esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Investigation on DOP
### Modification of Young Equations for DOP Prediction
In the end, the modified Young equation is as follows:  
\[DOP=0.00344 NK_{c}K_{h}(t_{c}T_{c})^{-0.06}(f_{c}^{-0.43})\ (292.8-5048V_{f}+77946V_{f}^{2})\ (m/A)^{0.7}\ (e^{0.003V_{0}}) \tag{17}\]  
where \(f_{c}\) is in the unit of \"MPa\", \(V_{f}\) is a unitless percentage, \(m\) is in the unit of \"kg\", \(A\) is in the unit of \"m\({}^{2}\)\", \(V_{0}\) is in the unit of \"m/s\", and eventually the DOP is in the unit of \"cm\". The correlation index \(R^{2}\) of the fitted function is 0.9977, indicating that the modified equation for the DOP prediction has high goodness of fit. The DOPs calculated by the modified Young equation and the original Young equation are compared with the experimental data presented in this study and References [3; 5; 33], as listed in Table 8. It can be seen that the original Young equation greatly underestimates the penetration resistance of UHPC, and the maximum relative error is 26.9%. However, the relative error between the calculated DOPs by the modified Young equation and experimental data is within 14%, which indicates that the modified Young equation proposed in the present study can accurately predict the DOP of UHPC targets subjected to projectile impact. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{\(f_{c}\)**} & \multirow{2}{*}{\(V_{f}\)**} & \multirow{2}{*}{\(m\)**} & \multirow{2}{*}{\(d\)**} & \multirow{2}{*}{\(V_{0}\)**} & **DOP in** & **DOP of** & **DOP of** & **DOP of** & **Relative** \\  & & **(MPa)** & **(\%)** & **(kg)** & **(cm)** & **(m/s)** & **Tests** & **Equation** & **Error** & **Equation** & **Relative** \\  & & & & & & & & & **(cm)** & **(cm)** & **(cm)** \\ \hline U-1 & 148 & 2 & 11.9 & 8 & 410 & 42.1 & 39.2 & \(-\)6.9\% & 52.7 & 25.2\% \\ U-2 & 148 & 2 & 11.9 & 8 & 664 & 78.6 & 84.0 & 6.8\% & 92.7 & 17.9\% \\ UHPC-SF-1 [3] & 140 & 3 & 0.329 & 2.53 & 553 & 12.9 & 12.9 & \(-\)0.2\% & 16.4 & 26.9\% \\ UHPC-SF-2 [3] & 140 & 3 & 0.329 & 2.53 & 683 & 16.6 & 18.7 & 12.7\% & 20.4 & 23.1\% \\ UHPC-SF-3 [3] & 140 & 3 & 0.329 & 2.53 & 808 & 20.8 & 23.7 & 13.9\% & 25.8 & 24.0\% \\ A-5-1 [5] & 114 & 3 & 0.341 & 2.53 & 510 & 13.4 & 12.7 & \(-\)4.9\% & 16.9 & 26.1\% \\
1:3 [33] & 153 & 1.5 & 6.3 & 7.5 & 622 & 51.0 & 52.2 & 2.4\% & 59.7 & 17.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of the DOPs between results of equation calculation and experimental results for different UHPC targets.
# Promoting Optimal User Experience through Composite Challenge Tasks
## 7 Experiment and Results
#### Results  
The hypotheses of flow and performance were analyzed independently. The results with the values of the means for the global flow score are shown in Table 13 where the global BG mean is greater than the global NBG mean. The Student's \(t\)-test for independent samples is used to validate whether this result is significant [26].  
The Student's \(t\)-test for independent samples assumes the homogeneity of the variance. [PERSON]'s test [29] is used to verify this latter condition. Table 14 shows the results of the Student's and [PERSON]'s tests considering whether equal variances are assumed or not, as indicated in the corresponding lowest two rows. A Sig. value greater than 0.05 indicates that the equality of variances is assumed and vice versa. In this case the value is 0.23; hence the variances are equal. Then the bilateral significance or \(p\)-value is considered. If this value is \(\leq\)0.05 the hypothesis is accepted and vice versa. In this case the value is 0.038; hence there is a significant difference between the means of the BG and NBG and the hypothesis is accepted--i.e., the mean of flow in BG is greater than the mean of flow in NBG.  
