esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Validation Approach  
Validation of forecasting models involved the comparison of predicted yields and reference official statistics that were not used for the model training. For each administrative unit, crop type, and GDD level, a cross-validation was performed. It followed the leave-one-_year_-out procedure, which is a special case of the \(k\)-fold cross validation where \(k\) equals a number of years in a time series. It must be noted that the selection of predictors was repeated at each iteration to avoid the predictor selection procedure to benefit from 'knowing' the data from the year that was used for validation.  
Three metrics were used to describe the model performance: Mean Bias Error (MBE, Equation (5)), Root Mean Square Error (RMSE, Equation (6)), and Modeling Efficiency (EF, Equation (7)) calculated by means of the following formulae:  
\[\text{MBE}=\frac{1}{n}\sum_{k=1}^{n}(E_{k}-M_{k}) \tag{5}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{k=1}^{n}(E_{k}-M_{k})^{2}} \tag{6}\]  
\[\text{EF}=1-\frac{\sum_{k=1}^{n}(E_{k}-M_{k})^{2}}{\sum_{k=1}^{n}(E_{k}-\overline {M})^{2}} \tag{7}\]  
where: \(E_{k}\) represents the predicted crop yield value, \(M_{k}\) represents the reference crop yield value, \(\overline{M}\) represents the average value of reference crop yield values, \(k\) represents the step of the time series (i.e., 1 year), and \(n\) represents the length of the time series.  
The RMSE and MBE were also expressed in relative values (0-100%) denoted as the RRMSE and RMBE, respectively, by dividing these quality metrics by the mean of a reference data (\(\overline{M}\)). The MBE, RMSE, and EF were also used to evaluate the accuracy of cross-calibration between MODIS and Sentinel-3 products. However, in this situation the \(E_{k}\) denotes re-calibrated MODIS NDVI/LST and \(M_{k}\), the original Sentinel-3 NDVI/LST time series.
# Analysis of the Working Performance of a Back-to-Back Geosynthetic-Reinforced Soil Wall
## 3 Results and Discussion
### Parametric Study on the Geogrid Length
#### 3.2.2 Tension in the Reinforcement  
[PERSON] et al. [13] suggested that no lateral earth pressure needs to be considered from the backfill for external stability if the overlap exceeds 0.3 \(H\), which is based on the vertical panel and the same length reinforcement. A varied overlap scenario was not discussed, which is the case for different reinforcement lengths. This was due to the consideration of the two walls acting as one wall with no unreinforced backfill to exert an external destabilizing thrust. As the maximum distance between the left blocks and right panel was 16.0 m at the bottom of the wall, there should have been reinforcement layers in the upper half that were fully covered with the geogrid.  
The maximum tension in the reinforcement is shown in Figure 12. A rising trend can be observed with the decrease in reinforcement length from 8.0 to 6.0 m. A peak tension value was observed for the reinforcement length of 6.0 m, indicating that if the required embedded length in the active zone remained the same, the length in the resistance zone  
Figure 11: Post-construction deformation with various reinforcement lengths. (**a**) Post-construction deformation on the left panel; (**b**) post-construction settlement at the top of the wall.  
would be shorter. Therefore, the reinforcement had to provide additional tension to resist being pulled out and maintain the stability of the entire structure.
