esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System and Channel Models  
Two different proposed network scenarios i.e., scenario-1 (S\({}_{1}\)) and scenario-2 (S\({}_{2}\)) in D2D-CRS-SM are shown in Figures 2 and 3, respectively (Part of this work was presented in [51]. It should be mentioned that a very initial result was presented in [51] by considering only the second scenario of the proposed network architecture in this paper. Moreover, the performance of [51] was studied in terms of BER and SE considering only BPSK and QPSK.). Each scenario consists of a BS, two cellular users (e.g., NU and FU) and a D2D transmitter-receiver pair (e.g., D\({}_{1}\) and D\({}_{2}\)). The direct link between the BS and FU is assumed to be weak for data transmission because of poor conditions of channel. Hence, the direct link is not suitable for communication and it is neglected for this reason. So the BS solely depends upon D\({}_{1}\) for the purpose of communication with FU, where D\({}_{1}\) acts as half-duplex DF relay. The above assumptions are very much valid for cellular users such as FU situated far from BS e.g., a user located at the edge of a cellular network and suffers from very low SNR. Each node is a multi-antenna system. The communication nodes BS, NU, FU, D\({}_{1}\) and D\({}_{2}\) are equipped with A\({}_{\text{BS}}\), A\({}_{\text{NU}}\), A\({}_{\text{FU}}\), A\({}_{\text{D}_{1}}\), and A\({}_{\text{D}_{2}}\) antennas, respectively. It is important to mention that the data transmission policy in S\({}_{1}\) and S\({}_{2}\) is identical except that antenna index of BS is the desired information of NU in S\({}_{1}\), whereas modulation symbol of BS is the desired information of NU in S\({}_{2}\).  
Figure 1: The organization of the article.  
For the channel between any two communication nodes \(x\) and \(y\), it is assumed that the channel experiences independent Rayleigh flat fading plus additive white Gaussian noise. The channel coefficient is denoted by \(h_{x,y}\sim\mathcal{CN}(0,\lambda_{x,y})\) with zero mean and variance \(\lambda_{x,y}\), where \((x,y)\in\{\text{BS},\text{NU},\text{FU},\text{D}_{1},\text{D}_{2}\}\) and \(x\
eq y\). So the link gain will be \(\alpha_{x,y}=|h_{x,y}|^{2}\), where \(\alpha_{x,y}\) is an exponentially distributed random variable with a scale parameter (or mean value) \(\lambda_{x,y}(\lambda_{x,y}>0)\)[52; 53]. The transmit powers of BS and D\({}_{1}\) are represented as \(P^{\text{BS}}\) and \(P^{\text{D}_{1}}\), respectively. The average SNR is denoted by \(\rho\). It is also assumed that the receivers are well aware of CSI i.e., CSI at NU and D\({}_{1}\) during \(P_{1}\) and CSI at FU and D\({}_{2}\) during \(P_{2}\). The data transmission process in the proposed D2D-CRS-SM is completed in two cooperative phases which are described in detail in the following sections.
## 3 Results
### Macro- and Micronutrient Status in the Soybean Grain
#### 3.1.3 Zn and Se Accumulation in the Grain  
In this study, the biofortification treatments have a very significant influence (\(p<0.001\)) on the Zn and Se content in the soybean seeds (Table 2).  
Foliar biofortification with Se did not increase Zn content, which was on average 45.92 mg/kg, and it was not significantly different from the control treatment (Figure 2a). With Zn application, the significant differences were determined between the lower (Zn_1) and higher (Zn_2) dose of applied Zn; thus, the highest accumulation was on average 66.14 mg/kg with Zn_2 and simultaneous Se_3 and Zn_2 application, which was about 3% higher compared to the Zn_1 treatment. The soybean varieties have different responses to foliar biofortification with Zn. The Toma variety has the highest content of Zn in the grain (61.47 mg/kg), which was significantly different from the other varieties. The lowest content of Zn in the grain was found for Lucija and Sonja (50.91 mg/kg on average), with no significant differences between them.  
