esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The PPV resulting from blasting operations--a critical environmental issue--needs to be predicted accurately for any blasting operations. The present research was centred upon the simulation of PPV in four quarry sites located in Malaysia. It took into account the input variables of stemming, powder factor, maximum charge per delay, burden-to-spacing ratio and the distance from the blast face. In addition, GEP was applied to the development of a predictive model, and the MC simulation was employed for the purpose of simulating all ranges of PPV results. All probable combinations of correlations and values of the variables were examined in the MC simulation process, and they were also analysed by carrying out 10,000 trials.  
The results of the performance indices achieved for the developed GEP equation (training datasets: RMSE = 4.2814 and R\({}^{2}\) = 0.7283, and testing datasets: RMSE = 4.0344 and R\({}^{2}\) = 0.6823) confirmed its  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & **BS** & **ST** & **PF** & **MC** & **D** \\ \hline Correlation coefficient & 0.02 & 0.17 & \(-0.08\) & 0.09 & \(-0.96\) \\ \hline \end{tabular}
\end{table}
Table 6: Correlation sensitivity analysis (CSA) for PPV results in MC simulation.  
Figure 10: Regression sensitivity analysis (RSA) for the inputs.  
high capacity in terms of predicting the PPV value and also its applicability as an input equation into the MC simulation technique.  
The range of PPV was simulated around 35 mm/s, and it starts from 1.13 mm/s up to 34.58 mm/s. Moreover, based on the results of the PPV, it can be stated with 100% confidence that the PPV is less than 50.8 mm/s, which is mentioned as the allowable maximum amount of PPV for surface mine blasting. Notably, based on the results of the RSA, the MC and ST have a direct relation with PPV, while any increase in the values of D and PF would lead to a reduction in the value of PPV. Moreover, based on the outcome of RSA and CSA, it was concluded that the result of the PPV is highly sensitive to the amount of D. These results are in good agreement with previous studies. The ST and PF are found to be the second and third most influential factors for PPV. Although BS was considered as an effective factor on PPV, its changes up to 50% were almost ineffective on PPV values. It is notable that the proposed equation is not applicable for other cases; however, the research framework can be adopted for other sites with different variables, values and functions for the purposes of predicting and simulating PPV.  
Conceptualization, D.J.A.; Data curation, A.A.; Formal analysis, A.M.; Supervision, K.Y.; Writing--review & editing, A.H.; Methodology, M.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors would like to express their sincere gratitude to University Malaysia Kelantan for providing financial supports. The authors confirm that this article content has no conflict of interest.
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 3 MIMO Detection: A System Model
### Conjugate Gradient Detector  
The CG method is preferred for large-scale systems due to its rapid convergence. It iteratively computes  
\[\hat{\mathbf{s}}^{(n+1)}=\hat{\mathbf{s}}^{(n)}+\alpha^{(n)}\mathbf{p}^{(n)}, \tag{15}\]  
where \(\mathbf{p}^{(n)}\) is the search direction and \(\alpha^{(n)}\) is the step size. The directions satisfy the \(\mathbf{A}\)-conjugacy condition:  
\[(\mathbf{p}^{(n)})^{T}\mathbf{A}\mathbf{p}^{(j)}=0\quad\text{for }n\
eq j. \tag{16}\]  
CG is computationally efficient but involves matrix-vector operations that may be burdensome in hardware-constrained environments [11; 32].
