esa-sceva/satcom-chunk-collection huggingface dataset:
# Forecasting Cost Risks of Corn and Soybean Crops through Monte Carlo Simulation
## 2 Materials and Methods
### Monte Carlo Simulation  
The main production costs per bag of soybean and corn were investigated in relation to all the variables through cumulative frequency analysis using the Crystall Ball software version 11.1.2.1.0 [32]. To analyze the probability of cost variation and gross net revenue, a Monte Carlo simulation (MCS) was used through a stochastic approach [5; 15; 21; 33]. The average result of the MCS for each variable was obtained using Equation (1).  
\[\mathrm{a_{m}}=\frac{1}{\mathrm{n_{i}}}\sum_{i=1}^{\mathrm{n}}x_{i} \tag{1}\]where \(a_{m}\) is the average result of the MCS for the variable, x represents the individual result of each simulated iteration, and n is the number of simulations (iterations).  
\(a_{m}\) is the normalization factor, indicating that the sum of the results is divided by the number of simulations carried out.  
\(1/r\) is the estimated value or the average of the simulated results. It can represent an estimate of the expected value of a variable or the average result of several simulations.  
N is the total number of simulations, the average can be given dividing by N.  
\(x_{i}\): represents the values obtained in different simulations or samples.  
Dividing by the total number of simulations (or the normalization factor, if r) gives the average of the simulated results, which is an estimate of the expected value of the model or system. The formula is used to estimate the mean or expected value of a random variable that is the result of the simulation.  
In this research, 50,000 iterations were made, which is the maximum number of iterations provided by the software and it is a significant number for this specific problem. Similar approaches were used in several studies, e.g., [5; 21]. To forecast future events, the Predictor tool version 11.1.2.1.0 was used, which is a stochastic simulation tool available from the software. It was used to make predictions based on time series data by analyzing historical data from which it was possible to identify trends and seasonal patterns. These insights were then used to forecast the most probable outcomes.  
The scenarios were forecasted using the software for a period of 24 months, based on data from 2018 to 2022. The MCS uses quantitative methods based on time series for forecasting [32], namely, Simple Exponential Smoothing (SES), Autoregressive Integrated Moving Average (ARIMA), Damped Trend Non-Seasonal (DTN-S), Double Moving Average (DMA), Non-Seasonal Smoothed Trend (TANS), and Double Exponential Smoothing (DES). The distribution used by the predictor was the triangular distribution. This model was employed due to the low dispersion of the values presented in the descriptive analysis of the MCS.  
Each method has its strengths and is chosen based on the characteristics of the time series data (e.g., trend, seasonality, stationarity).
## 1 Introduction
To tackle this problem, graph convolutional networks (GCNs) that can effectively handle non-Euclidean relations are integrated with RNNs [12] or CNNs [13] to embed prior knowledge of the road network and capture the correlations between sensors. The graph convolution network here represents the road network structure as a fixed weighted graph. [PERSON] et al. [14] integrated Wavenet [15] into the GCN to capture the dynamic spatial-temporal correlations of traffic data, while using an adaptive adjacency matrix to obtain hidden spatial dependencies in the road network. However, there are still some limitations in these methods: (i) RNN-based models are challenging to train well [16] due to the problem of gradient disappearance or gradient explosion, and the receptive field of RNNs is limited; (ii) many existing methods only consider localized spatial correlations but ignore non-local ones; and (iii) they do not utilize more complicated traffic-related features such as the existing periodicity, repeating patterns, and external factors. To capture the dynamic complex spatial-temporal correlations more effectively, we propose a novel global spatial-temporal graph convolutional network called GSTGCN to predict urban traffic speed, which consists of three independent spatial-temporal components with the same structure and one external component. Each spatial-temporal component contains a dynamic temporal module and a global correlated spatial module. The main contributions of this paper are as follows:  
* We develop a dynamic temporal module, which consists of multiple residual blocks stacked by dilated causal convolutions. It has a long receptive field and can capture dynamic temporal correlations effectively. * We design a global correlated spatial module, which contains a localized graph convolution and a global correlated mechanism. It is proposed to simultaneously capture the local spatial correlations and global ones between sensors in the traffic network. * The whole model fuses the output of the three spatial-temporal components considering multiple temporal dependencies and takes external factors into account to predict traffic speed. The experimental results demonstrate that the proposed GSTGCN outperforms the eight baselines.
