esa-sceva/satcom-chunk-collection huggingface dataset:
# Second-Order Statistical Properties of Vehicle-to-Vehicle Rician Fading Channel
## 3 LCR and AFD for the V2V Flat Fading Channel
The AFD \(T_{\zeta}(R)\) is the average time duration for which the signal level \(\zeta(t)\) remains below a specified threshold \(R\). For a Rician fading channel, the AFD is denoted by \(T_{\zeta}(R)\) and is generally defined as [22]  
\[b_{0}^{SBT}=\frac{\eta_{T}}{2(K+1)}\int\limits_{-\pi}^{\pi}\frac{e^{k_{T}cos( \alpha_{T}-\mu_{T})}}{2\pi I_{0}(k_{T})}\ da_{T}=\frac{\eta_{T}}{2(K+1)}, \tag{9}\]0  
where \(P_{\zeta}(R)\) denotes the cumulative distribution function (CDF) of \(\zeta(t)\) with \(Q(a,b)\) representing the generalized Marcum \(Q\) function. The derived LCR in (6) and AFD in (22) expressions are generalized formulations encompassing various special cases previously mentioned in the literature. The simplest scnario is that they can reduce to the Clarke LCR \(\sqrt{2\pi}f_{Rmax}Re^{(-R^{2})}\) and AFD \(\frac{(e^{R^{2}}-1)}{\sqrt{2\pi}f_{Rmax}R}\)[22] which is obtained by setting \(K=0\) (NLOS condition), \(k_{T}=0\) (isotropic scattering around Rx), \(f_{Tmax}=k_{R}=0\) (fixed and no scattering around Tx), \(\eta_{SB}=\eta_{DB}=0\) (zero power gain for SBT and DB). Similarly, the expressions for LCR \(\sqrt{2\pi(f_{Tmax}^{2}+f_{Rmax}^{2})}Re^{R^{2}}\) and AFD \(\frac{1}{\sqrt{2\pi(f_{Tmax}^{2}+f_{Rmax}^{2})}}e^{R^{2}-1}\) for NLOS non-isotropic DB rays [11] can be obtained by setting \(K=k_{T}=\bar{k}_{R}=0\). This demonstrates the broad applicability of the proposed LCR and AFD expressions, which we can tailor to capture a wide range of channel scenarios and modeling assumptions.
# A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
## 5 Simulation Analysis
### IEEE 33-Node Distribution Network
#### 5.1.2 Fault Tolerance  
In actual engineering, the distortion of fault information reported by FTU is divided into two cases of misreporting or omission of the reported information. Omission of the reported information refers to the fault current exceeding the set threshold and the FTU not reporting the fault information. Misreporting means that the FTU reports fault information, but the actual situation does not match it.  
In this section, different numbers and locations of false alarm and omission signals are set to simulate a variety of information distortion situations in real projects to test the information tolerance of the algorithm.  
As can be seen in Table 7, the accuracy of Algorithm 2 dropped to about 80%. Algorithm 3 requires high information reliability of the branch nodes and therefore produced misjudgment for fault cases 2 and 7, in which the branch nodes were distorted. The fault location model of the algorithm in this paper was derived using strict mathematical derivation and is fully equivalent to the classical localization model, which is fault-tolerant to distorted information with the help of redundancy of fault information in the whole network. The test results confirm that Algorithm 1 had 100% accuracy for the fault information distortion case in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Equipment Number**} & \multirow{2}{*}{**Preset Fault Section**} & \multicolumn{5}{c}{**Accuracy (\%)**} \\ \cline{3-6}  & & **Algorithm 1** & **Algorithm 2** & **Algorithm 3** & **Algorithm 4** \\ \hline
1 & \(x_{8}\) & 100 & 94 & 99 & 100 \\
2 & \(x_{26}\) & 100 & 80 & 100 & 100 \\
3 & \(x_{28}\) & 100 & 82 & 100 & 100 \\
4 & \(x_{30}\) & 100 & 78 & 99 & 100 \\
5 & \(x_{4},x_{29}\) & 100 & 96 & 100 & 100 \\
6 & \(x_{11},x_{28}\) & 100 & 84 & 96 & 100 \\
7 & \(x_{25},x_{30}\) & 100 & 81 & 100 & 100 \\
8 & \(x_{6},x_{27}\) & 100 & 95 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy test results.  