# OQPSK Synchronization Parameter Estimation Based on Burst Signal Detection
## 2 Algorithm Description
### Synchronization Parameter Estimation
#### 2.2.3 Timing Error Estimation
According to the Euler formula, \(e^{i\pi\mu/2}=\cos\frac{\pi\mu}{2}+j\sin\frac{\pi\mu}{2}\), \(e^{i\pi\mu}=\cos\pi\mu+j\sin\pi\mu\) can be obtained; thus, Equation (24) can be rewritten as:  
\[\Lambda(\tilde{\tau})=\left|\sum_{k=0}^{N-1}\tilde{c}^{*}(k)r(kT+\tilde{\tau}) \right|^{2} \tag{18}\]0  
The derivative of the above equation:  
\[\Lambda(\tilde{\tau})=\left|\sum_{k=0}^{N-1}\tilde{c}^{*}(k)r(kT+\tilde{\tau}) \right|^{2} \tag{18}\]1  
Take zero for the above equation, then solve it; we can obtain:  
\[\Lambda(\tilde{\tau})=\left|\sum_{k=0}^{N-1}\tilde{c}^{*}(k)r(kT+\tilde{\tau}) \right|^{2} \tag{18}\]2  
According to Equation (27), the corresponding timing error can be calculated as:  
\[\Lambda(\tilde{\tau})=\left|\sum_{k=0}^{N-1}\tilde{c}^{*}(k)r(kT+\tilde{\tau}) \right|^{2} \tag{18}\]3  
where \(P\) is the number of sampling points used for each symbol during interpolation, and \(P=2\) in this article. The value of \(\tau\) cannot be calculated by Equation (28) because the equation is already in its simplest form; thus, we have to do approximate processing. The four points adopted in the interpolation are approximately symmetrical, in which the values of \(z(0)\) and \(z(1)\) in the middle are larger and the values of \(z(-1)\) and \(z(2)\) on both sides are smaller. Therefore, we can get a larger \(A\) and a smaller \(C_{2}\), and further, \(\frac{2C_{2}}{A}\sin\pi\hat{\tau}\) can be ignored:  
\[\Lambda(\tilde{\tau})=\left|\sum_{k=0}^{N-1}\tilde{c}^{*}(k)r(kT+\tilde{\tau}) \right|^{2} \tag{18}\]4  
Although we obtain Equation (29) by ignoring \(\frac{2C_{2}}{A}\sin\pi\hat{\tau}\), simulation indicates that it still reaches high accuracy.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 4 Methodology and System Architecture
### Enhanced Security Analysis
#### 4.5.3 Latency and Throughput Models
In practical implementations, the latency and throughput models are primarily utilized during the design, configuration, and optimization phases of the FHSS system. These models help in predicting system performance under various operating conditions and in making informed decisions about parameter settings such as hopping rate \(R_{h}\), bandwidth \(B\), and modulation schemes. During active communication, the system may not need to run these calculations continuously. Instead, they can be computed periodically or when there is a significant change in system parameters or operating conditions. For example, if the system detects a change in the bit error rate \(P_{e}\) due to interference or other environmental factors, it may recalculate the effective data rate \(R_{\text{eff}}\) and throughput \(T\) to assess the impact on performance. This recalculation can trigger adjustments to system parameters to maintain optimal operation. The frequency of running these calculations depends on the specific requirements and constraints of the system. In scenarios where the communication environment is stable, the models may be recalculated infrequently or only when manual adjustments are made. In contrast, in dynamic or high-security environments where conditions can change rapidly, the system might perform these calculations more frequently--potentially after every significant event or at regular intervals defined by a monitoring frequency \(f_{\text{calc}}\). It is important to note that the calculations themselves are not computationally intensive and can be executed quickly without imposing significant overhead on the system. Modern communication systems often include real-time monitoring and control software capable of performing these calculations efficiently. By balancing the frequency of these calculations with system performance requirements, the FHSS system can maintain high data throughput and low latency while adapting to changing conditions. In our current setup, we did not implement the models for latency in the operational system. The focus of our implementation was on demonstrating the feasibility of integrating true random number generation into the FHSS mechanism and analyzing its impact on security and randomness properties. The latency and throughput considerations are included for comprehensiveness and to guide future work, where these factors could be empirically measured and optimized in real-world scenarios.