Selenium biofortification has a great impact on increased Se content in the grain (Figure 2b). Without Se foliar application (Zn_1 and Zn_2 treatment), the Se content in the grain was at the control level (55.29 mg/kg), with no significant differences determined between the control and Zn treatments. The foliar application of Se vigorously increased the Se grain content, which was increased linearly with increasing Se dose. Thus, the Se content in the grain was 1155.43 mg/kg at the Se_1 treatment, and increased by 77.5% at the Se_2 treatment and even more, by 156%, at the Se_3 treatment. Regarding the varieties, the lowest accumulation of Se in the soybean grain was in the Lucija grain (1070.71 mg/kg) and the highest amount of Se was found in the grain of the Sunce variety (1436.85 mg/kg).
## 3 Results and Discussions
### Selection of Nozzle Key Structural Parameters  
From the L\({}_{18}\) orthogonal test, the contribution of the relevant parameters to the performance index can be obtained, i.e., it reflects the degree of influence of the parameters on the performance index. Figure 8 depicts the contribution of decision parameters to the performance indicators (peak wall shear stress, action area, and cleaning water consumption). From Figure 8, it can be seen that the nozzle V-groove half angle \(\beta\) is the most significant effect of the maximum shear stress, and its contribution is 76.78%, followed by the nozzle  
Figure 6: Mesh independence test using different grid numbers.  
Figure 7: Model validation using experimental data of [PERSON] et al. (2008).  
incident section diameter \(d\), and its contribution is 20.22%. The influence of the nozzle incident section diameter \(d\) on the action area is most significant, and its contribution rate is 50.42%, followed by the nozzle V-groove half angle \(\beta\), and its contribution rate is 48.01%. The influence of nozzle incident section diameter \(d\) on the cleaning water consumption is the most significant, and its contribution is 63.81%, followed by the nozzle V-groove half angle \(\beta\), and its contribution rate is 34.20%. This is consistent with the results of [PERSON] et al. [50]. The reason for the above phenomenon is that the V-groove half angle \(\beta\) and incident section diameter \(d\) together determine the size of the contact surface between the nozzle incident section and the external flow field. The increase in the contact surface between the nozzle incident section and the external flow field will lead to an increase in the jet flow rate per unit time, which directly affects the maximum shear stress, the action area, and the cleaning water consumption.  
From Figure 8, it can be also seen that the nozzle incident section diameter d and V-groove half angle \(\beta\) have a more significant effect on the peak wall shear stress, action area, and cleaning water consumption, with a total contribution rate of about 97.00%, 98.43%, and 98.01%, whereas the inlet section diameter \(D\), inlet length \(L_{1}\), incident length \(L_{3}\), and shrinkage segment angle \(\alpha\) have a smaller effect on the cleaning performance. Therefore, to reduce the complexity of numerical simulation calculation and to guarantee the accuracy of the calculation, in this study, the nozzle incident section diameter \(d\) and the V-groove half angle \(\beta\) are selected as the key structural parameters of the nozzle, which are used as the decision-making independent variables for the construction of the subsequent model, and the other structural parameters that do not have a significant impact on the jet cleaning performance indexes are used as fixed values. Such a calculation mode reduces the number of structural variables from six to two, which greatly shortens the time of numerical simulation calculation and reduces the complexity of the model calculation work.  
Figure 8: Contribution of decision parameters to performance indicators.
Thus, _Sasa_ GPP and its controlling factors could be influenced by the structure of the forest canopy, both temporally and spatially, which would create different environmental conditions on the forest floor. Since _Sasa_ is widely distributed in forests in Japan and could contribute significantly to forest ecosystem GPP, it is of importance to accurately estimate its GPP and its seasonal trends under different canopy structure. In addition, understanding the factors that control the seasonal changes of _Sasa_ GPP in these forests is essential for predicting how _Sasa_ GPP will change under global warming. This study was conducted in three types of secondary forests: deciduous broadleaved, evergreen coniferous, and mixed forest consisting of both coniferous and deciduous trees. These three types of forest were chosen because they are the typical forest with _Sasa_-dominated understory. By combining data of the vegetation distribution (data of vegetation survey conducted by the Ministry of the Environment in Japan) and the data of the distribution of _Sasa senanensis_[20], the targeted dwarf bamboo species in this study, it appeared that deciduous broadleaved forest, mixed forest, and evergreen coniferous forest occupy 43.8%, 2.2%, and 24.4%, respectively. Even though the ratio of mixed forest was relatively low, it was higher than that of other types of forest, i.e., deciduous coniferous forest and evergreen broadleaved forest. These three forests have different canopy structures both temporally and spatially, which could largely influence the understory environment. In the deciduous broadleaved forest dominated by mature beech trees, there were two areas where the forest floor environment was significantly different, so we established two study areas: a closed-canopy area and a canopy gap area (canopy area and gap area, [12]). In this study, we tested two hypotheses: (1) the seasonal trends of _Sasa_ community GPP and _Sasa_ annual GPP would differ among forest types, (2) in addition to light intensity, the seasonal changes of _Sasa_ community GPP would be controlled by different factors in different forest types.