From Table 8, it can be seen that the computational efficiency of Algorithm 2 was around 130 ms, which is due to the fact that the algorithm needs to perform iterative operations in a high-dimensional solution space. Algorithm 3 applies a hierarchical strategy to reduce the fault search dimension, and its computational efficiency was around 90 ms. Algorithm 4 had a computational efficiency of about 127 ms. The analysis in Section 4.2 confirms that the algorithm proposed in this paper has a linear level of time complexity. The test results show that the computational efficiency of Algorithm 1 was below 20 ms, which is a significant advantage compared with the other three algorithms.
## 4 Results on a Real Case: Proof-of-Concept Validation
### Results of the Path Planner System  
#### 4.2.1 Detecting Tree Lines  
As a result of the Hough Transform application in Algorithm 7, all the orange tree rows are extracted as depicted in Figure 6. Although the order in which they are extracted does not correspond to the real order inside the grove, they are reordered and labelled according to its real order for further steps.  
Figure 5: Generating binary maps from the satellite maps. (**a**) satellite view of the current parcel, (**b**) _Path_ image of the current parcel, (**c**) _Trees_ image of the current parcel, (**d**) _Obstacles_ image of the current parcel.
## 2 Related Work
Future studies will concentrate on optimizing the complexity of BBMO and investigating various consistencies in estimated task completion times (ETCs). In ref. [17], the authors focus on load balancing in fog computing for healthcare applications. The authors present a probability-based strategy for determining job offloading to fog nodes with the least amount of latency. The fog master makes use of ant colonyoptimization (ACO) to assign tasks to appropriate nodes. Balancing load between fog nodes enhances the quality of service and reduces response time. The proposed work provides significant benefits for time-sensitive healthcare applications in fog computing. In their publication [18], the researchers propose and the writers detail a technique for arranging tasks with multiple goals in fog computing, which utilizes an upgraded ant colony algorithm. This approach is customized to the features of fog nodes and accounts for expenses related to computing resources, power consumption, and network usage. In this paper [19], PSO-GWO is a novel hybrid metaheuristic algorithm suggested for scheduling dependent tasks on virtual machines in cloud computing. To optimize the overall execution cost, the PSO-GWO technique combines Particle Swarm Optimization with gray wolf optimization. PSO-GWO beats the regular PSO and GWO algorithms in terms of average total execution cost and time, according to experimental data. When evaluated on scientific processes, the algorithm obtains significant percentage reductions in execution time and overall execution cost when compared to PSO and GWO, suggesting higher performance. In this paper [20], the author introduces the Dynamic and Fault-Tolerant Scheduling Algorithm (DRFTSA). Leveraging task priorities based on unit electricity cost and a deep reinforcement learning model (DQN) within CloudSim, DRFTSA dynamically optimizes scheduling. Real-time Google Cloud Jobs data, categorized into regular and large datasets, are used for evaluation. In this paper [21], the above-proposed method addresses the critical challenge of cloud task scheduling; this proposal introduces the Sine Cosine-based Elephant Herding Optimization (SCEHO) algorithm coupled with Improved Particle Swarm Optimization (IPSO), which enhances resource selection for tasks by incorporating load balancing and resource allocation parameters. Table 1 provides a concise overview of the methodologies and critical parameters addressed by each task scheduling algorithm in the mentioned references. We can say that these works did not consider the huge amount of data that workflow tasks create or how quickly the fog-cloud computing world changes, which generates a huge amount of energy. Therefore, we propose a new algorithm that considers cost and energy in addition to Makespan to control the energy consumption and give the tasks priority depending on the term (pay as you go). Pay-as-you-go (PAYG) cloud computing is a way to pay for cloud computing that charges you based on how much you use it. It is kind of like how electricity bills work; they only use the resources they need [22].
# Comparative Analysis of Data Detection Techniques for 5G Massive MIMO Systems
## 4 Complexity Analysis  
Multiplications and divisions are dominant operations in the computational complexity. The AMP, SOR, GS, and JA methods can be implemented by matrix-vector multiplications. The AMP algorithm requires only two matrix-vector multiplications per iteration. Therefore, the AMP algorithm is implementation-friendly. The computation of \(\mathbf{D}^{-1}\) requires \(K\) real number of divisions. The number of multiplications required to perform the first iteration in the SOR, GS, and JA methods are \(4K^{2}+4K\), \(4K^{2}\), and \(4K^{2}-2K\), respectively. Although initialization increases the number of multiplications, this complexity increment is trivial because a small number of iterations are required to obtain the target performance. Table 1 illustrates the number of required multiplications in each detection algorithm.
