esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliable User Datagram Protocol as a Solution to Latencies in Network Games
## 4 Performance Evaluation
### Simulation Scenario Construction for the Network Games
#### 4.1.3 Performance Evaluation of R UDP Scenario  
In conditions for the performance evaluation of the R UDP were the same as the above two cases and the result is shown in Figure 10, which shows the same achievements but with a different average end-to-end delay of 0.020 s. (i.e., from the client node to the server node).
# Performance Analysis and Parameter Optimization of the Optimal Fixed-Point Quantum Search
## 5 Discussion
### Complexity of \(L_{E,opt}^{Up}\left(\lambda_{0}\right)\)  
Based on Equations (13), (20), (23), (24), and (29), we can obtain the expression of \(L_{E,opt}^{Up}\left(\lambda_{0}\right)\) as below:  
\[L_{E,opt}^{Up}\left(\lambda_{0}\right) = \left\{\begin{array}{ll}\frac{2k+1}{1-\delta_{E}^{2}},&\text{if } \lambda_{0}\in\left[\lambda_{0,k},\lambda_{0,k-1}\right),\,k\geq 1,\\ \frac{1}{1-\delta_{0}^{2}},&\text{if }\lambda_{0}\in\left[\lambda_{0,0},4/5 \right],\end{array}\right. \tag{34}\]  
where \(\delta_{k}\) and \(\lambda_{0,k-1}\) are defined by Equations (25) and (26), respectively. To analyze the complexity of \(L_{E,opt}^{Up}\left(\lambda_{0}\right)\), first, for any known \(\lambda_{0}\ll 1\), we can determine an integer  
\[k=\left\lceil\frac{x}{4\text{arcosh}\left(1/\sqrt{1-\lambda_{0}}\right)}- \frac{1}{2}\right\rceil, \tag{35}\]  
Figure 3: (Color online.) The normalized relative value \(L_{E,rel}^{Up,nor}\left(\lambda_{0},\delta\right)\) as a function of \(\delta\) and \(\lambda_{0}\). The white dashed curves correspond to the optimal parameter \(\delta_{opt}\) of Equation (24).  
such that \(\lambda_{0}\in\left[\lambda_{0,k}^{\prime},\lambda_{0,k-1}^{\prime}\right)\), where \(\lambda_{0,k}^{\prime}\) is as defined by Equation (A26) and \(x>0\) is the solution of Equation (28). From Equations (26) and (27) it follows that  
\[\lambda_{0,k}^{\prime}\leq\lambda_{0,k-1}<\lambda_{0,k-1}^{\prime}, \tag{36}\]  
then, we have \(\lambda_{0}\in\left[\lambda_{0,k},\lambda_{0,k-2}\right)\). When \(\lambda_{0}\ll 1\), \(k\rightarrow\infty\), \(k\approx k-1\), thus,  
\[L_{E,opt}^{Up}\left(\lambda_{0}\right)\approx\frac{2k+1}{1-\delta_{k}^{2}}, \tag{37}\]  
where  
\[\delta_{k}\approx\cosh^{-1}\left(\frac{x}{2}\right) \tag{38}\]  
due to \(k\approx\frac{x}{4\mathrm{arcosh}\left(1/\sqrt{1-\lambda_{0}}\right)}-\frac{ 1}{2}\), and  
\[k\approx\frac{x}{4\sqrt{\lambda_{0}}}-\frac{1}{2} \tag{39}\]  
due to \(\mathrm{arcosh}\left(1/\sqrt{1-\lambda_{0}}\right)\approx\sqrt{\lambda_{0}}\). Combining Equations (37), (38), and (39), we can obtain  
\[L_{E,opt}^{Up}\left(\lambda_{0}\right) \approx \frac{x}{2\left[1-\cosh^{-2}\left(\frac{x}{2}\right)\right]} \frac{1}{\sqrt{\lambda_{0}}} \tag{40}\] \[\equiv L_{E,opt}^{Up,\approx}\left(\lambda_{0}\right).\]  
Therefore, we conclude that \(L_{E,opt}^{Up}\left(\lambda_{0}\right)=O\left(1/\sqrt{\lambda_{0}}\right)\). The corresponding graphs of \(L_{E,opt}^{Up}\left(\lambda_{0}\right)\) and \(L_{E,opt}^{Up,\approx}\left(\lambda_{0}\right)\) as functions of \(\lambda_{0}\) are shown in Figure 4, which shows a good agreement. Note that, for an unknown \(\lambda\) and a given \(\lambda_{0}\), there also exists an upper bound of the expected queries of the classical search, namely, \(1/\lambda_{0}\). This means that the OFPQS algorithm with the optimal parameters achieves a quadratic speedup over classical algorithms.
