produced by the model, and the analysis was conducted at three altitudes (50, 500, and 1000 m) from the time when the concentrations in Busan reached their maximum levels in each case.
## 3 Methodology
### Feature Compression  
In order to select efficient features, this paper adopts the SDAE to compress the features in the time domain and frequency domain.  
The SDAE network is used to realize feature compression. Due to the large number of time-domain and frequency-domain features, it is difficult to combine different time  
Figure 3: The structure of the bearings RUL prediction model proposed.  
domain and frequency-domain features to predict the bearing RUL. However, the SDAE network can realize feature compression, ensure the similarity between the decoding results and the input data, and restore the original features to the maximum extent. In the training stage of SDAE network, the input and output are both time domain and frequency domain feature vectors extracted from the original training set. We put the features compressed by SDAE into feature subset \(F_{SDAE}\) as the basis for the construction of the optimal DI set \(Opt_{DI}\).
# A Model-Driven Approach for Solving the Software Component Allocation Problem
## 5 Methodology
### Optimization  
After defining the model and optimization problem, (optimal) component allocations shall be found. Since the problem is multi-objective in nature, not only a single optimal solution but a set of Pareto-optimal solutions is desired. In Algorithm 1 the pseudo-code for solving the MOCAP is shown. The solver starts by initialization an empty archive \(A\). Then, uniform weight vectors \(W\) are created using the Das and Dennis sampling method [35]. The uniformity of weight vectors is important to ensure a diverse set of solutions. In our experiments, we have used a partition number \(p=12\) for creating the weight vectors. For each of the weights \(w\in W\), a single-objective optimization problem is solved. An efficient mixed-integer linear programming (MILP) solver can be applied to obtain an optimal solution \(s\) because the optimization problem consists of only linear functions. The solution for the scalarized problem \(f_{m}(\mathbf{x})\) is known to be a non-dominated solution. However, runs with different weight vectors \(w\) might find the identical solution \(s\). Thus, \(s\) is only added to the archive if it is not present yet. It is worth noting that the weighted sum approach cannot find any solutions on the concave part of the front.  
```
Input: Number of Units \(U\), Number of Components \(C\), Number of Resources \(M\), Resource Consumptions \(T\), Resource Capacities \(R\) /* Archive of Pareto-optimal solutions */ \(A\leftarrow\{\}\) /* Uniformly distributed weight vectors */ \(W\leftarrow\) uniform_weights() foreach\(w\in W\)do /* Solve a linear single-objective optimization problem */ \(s\leftarrow\) solve_milp(U, C, M, T, R) /* if a new solution has been discovered, add to non-dominated archive \(A\) */ if\(s\
ot\in A\)then \(A\gets A\cup\{s\}\) end foreach return\(A\)
```  
**Algorithm 1**Solver for MOCAP.