## 1 Introduction
In these papers, where temperature and relative humidity control is provided according to the building and environmental conditions, the room/building comfort is brought to the desired conditions quickly and focused on energy efficiency. However, there are very few studies in the literature on temperature and humidity control about evaporative cooling systems and basic control methods (ON/OFF, P, PI, etc.) applied to reduce energy consumption without reducing room comfort in these studies. As can be seen from the studies in the literature, the best energy-saving application for DPC systems is the control of the speeds of the fans used for air intake-exhaust according to the conditions. [PERSON] et al. [23] investigated developed capacity and improved controllers' performances of the three evaporator systems for indoor temperature and humidity control. The proportional-integral (PI) [24] and high-low speed control strategies [25; 26] are used to provide energy efficiency for the Indirect EC with the control of the fans at a variable speed. Energy efficiency can be achieved by regulating the operations and working speed of the water pump, which is another actuator. [PERSON] et al. [27] achieved energy savings for the direct EC system by adjusting the operation times of the water pump. By using these control strategies, it is seen that energy efficiency is increased by between 15% and 30% for DPC systems. It shows that the comfort conditions can be achieved faster and with less energy consumption by regulating the operation of the actuators in the cooler. In this study, an independent temperature and relative humidity control strategy is developed for the extra cooling performance with an extra energy saving of pre-cooled desiccant-wheel-based dew-point evaporative cooling system. This controller strategy increases the environmental compatibility of the cooling system, cooling performance, and energy efficiency. For this purpose, the control strategy is based on the proportional (P) control technique, in which the speeds of the actuators (fans and desiccant wheel) are adjusted according to the environment and room conditions throughout the process. The energy consumption of the system has been tried to be minimized by adding different operating modes for each actuator and adding extra automatic run/stop property in the control strategy. The results show that the temperature and relative humidity of the room reach the set values quickly and robustly, despite the disturbance effects such as heat gains from windows/roof/doors and humans. During this process, the proposed control strategy shows an extra energy-saving performance than the classical ON/OFF control method.
## 2 The Greek Power System
### Wind and PV Development  
Renewable energy sources shall substantially contribute towards the achievement of decarbonization targets set for the energy sector. One of the main objectives of energy planning is the increase of renewable energy sources' share in the energy mix, in order to substitute part of the conventional units' production. In the renewable sources capacity development scenarios, reference is mainly made to large penetration of wind and photovoltaic systems. In the corresponding studies, wind installations' capacity is projected to reach, according to Ministry of Environment and Energy in 2030, approximately 7 GW, while by 2050 11-18 GW (off-shore wind installation included) [27]. According to a study conducted by European Commission, by 2030, wind installations' capacity may reach 6 GW and by 2050 7.8 GW [28]. In addition, World Wide Fund for Nature (WWF) estimates that the wind installations will account for 5 GW, in 2030, and 6.7 GW, in 2050 [29]. Moreover, the increase of PV capacity is projected, according to the Energy Roadmap by the Ministry of Energy (the National Energy and Climate Plan and the Long-term strategy towards 2050), for 2030 at 7.7 GW, while for 2050 it is estimated at 8-12 GW, depending on the scenario considered [26; 27]. According to a study conducted by the European Commission for Greece, in 2030, it is estimated that 5.6 GW of PV will have been constructed, while by 2050 9 GW [28]. Furthermore, according to WWF's projections, PV capacity will reach 4.8 GW by 2030 and 7.1 GW by 2050 [29]. Table 1 presents forecasts on the cumulative capacity of PV and Wind in Greece by 2030 and 2050 [26; 27; 28; 29].  
There are significant differences between the figures predicted for wind and PV capacity by the competent bodies. In order to achieve a comparative assessment of the various development scenarios, the ratio of the forecasted wind and photovoltaic capacity by the annual mean load of the country is presented in Figure 1. In 2030 the wind and PV capacity lies between 150-205% of the annual mean load demand, while in 2050 between 200-255%. The ratio between Wind and PV in terms of installed capacity is almost 1 by 1 in most of the forecasts. More PV than Wind capacity is expected by 2050 according to the Energy Roadmap of the Ministry of Energy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Ministry of Environment and Energy**} & \multicolumn{2}{c}{**WWF**} & \multicolumn{2}{c}{**European Commission**} \\ \cline{2-6}  & **PV (GW)** & **Wind (GW)** & **PV (GW)** & **Wind (GW)** & **PV (GW)** & **Wind (GW)** \\ \hline
2030 & 7.7 & 7 & 4.8 & 5.0 & 5.6 & 6 \\ \hline
2050 & 8–12 & 11–18 & 7.1 & 6.7 & 8.9 & 7.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Forecasts for the cumulative capacity of PV and Wind capacity in Greece by 2030 and 2050 [26; 27; 28; 29].