To assess performance, the condition was divided in two parts: (1) performance (BG) correlated with flow (BG), and (2) performance(NBG) correlated with flow (NBG). Each part was analyzed to obtain [PERSON]'s correlation coefficient using PSPP. The BG data analysis revealed a correlation coefficient \(r=0.87\) while the NBG data analysis revealed a correlation coefficient \(r=0.50\). Therefore, the first and second condition show a high and low correlation, respectively. This means that indeed flow is correlated with high performance but this is not the case for a low state of flow.
## 3 Calibration of System Parameters
### Calibration of Internal Parameters  
To apply a machine vision-based method to determine distance \(d_{1}\) and \(d_{2}\) by a CCD camera, the internal parameters need to be calibrated by using the following equation:  
\[\lambda\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=A\left[\begin{array}{cc}R&T\end{array}\right]\left[ \begin{array}{c}X_{W}\\ Y_{W}\\ 2_{W}\\ 1\end{array}\right], \tag{6}\]  
where \(R\) is a matrix representing the three rotation angles and \(T=[t_{x}\), \(t_{y}\), \(t_{z}]\) is a vector representing the three linear translations. \([X_{w}\), \(Y_{w}\), \(Z_{w}]\) is the coordinate vector of a point in the world coordinate system, while \([u\), \(v]\) is a coordinate vector of the corresponding point in the pixel coordinate system. \(\lambda\) is an arbitrary scaling factor. A is a matrix of the CCD camera internal parameters, including two focal lengths (\(F_{u}\) and \(F_{v}\)), two principal point coordinates (\(P_{u}\) and \(P_{v}\)), and four image radial and tangential distortion coefficients (\(K_{1}\), \(K_{2}\), \(K_{3}\), and \(K_{4}\)). The eight internal parameters need to be calibrated beforehand and [PERSON]'s camera calibration method is used to obtain their values by using a checkerboard at different positions [40].
# Transaction Mechanism Based on Two-Dimensional Energy and Reliability Pricing for Energy Prosumers
## 2 Dynamic Energy-Reliability Pricing-Based Transaction Mechanism  
### Energy-Reliability Pricing-Based Transaction Mechanism  
A conventional energy-based dynamic price is announced by a utility or retailer, and consumers can control their demand considering the publicly announced price. The utility provides price information in one-way communication, and a consumer responds with their energy usage for each time period \(t\). Here, the information is the only price on time, so the information can be considered one-dimensional. The conventional pricing is expressed as  
\[P_{Conv}=\left\{p_{1}^{C},\cdots,p_{t}^{C},\cdots,p_{T}^{C}\right\}, \tag{1}\]  
where \(T\) is the operation time period.  
Under the proposed dynamic energy-reliability pricing-based transaction mechanism (ERT), however, the utility announces reliability bands on time as well as price on time to prosumers, as shown in Figure 1. A prosumer responds to the utility with their expected demand and selected reliability band. The utility can announce the reliability price for each band for each time. Therefore, the communication is two-way. The proposed ERT is two-dimensional, since both the energy price and reliability price are included, as follows:  
\[P_{ERT}=\left\{\alpha_{1}p_{1}^{E},\cdots,\alpha_{t}p_{t}^{E},\cdots,\alpha_{ T}p_{T}^{E}\right\}, \tag{2}\]  
where \(\alpha_{t}\) is a relative price in the range of [0, 1], as shown in Figure 1. The relative price is a multivariable function according to reliability \(\gamma_{t}\in(0,\ 1]\) and band selection indicator \(\beta_{i,t}\in\left\{0,1\right\}\),  
\[\alpha_{t}(\beta_{i,t},\gamma_{t})=\sum_{i\in B}\beta_{i,t}f_{i}(\gamma_{t}), \tag{3}\]  
where \(B\) is a band set, e.g., three band (Band 1, Band 2, Band 3) in Figure 1.  
A comparison between the conventional pricing mechanism and the proposed ERT is summarized in Table 1.  
Energy usage intentions are indirectly controlled in conventional price-based systems using unit prices for specific times. Under the ERT, additional information is used to understand prosumers' intention regarding energy usage, and uncertainty is managed differently than in conventional price-based systems.  
Figure 1: The proposed dynamic energy reliability pricing concept.
# The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review
## 4 The Future of Farming: 6G-IoT Applications in Agriculture 5.0
### Transforming Farming with Collaborative Robotic Systems  
Future agricultural IoT systems will be based on robotic communication, which is essential for automating processes. In the smart agriculture industry, the use of collaborative production robots to achieve single or multiple objectives is already widespread and is expected to further increase in the future [16]. In Agriculture 5.0, these AI-based robots will perform a variety of tasks, including identifying patterns of anomalies and diseases across the plant, seed, soil, and weed spectrum and administering treatments to preserve the health and quality of plants [113; 114].  