## 3 Results and Discussion
### Case Study  
Two ten-layer deposition cases were compared under the same process parameters: \(WFR=4\) m/min and \(TS=0.4\) m/min. One has the same heat dissipation condition as that in Group III-2 and the other as that in Group II. In the experiments, the interlayer temperature was retained at room temperature to avoid its effect on the bead geometry [15]. Figure 8 shows that the effective bead width and the total wall height are 9.9 mm and 26.8 mm, respectively, with thermoelectric cooling, compared to 12.4 mm and 21.6 mm without thermoelectric cooling. The bead tends to be narrower and taller when the thermoelectric cooling is employed, which is particularly beneficial for the fabrication of thin-walled structures in terms of geometric accuracy and productivity. For example, for when the dimensions of the final part are 100 mm (length) \(\times\) 8 mm (width) \(\times\) 100 mm (height), the comparison between the two test cases are given in Table 3. When thermoelectric cooling is employed, the bead width error is reduced by 56.8% and the material utilization is increased by 16.3% because a smaller amount of material needs to be removed through post-processing. On  
The effect of thermoelectric cooling on microstructures was also analyzed. Specimens were taken from both the middle and the bottom parts of the two walls. As shown in Figure 9a,b, the microstructures in the middle parts are characterized by dendrite grains. The average grain size is 5.97 \(\upmu\)m with thermoelectric cooling, which is much finer than that without thermoelectric cooling (7.96 \(\upmu\)m). This is because the former undergoes a much higher cooling rate that helps to refine the grain size by increasing the number of particles that nucleate grains and by affecting the development of constitutional undercooling [27]. The microstructures in the bottom parts, as shown in Figure 9c,d, are mainly slender columnar grains perpendicular to the substrate as the direction of the grain growth is mainly along the largest temperature gradient [28]. Their difference is not obvious.
## 4 Comparative Simulation Analysis
### Comparative Analysis with DLC
The absolute values of the vehicle body vertical velocity obtained by the MPC strategy were, in most of the cases, lower than those obtained by the DLC strategy. The prediction curve is finally stable near the zero value. The changes in the vertical velocity of the front and rear wheels are shown in Figure 9a,b, respectively. The changes in the vertical velocity of the wheels under the two control strategies are basically the same, and the final values of the wheel vertical velocity prediction curve are also near the zero value. The dynamic deflection changes of the front and rear suspensions are shown in Figure 10a,b, respectively. Compared with the DLC strategy, the absolute values of the front and rear maximum suspension dynamic deflections obtained by the MPC strategy decreased by more than 74%. The changes in the pitch angle and pitch angle velocity of the vehicle body are shown in Figure 11a,b, respectively. Compared with the DLC strategy, the absolute values of the maximum pitch angle and pitch angle velocity of the vehicle body obtained by the MPC strategy decreased by more than 74.6%. Similarly, the changes in the vertical velocity and vertical acceleration of the body mass center are shown in Figure 12a,b, respectively. The absolute values of the vertical velocity and acceleration of the body under the MPC strategy were lower than those under the DLC strategy in most of the cases. Further, the changes in the front and rear suspension control forces are shown in Figure 13a,b, respectively. The suspension control force under the two control strategies did not exceed the specified values. Moreover, Table 2 shows the RMS of some parameters and compares the two strategies to verify that the MPC strategy improves the comfort of the vehicle.
# Intelligent Packaging for Real-Time Monitoring of Food-Quality: Current and Future Developments
## 2 Food Quality Indicators
### Nitrogen Related Compounds  
Animal-derived food products, such as meat, fish, and dairy, represent a class of consumables that are highly susceptible to the undesired proliferation of pathogenic microorganisms, which causes their rapid degradation and the creation of biogenic amines [54; 55; 56]. Generally speaking, the increase in the total volatile basic nitrogen (TVB-N) concentration is known to be an indicator of food spoilage and the formation of compounds that are hazardous to health, such as histamine, cadaverine, and putrescine [57; 58; 59]. Thus, TVB-N and related compounds are a promising figure of merit for the continuous assessment of the quality of fresh protein-based products.
# Damping and Natural Period Evaluation of Tall RC Buildings Using Full-Scale Data in Korea
## 4 Natural Period and Damping Ratio Identification from Measured Full-Scale Data
### Equation for Natural Period Estimation  
Figure 8 presents the results of a regression analysis of 36 measured natural periods and 22 natural periods obtained from references [5; 6]. The linear regression was performed following previous research [5; 8; 9; 12], such that the equation included a single term of building height, even though statistical dispersion increases as the building height increases.  
The proposed equation of natural period for the wind design of RC buildings as obtained from the linear regression analysis is as follows:  
\[T=0.0196H=H/51 \tag{19}\]  
Equation (19) can be easily converted into an equation for natural frequency estimation as follows:  
\[\mathrm{f_{1}}=51/\mathrm{H} \tag{20}\]  
The coefficient of correlation of Equation (19) is 0.9861, which implies a very close correlation with the measured data. Furthermore, Equation (19) is very similar to Equation (7) of [PERSON] and [PERSON], who derived their empirical formula from the measured data of Korean buildings shorter than 66 m and having shear walls.  
