esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Crop Yield and Reducing Energy Consumption in Greenhouse Control Using PSO-MPC Algorithm
## 2 Literature Reviews
MPC can find an optimal solution to achieve various objectives (e.g., maximizing crop yield, maintaining stable greenhouse climate, and minimizing energy costs) by modeling the system dynamics with reasonable accuracy. For example, in [PERSON] et al. [12], a nonlinear MPC algorithm based on particle swarm optimization (PSO) was proposed to minimize energy consumption while regulating climatic temperature variables. A similar approach was presented by [PERSON] et al. in [13], where MPC was used to minimize the total energy costs for greenhouse heating and cooling while controlling greenhouse climatic conditions. Comparison results demonstrated that MPC outperformed open-loop control algorithms. Furthermore, as proposed by [PERSON] et al. in [17], a more complex multi-objective compatible control (MOCC) strategy was proposed with an extended greenhouse model to achieve climate control through feedback control architecture. Multi-objective evolutionary algorithms (MOEAs) are applied to optimize an objective function that controls greenhouse climate parameters. Simulation experiments were conducted to validate the feasibility of the proposed algorithm through various comparative studies. An MPC algorithm for greenhouse air temperature predictive control using the particle swarm optimization algorithm was proposed by [PERSON] et al. in [18]. In this work, we propose an MPC algorithm to control factors related to heating, lighting, and ventilation in order to adapt greenhouse climatic conditions for optimizing crop yield while minimizing energy costs. Our MPC algorithm consists of a greenhouse climate model proposed by [PERSON] et al. in [19] that estimates greenhouse climatic factors (e.g., CO\({}_{2}\), temperature, humidity) on the basis of outdoor environmental information, and a crop yield model proposed by [PERSON] et al. in [20] that estimates crop growth on the basis of greenhouse climatic factors. Using these two models, we adopted the particle swarm optimization (PSO) algorithm proposed by [PERSON] et al. in [21] to tune controllable parameters and minimize an objective function that targets maximizing crop yield and minimizing energy costs. Our proposed approach does not require any sensor equipment, like [4; 5], nor high amounts of training data, like data-driven approaches [8; 9; 10; 15], rendering it easy to implement. Furthermore, our approach fully takes into account different types of lighting conditions (LED and HPS) that affect crop growth and targets while aiming to balance both energy and yield for achieving the best profits. The simulation results show that our proposed approach achieves better performance than that of other control strategies for different periods of time. Additionally, the PSO optimization algorithm performs better in optimizing the objective function in our proposed MPC strategy than the traditional genetic algorithm (GA) does.
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 4 Results
### Comparative Analysis of Alternative Models  
The proposed ST-GAT model is compared with several alternative models by evaluating their deflection prediction performance using MSE and MAE metrics. The hyperparameters for these models are summarized in Table 4. Graph convolutional network-long short-term memory (GCN-LSTM) and GCN-gated recurrent unit (GRU) both use GCN to capture spatial relationships, with LSTM and GRU, respectively, focusing on temporal feature extraction. LSTM typically excels in handling longer sequences, while GRU offers a more computationally efficient alternative. CNN, although effective in extracting spatial features, is less suited for time-series prediction compared to other models. RNN and BiLSTM are recurrent models with BiLSTM offering the advantage of processing data in both forward and backward directions. Temporal convolutional network (TCN), which applies convolutional techniques, presents an alternative to traditional RNNs for sequence data. Transformer architectures, known for their attention-based mechanism, efficiently handle sequential data without relying on recurrence, offering a distinct advantage in many applications like weather forecasting and speech recognition.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
Figure 13: Performance of ST-GAT neural network under different monitoring point combinations.  
Figure 14 compares these models using MSE and MAE with ST-GAT delivering the best performance on both metrics (MSE: 0.485 mm\({}^{2}\), MAE: 0.567 mm). The GCN-GRU and GCN-LSTM models also show relatively low errors (MSE: 1.331 mm\({}^{2}\) and 1.418 mm\({}^{2}\)), highlighting their effectiveness in capturing spatial-temporal relationships. However, CNN performs poorly, with significantly higher MSE (1.913 mm\({}^{2}\)) and MAE (2.88 mm), which is likely due to its limitations in handling sequential data. Other models, such as BiLSTM and RNN, produce the highest errors, with BiLSTM reaching an MSE of 3.283 mm\({}^{2}\) and an MAE of 3.240 mm. This suggests that while they handle temporal sequences well, they may struggle with complex spatial dependencies in this application. Similarly, Transformer, though effective in many sequence tasks, underperforms here with an MSE of 5.768 mm\({}^{2}\), showing difficulties in predicting continuous deflections. Overall, GAT-GRU proves to be the best-performing model, outperforming alternatives by efficiently utilizing both spatial and temporal data. The incorporation of attention mechanisms through GAT and temporal processing via GRU gives it a distinct advantage in reducing prediction errors in predicting time-series deflections at multiple monitoring locations.