## 5 A Detailed Case of CPSS-UPTDN  
There are various potential applications of CPSS-UPTDN based on AI and big-data technologies. We discuss a typical case for bus dispatching based on our proposed framework in detail. By combining cellular data, a deep learning algorithm is designed to predict the road congestion extent and facilitate the follow-up dispatching in public transportation. The big data comes from physical network resources and social systems. Next, we will describe the detailed processes of dynamic dispatching for urban buses.
## 2 Materials and Methods
### Vegetation Indices  
To improve the characterization of nitrogen-saturated grassland plots, numerous vegetation indices were developed from the spectral profile of the Worldview-3 image. These indices included the: Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), and the Green Difference Vegetation Index (GDVI) [47,66,67,68]. However, due to the spectral arrangement of the Worldview-3 image, these vegetation indices had to be computed for several near-infrared variations, namely, Near-Infrared 1 (NIR1), Near-Infrared 2 (NIR2)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Band** & **Wavelength Range (nm)** & **Designation** \\ \hline
1 & 400–450 & Coastal \\
2 & 450–510 & Blue \\
3 & 510–580 & Green \\
4 & 585–625 & Yellow \\
5 & 630–690 & Red \\
6 & 705–745 & Red-Edge \\
7 & 770–895 & Near–Infrared \\
8 & 860–1040 & Near–Infrared 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed spectral range of the 8-band Worldview-3 image.  
and a combination of the two (hereafter referred to as NIR3). This resulted in a total of 24 spectral variables, including 15 vegetation indices and 9 spectral wavebands. Thereafter, both spectral wavebands and vegetation indices (\(n=24\)) were input into the RF classification algorithm to distinguish between nitrogen-saturated and -unsaturated grassland plots. The datasets were split into 70% training and 30% testing data [69]. Next, the performance of the RF classification algorithm in characterizing nitrogen saturation was evaluated. For comparative purposes, a Classification and Regression Tree (CART) algorithm was implemented together with RF using the R statistical software package [70].
## 3 Experimental Analysis
### Displacement Analysis  
The study provides detailed measurements of various displacement metrics across 102 shield rings, labeled sequentially from 1 to 102. These metrics include the average shield ring point cloud displacement, shield ring centerline displacement, and ovality change, offering valuable insights into the structural changes and alignment of the tunnels over time. To gain a deeper understanding of these changes, a dedicated algorithm was implemented for tunnel cross-section deformation analysis, which corroborated the findings from the shield ring-level analysis based on these metrics. Furthermore, a 3D tunnel cross-section deformation magnitude algorithm was employed to identify locations with significant displacement, providing a more comprehensive evaluation of the tunnel's structural integrity. The point cloud's displacement magnitude refers to the distance that points in the scanned data have shifted between two time periods or conditions, typically measured in millimeters or meters.