# Energy Efficiency of a Decode-and-Forward Multiple-Relay Network with Rate Adaptive LDPC Codes +
## 2 System Model  
We consider a two-hop wireless system, with a source node \(S\), a destination node \(D\), and intermediate relay nodes \(R_{i}\), where \(i\in\{1,2,...,N\}\). These relay nodes are located at different positions between the vicinity of \(S\) and \(D\) as shown in Figure 1. There is no direct link between \(S\) and \(D\) and the source node communicates with the destination via relays only. The relays are assumed to operate in a regenerative decode-and-forward mode. A time division based half-duplex relaying has been considered, i.e., there are two phases of the data transmission. In the first phase or time slot (TS-1), 5 encodes the data with a specific code rate \(C_{R}\) using LDPC codes and broadcasts it towards the relays. The relays receive the data and decode it. In the second phase or time slot (TS-2), the data is encoded at the relays with the same or varying code rates and are sent towards the destination \(D\). The destination receives the data from all the relays on orthogonal channels and performs decoding. The additive white Gaussian noise (AWGN) has been assumed on all the receivers with zero mean and \(N_{0}/2\) variance per real dimension.  
The received signal at the \(i^{th}\) relay is given as:  
\[y_{r_{i}}=\sqrt{P_{\rm s}}x_{s}h_{sr_{i}}+n_{r_{i}}, \tag{1}\]  
where \(x_{s}\) is the transmitted symbol from the source with the transmit power \(P_{\rm s}\). In Equation (1) the channel coefficient between the source and \(i^{th}\) relay is given as \(h_{sr_{i}}\) and \(n_{r_{i}}\) is the additive white Gaussian noise (AWGN) sample added at the \(i^{th}\) relay. It has been assumed that all the nodes are equipped with single antennas and the wireless channel on all links undergoes frequency flat fading. The channel coefficients are independent and identically distributed (i.i.d) and follow the Nakagami-\(m\) distribution. Moreover, we assume that all the nodes have perfect channel state information (CSI) of the links on which they receive data, hence, a perfect coherent detection is possible at the receiver.  
Likewise, at the destination \(D\), the received signal from the \(i^{th}\) relay can be represented as:  
\[y_{d_{i}}=\sqrt{P_{\rm i}}x_{r_{i}}h_{r_{i}d}+n_{d}, \tag{2}\]  
where \(x_{r_{i}}\) is the transmitted symbol from the \(i^{th}\) relay with transmit power \(P_{i}\). \(h_{r_{i}d}\) is the channel coefficient between the \(i^{th}\) relay and the destination and \(n_{d}\) is the AWGN sample added to the received symbol at the destination. The destination receives signals from the multiple relays on orthogonal channels.
## 6 Conclusions  
To solve the problem of system performance deterioration caused by channel asymmetry in the cooperative FSO communication system, this paper proposed a scheme of using layered modulation and joint physical-layer network coding in the cooperative FSO communication system. With the use of layered modulation for the transmitted source information, and using physical-layer network coding at the relay node, the performance of the cooperative FSO communication system is improved. Through the simulation and analysis of different constellation priority parameters and different channel asymmetry parameters, it can be concluded that the higher the constellation priority parameters and channel asymmetry parameters, the better the system performance. Under different atmospheric turbulence intensities, when the average signal-to-noise ratio of the channel rises from \(SNR=5\) dB to \(SNR=15\) dB, the bit error rate of the cooperative FSO communication system can be reduced to below \(10^{-8}\), and under strong atmospheric turbulence channel conditions, the system can obtain a signal-to-noise ratio gain of about 1.5 dB. The higher the average signal-to-noise ratio of the channel, the faster the bit error rate of the system decreases. In atmospheric turbulence of different intensities, the average channel capacity of the system increases with the increase of the constellation priority parameters. The simulation results show that the layer modulation combined with the physical-layer network coding scheme can improve the BER performance and average channel capacity performance of the cooperative FSO communication system; it is suitable for the channel, with strong atmospheric turbulence and high signal-to-noise ratio, and shows better anti-interference performance for the channel with strong atmospheric turbulence.  