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 2 Data and Methods  
### Study Area  
The Three-North region (73\({}^{\circ}\)26\({}^{\prime}\) E-127\({}^{\circ}\)50\({}^{\prime}\) E, 33\({}^{\circ}\)30\({}^{\prime}\) E-50\({}^{\circ}\)12\({}^{\prime}\) E) is located in the ecologically fragile area of China including the western part of northeastern China, northern China and most of the northwestern region, as shown in Figure 1. This region covers 551 counties in 13 provinces, covering an area of more than 4 million square kilometers, accounting for 42.4% of the country's total land area. The climates of the Three-North region are quite different, including arid climate, semiarid climate, and semiarid and semihumid climate. Arid and semiarid regions account for two-thirds of the Three-North region. From the northeast end to the southwest section, the average temperature ranges from \(-\)2 \({}^{\circ}\)C to 14 \({}^{\circ}\)C, and the temperature in most areas ranges from 2 \({}^{\circ}\)C to 5 \({}^{\circ}\)C. The precipitation decreases from east to west and from south to north, and the average annual precipitation in most areas is 20-450 mm. Affected by precipitation, the types of natural vegetation from east to west are forest, forest-grassland, grassland, and desert. The topographical featuresof this area are also very complex. The overall terrain is low in the east and high in the west, mainly including plains, plateaus, mountains, basins, and other topographies.
# Investigation of the Inverse Magnus Effect on a Rotating Sphere in Hypersonic Rarefied Flow
## 4 Results and Discussion
### Surface Quantities
#### 4.2.1 Distribution of Wall Friction  
When the sphere is at rest (Figure 8a), the distribution of the shear stress vector on the sphere is axisymmetric. As the rotation speed increases (Figure 8b-e), the symmetry is broken significantly, and the strength of skin friction is enhanced. The point of \(C_{f}=0\) departs the leftmost pole on the sphere and moves toward the bottom. The region with a large magnitude of \(C_{f}\) is mainly the upper half of the windward side, where the directions of shear stresses are basically upward. The resultant force of these shear stresses becomes a lift \(F_{y}\) exerted on the sphere, directed toward the positive \(y\)-axis.  
Figure 7: The DSMC results for the flow fields for different rotation parameters \(W=\omega R/V_{\infty}\). Subfigures **(a–e)** correspond to \(W=0.00\), \(0.25\), \(0.50\), \(0.75\), and \(1.00\), respectively. The contour shows the distribution of the local Mach number on the symmetry plane and the sphere. Streamlines on the symmetry plane and the sphere are plotted. Subfigure **(f)**: velocity profiles along Line 2 in Figure 5.  
These findings can be explained by comparing the distribution of shear stress with the field of relative velocity on the sphere. In Figure 9a, the magnitudes and directions of the wall shear stresses under the condition of \(W=0.5\) are demonstrated. In Figure 9b, the relative velocity is the difference between the absolute gas velocity shown in Figure 7c and the linear velocity of the rotating wall surface. It can be observed that the direction of wall friction coincides with the direction of gas motion relative to the spinning sphere. The point at which \(C_{f}=0\) is just the point where the gas velocity equals the wall velocity. According to the gas-kinetic interpretation of wall shear stress, the magnitude of \(\tau_{w}(x_{w})\) is determined by the combining effects of the incident mass flux on the wall and the magnitude of relative velocity. The incident mass flux is large only on the windowal side, while the relative velocity is large only on the top of sphere. As a result, strong friction is concentrated on the  
Figure 8: The DSMC results of the wall friction for different rotation parameters \(W=\omega R/V_{\infty}\). Subfigures (**a–e**) correspond to \(W=0.00\), \(0.25\), \(0.50\), \(0.75\), and \(1.00\), respectively. The contour shows the magnitude of \(C_{f}\). The vectors show the directions of shear stresses.  
upper half of the windward side, where the tangential momentum transfer between the gas molecules and the wall surface is rapid and effective.
