esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Positioning Deviations according to the Scenario Phase
From the proportional comparison of the \(SSR\) indicators, it was clear that Receiver A and Receiver B measured relatively good accuracy in both the static and dynamic modes. However, even the best of the receivers (Receiver A) showed in the dynamic mode 4.8-6.4 times worse values of the error indicators (\(RMS_{err}\) and \(Me\)) than in the static mode. The other receivers compared showed much worse, especially in the case of \(RMS_{err}\). Based on these results, it can be concluded that the errors of all the tested GNSS receivers with RTK correction in positioning were many times worse in the dynamic mode of measurements than in the static one. Thus, as described by collective authors of reference [50], with the improvements of sensors for autonomous navigation, further improvements in tracking accuracy cannot be expected, as the proposed 1.6-cm straight-line tracking error is approximately the same as the RTK-DGPS receiver error. However, as presented in our study, the awareness of the limits given by the generally known accuracy of RTK receivers can be further increased by the error of RTK receivers acting in dynamic scenarios (e.g., during the just-mentioned rollovers or during the use of fast drone platforms). The same limit-causing distortion of the input point cloud data must then be considered by systems for the detection, classification and precise localisation of vegetation or, directly, fruits, as described, for example, by \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Whole Measurement**} & \multicolumn{3}{c}{**“FIX” State Epoch**} \\ \cline{2-5}
**RTK Receiver** & \(\frac{SSR_{signal}}{SSR_{signal}}\) & \(RMS_{signal}\) & \(Me\)(Whole\(|_{signal}\)) & \(RMS_{signal}\) & \(Me\)(FTX\(|_{signal}\)) \\  & \(\frac{SSR_{signal}}{SSR_{signal}}\) & \(\frac{RMS_{signal}}{ME(Whole\(|_{signal}\))}\) & \(\frac{Me\)(Whole\(|_{signal}\))}{RMS_{signal}}\) & \(Me\)(FTX\(|_{signal}\) \\ \hline Receiver A & 0.97 & 6.4 & 4.9 & 5.9 & 4.8 \\ Receiver B & 0.91 & 59.6 & 3.1 & 24.5 & 2.9 \\ Receiver C & 0.64 & 184.9 & 4.1 & 3.6 & 2.9 \\ Receiver D & 0.36 & 46.4 & 6.1 & 4.0 & 3.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Proportional comparisons of the error indicators of the dynamic measurements compared to the static measurements. The RTK receivers with build-in INS (Receiver A and Receiver B) had similar results for \(SSR\) in the static and dynamic measurements. All four RTK receivers mostly had better error indicators (\(RMS_{err}\) and \(Me\)) during the static measurements than in the dynamic measurements. references [10; 12; 51; 52]. Therefore, to accelerate the development and improve the quality of such functionalities, it is advisable to use the most accurate INS or to look for reference devices other than RTK-based technologies.
# Application Research of Multi-Mode Relay in Future Heterogeneous Networks
## 4 Theoretical Calculation and Simulation Analysis
Figure 9 displays the performance of system outage probability, which is plotted by using Equation (29) in the circumstance with different noise power: \(p_{n}=\sigma_{E}^{2}=10^{-2},10^{-3},10^{-4},10^{-5},10^{-6}\), respectively. We can observe that the power level of noise significantly affects the system performance. Notice that when \(p_{n}=10^{-2}\), the simulation results are approximate with the theoretical value before \(p_{s}\) reaches 28 dB. One interesting point is that with the increase of \(p_{s}\), the system performance grows better than the theoretical analysis to a certain extent. Figure 8: Spectral efficiency of HHWN with MMR as different relay power \(p_{r2}\) equals 9/10,1/2,1/3,1/4,1/5 times \(p_{r1}\). Figure 7: Outage probability of HHWN with MMR as different relay power \(p_{r2}\) equals 9/10,1/2,1/3,1/4,1/5 times \(p_{r1}\).