# Deep Q-Learning for Two-Hop Communications of Drone Base Stations
## 6 Simulation and Results
However, it must be noted that after establishing a Q-table for QL model, the computational cost reduces tremendously. In the two-hop greedy model, for any user location all eight different directions must be examined. Additionally, to see the impact of the location of the MBS on the performance of the proposed models, we have conducted the simulation with an MBS, which is located closer to the target area, at location [300 m, 300 m]. The results are illustrated in Figure 8. From this figure, we can observe the following points:  
* The average performance for all models has increased, which is the direct impact of a closer MBS to the environment. * The average obtained performance in Q-learning and DQN are more than the greedy models, with the average of 6.42, 6.5, 6.59 and 6.98 bps/Hz, respectively. * Although Q-learning shows slightly better performance than two-hop greedy model (1.3% improvement), their performance still remain very close. * The average user-DBS distance in one-hop greedy model has not changed significantly, as in this model the drone tries to maximize the access link performance, not taking account the MBS location and the backhaul link. * As expected, the average user-DBS distance in both QL and DQL models has reduced compared to the further MBS location, which is a direct result of a shorter distance between the DBS and the MBS. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & 1 Hop Greedy & 2 Hop Greedy & QL & DQN \\ \hline
**Performance [bps/Hz]** & 6.03 & 6.3 & 6.28 & 6.80 \\ \hline
**User-DBS Distance [m]** & 28.4 & 138.2 & 135.5 & 159.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparing the models for a Macro Base Station (MBS) located at [1000 m, 1000 m]. Figure 7: The average distance between the user and the Drone Base Station (DBS) in meter.
## 2 Materials and Methods
### Data Analysis  
Considering that both a diagnosis based on the application of monolingual tests and the absence of a diagnosis in a population with a migration history cannot be considered reliable, a simple distinction between children with and without a DLD diagnosis could not provide a solid and sufficient benchmark for validation. Therefore, the results obtained in the standardised language tests were used to identify children at risk of DLD (below the cut-off in at least one of the standardised tests) and children without DLD (standardised test scores above the cut-off and no DLD diagnosis, see Section 2.1, Participants section). Moreover, to provide an accurate validation of each of the battery subtests (assessing lexical, phonological, or morphosyntactic abilities), a further, more specific criterion for classifying children at risk was applied for the various linguistic areas assessed individually. The participants were divided into two groups (risk/no risk) based on the specific clinical cut-offs described in the respective standardised tests' manual. Thus, the at-risk group consisted of those children without a formal diagnosis of DLD, but who showed at least one score significantly below the norm on standardisedtests in Italian, according to the manual of the respective test. The information on whether the child had scored below cut-off in the TFL comprehension subset determined the presence or absence for the lexical risk (children with lexical risk scored \(\leq\) 5 th percentile: \(n\) = 5), the BVL scores in the subtests on sentence completion and repetition determined the morphosyntactic risk (children with a morphosyntactic risk score \(\geq\) 2 SD: \(n\) = 14), and the phonological risk was based on the BVL nonword repetition task (children with a morphosyntactic risk score \(\geq\) 2 SD: \(n\) = 19).  
With the aim of assessing the overall capacity of the screening in identifying the risk of DLD, three different scores were created: (1) the Italian overall score (ITscore) which consists of the weighted average of the percentages of correct answers in the Italian tasks in the different linguistic areas (lexical, phonological, and morphosyntactic); (2) the Spanish overall score (SPscore) which consists of the balanced average of the percentages of correct answers in the Spanish tasks in the different linguistic areas; and (3) the bilingual overall score (BILscore), i.e., weighted average of the percentages of correct answers in the whole linguistic areas in the two different languages. More precisely, since in both languages there are two tasks assessing morphological skills (WSIR agreement and finiteness), the weighted average of the percentages of correct answers in both morphology tasks was calculated before the creation of the overall scores (i.e., each of the two tasks was assigned a weight of 0.5), so that each language area contributes equally to the final score.