# Cryptanalysis of a New Color Image Encryption Using Combination of the 1D Chaotic Map
## 5 Conclusions  
This paper attacks a new color image encryption algorithm combining a few the 1D chaotic maps which has been recently proposed in [37]. The encryption process depends on the linear-nonlinear-linear  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Image Size**} & \multirow{2}{*}{**Image**} & \multicolumn{3}{c}{**The Proposed Scheme**} & \multicolumn{3}{c}{**[PERSON]’s Scheme**} \\ \cline{3-8}  & & **Attack \(l\)\(p\) and** & **Attack** & \multirow{2}{*}{**Total Time**} & **Attack \(l\)\(p\) and** & **Attack** & \multirow{2}{*}{**Total Time**} \\  & & **Diffusion** & **Permutation** & & & & & \\ \hline \multirow{4}{*}{256 \(\times\) 256} & [PERSON] & 1.2386 & 2.7358 & 3.9744 & 1.2173 & 3.8253 & 5.0426 \\  & [PERSON] & 1.0537 & 2.8336 & 3.8893 & 1.1562 & 3.9376 & 5.0938 \\  & [PERSON] & 0.9697 & 2.9121 & 3.8818 & 1.0127 & 4.5862 & 5.5989 \\ \hline Average time & & 1.0873 & 2.8278 & 3.9151 & 1.1287 & 4.1164 & 5.2451 \\ \hline \multirow{4}{*}{512 \(\times\) 512} & [PERSON] & 1.5413 & 10.0327 & 11.574 & 1.7135 & 13.7236 & 15.4371 \\  & [PERSON] & 1.6331 & 11.7731 & 13.4062 & 1.5233 & 13.1027 & 14.626 \\  & [PERSON] & 1.6975 & 10.9173 & 12.6148 & 1.5872 & 14.3379 & 15.9251 \\ \hline Average time & & 1.6240 & 10.9077 & 12.5317 & 1.6080 & 13.7214 & 15.3294 \\ \hline \multirow{4}{*}{1024 \(\times\) 1024} & [PERSON] & 2.5136 & 32.4377 & 34.9513 & 2.4759 & 47.7536 & 50.2295 \\  & [PERSON] & 2.7572 & 35.7728 & 38.53 & 2.8364 & 58.5927 & 61.4291 \\  & [PERSON] & 2.6673 & 39.8154 & 42.4827 & 2.6581 & 56.3619 & 59.02 \\ \hline Average time & & 2.6460 & 36.0086 & 38.6546 & 2.6568 & 54.2361 & 56.8929 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Execution time (seconds).  
Figure 3: Images from Column 1 to Column 5 are original images, cipher images, retrieved permutation-diffusion images, retrieved permutation-only images, restored images.  
structure of the encryption algorithm. The vulnerability of this algorithm is revealed, and the attacking scheme is developed by the chosen plaintext attack in Section 3, based on the security weakness. Experimental results demonstrate the attack scheme of this paper can completely collapse the encryption algorithm and has low computation complexity.  
Conceptualization, M.L.; Methodology, M.L. and Y.D.; Software, Y.D.; Validation, Y.D.; Formal Analysis, Y.D.; Investigation, M.L. and Y.D.; Writing--Original Draft Preparation, Y.D.; Writing--Review and Editing, M.L. and Y.D.; Visualization, Y.D.; Supervision, M.L.; Funding Acquisition, M.L. and Y.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China by the Ph.D Scientific Research Foundation of Henan Normal University (Grant no. qd18027) and the National Natural Science Foundation of China (Grant no. 61602158).  
The authors declare no conflicts of interest.
## 4 Discussion
### Future Work  
In summary, there are still several limitations of this study, although it has been proven to have potential for winter wheat yield estimation at the regional scale. We have summed up three points to help improve the assimilation framework in the future. Firstly, as a semi empirical model, the accuracy of the water cloud model strongly relies on in situ measurements, which causes errors and is difficult to extend to a large regional scale. A more mechanistic model can be imported to replace the water cloud model (e.g., Michigan Microwave Canopy Scattering (MIMICS)). Secondly, owing to the deficiency of the WOFOST model in the irrigation module [4; 44], the irrigation and soil water balance only received cursory attention in this study. A soil water atmosphere plant (SWAP) model with more complex soil subroutines could be taken into account. The principal difference between the SWAP and WOFOST models is in the soil water balance component calculations. The SWAP model will have a more accurate modelling of the soil water balance [54]. Thirdly, this study was simulated at 10 m scale in Hengshuicity, which has more than one million pixels. There is no doubt that this will increase the demand on the computing power of computers if we want to run the model at a larger scale. As such, a higher efficiency assimilation strategy needs to be considered to improve computing efficiency, for example a cluster strategy on the grid can effectively improve the computing efficiency [55; 56]. Therefore, it would be interesting to try to achieve a better result using the aspects above.