## 2 Nutritional Properties of Honey Corresponding to Its General and Specific Chemical Composition
### General Chemical Composition of Honey
#### 2.1.3 Proteins  
The content of protein in honey (0.5%) has been attributed to both bees and plants and it related to the enzymes and free amino acids contained in the bee's salivary glands or in the nectar and pollen of floral sources [53]. Therefore, we can distinguish between proteins originating from bees and the proteins that come from floral sources. Honey proteins that originated from bees consists of enzymes secreted from the salivary and hypopharyngeal glands of worker bees [61] during ripening, the most well-known being invertase (\(\alpha\)-glucosidase), amylase (diastase) and glucose oxidase. Along with these enzymes, the protein content of honey is also represented by individual amino acids (around 20 aminoacids), of which proline is taught to be the most important [60] along with antimicrobial proteins such as bee-Defensine-1 and royal jelly protein [65]. Invertase is important for the sugar stability matrix, diastase is a parameter of honey freshness [66], while glucose oxidase has the role of producing hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), which is responsible for the antimicrobial properties of honey [55]. Proline amino acid con-centration in honey varies from 50% to 85%, and results from the salivary secretions of honeybees [9]. Besides the fact that its main role is to protect membranes and proteins from stress conditions, and therefore its role in antioxidant activity, proline is also used to evaluate the degree of honey maturation, which is an indicator of quality [59].  
Several studies have analysed the protein content of various honey samples. [PERSON] et al. (2018) [67], revealed that the protein content of the tested samples (5 types of Australian honey) varied from 900 to 2200 \(\mathrm{\SIUnitSymbolMicro g}\)/g. Among these, _Agastache rugosa_ honey had the highest protein content, while Manuka honey the lowest. A recent study [42] reviewed the chemical composition and quality parameters of Eucalyptus honey, underlining that the amount of protein found in European Eucalyptus honeys (0.9-1.24 mg/g honey), is higher compared to other honey varieties such as Orange (0.58-0.66 mg/g honey), Chestnut (0.59-0.71 mg/g honey) or Acacia (0.1 mg/g honey). Moreover, [PERSON] et al. (2017) [68], observed during a comparative proteomic profiling that the content of protein in honey Blossom (0.016 g/100 g\({}^{-1}\)) is lower than in Honeydew honey (0.042 g/100 g\({}^{-1}\)), from the same botanical species of _Mimosa scabrella Bentham_. This latter observation can be a turning point for the assessment of the protein profile for the differentiation between various honey types.
## 3 Computational Methods
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Space Group** & **a(Å)**, **b(Å)**, **c(Å)**, \(\alpha\), \(\beta\), \(\gamma\) & **Volume (Å3)** & **No. of Atoms** \\ \hline
**C1** & _P_-1(Ci) & 6.954, 7.503, 7.589, 81.166\({}^{\circ}\), 75.522\({}^{\circ}\), 84.257\({}^{\circ}\) & 378.05 & 31 \\ \hline
**C2** & _P_-1(Ci) & 7.178, 7.424, 7.808, 81.007\({}^{\circ}\), 71.428\({}^{\circ}\), 90.952\({}^{\circ}\) & 388.58 & 39 \\ \hline
**C3** & _P_-1(Ci) & 7.101, 7.430, 7.609, 78.761\({}^{\circ}\), 71.657\({}^{\circ}\), 83.958\({}^{\circ}\) & 373.38 & 31 \\ \hline
**C4** & _Pnma_(D\({}_{\rm 2h}\)16) & 9.757, 11.134, 13.728, 90.000\({}^{\circ}\), 90.000\({}^{\circ}\), 90.000\({}^{\circ}\) & 1491.35 & 124 \\ \hline
**C5** & _Pnma_(D\({}_{\rm 2h}\)16) & 9.770, 11.166, 13.746, 90.000\({}^{\circ}\), 90.000\({}^{\circ}\), 90.000\({}^{\circ}\) & 1499.60 & 124 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Lattice parameters of the five crystals. For the calculation of interband optical properties in the form of frequency-dependent complex dielectric function (\(\hbar\omega\)) = \(\varepsilon_{1}(\hbar\omega)\) + i\(\varepsilon_{2}(\hbar\omega)\), the imaginary part \(\varepsilon_{2}(\hbar\omega)\) is calculated first according to  
\[\varepsilon_{2}(\hbar\omega) = \frac{e^{2}}{\pi n\omega^{2}}\int_{BZ}dk^{3}\sum_{nl}|\langle\psi_ {n}(k,r)|-i\hbar\
abla|\psi_{l}(k,r)\rangle|^{2}\ f_{l}(k)[1-f_{n}(k)]\delta[ E_{n}(k)-E_{l}(k)-\hbar\omega] \tag{3}\]  
where \(l\) and \(n\) are for the occupied and unoccupied states respectively. \(\psi_{n}(k,r)\) are the ab initio Bloch functions from OLCAO calculation using a full basis (FB) set and a larger k-point sampling. \(f_{l}(k)\) and \(f_{n}(k)\) are the Fermi distribution functions. The real part \(\varepsilon_{1}\) (\(\hbar\omega\)) is obtained from the imaginary part \(\varepsilon_{2}\) (\(\hbar\omega\)) through Kramers-Kronig transformation [46]. The combination of using VASP and OLCAO packages with two different basis expansions is highly effective in revealing the subtle features in the material properties especially on the atomic scale details. The method is particularly useful for highly complex crystals or non-crystalline materials as demonstrated in some of recent publications [35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45].