## 5 Case Study
### Weighting of Robustness Criteria  
#### 5.2.1 Subjective Weighting  
The subjective weights are determined. Taking underground power stations as an example, experts' judgments on the mechanical malfunction probability of different machines are not identical to the expected utility curve. Although hand drills often fail, the malfunction rate given by experts is lower than the actual statistical results due to their low price. On the contrary, though three-arm trolleys have low failure frequency, their failure rate provided by experts is higher than the actual statistical results because of their high costs [49]. Thus, prospect theory is adopted and improved to calculate the subjective weights.  
The evaluation results given by ten experts are shown in Table 2, where _RPs_ are calculated. It can be known that the subjective weighting method adapted with improved prospect theory is capable of handling interval-valued weights.  
The gain/loss matrix \(M\) and value function matrix are obtained:  
\[M=\left[\begin{array}{cccc}0.1100&-0.1050&0.0350\\ 0.0125&-0.0550&0.0694\\ -0.0900&0.0950&-0.1150\\ -0.0400&0.0450&-0.0150\\ -0.0400&-0.1050&0.0850\\ 0.0600&0.0450&0.0386\\ -0.0900&0.1450&-0.0150\\ 0.0600&0.0634&0.1850\\ 0.1100&0.0228&-0.0150\\ -0.0900&0.0277&-0.1150\\ \end{array}\right]\), \(v=\left[\begin{array}{cccc}0.1402&-0.2829&0.0506\\ 0.0202&-0.1561&0.0931\\ -0.2455&0.1231&-0.3076\\ -0.1164&0.0633&-0.0472\\ -0.1164&-0.2829&0.1115\\ 0.0818&0.0633&0.0553\\ -0.2455&0.1793&-0.0472\\ 0.0818&0.0859&0.2227\\ 0.0345&-0.0472\\ -0.2455&0.0411&-0.3076\\ \end{array}\right]\)  
Figure 4: Details of the underground power station.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Expert Criterion** & \(r_{s}\) & \(r_{p}\) & \(r_{c}\) \\ \hline E1 & [0.2,0.5] & [0.3,0.4] & [0.4,0.5] \\ E2 & [0.3,0.5] & [0.2,0.3] & [0.3,0.4] \\ E3 & [0.5,0.6] & [0.1,0.2] & [0.2,0.4] \\ E4 & [0.4,0.6] & [0.1,0.3] & [0.3,0.5] \\ E5 & [0.3,0.4] & [0.1,0.3] & [0.4,0.6] \\ E6 & [0.4,0.6] & [0.2,0.4] & [0.3,0.6] \\ E7 & [0.5,0.7] & [0.1,0.2] & [0.3,0.5] \\ E8 & [0.2,0.6] & [0.2,0.4] & [0.5,0.7] \\ E9 & [0.3,0.6] & [0.3,0.4] & [0.3,0.5] \\ E10 & [0.4,0.5] & [0.1,0.2] & [0.2,0.4] \\ RP & [0.35,0.56] & [0.17,0.31] & [0.32,0.51] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation results given by experts.  