## 3 Results
### Accuracy Test
#### 3.1.3 Conventional Object Measurement
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Standard Value**} & \multicolumn{3}{c}{**Measurement Value**} & \multicolumn{3}{c}{**Error**} \\ \cline{2-13}  & \(d_{x}\) & \(d_{y}\) & \(d_{z}\) & \(d\) & \(d^{\prime}_{x}\) & \(d^{\prime}_{y}\) & \(d^{\prime}_{z}\) & \(d^{\prime}\) & \(\Delta d_{x}\) & \(\Delta d_{y}\) & \(\Delta d_{z}\) & \(\Delta d\) \\  & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline
**1** & 2.026 & 17.512 & 431.588 & 431.948 & 2.215 & 16.812 & 431.788 & 432.137 & 0.189 & 0.402 & 0.254 & 0.189 \\
**2** & 411.314 & 25.527 & 22.123 & 412.699 & 411.616 & 25.321 & 21.945 & 412.978 & 0.302 & \(-0.206\) & \(-0.178\) & 0.302 \\
**3** & 112.564 & 81.495 & 710.525 & 723.987 & 112.122 & 81.652 & 711.121 & 724.522 & \(-0.442\) & 0.157 & 0.596 & \(-0.442\) \\
**4** & 919.468 & 138.619 & 511.203 & 1061.115 & 920.210 & 139.021 & 510.899 & 1061.664 & 0.742 & 0.402 & \(-0.304\) & 0.742 \\
**5** & 2412.852 & 215.665 & 262.421 & 2436.643 & 2413.352 & 215.268 & 262.021 & 2437.060 & 0.500 & \(-0.397\) & \(-0.400\) & 0.500 \\
**6** & 365.133 & 301.214 & 416.230 & 603.317 & 364.847 & 301.521 & 415.889 & 630.073 & \(-0.286\) & 0.307 & \(-0.341\) & \(-0.286\) \\
**7** & 165.249 & 118.259 & 687.255 & 716.667 & 165.145 & 117.966 The RMS error of the feature distance is 0.508, and (0.539, 0.297, 0.435) in \(X\), \(Y\) and \(Z\) directions. The mean error of the feature distance is 0.159 mm, and the maximum errors in \(X\), \(Y\) and \(Z\) directions are 0.782 mm, 0.402 mm and 0.625 mm, respectively.
# Efficient Resource Management for Sum Capacity Maximization in 5G NOMA Systems
## 1 Introduction  
The existing generations of communication networks like third generation (3G) and fourth generation (4G), orthogonal multiple access (OMA) technologies such as code-division multiple access (CDMA) and orthogonal frequency division multiple access (OFDMA) have been widely adopted by academia and industry. Although these technologies can significantly reduce the inter-user interference, the objective of high spectral efficiency and massive connectivity is a far cry and need to be further improved [1]. The next generations such as fifth generation (5G) and beyond 5G (5 GB) mobile networks are expected to provide ultra-high data rates and meet with the explosive growth of wireless connections [2]. In order to fulfill these demands, new radio access techniques are actively pursued and explored. Recently, non-orthogonal multiple access (NOMA) has been emerged as one of the best candidates for 5G and B5G cellular networks and received tremendous interest from research community due to its potential of connecting more users on the same system resources [3, 4]. In the downlink wireless transmission, with the help of superposition coding at the base station (BS), NOMA can multiplex multiple users at the same resource and time [5]. The BS assigns low transmit powerto the user with better channel and high transmit power to the one with weaker channel to facilitate successive interference cancellation (SIC) process at the receivers [6]. After receiving the signals from BS, the strong user applies SIC technique to remove the signal of weak user before decoding its own signal. However, weak user can not apply SIC and receives the signal with the interference of a strong user [7].  
Besides the advantages of high spectral efficiency and massive connectivity, NOMA faces a number of challenges for successful implementation. The users are required to satisfy the minimum gap among their transmission powers to guarantee the successful process of SIC [8]. The high number of users sharing the same system resources can significantly increase the complexity of SIC at the receivers [9]. Furthermore, different users sharing the same system resources can cause inter-user interference [10]. To overcome these limitations, the network resources need to be efficiently utilized--the process is called optimal resource allocation [11].