As part of the Agriculture 5.0 framework, it is anticipated that an increased number of robotic systems will be employed and equipped with ultra-high-precision (UHP) sensors [98]. This will result in a nearly constant flow of a large volume of time-sensitive information between the management system and the involved robotic systems. Therefore, future agricultural collaborative robotic systems [115] will rely on umMTC and eRLLC 6G services, which will enable autonomous machinery to operate safely and efficiently in the field, thereby reducing labor costs and increasing productivity. The ultra-low-latency communication will also enable faster response times to unanticipated events in the field, thereby enhancing system resilience and reducing downtime. Furthermore, by creating digital twins of farm machinery and equipment, farmers could monitor their performance and detect potential issues before they occur [87].  
In summary, the 6G-IoT standard will pave the way for secure, reliable, and ultra-low-latency communication between robots and between robots and the management system, which is crucial to optimizing the operation of future collaborative robotic systems in Agriculture 5.0.
## 4 Case Studies
### SBSS Scenario
As mentioned earlier, the on-board payload for Earth observation is HyperScout-2, as specified in Table 2, whereas, for RSO tracking, the DSS assets are equipped with an MAI-SS star tracker, which has the specifications tabulated in Table 3. The two payloads are illustrated in Figure 7. \begin{table}
\begin{tabular}{c c} \hline
**Performance Parameter** & **Specification** \\ \hline Accuracy (Cross Axis/Boresight) & 5.7 arcsec/27 arcsec \\ \hline Acquisition Time & 130 ms Acq, 105 ms Track (typical) \\ \hline Max Tracking Rate & \textgreater{}2.0\({}^{\circ}\)/s \\ \hline Update Rate & 4 Hz \\ \hline Lens & 0.9 in f1.2 BKY Glass \\ \hline \end{tabular}
\end{table} 
Table 3: Star tracker payload specifications. Figure 6: Inter-Satellite links between the DSS assets. For the proposed SBSS mission architecture, the following assumptions are critical:  
* The star trackers on board track the RSOs with the stars in the background. * The participating spacecraft are equipped with state-of-the-art GPS for positioning and navigation that provide a full set of navigation data. * The RSO position is estimated by simultaneous optical measurements obtained from two different spacecraft. * The participating spacecraft share their position information and the estimated RSO position through a network. * Mutual separation between the spacecraft belonging to the DSS constellation is guaranteed using intersatellite links and continuous monitoring from the ground stations. The proposed AN system comprises the following components. Figure 8 illustrates the system architecture and its individual components:  
* **Navigation hardware** comprises the state-of-the-art GPS to obtain a full set of navigation data comprising the DSS satellite positions, velocities, and attitude rates. * **Tracking hardware** comprises star trackers that track the RSO. * The obtained data from the hardware is used as inputs by the on-board **Tracking System** to obtain the RSO position estimates, error measurement budget, and to generate the uncertainty ellipsoids. * The **navigation and guidance system** exploits the data generated by the tracking system for trajectory optimization and AN/manoeuvring to generate the steering commands. * **Actuators** use the steering commands to perform the collision avoidance manoeuvres in order to avoid a collision with the RSO. Figure 8: AN system architecture for SBSS. Figure 7: (a) HyperScout-2; (b) MAI-SS star tracker, Adcole Maryland Aerospace [89; 90].
# Practical Use of Starlink Downlink Tones for Positioning
## 4 Multi-Epoch Positioning  
As shown in Figure 11, Starlink downlink tones are down-converted by the LNB, then, the tones are acquired and tracked. The measured tone frequencies are then aggregated and used to estimate the user's position. In this section, we first build the frequency observation model which will be simplified, then present the navigation algorithm and discuss the positioning results.  