## 3 Kriging (Gaussian Process) Model
### Covariance Function  
The covariance function \(\kappa(\mathbf{x},\mathbf{x}^{\prime};\mathbf{\Theta})\) plays a critical role in Kriging predictors by encapsulating the underlying assumptions about the approximation function. This function quantifies the extent of \"similarity\" between observations and newly acquired data points, with its value dependent on the spatial separation between these input points. Consequently, the proximity of input points to each other implies a higher likelihood of similar outcomes or responses. The covariance functions under consideration are of a stationary nature, implying that their characteristics solely rely on the relative position between their two inputs. Furthermore, these covariance functions are one-dimensional in nature and are defined for a pair of one-dimensional inputs denoted as \(\mathbf{x}\) and \(\mathbf{x}^{\prime}\), both belonging to the real number set (\(\mathbb{R}\)), and are parameterized by \(\mathbf{\Theta}\in\mathbb{R}\). For instance, the linear, exponential, and Gaussian covariance functions [16] can be written as  
\[\text{Linear: }\bar{\kappa}(x,x^{\prime};\Theta) =\max\left(0,1-\frac{\bar{d}}{\Theta}\right) \tag{13}\] \[\text{Exponential: }\bar{\kappa}(x,x^{\prime};\Theta) =\exp\left(-\frac{\bar{d}}{\Theta}\right)\] \[\text{Gaussian: }\bar{\kappa}(x,x^{\prime};\Theta) =\exp\left(-0.5\cdot\left(\frac{\bar{d}}{\Theta}\right)^{2}\right)\]  
where \(\bar{d}=\|x-x^{\prime}\|\) denotes the distance between the two input points \(x\) and \(x^{\prime}\). The Matern function, widely employed as a covariance function and utilized in this study, is represented as follows [31]:  
\[\text{Matern: }\bar{\kappa}(x,x^{\prime};\Theta)=\left(1+\sqrt{3}\cdot\frac{ \bar{d}}{\Theta}\right)\cdot\exp\left(-\sqrt{3}\cdot\frac{\bar{d}}{\Theta}\right) \tag{14}\]  
Figure 2: Illustration of the Kriging (Gaussian process) model and predictions.  
In cases where the input dimension \(M\) exceeds one, multi-dimensional covariance functions can be formed by employing one of the methods, the separable and ellipsoidal function construction approaches, which are expressed as:  
\[\begin{split}\text{Ellipsoidal}:\kappa(\mathbf{x},\mathbf{x}^{ \prime};\mathbf{\Theta})&=\kappa\left(\sqrt{\sum_{i=1}^{M}\left( \frac{\tilde{d}_{i}}{\Theta_{i}}\right)^{2}}\right),\tilde{d}_{i}=\left\|x_{i} -x_{i}^{\prime}\right\|\\ \text{Separable}:\kappa(\mathbf{x},\mathbf{x}^{\prime};\mathbf{ \Theta})&=\prod_{i=1}^{M}\tilde{\kappa}(x_{i},x_{i}^{\prime}, \Theta_{i})\end{split} \tag{15}\]
# Research on Hybrid Relay Protocol Design and Cross-Layer Performance Based on NOMA
## 3 Cross-Layer Performance Analysis of Two-Hop Relay System Based on NOMA
### Physical Layer Outage Probability
For user D\({}_{1}\), the outage occurs when either D\({}_{1}\) or R fails to decode \(x_{1}\)[6], then the outage probability of user D\({}_{1}\) is expressed as follows:  
\[P_{\text{out}}^{\text{D}_{1}}=1-P_{r}\big{(}\gamma_{\text{R},x_{1}}>\gamma_{ \text{th}_{1}},\gamma_{\text{D}_{1},x_{1}}>\gamma_{\text{th}_{1}}\big{)} \tag{15}\]  
where the expression of the correlation probability is as follows:  
\[\text{P}_{r}\big{(}\gamma_{\text{R},x_{1}}>\gamma_{\text{th}_{1}}\big{)}=\text {P}_{r}\Bigg{(}\left|h_{\text{SR}_{1}}\right|^{2}>\frac{\lambda_{1}}{\Delta wd _{\text{SR}_{1}}^{-2\