# Progress in Energy Storage Technologies and Methods for Renewable Energy Systems Application
## 3 The Progress of Energy Storage Technologies
### Flywheels  
FES is the use of varying inertia of the object to achieve energy input/storage or output/release. A total of 60 years of development in mechanical bearing technology has matured compared to superconducting magnetic bearings. A modern FES system consists of five main components: a flywheel, a set of bearings, a reversible motor/generator, a power electronics unit, and a vacuum chamber [65]. The working principle of flywheel energy storage is that the flywheel is driven by electric energy to rotate at high speed under the condition of electric affluence, and the electrical energy is transformed into mechanical energy for storage. The flywheel energy storage can realize the deposit and release of electric energy through the acceleration and deceleration of the rotor. Compared with other forms of energy storage technologies, flywheel energy storage has the advantages of a long service life, high energy density, not being limited by the number of times of charging and discharging, being easy to install and maintain, and being less harmful to the environment [66]. At present, flywheel energy storage technology focuses on high-strength composite material technology, high-speed low loss bearing technology, high speed high-efficiency power generation/motor technology, flywheel energy storage grid-connected power regulation technology, and vacuum technology [67]. High-speed FES uses advanced composite materials as flywheels, such as carbon fiber [43]. The high-speed FES system uses on-contact magnetic bearings to reduce bearing wear, thus increasing efficiency. The applications of high-speed FES are expanding, mainly in the traction and aerospace industries for high power quality and traversing power services [68]. In June 2011, a 20 MW modular power plant built by Beacon Power, the largest advanced FES facility in North America, went into commercial operation in New York [45]. It provides fast-response FM service to the grid using a 200-speed flywheel system, providing 10% of the statewide FM demand [69]. [PERSON] and [PERSON] [70] proposed a simple control method for flywheel energy storage systems, modeled in the charging and discharging states, respectively. In the charging state, current control is mainly considered, and a closed-loop speed detection controller is applied for speed regulation. In the discharging state, the PWM inverter is used as a PWM boost rectifier. It is proposed to use the current reference instead of the speed reference to drive the charging mode. [PERSON] et al. [71] used superconducting energy storage technology to achieve the highest peak energy demand and a significant increase in energy efficiency in a tram track electrical system. The peak energy of the whole system was reduced by 36.7%, and the annual power saving was about 48 MWh.
## 4 Numerical Results and Discussion
### Comparison of the Influence of Up-Arch and Settlement on Vehicle Operational Safety
The most common foundation deformation of the track can be roughly divided into settlement and up-arch. Considering only the influence of foundation deformation on track irregularity, in the case of 12 mm up-arch, the equiprobability curve of reduction rate of wheel load probability density is shown in Figure 11, and the reliability of vehicle-vertical safety was 0.9978, as shown in Figure 12.  
Figure 8: Track irregularity after overlaying. Figure 9: Equal probability curve of the reduction rate of wheel load at 12 mm settlement. It can be seen that the settlement was lowered with respect to the up-arch by 0.0293, which is about 3.02%, and this indicated that the foundation settlement was more unfavorable to the vehicle vertical safety. The reason for this are varied, for example, different states of the track structure support will have an influence in different degrees. Only the influence of foundation deformation on track irregularity was considered in this paper, while the influence on the other parts of the track structure was not considered.
## 3 Experimental Methods and Results  
### Experimental Platform  
Figure 1 shows the HIFU experimental system, which is mainly composed of a water tank, a thermometer, a HIFU transducer, a hydrophone probe, a 3D position system, a digital oscilloscope and a computer control terminal. The input power of the HIFU transducer can be adjusted by the HIFU control system. The drive power of the HIFU transducer is 210-300 W and the irradiation period is 0.1-0.2 ms. Fresh porcine muscle tissues were selected as the experimental material. The preparation process of the tank liquid is as follows: firstly, 95% ethanol was mixed with poyidone in a ratio of 4:1, then the mixture was poured into the tank and mixed with water in a ratio of 1:20 to remove oxygen. In order to stabilize the mixture, we waited one hour before the experiment. The experimental material was fixed on an acoustic rubber plate and placed in the mixture under the HIFU transducer (PRO2008, Shenzhen, China). The geometric focal length of the HIFU transducer was 13 cm, the aperture of transducer was 11 cm, the diameter of the circular hole was 4.7 cm, and the source pressure of transducer was 0.23 MPa. In addition, the ellipsoidal \(-6\) dB focal beam width of this transducer was 1.6 mm. The 3D position system can control the position change of the HIFU irradiation target area. There is a channel above the HIFU transducer that allows a hydrophone probe (FOPH2000, Lautenbach, Germany) to be placed. A thermometer (DT-3891G, Shenzhen, China) was inserted into fresh porcine tissues to measure the temperature and estimate the extent of the damage. The sampled signals were obtained through a hydrophone probe, amplified by a broadband signal amplifier, and sent to the digital oscilloscope (MDO3032, Tektronix, Beaverton, OR, USA) for observation and preservation.  
In this experiment, 188 groups of data were obtained from 10 specimens of in vitro fresh porcine muscle tissues, including 90 groups that were sampled while the HIFU source was on and 98 groups that were sampled while the HIFU source was off. The HIFU echo signals can be obtained while the HIFU source is on. The center frequency of the HIFU echo signal was 1.39 MHz, the second harmonic frequency was 2.78 MHz.