Based on the value function matrix above, the comprehensive prospect value is obtained:  
\[V=\left[\begin{array}{c}1\\ 1\\ 1\\ 1\\ 1\\ 1\\ 1\\ 1\\ 1\\ 1\\ 1\\ 1\\ 1\\ 1\\ 1\\ \end{array}\right]^{T}\times\left[\begin{array}{ccc}0.1402&-0.2829&0.0506\\ 0.0202&-0.1561&0.0931\\ -0.2455&0.1231&-0.3076\\ -0.1164&0.0633&-0.0472\\ -0.1164&-0.2829&0.1115\\ 0.0818&0.0633&0.0553\\ -0.2455&0.1793&-0.0472\\ 0.0818&0.0859&0.2227\\ 0.0345&-0.0472\\ -0.2455&0.0411&-0.3076\\ \end{array}\right]=\left[\begin{array}{ccc}-0.5052&-0.1314&-0.2237\\ \end{array}\right]\]  
Here the subjective weights of each criterion, considering bounded rationality, can be gained according to the value function and the comprehensive prospect value:  
\[\rho_{\text{subjective}}=\left[\begin{array}{ccc}0.59&0.15&0.26\\ \end{array}\right]\]
###### Abstract  
Minature hyperspectral and thermal cameras onboard lightweight unmanned aerial vehicles (UAV) bring new opportunities for monitoring land surface variables at unprecedented fine spatial resolution with acceptable accuracy. This research applies hyperspectral and thermal imagery from a drone to quantify upland rice productivity and water use efficiency (WUE) after biochar application in Costa Rica. The field flights were conducted over two experimental groups with bamboo biochar (BC1) and sugarcane biochar (BC2) amendments and one control (C) group without biochar application. Rice canopy biophysical variables were estimated by inverting a canopy radiative transfer model on hyperspectral reflectance. Variations in gross primary productivity (GPP) and WUE across treatments were estimated using light-use efficiency and WUE models respectively from the normalized difference vegetation index (NDVI), canopy chlorophyll content (CCC), and evapotranspiration rate. We found that GPP was increased by \(41.9\pm 3.4\%\) in BC1 and \(17.5\pm 3.4\%\) in BC2 versus C, which may be explained by higher soil moisture after biochar application, and consequently significantly higher WUEs by \(40.8\pm 3.5\%\) in BC1 and \(13.4\pm 3.5\%\) in BC2 compared to C. This study demonstrated the use of hyperspectral and thermal imagery from a drone to quantify biochar effects on dry cropland by integrating ground measurements and physical models.  
unmanned aerial vehicle (UAV); hyperspectral and thermal imagery; gross primary productivity (GPP); water use efficiency (WUE); biochar; upland rice 2021131866. [[https://doi.org/10.3390/rs13101866](https://doi.org/10.3390/rs13101866) [https://www.mdpi.com/journal/renotesensing](https://www.mdpi.com/journal/renotesensing)]([https://doi.org/10.3390/rs13101866](https://doi.org/10.3390/rs13101866) [https://www.mdpi.com/journal/renotesensing](https://www.mdpi.com/journal/renotesensing))
# Time Coding OTDM MIMO System Based on Singular Value Decomposition for 5G Applications
## 3 Simulation Results
### Effect of Subcarriers  
Figure 6 shows the effect of increasing the number of subcarriers at all modulation techniques. The proposed system gives SNR = 10 dB at BER = \(10^{-3}\) at QPSK modulation technique when N = 16. The performance of STC OTDM when N = 64 and 128 is nearly equal. So, this is great evidence to prove that STC OTDM gives high performance with less number of subcarriers at all the modulation techniques as it clear in the figure. The performance decreases at 64-QAM and 256-QAM to SNR = 22.5 dB and 28 dB, respectively, at BER = \(10^{-3}\). Thus, the required performance can be obtained with a lower number of subcarriers and then lower complexity. This is in addition to the fact that the proposed technique is based on linear equalization and an SVD algorithm which is widely used in different present wireless communication systems like in channels estimation and beamforming algorithms [15]. The complexity of the SVD computation is of order O (\(mn^{2}\)), where m and n are the matrix dimensions that will apply the SVD on it [17]. Thus, the proposed system is applicable and can practically be deployed.  
Figure 4: BER of the proposed technique (STC OTDM) in comparison with other multicarrier techniques at N = 16 and L = 9 using 64-QAM.  
Figure 5: BER of the proposed technique (STC OTDM) in comparison with other multicarrier techniques at N = 16 and L = 9 using 256-QAM.