u}}\Bigg{)}=1-F_{\left|h_{\text{SR}_{1}}\right|^{2}} \Bigg{(}\frac{\lambda_{1}}{\Delta wd_{\text{SR}_{1}}^{-2\
u}}\Bigg{)}, \tag{16}\]  
\[\text{P}_{r}\Big{(}\gamma_{\text{D}_{j},x_{1}}>\gamma_{\text{th}_{1}}\Big{)}= \text{P}_{r}\Bigg{(}\left|h_{\text{RD}_{j}}\right|^{2}>\frac{\lambda_{1}}{ \Delta_{pl_{k}}d_{\text{RD}_{j}}^{-2\kappa}}\Bigg{)}=1-F_{\left|h_{\text{RD}_ {j}}\right|^{2}}\Bigg{(}\frac{\lambda_{1}}{\Delta_{pl_{k}}d_{\text{RD}_{j}}^{ -2\kappa}}\Bigg{)}\,. \tag{17}\]  
In the above equation, \(\lambda_{1}=\gamma_{\text{th}_{1}}/\left(a_{1}-a_{2}\gamma_{\text{th}_{1}}\right)\). By substituting Formulas (11) and (14) into Formulas (16) and (17), respectively, the closed expression of outage probability of user D\({}_{1}\) can be obtained. For user D\({}_{2}\), the outage occurs when either R or D\({}_{2}\) fails to decode \(x_{2}\), then the outage probability of user D\({}_{2}\) is expressed as  
\[P_{\text{out}}^{\text{D}_{2}}=1-P_{r}\big{(}\gamma_{\text{R},x_{1}}>\gamma_{ \text{th}_{1}},\gamma_{\text{R},x_{2}}>\gamma_{\text{th}_{2}},\gamma_{\text{D} _{2},x_{1}}>\gamma_{\text{th}_{1}},\gamma_{\text{D}_{2},x_{2}}>\gamma_{\text{th }_{2}}\big{)} \tag{18}\]  
where the expression of the correlation probability is as follows:  
\[f_{\big{|}h_{\text{SR}_{k}}\big{|}^{2}}(x)=\bigg{(}\frac{m_{w_{k}}}{\Omega_{ w_{k}}}\bigg{)}^{m_{w_{k}}}\frac{x^{m_{w_{k}}-1}}{\Gamma\big{(}m_{w_{k}} \big{)}}x, \tag{10}\]0  
\[f_{\big{|}h_{\text{SR}_{k}}\big{|}^{2}}(x)=\bigg{(}\frac{m_{w_{k}}}{\Omega_{ w_{k}}}\bigg{)}^{m_{w_{k}}}\frac{x^{m_{w_{k}}-1}}{\Gamma\big{(}m_{w_{k}} \big{)}}x, \tag{10}\]1  
In the above equation, \(\lambda_{2}=\gamma_{\text{th}_{2}}/a_{2}\). By substituting Formulas (11) and (14) into Formulas (19) and (20), the closed expression of outage probability of user D\({}_{2}\) can be obtained. When either R or D\({}_{2}\) fails to detect both signals \(x_{1}\) and \(x_{2}\), or D\({}_{1}\) fails to detect \(x_{1}\), the system will be interrupted. Then the system outage probability is expressed as  
\[f_{\big{|}h_{\text{SR}_{k}}\big{|}^{2}}(x)=\bigg{(}\frac{m_{w_{k}}}{\Omega_{ w_{k}}}\bigg{)}^{m_{w_{k}}}\frac{x^{m_{w_{k}}-1}}{\Gamma\big{(}m_{w_{k}} \big{)}}x, \tag{10}\]2
## 4 Security Threat Landscape in 5G Networks
### Analysis of Inherent Vulnerabilities in Network Slicing

Table 6: Vulnerabilities in network slicing in 5G networks and their associated mitigation strategies [56; 57; 58; 59]. In Table 6, the complex landscape of vulnerabilities inherent in 5G network slicing is succinctly elucidated, categorizing each vulnerability by its potential impact, exploitation techniques, and recommended mitigation strategies. The table underscores the need for robust security protocols and stringent configuration management practices in dealing with complex slice configurations. It also highlights the urgency for securing orchestration interfaces and implementing strong authentication mechanisms. Additionally, vulnerabilities tied to SDN and NFV are indicated, necessitating regular software updates and rigorous security audits. The table serves as a comprehensive guide for understanding and addressing the multifaceted security challenges in 5G network slicing.