# Wind Load Distribution in Float Photovoltaic System
## 1 Introduction  
For decades, photovoltaic power generation technology has mainly focused on research that increases the efficiency of solar panel modules, and as a result, it has achieved \"grid parity\", which is the point that the cost of photovoltaic power generation becomes the same as the cost from fossil fuels. However, considering the reality of the narrow land and an area-intensive industry, it seems difficult to build a large-scale power plant. Alternatively, floating photovoltaic generation is a technology derived to solve the problem of environmental destruction of such large-scale terrestrial photovoltaic power generation systems [1; 2; 3]. [PERSON] et al. found that the average power capacity of floating photovoltaic (PV) panels is 11 higher than the average capacity of PV panels installed on the ground [1].  
Recently, efforts have been actively made to preemptively prepare for climate disasters through carbon neutrality. Accordingly, research on PV structures, one of the new renewable energies, is actively underway.  
[PERSON] and [PERSON] confirmed the effects of wind load, flow characteristics, and inclination angle on floating PV panels and buoys through numerical analysis. As a result, the wind load increased as the inclination angle of the solar panel increased [4].  
[PERSON] et al. confirmed the structural safety evaluation of a 2 MW floating PV power generation structure through flow analysis and seaworthiness analysis. As a result, it was confirmed that the largest load occurred in the 1-row unit [5].  
[PERSON] et al. confirmed the effect of wind load on the solar panel array of a floating PV system through an indoor model experiment. Experiments have shown that the first and last rows of panels have the highest drag and lift coefficients because they are the first to encounter the wind [6].  
[PERSON] et al. confirmed the effect of wave angle due to wind load on offshore PV panels through numerical analysis. The wind load on the offshore PV panels was determinedthroughout the wave cycle. Additionally, to maximize PV panel performance, pontoon movement should be considered when designing the PV system [7].  
The load distribution caused by the wind load in the floating PV system was assessed using possible parametric studies with design parameters including wind speed, wind direction, and installation angle of PV modules. In this study, the design load was confirmed to install a floating PV power generation structure in salt-reclaimed land. In addition, the load effect on the structure was evaluated through the CFD analysis.
## 2 Theory and Method
### Theory of the PSO-GD Algorithm
In addition, the following conditions should be considered during the selection of accelerations to ensure stability [40]:  
\[\alpha_{1}+\alpha_{2}<4 \tag{16}\]  
\[\frac{\alpha_{1}+\alpha_{2}}{2}-1<\omega<1 \tag{17}\]  
By performing a first-order Taylor series expansion of the linear approximation equation, we obtain the following conventional GD inversion:  
\[d_{obs}-e_{obs}\cong G\delta m_{true}+g\Big{(}m_{ref}\Big{)} \tag{18}\]  
where \(d_{obs}\) is the observed data, \(e_{obs}\) is the observation error, \(g\) is considered the response value of parameter \(m_{ref}\) in the data-space model, \(\delta m_{true}=m_{true}-m_{ref}\), \(m_{true}\) is the true geoelectric model, and \(m_{ref}\) is an arbitrarily selected electrical resistance model. Simplification of Equation (18) gives  
\[G\delta m_{true}=\delta d_{obs}-e_{obs} \tag{19}\]  
where \(\delta d_{obs}=d_{obs}-g(m_{ref})\). Then, the model correction term is  
\[x_{i}^{t+1}=x_{i}^{t}+v_{i}^{t+1} \tag{11}\]0  
Introducing the model-correction term in the GD algorithm to PSO yields the following equation:  
\[x_{i}^{t+1}=x_{i}^{t}+v_{i}^{t+1} \tag{11}\]1  
\[x_{i}^{t+1}=x_{i}^{t}+v_{i}^{t+1} \tag{11}\]2  
where \(\alpha_{3}^{\min}\) and \(\alpha_{3}^{\max}\) are the minimum and maximum values of the gradient descent acceleration parameter, respectively. To ensure that the global PSO and GD algorithms take precedence at the early and late stages, respectively, a weight-based improvement strategy is further introduced:  
\[x_{i}^{t+1}=x_{i}^{t}+v_{i}^{t+1} \tag{11}\]3  
A cosine probability function, \(w^{k}\), which dynamically decays from 1 to 0 as the number of iterations increases, is used as a weighting term to realize a dynamic transition from the GD algorithm to the PSO algorithm. The calculation equation for \(w^{k}\) is given below:  
\[x_{i}^{t+1}=x_{i}^{t}+v_{i}^{t+1} \tag{11}\]4  
where \(k\) and \(\max(k)\) are the current and maximum numbers of iterations, respectively. In the PSO-GD algorithm, the GD algorithm guarantees the convergence speed in the early stage, and the global convergence of the PSO algorithm prevents it from falling into a local minimum.