The range rate is the opposite sign of the Doppler shift times of the wavelength, i.e., \(\dot{\rho}^{k}=-\lambda f^{k}\). We have shown in [21] that the \(k\)th-satellite range rate error at time \(t\), \(\Delta\dot{\rho}^{k}(t)=\dot{\rho}^{-k}(t)-\dot{\rho}^{k}(t)\), which is the difference between the measured range rate and the predicted one, can be expressed as a function of the user's position error \(\delta r=r-\hat{r}\), the user's velocity error \(\delta v=v-\hat{\theta}\), the user clock bias error \(\delta t=t-\hat{t}\), and the user clock drift error \(\dot{\delta t}=\hat{t}-\hat{\hat{t}}\) :  
\[\Delta\dot{\rho}^{k}(t)\simeq\left(\frac{\left(v-v^{k}\right)^{T}}{\|r-r^{k} \|}-u^{kT}\!\left(v-v^{k}\right)\!\frac{u^{kT}}{\|r-r^{k}\|}\right)\!\delta r +u^{kT}\delta v+\frac{1}{c}\left(\frac{\left(u^{kT}\hat{r}^{k}\right)\!u^{kT} -\hat{r}^{kT}}{\|r-r^{k}\|}\!\left(v-v^{k}\right)-u^{kT}\hat{v}^{k}\right) \!\dot{\delta t}+c\dot{\delta t}-\lambda\delta f^{k}_{sv}+\eta_{\dot{\rho}} \tag{16}\]  
Figure 11: A block diagram showing the processing steps used to perform positioning from Starlink downlink tones.  
Figure 10: (**a**) Measured frequency for SV#1 after aggregating the multi-tone measurements of Figure 8a; (**b**) Measured frequency for SV#2 after aggregating the multi-tone measurements of Figure 8b.  
where \(\eta_{\rho}\) is the range rate noise, and \(u^{k}\) is the line-of-sight unit vector between the receiver and the satellite given by:  
\[u^{k}=\frac{r-r^{k}}{\|r-r^{k}\|} \tag{17}\]  
In (16), we have added the term \(\lambda\delta f^{k}_{sy}\), representing the \(k\)th-satellite clock drift range-rate, which we purposely not included in [21]. The tropospheric induced frequency error is assumed to have been compensated based on a model (for instance, the MOPS [32]). For clarity, we can set (16) in the form:  
\[\Delta\widetilde{\rho}^{\widetilde{k}}_{i}=h^{k}_{r,i}\times\delta r+h^{k}_{v,i}\times\delta v+h^{k}_{c\delta t,i}\times c\delta t_{i}+c\delta t_{i}- \lambda\delta f^{k}_{syi}+\eta_{\rho,i} \tag{18}\]  
where the index \(i\) referes to time \(t_{i}\), and the coefficients \(h^{k}_{r,i}\), \(h^{k}_{v,i}\), and \(h^{k}_{c\delta t,i}\) can be easily deduced by term-to-term identification between (16) and (18).
# Fast Connectivity Construction via Deep Channel Learning Cognition in Beyond 5G D2D Networks
## 1 Introduction
This paper addresses the seamless network coverage issues to support URLLC in beyond 5G D2D, and then proposes a solution based on the context-aware service loop to handle the issues. The contribution of this study will be represented as follows:  
* This paper makes closely investigates of seamless connectivity issues with mobility which are eventually related to real-time demands on applications based on beyond 5G D2D networks. * In addition, the fast connectivity construction framework relying on on-node analyses of networking contexts is proposed with the goal achievement on low latency. * On-node analyses are designed through a light-weight deep neural network (DNN) technique, and it is examined by various experimental scenarios. The rest of this paper is organized as follows. In Section 2, we present the state-of-the-art in terms of the 5G networks, D2D, and cognitive computing. In Section 3, we explain of our fast connectivity construction scheme. Then, we address DNN based analyses in detail in Section 4. Finally, we conclude this paper with further discussions and future works in Section 5.
## 5 Experiment and Result Analysis
### Data Sets and Data Preprocessing  
The training, testing, and validation sets were divided into 60%, 20%, and 20% of the total dataset. Normalization was conducted in this study to address the different magnitudes among the data. We adopted the min-max normalization method (Equation (25)):  
\[Y_{i}=\frac{Y-Y_{\min}}{Y_{\max}-Y_{\min}} \tag{25}\]  
Min-max normalization can map the data clearly and directly to the [0, 1] interval, which can prominently exhibit the data's relative magnitudes and distribution characteristics. It allows the differences and relationships of the data to be presented more intuitively and clearly, facilitating our understanding and analysis of the data. In contrast, mean normalization is mainly adjusted based on the mean of the data and may not fully reflect the extreme values and distribution range of the data in some cases. Although z-score normalization can standardize the data, it is less intuitive and direct than min-max normalization in emphasizing the data's relative magnitudes and original distribution characteristics. Considering the data's characteristics and the analysis requirements in this study, min-max normalization can serve our research purposes and data processing requirements more effectively.
## 4 Simulation and Numerical Results
### Radar Performance
#### 4.1.1 Pslr Performance  
First, we pay attention to the peak sidelobe ratio (PSLR) of range and velocity two-dimensional images. The calculation method of the PSLR can be expressed as follows:  
\[\mathrm{PSLR}=10\mathrm{log}_{10}\frac{P_{sl}}{P_{ml}} \tag{48}\]  
where \(P_{sl}\) denotes the peak level of the highest sidelobe and \(P_{ml}\) denotes the peak level of the main lobe.  