# Can LiDARs Replace Meteorological Masts in Wind Energy?
## 1 Introduction
To this end, a LiDAR-based measurement campaign is conducted for a period of approximately one year. In addition to validating the wind data collected using LiDAR against those measured with sonic and cup anemometers mounted on a neighbouring met-mast, the wind power potential of the site and the fatigue load for the NREL-5 MW reference wind turbine estimated using wind speeds measured by the LiDAR and the sonic anemometer are also compared. The fatigue loads of the wind turbine are computed using OpenFAST, which is a highly efficient and extensively used open-source tool for simulating the performance and response of wind turbines [25]. To the knowledge of the authors, thorough evaluation of LiDAR measurement for an extended period of one year--which is necessary in actual wind resource assessments--was not conducted. The paper is further organized as follows. Section 2 describes the test site and the measurement devices. A comparison of mean wind speeds, turbulence intensities and peak wind speeds measured by LiDAR against those measured by the cup and sonic anemometers are presented in Section 3. Section 4 evaluates wind resource and wind power potential of the site. This section also compares wind load estimated for the NREL-5 MW wind turbine subjected to wind speed and turbulence intensity distribution corresponding to the sonic anemometer and the LiDAR measurements. Finally, the main conclusions of this work are summarized in Section 5.
## 2 Related Work
Although STANAG 4539 defines six interleaver lengths, the author recommended to use only Long (4.305 s for fading channels) or Short (1.07625 s in AWGN channels) configurations, as well as keeping the packet size for long messages between 6000 and 8000 bits. As it usually occurs in HF, the packet delays are high, and these recommendations help in alleviating this issue. In a similar work, [PERSON] et al. studied the optimization of the ARQ parameters of STANAG 5066 when used in an HFDVL (HF Data+Voice Link), which relies on Orthogonal Frequency Division Multiplexing (OFDM) with 16-QAM symbols and 73 sub-carriers [31]. The main analyzed parameter was the presence of spatial diversity on the reception side, although ARQ parameters were also evaluated. Unlike [PERSON] and [PERSON], the authors carried out an experimental evaluation, resulting in a big marginal performance improvement from one antenna to two antennas. In Reference [32], [PERSON] et al. carried out TCP over STANAG 5066 simulations using OMNeT++. The authors focused on properly simulating the physical behavior, including a specific layer dedicated to MIL-STD-188-110C narrowband HF waveforms. The results were focused on analyzing the impact of different TCP settings on the system performance under several channel conditions, as [PERSON] et al. in Reference [31]. After detecting the susceptibility of TCP to the huge ionospheric SNR variations, which generate retransmissions and hence big delays, the authors proposed a Performance Enhanced Proxy to mitigate this effect with successful results.
# Effect of Thermal via Design on Heat Dissipation of High-Lead QFN Packages Mounted on PCB
## 3 Simulation Results
### Steady-State Analysis of High-Lead QFN Package under Reference Conditions  
To ascertain the temperature distribution, we have employed convective boundary conditions. Our assumption is that the model operates under natural convection conditions, with a convection coefficient set at 20.0 W/(m\({}^{2}\)\({}^{\circ}\)C). The internal heat generation rate of the QFN component during operational states is considered to be 0.1 W/mm\({}^{3}\). The power dissipation of the device is about 0.24 W. Furthermore, radiation effects have been disregarded. Under these established conditions, the calculated temperature distribution is depicted in Figure 4a. The observations reveal a notably uniform temperature distribution around the high-lead QFN package, signifying the transfer of heat from the die to the PCB, subsequently diffusing further across the PCB. Through computation, it is evident that the maximum temperature is concentrated around the chip, reaching a peak value of 58.4 \({}^{\circ}\)C.  
In the context of static structural analysis, we have imposed displacement constraints in the X, Y, and Z directions at the bottom of the PCB board. Additionally, symmetric constraints have been enforced on the vertical planes in the X and Z directions to restrain displacements beyond the plane and rotations within the plane. By employing the outcomes of the fundamental steady-state thermal analysis, we have fed inputs into the static structural analysis module, rendering the stress distribution depicted in Figure 4b. The calculated outcomes underscore the occurrence of the maximum stress at the junction of the solder joint and the I/O pads, with a peak stress value of 386.3 MPa.  