# Segmentation of Rice Seedings Using the YCrCb Color Space and an Improved Otsu Method
## 4 Conclusions  
This paper supplies an improved Otsu method for rice seedling segmentation in the YCrCb color space. We built the color-index 2 Cg-Cr-Cb based on the YCrCb color space rather than the RGB color space in the image graying process, which reduces the influence of illumination variation on image graying. We then used the cumulative probability of rice seedling occurrence as an adaptive weight on the background class variance of within-class variance. Comparing the weighted method with the traditional Otsu method and the NVE method, the threshold determined by the weighted method is close to the desired threshold. The Otsu method's threshold biases towards the background class, which misclassifies background pixels as seedling pixels, while the NVE method's threshold causes over-segmentation. We constrained the search range to improve the efficiency of the weighted method. Experimental results demonstrate that the proposed method can achieve better segmentation performance than the other two algorithms while reducing the cost of calculating the threshold which can provide reliable seedling row information for visual navigation system of automated rice tranplanters. Moreover, accuracy navigation line can ensure the transplanter plants seedlings along the seedling row without damaging seedlings and improve the working quality of rice transplanter, which are beneficial to increasing rice yield.  
Methodology, J.L.; Data Curation J.L. and Y.W.; Validation, J.L. and J.Y.; Resources, L.L. and S.Z.; Writing-Original Draft Preparation, J.L.; Writing-Review and Editing, L.L. and D.Z.; Funding Acquisition, D.Z.  
This work was supported by the National Key Research and Development Program of China (No. 2018 YFD0700304), the Natural Science Fund of Anhui Province (No. 1708085 QF148 and No. kj2016A233) and the Science Foundation of Anhui Agricultural University (No. 2016 ZR2016).  
The experiment fields and test images in this work was supported by the National Project on Transformation of Agricultural Scientific and Technological Achievements (No. 2014C30000162).  
The authors declare no conflict of interest.
# PCONet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
## 2 System Model
### Uplink MU-MIMO System  
Without loss of generality, we consider the uplink MU-MIMO system consisting of one BS equipped with \(N_{r}\) antennas and \(K\) UEs as depicted in Figure 1. For convenience, \(\mathcal{K}=\{1,\dots,K\}\) denotes the set of UEs. The \(k\)-th UE equipped with \(N_{k,t}\) antennas transmits \(N_{k,s}\) modulated data streams. We denote \(N_{t}=\sum_{k=1}^{k}N_{k,t}\), \(N_{s}=\sum_{k=1}^{K}N_{k,s}\) as the total numbers of transmit antennas and independent data streams of all UEs, respectively. For simplicity, we consider the case where \(N_{k,t}\) and \(N_{k,s}\) are constants. The channel matrix can be represented as \(\mathbf{H}\triangleq[\mathbf{H}_{1},\dots,\mathbf{H}_{K}]\in\mathbb{C}^{N_{t} \times N_{t}}\), where \(\mathbf{H}_{k}\in\mathbb{C}^{N_{t}\times N_{k,t}}\) denotes the channel matrix from the \(k\)-th UE to the BS.  
Figure 1: Block diagram of an uplink MU-MIMO wireless system with an \(N_{r}\)-antenna BS and \(K\) UEs each with \(N_{k,t}\) antennas.  
The BS firstly obtains the CSI of all UEs and calculates the optimal precoding matrices \(\mathbf{F}_{k}\in\mathbb{C}^{N_{k,\times}N_{k,s}}\) of the \(k\)-th (\(k\in\mathcal{K}\)) UE which will be fed back to the UEs for the deployment of uplink data transmission. The received signal vector \(\mathbf{y}\in N_{r}\) at the BS can be represented as  
\[\mathbf{y}=\mathbf{H}_{k}\mathbf{F}_{k}\mathbf{s}_{k}+\sum_{i\
eq k}\mathbf{H} _{i}\mathbf{F}_{i}\mathbf{s}_{i}+\mathbf{n} \tag{1}\]  
where \(\mathbf{s}_{k}\in\mathbb{C}^{N_{k,\times}1}\) represents the data symbol vector with a covariance matrix \(\mathbf{\Phi}_{\mathbf{s}_{k}}=\mathbb{E}\big{[}\mathbf{s}_{k}\mathbf{s}_{k}^ {H}\big{]}=\mathbf{I}_{N_{k,s}}\) and \(\mathbf{n}\in\mathbb{C}^{N_{r}\times 1}\) is the received complex white Gaussian noise vector consisting of independent and identically distributed (i.i.d.) elements with the distribution \(\mathcal{CN}\big{(}\mathbf{0},\sigma^{2}\mathbf{I}_{N_{r}}\big{)}\). The noise covariance matrix is \(\mathbf{\Phi}_{n}=\sigma^{2}\mathbf{I}_{Nr}\).  