## 3 Nasal Acoustic Signal Processing Methods
### Nasal Cavity Acoustic Signal Preprocessing Method
#### 3.1.3 Removal of High-Frequency Noise From Incident and Reflected Waves  
When the input impulse response is obtained in the frequency domain, the response includes high-frequency noise. This noise impacts the smoothness of the input impulse response and consequently affects the nasal cavity depth-cross-sectional area curve. This ultimately leads to increased variance between the cross-sectional area values and the standard values, as well as excessive errors at some points, which does not satisfy the accuracy requirement of the nasal cavity three-dimensional structure reconstruction.  
As shown in Equation (2), if the acoustic signal is particularly weak near a certain frequency \(\omega_{1}\), i.e., the value of \(P_{i}(\omega_{1})\) is very small, then the value of \(x(\omega_{1})\) will be large. The result of the inverse convolution will greatly amplify the noise at this frequency. Analyzingthe frequency spectrum of the incident wave, it can be seen that the effective frequency spectrum of the incident wave is primarily concentrated within the low-frequency range, with minimal amplitude in the high-frequency spectrum. However, high-frequency signals will be enlarged into high-frequency noise under the effect of the inverse convolution. So, in practice, in order to eliminate the interference of high-frequency noise, Equation (2) is optimized as a constrained inverse convolution [19; 20; 21]:  
\[x(\omega)=\frac{P_{r}(\omega)P_{i}^{*}(\omega)}{P_{i}(\omega)P_{i}^{*}(\omega )+q} \tag{3}\]  
where \(P_{i}^{*}(\omega)\) denotes the complex conjugate of the incident wave \(P_{i}(\omega)\). The frequency domain separation between the reflected wave \(P_{r}(\omega)\) and the input impulse response \(x(\omega)\) is considered an uncertain issue [22; 23]. A constraint factor \(q\) is introduced in the inverse convolution to prevent the input impulse response from decreasing to the background noise level at high frequencies. Therefore, the role of the constraint factor \(q\) is similar to that of a low-pass filter, and the different values of the constraint factor \(q\) also have varying degrees of impact on the results of high-frequency noise reduction. In this research, the appropriate value of the constraint factor was found by taking the average of several trials.
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 2 Comparative Study of BPv7 and IPv6 Functionalities
### Feature Analysis
#### 2.3.3 Support for Reliability  
As explained before, IPv6 lacks reliability at the network layer and typically delegates the task to transport-layer protocols. Unlike its predecessor BPv6, which supported retransmissions via custody transfer, BPv7 does not perform retransmission itself. Instead, it provides optional status reporting to the application layer, leaving it to the application to decide whether to retransmit lost data. Reliability at the link layer is handled separately using the Licklider Transmission Protocol (LTP).  
While IPv6 does not inherently support these characteristics, it could be extended with a custom signaling mechanism, potentially using ICMPv6, to emulate BPv7's status report mechanism without compromising IP compliance. This approach would establish a parallelism between the BPv7 model, where the protocol reports status and the application manages retransmission, and an IPv6-based solution, where ICMPv6 reports status while a transport layer protocol handles retransmissions.