In order to further validate the excellent thermal performance of the high-lead QFN package, we used two additional QFN packages (the conventional QFN package and the dual-row QFN package) as references. Figure 5 illustrates the temperature distribution of these two QFN packages under reference conditions. The maximum temperature of the conventional QFN package is 58.6 \({}^{\circ}\)C. Compared to the high-lead QFN package, the heat dissipation of the conventional QFN package is poor. While the maximum temperature of  
Figure 4: (**a**) Temperature distribution and (**b**) Stress distribution of high-lead QFN package.  
the dual-row QFN package is 58.8 \({}^{\circ}\)C. The heat dissipation in the dual-row QFN package is the worst among the three packages.
# Analyzing Zone-Based Registration Using a Three Zone System: A Semi-Markov Process Approach
## 4 Numerical Results
### Total Cost for Various Gamma Distributions  
Let us consider the case where the UE's sojourn time in a zone, \(T_{m}\), follows a gamma distribution with a mean of 1 and focus on the comparison between 2Z and 3Z systems since they are both far superior to a 1Z system.  
Assuming \(\theta\) = 0.3, Figure 11 compares the performance results for various CMRs and variances. The figure shows that the 3Z system is superior to the 2Z system for small CMRs. We can also see that, as the variance decreases, this tendency becomes more definite. Note that, from the figure, the smaller the CMR and the larger the variance, the better the 3Z system is compared to the 2Z system but the difference is very small (about 1%).
## 3 IEC'S Characteristics and Benefits
This is achieved with a single, integrated and highly optimized platform consisting of basic mobile network and LAN functions such as vProbe, CG-NAT, deep packet inspection (DPI), optimization and load balancing. * _Virtualization and analytics:_ cloud computing service providers look to complement the various service offerings in the related businesses. Intelligence edge computing has the ability to provide a real-time network and insight into customer behavior. For instance, IECs define operational efficiencies, anticipate future demand and deliver service innovation which is considered to be a core value-addition. Cloud Edge supports audit paths including security data audits and provides these capabilities, along with real-time analytics. * _Automation:_ In the inevitable decoupling of mobile networks, intelligence edge computing has the ability to automate the process of integrating enterprises, end-user services, applications and dynamically expanding network infrastructure, especially with IEC applications. Applications and tools of cloud edge automation enable cloud computing services to provide the ability to quickly adjust traffic rises and falls automatically which leads to reduce the operational costs and reduce the time needed to generate revenue.
# Effect of Subject-Specific Region of Interest on Motor Imagery Brain-Computer Interface
## 3 Results and Analysis  
### Effect of ROI on Intrinsic Time-Scale Decomposition  
The effects of ROI on the four classes of motor imagery using ITD are presented in Figure 4. The ROI was based on the ERD/ERS and the significant features of the recorded data. It was evaluated using the kappa coefficient (k). Kappa coefficients in the range of 0.70\(-\)0.95 were found for all subjects. For ITD with ROI estimation, varied time\(-\)frequency distributions across the subjects were observed. These are clearly seen with higher kappa coefficients; however, certain subjects, such as 3, 4, 6 and 15, show lower kappa coefficients; this is due to the distinctions in the physiologies and brain states of the subjects [24]. On average, the kappa coefficient increased by \(\sim\)14.20% for ITD with ROI estimation compared to that without ROI estimation. Thus, it can be concluded that the ITD with ROI estimation enhances the classification performance.  
Figure 3: Validation timeline trial.  