The precoding and the linear detection are jointly optimized to obtain the best system performance. The recovered data symbol of the \(k\)-th UE can be represented by  
\[\mathbf{\hat{s}}_{k}=\mathbf{G}_{k}\mathbf{y} \tag{2}\]  
where \(\mathbf{G}_{k}\) represents the detection matrix of the BS. The centralized implementation in [9] aims to jointly optimize the transceiver to eliminate the MU interference in the uplink system by minimizing the mean squared error (MSE) between the estimated symbols and the transmitted symbols. The MSE between the estimated symbols and the actual symbols for the \(k\)-th UE is given by  
\[\boldsymbol{\eta}=\sum_{k=1}^{K}\mathbb{E}\big{[}(\mathbf{\hat{s}}_{k}-\mathbf{ s}_{k})^{H}(\mathbf{\hat{s}}_{k}-\mathbf{s}_{k})\big{]} \tag{3}\]
## 2 Materials and Methods
### Datasets
#### 2.1.4 Contourlet Parametric (CP) Image  
The Rician Inverse Gaussian (RiIG) distribution is proposed by [PERSON] et al. [25]. It is a mixture of Rician distribution and Inverse Gaussian distribution. The PDF of RiIG distribution is given by  
\[P_{RiIG}(r)=\sqrt{\frac{2}{\pi}\alpha^{\frac{3}{2}}}\delta\ exp(\delta\gamma) \times\frac{r}{(\delta^{2}+r^{2})^{\frac{3}{4}}}K_{\frac{3}{2}}\left(\alpha \sqrt{\delta^{2}+r^{2}}\ \right)I_{0}(\beta r) \tag{1}\]  
where \(\alpha\), \(\beta\) and \(\delta\) are the three parameters of this PDF. \(\alpha\) controls the steepness of the distribution; \(\beta\) regulates the skewness; \(\beta\) < 0 suggests skewed to the left; \(\beta\) > 0 suggests skewed to the right, and \(\delta\) is a dispersion parameter similar to the variance in the Gaussian distribution. The symbol \(r\) denotes the corresponding image which is subjected to the model by RiIG distribution. Moreover, \(\gamma=\sqrt{\delta^{2}-\beta^{2}}\); \(I_{0}(.)\) is the modified Bessel function of the first kind, and \(K_{3/2}(.)\) is the modified Bessel function of the second kind. A few realizations of the RiIG PDFs for a few selected values of the parameters are shown in Figure 4.  
The Contourlet Parametric (CP) image is constructed from the RiIG parameter (\(\delta\)) map, which is attained by employing a square sliding window to process the Contourlet coefficient image. This process is depicted in [14], where the author used this process to construct Nakagami parametric images with the image parameters being calculated for each image. It should be noted that in [14; 31; 32], the parametric images are obtained in the spatial domain, whereas we generated the images in the Contourlet transform domain. The results observed in previous studies recommend that the most appropriate sliding window for constructing the parametric image is a square with a side length equal to three times the pulse length of the incident ultrasound. In this study, the parametric imaging employed a 13 \(\times\) 13 pixel sliding window within the Contourlet sub-band coefficient image to analyze each local RiIG parameter (\(\delta\)). The employed sliding window size should be larger than the speckle and should discriminate variations of the local structure in tumors. The window was moved through the entire Contourlet sub-band coefficient image in steps of 1 pixel, with the local RiIG parameter (\(\delta\)) assigned as the new pixel located at the center of the window at each position. This process yielded the RiIG parametric image as the map of RiIG parameter \(\delta\) values. The suitability of the RiIG statistical model over the Nakagami statistical model is shown in Figure 5 by CP images and percentile probability plot (pp-plot) [33; 34; 35].  
Figure 4: Examples of the PDFs residing in the RiIG model with various \(\alpha\), \(\beta\), and \(\delta\) values.