Conceptualization, Y.C. and H.Q.; methodology, H.Q.; software, H.Q.; validation, X.P.; data curation, Z.Z.; writing--original draft preparation, H.Q.; writing--review and editing, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Chongqing Education Commission Fund (KJ120827); the Science and Technology Project of the Chongqing Education Commission (KJ1500934, KJ1709205); the Chongqing Graduate Scientific Research Innovation Project (CYS18311); the Chongqing Basic and Frontier Research Program (cstc2015 cyjA40051); and the Science and Technology Research Youth Project of the Chongqing Education Commission (KJQN202101124).  
Not applicable.  
The authors declare no conflict of interest.
## 3 The Proposed Approach
### Maintenance Optimization
#### 3.2.1 Representation and Decoding of Candidate Solutions  
The proposed optimization approach uses a real representation for candidate solutions, which considers a variable number of maintenance actions, with their respective severities, in a user-defined time horizon of \(N\) months. The approach determines the number of PM activities (\(c\)) and the times and intervention level for these activities, \(T_{j}\text{es}_{j},\text{for}j=1,\ldots,c\).  
Numerically, each solution is represented as a vector of \(N\) real numbers, denoted as \(X=\{x_{0},\cdots,x_{N-1}\}\). Each element \(x_{i}\) of a solution \(X\) is decomposed into an integer part \(\lfloor x_{i}\rfloor\) and a fractional part \(\{x_{i}\}\), such as \(\{x_{i}\}=x_{i}-\lfloor x_{i}\rfloor\).  
The occurrence of a preventive maintenance activity in a given month is defined by the value of \(\lfloor x_{i}\rfloor\). Each element \(x_{i}\) of a candidate solution is defined in the interval \((0;2|s|-1)\), in which \(|s|\) is the number of intervention levels considered. Thus, the solution is decoded by calculating the rest of the division of \(\lfloor x_{i}\rfloor\) by \(2\), \(mod(\lfloor x_{i}\rfloor,2)\), indicating the occurrence of a maintenance action in \(i\)-th month if and only if \(mod(\lfloor x_{i}\rfloor,2)=1\). This choice of limits for \(x_{i}\) values ensures equal probability for even and odd values.  
For cases in which \(mod(\lfloor x_{i}\rfloor,2)=1\), the values of \(\lfloor x_{i}\rfloor\) also indicate the intervention level of the maintenance to be performed in the \(i\)-th month, as \(s_{j}=\left(s_{\lfloor x_{i}\rfloor}+1\right)/2\). The time \(T_{j}\) to perform the PM activity is calculated from the fractional part \(\{x_{i}\}\), of the form \(T_{j}=30(i+\{x_{i}\})\).  
It is noteworthy that, in practice, the intervention level of the maintenance activity can be defined based on the number of tasks performed in the maintenance planning, the time in hours required to perform the planning, and the number of items replaced in maintenance and can be adapted as a function of the system specificity.
## 1 Introduction
### Our Routing Approach  
This work investigates packet losses due to the channel characteristics while all satellite nodes have equal buffer capacity. We tried to optimize latency and packet loss together without any weights or priorities by using the simplified Kleinrock's power metric [21] in the snnr routing algorithm, which uses the ratio of delay and packet loss. We compared the performance of snnr with the other routing approaches with the following goal functions:1. Linear combination of delay and packet loss; 2. Delay-only; 3. Packet-loss-only; 4. Contact graph routing (CGR), which uses the shortest path with the earliest available links. We evaluated the performance of the routing approaches in a laboratory testbed using virtual machines under various testing scenarios with different emulated delay, packet loss, and bandwidth configurations. We implemented our routing algorithms using the CSG routing approach with the HDTN software running on each virtual machine. Though we briefly explain HDTN and CSG in the following subsections, the readers are encouraged to read the references for complete insights.