To further investigate the effects of ROI on ITD, we used the receiver operating characteristic (ROC) to evaluate the discrimination of motor imagery movements of both hands, both feet, right hand and left hand. The ROC curves are shown in Figure 5a,b for Subject 1. Figure 5a shows the classification evaluation of ITD without ROI estimation. On average, the classification accuracy, sensitivity, specificity and area under the curve (AUC) values of ITD without ROI estimation for the right hand, left hand, both hands and both feet were, respectively: 75.00%, 75.00%, 75.00% and 81.91%; 81.66%, 79.79%, 77.91% and 87.03%; 79.58%, 80.21%, 80.83% and 88.08%; and 74.16%, 78.12%, 82.08% and 83.87%, as shown in Figure 5a. The corresponding values of classification accuracy, sensitivity, specificity and AUC for ITD with ROI estimation were, respectively: 84.58%, 86.25%, 87.91% and 92.99%; 91.66%, 91.46%, 91.45% and 97.19%; 98.33%, 98.12%, 97.91% and 99.64%; and 90.00%, 91.45%, 92.91% and 97.20%, as shown in Figure 5b. From the results, it is clear that the ITDs with ROI estimation outperform those ITDs without ROI estimation in all classification measurements. Thus, it can be stated that ROI features or ERD estimations greatly improve the classification performance. For Subject 2, similar observations, with slightly different values of classification accuracy, sensitivity, specificity and AUC, were observed, as seen in Figure 6 and Table 1. The effectiveness of ROIs in distinguishing among the four tasks' motor imagery is supported by the higher AUC, sensitivity and specificity. The ROCs of ITD for subjects 3 to 14 and the subjects' performance with and without ROI estimation are shown in Supplementary Figure S1 and Table S1, respectively.  
Figure 4: Kappa coefficients of ITD with and without ROI.  
Figure 5: ROCs of ITD for Subject 1: (**a**) without ROI and (**b**) with ROI estimation.
###### Abstract  
In this article, we present a novel structural health monitoring system based on a wireless sensor network for GNSS (global navigation satellite system) receivers. The GNSS network presented here consists of three GNSS rover stations and one base station that are deployed at the Neckartal bridge on the Autobahn A81 in southwest Germany. The newly-developed GNSS sensor nodes support satellite data logging up to a sampling rate of 20 Hz. Due to the ultra-low-power consumption achieved by the wake-up receiver during inactive periods, the nodes offer a lifetime from 20 to almost 200 days, without energy harvesting and depending on the satellite data logging period. By performing differential post-processing, precise positioning information in the millimeter range could be achieved. Using the GNSS sensors, we determined resonant frequencies at 0.33 Hz and 1.31 Hz, mainly in the lateral direction of the bridge. To verify the GNSS results, we placed an accelerometer on the bridge. The frequencies detected by the acceleration sensor correspond well to the frequencies found by the GNSS sensors, although the accelerometer measured further higher frequencies as it is probably more sensitive to small amplitudes.  
Keywords:wireless sensor network; structural health monitoring; wake-up receiver; GNSS sensor network; GPS receiver; bridge; spline +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Facial Regions of Interest  
There are non-skin regions that cannot be used to observe the HR in the face, such as the eyes, eyebrows, and mustache. However, because typical face detector algorithms (e.g., the Viola-Jones face detector) include non-skin regions, they are not adequate for selecting the RoIs for HR estimation [18]. Previous HR estimation research also showed that the strength of PPG signals differs between different regions of the face, with the cheek and forehead regions tending to produce the strongest PPG signals [19]. Due to forehead occlusion depending on the hair style, the cheek region was selected as the RoI using a discriminative response map fitting (DRMF) face detector that detects 66 facial landmark points to extract only the region needed to detect the PPG signals (Figure 1) [20].  
However, face detection in every frame not only requires significant resources, but slight changes to the face or fading caused by light saturation or shadows can also lead to face detection failure. For this reason, our method adopted kernelized correlated filter (KCF) face tracking [21]. Even though face detection and tracking can be used to select the target facial region, regions that are not suitable for the extraction of the HR may be included within the tracked region. In order to obtain the skin region directly associated with HR variation, hue channels were employed as in Reference [14], which demonstrated reasonable performance.