# Experimental Study on Grouting Visualization of Cover Layer Based on Transparent Soil
## 3 Grouting Test
### Test Process  
The main procedures for conducting the transparent soil grouting test are as follows: (1) Preparation of transparent soil materials: Prepare a sufficient quantity of transparent soil according to the method described in Section 2.1. (2) Inspection of grouting pipeline and sensors: Ensure that all valves in the grouting pipeline are open and properly sealed. Check the integrity of the signal cables for all sensors to confirm there is no damage. (3) Laser setup: Turn on the laser and adjust its power and incidence angle to generate a high-quality speckle field. Ensure that the laser sheet, the centerline of the model slot, and the grouting axis are properly aligned to guarantee effective laser projection. (4) Pressure valve adjustment: Adjust the pressure-limiting valve to control the maximum grouting pressure in the pipeline and maintain pressure stability. (5) Image acquisition setup: Adjust the image capture device to ensure high-quality imaging of the observation surface. Firmly attach a ruler to the surface of the model slot to aid in the calibration and identification process during transparent soil image analysis. (6) Slurry preparation: Prepare the stable slurry according to the specified mix ratio. Stir thoroughly, transfer to the storage tank, and configure the return slurry pipeline. (7) Start data recording: Activate the timed shutter switch to begin recording the entire grouting process. (8) Pressure ramp-up grouting: Close the return valve and open the grouting valve to initiate the formal grouting process. To prevent early overflow of slurry at the transparent soil surface and to clearly visualize the diffusion process, the pressure increase is divided into three stages: 50 kPa, 100 kPa, and 200 kPa. Pressure is applied continuously until it reaches 50 kPa, maintained for 50 s, then increased to 100 kPa for 100 s, and finally to 200 kPa for another 100 s. At each stage, ensure the grouting rate remains below 2 mL/s for at least 30 s. The total grouting duration is 300 s.
# A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network
## 4 MAC/NET with Updated Genetic Algorithm--A Cross-Layer Approach (MNUG-CLA)
### Updated Genetic Algorithm (UGA) for GPSR Protocol
The probability is given by,  
\[w_{i}=\frac{fit_{i}}{\sum_{j=1}^{N}fit_{i}} \tag{3}\]  
Once the crossover is applied with probability (Pc) to produce the pair of offspring, the combination of the middle part and the first part is done and vice versa. The probability of mutation (Pm) can be fed to every offspring by the utilization of a unary operator, which had the chance to modify it to another mutation structure. This method is also known as a two-point mutation point. The final phase is the stopping criterion, with the adoption of the dynamic concept. The maximum threshold (max_th) can be fixed with the adoption of the stagnating state (min_th). These two thresholds can be adopted with empirical values. The control channel conveys emergency alerts and data negotiator signals in a multi-channel environment; meanwhile, the service channel communicates with data. When an emergency occurs, all locations respond to the control channel, and a control packet is created, which passes across the control channel based on the importance of the node. During the data time of negotiation, the node determines the relative importance and then correlates it to the protocol-specific channel-access characteristics. The specifications of the delivery channel selected by the originator, as well as the projected time of data transmission, are sent, including the data negotiation message. The broadcaster then changes to the designated service channel and listens for the receiver to send the ready (RD) signal. If the channel is available on the recipient side, it switches to the designated channel and sends the ready (RD) signal. The transmitter starts transmission after obtaining the ready signal, and the receiver sends back an acknowledgement through the control channel after collecting all data, prompting all nodes to change their local tables. Each node keeps local tables with information about its neighbors and a list of available channels. The channel list includes information on free channels, channels that have been tagged as busy for an expected time, and networks that have been used previously. The service channel for negotiation is assigned to the channel with the lowest load/relatively free bandwidth. If the requested channel is not accessible at the receiver, the receiver responds with a negative acknowledgement and the available channel data after one slot. When the transmitter receives this, it switches to the next available channel and begins transmitting. The free channel list on all other nodes is updated.