## 5 Sub-Areas of Traffic Prediction Applications Using RNNs
### Traffic Speed Prediction
It captures spatial dependencies through adjacency matrices and graph convolutions and learns temporal information using an RNN structure. The proposed attention-enabled model outperforms traditional forecasting models in prediction tasks. These studies collectively demonstrate the significant advancements in traffic speed prediction using RNN-, LSTM-, and GRU-based models. The integration of different neural network architectures, attention mechanisms, and the consideration of spatiotemporal correlations have shown promising results in improving the accuracy and stability of traffic speed predictions. Overall, the literature indicates a growing consensus on the effectiveness of RNN variants for traffic speed prediction, with ongoing research focusing on enhancing the models' ability to capture complex spatiotemporal patterns and network-wide traffic behaviors.
# Dual Histogram Equalization Algorithm Based on Adaptive Image Correction
## 2 Histogram Equalization  
The main idea of the histogram equalization algorithm is to extend the probability density function (_PDF_) of the gray levels in the whole image and remap the gray levels of the pixels in the original image. First, the histogram of the original image F is normalized and its cumulative histogram is constructed. The conversion formula is mainly composed of the cumulative distribution function (_CDF_). Then, the cumulative histogram is quantized to the gray level of the output image. The three steps of the algorithm are detailed as follows:  
Count the percentage of pixels for each gray value to obtain the _PDF_ of the histogram:  
\[PDF(i)=\frac{n_{i}}{n},i=0,1,2\cdots k \tag{1}\]  
where \(i\) is the gray level of the input image, \(n\) is the total number of pixels in the input image, and \(n_{i}\) is the total number of pixels in the image with gray level \(i\).  
Accumulate the _PDF_ of each gray level to obtain the _CDF_ of the histogram:  
\[CDF(i)=\sum_{i=0}^{k}PDF(i) \tag{2}\]  
where _CDF_ is cumulative distribution function.  
Quantize the _CDF_ and map it to the output image:  
\[F(i)=start+(end-start)\times CDF(i) \tag{3}\]  
where _start_ and _end_ denote the minimum and maximum gray levels of the mapping interval, respectively.  
Based on the _CDF_, the traditional histogram equalization algorithm selectively enhances the gray levels that occupy more pixels and extends the distribution range of gray levels. However, it will over-enhance the gray levels with higher frequency, and it will merge the gray levels with fewer pixel points, resulting in the loss of details, which is also the drawback of traditional histogram equalization.
# The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review
## 3 6G-IoT: New Technologies and Emerging Services
### Reconfigurable Intelligent Surfaces  
Despite the fact that current 5G networks are more energy- and spectrum-efficient than their predecessors, it is anticipated that future 6G networks will utilize the innovative technology of Reconfigurable Intelligent Surfaces (RIS) to further enhance energy and spectrum efficiency [80]. The aim of RIS technology is to transform the wireless environment dynamically, thereby overcoming the stochastic nature of the propagation channel. In this sense, the wireless environment could be an additional variable in network design with specific, measurable, and modifiable properties. In addition, the use of RIS in THz communications could be more challenging but extremely promising in reducing the high propagation attenuation of ultra-high-frequency signals.  
Consequently, the employment of RIS technology could be incredibly useful in a variety of Agriculture 5.0 scenarios, as it could extend the monitoring range (i.e., the coverage area) and reduce the amount of power consumed, both of which are essential for IoT systems [81]. RIS can also be combined with wireless power transfer systems to focus transmitted energy on the intended sensors, enhancing the overall efficiency of the procedure [82]. Additionally, RIS could be integrated with Simultaneous Wireless Information and Power Transfer (SWIPT) systems, allowing the end device to concurrently harvest energy and decode information [83]. Therefore, it is anticipated that RIS technology will be an essential component for efficiently powering edge IoT devices in 6G-IoT systems, thereby providing a number of benefits for Agriculture 5.0 deployment scenarios.