## 1 Introduction
[10] analyzed the structural reliability under both static and fatigue load, and then the optimal design with different variables was carried out by making use of an improved genetic algorithm. However, this work characterized the randomness of variables utilizing the traditional distribution function, and the fatigue reliability performance function could be directly obtained. As for engineering structures, in fact, the fatigue reliability performance function was difficult to ascertain on the basis of limited samples. Plate high-strength steel with different thicknesses welded this A-type frame [11]. In the manufacturing stage, the material properties of the weld metal are scattered, which is caused by man-made factors. In addition, the dimension of A-type frame is usually obtained from the traditional experience, which could be different from the \"ideal\" structure. The steering loads at its connection sites should be random during the in-service stage, because the excitation source is stochastic. These parameters are all relative with the structural fatigue reliability. According to the traditional stress-strength interface model [12; 13; 14], only stress and strength are directly considered as random variables to evaluate fuzzy fatigue reliability, which is unreasonable to ignore the uncertainties of dimension, material properties, and loads. More importantly, the common fuzzy fatigue reliability analysis is based on the traditional membership function, such as normal distribution, parabolic distribution, et al. However, a large number of sample data is needed to ascertain the characteristic parameters of traditional membership function, so that the calculated fuzzy fatigue reliability that is based on this method has certain errors for engineering structures without enough samples. The performance function used for calculating fatigue reliability is also not easy to determine on account of a small amount of sample data. Therefore, the multi-source uncertainties were taken into account to analyze the fuzzy fatigue reliability of welded A-type frame to accurately establish the membership function and performance function. In this paper, a new membership function was constructed based on the limited sample data, whose accuracy was testified by experimental data. According to the fuzzy theory, the failure probability formula was deduced. Subsequently, the response surface method was employed to establish fuzzy fatigue reliability performance function, while considering the uncertainties of dimension, material properties, and loads. The fuzzy fatigue reliability of the welded A-type frame was evaluated by the self-configuring membership function, and the optimal design for improving fatigue reliability was conducted through the genetic algorithm.
## 4 Results and Analyses  
Prior to testing the proposed algorithm for an integrated navigation system, the suitability of using a heavy tailed distribution was examined through an initial test carried out using only GNSS range and Doppler measurements. GNSS data was collected in pedestrian mode under fairly open sky conditions. The OEM6 receiver inside the NavCube along with the GPS-702-GG antenna was used to collect the data. Simulated errors were added to three satellite measurements for nearly 70% of the data in order to analyze the performance of the filters in presence of known faults. The simulated errors on the three satellites consisted of uniformly distributed pseudorange (\(\rho\)) errors ranging from 10-70 m and uniformly distributed Doppler (\(\hat{\vartheta}\)) errors ranging from -10 Hz to 10 Hz (\(\pm\)1.9 m/s). The results obtained with the navigation filter implemented with the assumption of \(t\) distributed measurement errors were compared against the results obtained with the assumption of normally distributed measurement errors. The comparisons were done for three cases, namely: (i) measurements without simulated errors; (ii) measurements with simulated pseudorange errors and (iii) measurements with simulated pseudorange as well as Doppler errors. Root mean square errors (RMSE) as well as reliability were calculated in local coordinates as shown in Table 2. It can be seen that the filter with the \(t\) distribution for measurement errors are least affected by the faults. It is also noted that, for the case with no added errors, the results are analogous for the two filter types. In fact, for an open sky data similar to the one used in this particular test, GNSS measurement errors follow the normal (N) distribution more closely as compared to the \(t\) distribution. This is supported by the slightly better horizontal accuracy as well as better reliability along the local axes in case of the filter that assumes normal distribution for the measurement errors.  
Figure 7: Reference trajectory (natural canyon).  
Taking the above result as an affirmation of the suitability of the \(t\) distribution for GNSS challenged environments, the VB filter was further implemented in the GNSS/INS integrated system. The GNSS/INS filter itself was realized using the tight-coupling technique implemented in a C\(++\) software application [12]. The proposed algorithm was finally assessed by analyzing the results obtained by processing the two pedestrian data sets described earlier.