## 2 Materials and Methods
### Unconsolidated-Undrained Triaxial Testing and Analysis  
Unconsolidated-undrained (UU) triaxial compression tests were conducted in accordance with BS 1377: Part 7 (1990) [30] to evaluate the shear strength characteristics of the treated and untreated soil samples. All samples were tested at the end of their designated wetting-drying (WD) cycles. The samples were placed in a triaxial chamber and subjected to a constant confining pressure (cell pressure) of 50 kPa. Axial loading was applied under undrained conditions, and the test was continued until either failure occurred (indicated by a peak in deviator stress) or an axial strain of 20% was reached. The tests in triplicate were conducted at three moisture states (Figure 1).  
* \(-1500\) kPa matric suction: This represents a dry-season or post-compaction condition in which matric suction contributes to higher strength.
* \(-33\) kPa matric suction: Samples were first fully saturated and then allowed to dry partially. This condition simulates field capacity, such as a few days after the rainfall.
* Full saturation: Achieved through prolonged soaking to eliminate matric suction, this represents the worst-case scenario, such as heavy rainfall or flooding, when strength is lowest due to the absence of suction.  
Each sample was tested at room temperature (\(\sim\)20 \({}^{\circ}\)C), and axial loading was applied using a compression machine at a constant strain rate of 0.1 mm/min. For soft or fully saturated samples, a slower rate of 0.01 mm/min was used to reduce the risk of sudden failure and to ensure accurate observation of stress-strain behaviour. The applied strain rate was selected to maintain undrained conditions by preventing significant pore-pressure dissipation during the test. From each test, the deviator stress-axial strain response was recorded. The undrained shear strength (\(S_{\text{u}}\)) was calculated as half of the peak deviator stress at failure. The undrained secant modulus of elasticity (\(E_{50}\)) was calculated using Equation (2), and the modulus of toughness (\(U_{\text{t}}\)) was calculated using Equation (3).  
\[E_{\text{50}}=\frac{\mathbf{0.5}\times\Delta\sigma_{\text{max}}}{\varepsilon_ {\text{50}}} \tag{2}\]  
\[\mathbf{U_{t}}=\int_{\mathbf{0}}^{\varepsilon_{\text{failure}}}\Delta\sigma( \varepsilon)d\varepsilon \tag{3}\]  
where \(\Delta\sigma_{\text{max}}\) is the maximum deviatoric stress, \(\varepsilon_{\text{50}}\) is the strain corresponding to 50% of the maximum deviatoric stress, \(\varepsilon\) is the strain, and \(\Delta\sigma\) is the deviatoric stress.  
After testing, samples were oven-dried to determine the actual moisture content. Visual observations of each specimen's failure mode (e.g., bulging and shear plane development) were also made to assess ductility and brittleness qualitatively.
# Downlink Training Design for FDD Massive MIMO Systems in the Presence of Colored Noise
## 8 Conclusions  
This paper has addressed the problem of DTS design and CSI estimation of the FDD MaMi systems in the presence of colored noise environments. To this end, a feasible DTS has been proposed, which is constructed from the effective unitary eigenvectors of channel covariance and noise covariance matrices. In addition, an analytical solution for the MSE based on the MMSE channel estimation was provided. Furthermore, a random matrix theory method was used to obtain an explicit analytical solution for the achievable sum rate of the RZFBF precoder with low-complexity. The MSE and the sum rate performances of the proposed DTS design were compared with the conventional DTS designs based on white noise. The results showed that the porpoised DTS design provides a significant gain in the CSI estimation performance, and thus, the sum rate of FDD operation in MaMi systems is considerably improved in comparison to the conventional designs. The results also demonstrated accurate matching between the theoretical and simulated results, which underpin the main contributions of this paper. The proposed DTS design would allow the FDD to operate with MaMi systems in more realistic scenarios, particularly with limited coherence time and colored noise environments.  