## 3 Results
### LVP Transmission  
Acceleration Outputs  
The \(a_{out}^{RMS}\) showed differences between body sites (F = 297.56, \(p\) < 0.001, \(\eta_{p}^{2}\) = 0.940), \(f_{set}\) (F = 110.25, \(p\) < 0.001, \(\eta_{p}^{2}\) = 0.853), postures (F = 218.79, \(p\) < 0.001, \(\eta_{p}^{2}\) = 0.920), and its interactions body sites \(\times\)\(f_{set}\) (F = 82.11, \(p\) < 0.001, \(\eta_{p}^{2}\) = 0.812), body sites \(\times\) postures (F = 109.18, \(p\) < 0.001, \(\eta_{p}^{2}\) = 0.852), \(f_{set}\)\(\times\) postures (F = 29.75, \(p\) < 0.001, \(\eta_{p}^{2}\) = 0.610), and body sites \(\times\)\(f_{set}\)\(\times\) postures (F = 21.16, \(p\) < 0.001, \(\eta_{p}^{2}\) = 0.527). A decreased \(a_{out}^{RMS}\) was identified as the forces were transmitted from the AN upward (Figure 4), especially from the HI to the HE (HI-L3, \(p\) = 0.014; HI-HE, \(p\) = 1; L3-HE, \(p\) = 0.120). It should be noted a strong decrease between KN and HI (AN, 1.26 \(\pm\) 0.07, 95% CI: 1.12-1.40; KN, 0.94 \(\pm\) 0.04, 95% CI: 0.85-1.03; HI, 0.10 \(\pm\) 0.01, 95% CI: 0.09-0.11; L3, 0.09 \(\pm\) 0.01, 95% CI: 0.08-0.09; HE, 0.10 \(\pm\) 0.01, 95% CI: 0.09-0.11). Between \(f_{set}\), the highest \(a_{out}^{RMS}\) were detected at 25 Hz (0.64 \(\pm\) 0.03, 95% CI: 0.583-0.690) followed by 30 Hz (0.49 \(\pm\) 0.03, 95% CI: 0.427-0.544) and 20 Hz (0.37 \(\pm\) 0.01, 95% CI: 0.346-0.384). Among postures, higher \(a_{out}^{RMS}\) were found the more crouched the posture was, especially when comparing TS versus ER, HS, DS, and LU (TS, 0.25 \(\pm\) 0.01, 95% CI: 0.219-0.273; ER, 0.50 \(\pm\) 0.02, 95% CI: 0.458-0.550; HS, 0.55 \(\pm\) 0.02, 95% CI: 0.503-0.595; DS, 0.57 \(\pm\) 0.02, 95% CI: 0.522-0.622; LU, 0.61 \(\pm\) 0.03, 95% CI: 0.555-0.661).
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 3 Analysis of the Forecasting Results
### Forecasts Accuracy Measures  
#### 3.5.1 Point Forecasts Accuracy Measures  
This section presents the accuracy measures for our point forecasting models. This study made use of five forecasting models. The main model is the ANN model and compared with two benchmark models, SGB (a machine learning model) and GAM (a statistical learning model). The other two models are statistical models called additive quantile regression averaging and linear quantile regression averaging (LQRA). They are used to generate results from the point forecasts combination of the other three models. We present three accuracy metrics to evaluate these five point forecasting models. The various accuracy of point forecasts from these models are presented in Table 5.  
In Table 5, model BNN is the first model whose forecasts is from the ANN, the second model is the SGB forecasts, the third model is the GAM forecasts, while the fourth and fifth models are the forecasts combination models, which are the AQRA forecasts and the forecasts corresponding to the LQRA model, respectively. Our discussion evaluates Table 5 with regards to individual and combined models, respectively.  
BNN made use of 14 variables from its Lassocv variable selection model to fit the neural networks. It recorded the lowest RMSE value followed by GAM amongst the individual point forecasting methods. SGB, on the other hand, recorded the lowest MAE value followed by BNN while GAM recorded the highest MAPE value followed by BNN. Hence, accuracy measures using MAE and MAPE present SGB as the best individual point forecasting model followed by BNN while the GAM performed the least amongst these three models.  
Considering the combined forecasting models, the fourth model, which is AQRA, performed better than its counterpart fifth model, which is LQRA, in all accuracy metrics axis. AQRA also outperformed all of the individual point forecasting models, as shown in Table 5. This is in tandem with most literature findings that combining point forecasts presents forecasts whose accuracy measures are lower than the individual forecasting models [19; 20; 21]. These findings hold sway for both methods of forecasts combination, as can be seen from their accuracy metrics' values, which are lower than that of the three individual accuracy metrics' values for the point forecasting models. When we combine forecasting models, the resultant combined forecasts perform better than the individual point forecasting models. Hence, the authors retain AQRA as the main forecast combination model for interval forecasting, while we drop LQRA going forward.
# GPRS Sensor Node Battery Life Span Prediction Based on Received Signal Quality: Experimental Study
## 3 Material and Methods
### Battery/Network Life Span  
The information about the signal quality in a given area can provide an idea about the energy which is being consumed when the device is trying to connect and send/receive data.