The PSLR performance of the proposed scheme is shown in Figure 5. In the simulation, \(N_{t}=N_{r}=4\), the standard Monte Carlo technique with \(10^{4}\) independent trials is used for different SNR levels, and \(K=4\) in Figure 5a. In Figure 5a, the PSLR of the improved method under a different number of pulse groups and the PSLR of the traditional processing method [27] are plotted as a function of the SNR. It can be observed that the PSLR of the traditional FFT method with information compensation gradually decreases with the SNR, but tends to a constant value at a high SNR. This is because this method does not filter the valid data, which leads to the deterioration of the results with the uncertainty of index modulation. However, the PSLR of the proposed method decreases almost linearly with the SNR. Moreover, because a smaller number of pulse groups will result in more pulses in the same pulse group, the PSLR will decrease with the decrease in \(H\). In addition, the PSLR performance of the multichannel is slightly improved compared with that of a single channel. Figure 5b shows the variation in the PSLR of different methods with \(K\) under SNR = 10 dB. It can be observed that the PSLR of the traditional FFT method based on information compensation decreases with the increase in \(K\). When \(K<4\), the PSLR drops rapidly with increasing \(K\). On the other hand, when \(K>4\), the PSLR drops very slowly. However, the PSLR of the proposed method is much lower and hardly changes with \(K\) under the same SNR, which shows that the uncertainty of index modulation has little influence on the proposed method and the overall performance is better.  
Figure 6 shows the variation in the PSLR with the SNR for different methods when \(K\) is 3, 5, and 7. It can be found that the PSLR of FFT methods decreases with the increase in the SNR, but when the SNR reaches a certain value, it remains almost unchanged. When all subcarriers in a subblock are activated, the MIMO-OFDM-IM signal reverts to the traditional MIMO-OFDM signal, and the PSLR decreases linearly with an increasing SNR. The PSLR performance of the improved parameter estimation method (IPEM) based on the MIMO-OFDM system in [15] is better compared with the MUSIC algorithm. In contrast, the proposed method has a lower PSLR under the same conditions and decreases with the increase in the SNR. Also, Figure 6 validates that the PSLR performance of the proposed scheme hardly varies with \(K\) at different SNRs, which is consistent with the conclusion drawn in Figure 5. It is also noted that, while the IPEM [15] exhibits the same advantage, its performance is consistently about 6 dB worse than the proposed method.
## 3 Results and Discussions
### Water Distribution by LF NMR Analysis  
LF-NMR has been perceived as an efficient technique to evaluate the freshness of fish, and MRI is also a complementary technology for understanding water migration [39]. In the present study, the shortest relaxation time \(T_{21}\) representing the bound water varied, ranging from 0.79% to 1.01% during storage (Table 4), which was due to the water entrapped within highly organized myofibril structures [40]. \(T_{22}\) with intermediate population of relaxation time was considered as immobile water within the myofibril and diminished progressively during storage (\(p<0.05\)). The shortest relaxation time \(T_{23}\) representing free water increased constantly, which indicted that water located within myofibrillar macromolecules was released or translated to free water based on the destruction of muscle fiber [41]. Besides, this process of water migration was also well reflected in the phenomenon of chitosan-based coating retarding the changes of \(T_{22}\) and \(T_{23}\). The \(T_{2}\) proton density weighted images of all the samples are shown in Figure 4. It concluded that no significant difference was detected in image brightness of samples of different treatments in the early storage. From the ninth day, the initial red color presented a tendency towards the blue color. Furthermore, the color of treated HTS fillets was brighter than that of CK1 and CK2, which suggested that the degradation of myofibril and destruction of microstructure was suppressed by the synergistic effect of \(\varepsilon\)-PL and RA, corresponding to the conclusions of LF-NMR results.  
Figure 3: Changes in hardness (**a**), springiness (**b**), adhesiveness (**c**), and cohesiveness (**d**) of half-smooth tongue sole fillets during refrigerated storage (CK1: Chitosan coating without rosmarinic acid or \(\varepsilon\)-PL; CK2: Chitosan coating with 30 mg/kg rosmarinic acid; C1/C2/C3/C4: Chitosan coating with 30 mg/kg rosmarinic acid and 0.05%/0.10%/0.15%/0.20% \(\varepsilon\)-PL).