Conceptualization, M.A.N. and M.A.; methodology, M.A.N. and M.A. and B.M.M.; software, M.A.; validation, M.A.; investigation, M.A.N., B.M.M. and S.H.A.; resources, S.H.A.; data curation, M.A., N.K.N.; writing--original draft preparation, M.A.N. and B.M.M.; writing--review and editing, M.A., N.K.N.; visualization, M.A., N.K.N. and T.B.; project administration, N.K.N.; funding acquisition, N.K.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by UPM PUTRA BERIMPAK under Fundamental Research Grant 9584300.  
We would like express our heartfelt thanks to all of the anonymous reviewers for their efforts, valuable comments and suggestions. The authors would like to acknowledge University of Baghdad for general support.  
The authors would like to declare that there is no conflict of interest.
## 5 PV Power Output Forecasting  
The authors proposed the power output forecasting technique of PV system based on hourly solar radiation forecasting one day ahead by using weather reported data, Fuzzy theory and Recurrent Neural Network (RNN) [8], as shown in Figure 3. This chapter shows the summary of the PV power output forecasting method. Since the detailed value of solar radiation prediction is not reported by the Meteorological Agency, the Fuzzy theory is adopted to determine the hourly solar radiation by using weather reported data such as humidity and cloud cover. This technique also includes the correction method for annual solar radiation forecast errors. Additionally, since the power output of the PV system fluctuates depending on weather conditions, the training process of NN tends to be unstable. However, Recurrent NN (RNN) is known to be a good tool for time-series data forecasting. The RNN has \(l\) = 24 and \(m\) = 1-30 neurons in the input layer and hidden layer, and \(n\) = 24 neurons in the output layer. These neurons are connected with linear coupling, and \(x_{1}\)-\(x_{l}\) are input data to RNN. Input data \(x_{1}\)-\(x_{l}\) (24 h \(\times\) 30 days) for the training dataset are determined by Fuzzy theory. There are connection weights between each neuron. The output of hidden layer neurons are converted to nonlinear values by hyperbolic tangent sigmoid-function. RNN has a context layer. This layer contains a copy of the hidden layer with time-delay lines, and added as feedback structure [20]. The context layer reflects both the input and output layer's information to the structure of RNN. In this research, information of one time-step lag from the output units is used in the context layer. After each output unit provides the information relating to one time-step interval, the training of the network becomes stable. Based on conventional research [21], the authors think that it is convenient to make the forecast model by a trial-and-error approach. As a consequence, the past information is maintained to RNN with the progress of learning. The proposed technique for the application of RNN is trained by power output data based on Fuzzy theory and weather reported data. Because the Fuzzy model determines the solar radiation forecast data, RNN will train the power output smoothly.
###### Abstract  
The mobile satcom antenna (MSA) enables a moving vehicle to communicate with a geostationary Earth orbit satellite. To realize continuous communication, the MSA should be aligned with the satellite in both sight and polarization all the time. Because of coupling effects, unknown disturbances, sensor noises and unmodeled dynamics existing in the system, the control system should have a strong adaptability. The significant features of terminal sliding mode control method are robustness and finite time convergence, but the robustness is related to the large switching control gain which is determined by uncertain issues and can lead to chattering phenomena. Neural networks can reduce the chattering and approximate nonlinear issues. In this work, a novel B-spline curve-based B-spline neural network (BSNN) is developed. The improved BSNN has the capability of shape changing and self-adaption. In addition, the output of the proposed BSNN is applied to approximate the nonlinear function in the system. The results of simulations and experiments are also compared with those of PID method, non-singularity fast terminal sliding mode (NFTSM) control and radial basis function (RBF) neural network-based NFTSM. It is shown that the proposed method has the best performance, with reliable control precision.  
mobile satcom antenna; inertial sensor; stabilized platform; B-spline neural network; terminal sliding mode (TSM) +
Footnote †: journal: Sensors