Figure 9 compares the measured natural frequency data to the results obtained using Equation (19) and the other empirical formulae described in the previous section. All empirical formulae in Figure 9 yield results that are very close to the measured results because they were obtained from actual measured data, unlike the equations provided in ASCE 7-10, which are presented in Figure 7. In particular, the results obtained using [PERSON]'s empirical formula were the closest to the measured values, with the exception of [PERSON] and [PERSON]. The empirical formula provided in Eurocode 1 overestimated the natural periods, whereas the formula proposed by [PERSON] et al. underestimated the natural periods.  
Figure 8: Regression analysis of measured natural period.
## 4 Application to Parametric Network Games
### The Linear-Quadratic Network Nash Equilibrium Problem  
Let \(A_{i}(t)=[0,U_{i}(t)]\) for any \(i\in\{1,\ldots,n\}\), \(t\in[0,T]\), where, for each \(i\), \(U_{i}(t)\) is a positive and Lipschitz continuous function on \([0,T]\). The payoff of player \(i\) is given by:  
\[u_{i}(t,x)=k(t)x_{i}-\frac{1}{2}x_{i}^{2}+\phi\sum_{j=1}^{n}g_{ij}x_{i}x_{j}, \tag{27}\]  
In this model \(k:[0,T]\rightarrow\mathbb{R}\) is a positive Lipschitz function, and \(\phi>0\) describes the interaction between a player and their neighbors, which corresponds to the case of strategic complements. Moreover, since \(k(t)\) is the same for all players, they only differ because of their position in the network. The pseudo-gradient's components are given by:  
\[F_{i}(t,x)=-k(t)+x_{i}-\phi\sum_{j=1}^{n}g_{ij}x_{j},\qquad i\in\{1,\ldots,n\}, \tag{28}\]which can be written in compact form as:  
\[F(t,x)=(I-\phi G)x-k(t)\mathbf{1},\]  
where \(\mathbf{1}=(1,\ldots,1)^{\top}\in\mathbb{R}^{n}\). We will seek Nash equilibrium points by solving the variational inequality: for each \(t\in[0,T]\), find \(x^{*}(t)\in A(t)\) such that  
\[\langle(I-\phi G)x^{*}(t)-k(t)\mathbf{1},x-x^{*}(t)\rangle\geq 0,\qquad\forall x \in A(t). \tag{29}\]  
**Lemma 1**: \(F\) _is uniformly strongly monotone if \(\phi\rho(G)<1\), where \(\rho(G)\) is the spectral radius of \(G\)._  
Proof.: The symmetric matrix \(I-\phi G\) is positive definite if and only if its minimum eigenvalue \(\lambda_{min}(I-\phi G)\) is positive. On the other hand, \(\lambda_{min}(I-\phi G)=1-\phi\lambda_{max}(G)\). Since \(G\) is a symmetric non-negative matrix, the Perron-Frobenius Theorem guarantees that \(\lambda_{max}(G)=\rho(G)\), hence \(I-\phi G\) is positive definite if and only if \(\phi\rho(G)<1\). This condition does not depend on \(t\in[0,T]\).  
Notice that, when \(\phi\rho(G)<1\), the map \(F\) in (28) satisfies the assumptions of Theorems 2 and 3 with constants \(\alpha=1-\phi\rho(G)\), \(L_{x}=\|I-\phi G\|_{2}\), \(L_{t}=L_{k}\sqrt{n}\), where \(L_{k}\) is the Lipschitz constant of \(k(t)\), and \(L=\max\{L_{x},L_{t}\}\). Moreover, the feasible region \(A(t)\) satisfies assumption (3) with constant \(M=\|(L_{1},\ldots,L_{n})\|_{2}\), where \(L_{i}\) is the Lipschitz constant of \(U_{i}(t)\) for any \(i=1,\ldots,n\) (see [11]). Therefore, it follows from Theorems 2 and 3 that the Nash equilibrium \(x^{*}(t)\) is a Lipschitz continuous function with estimated constant equal to \(\min\{\Lambda_{1},\Lambda_{2}\}\).  
**Remark 1**: _The game under consideration also falls, for each \(t\in[0,T]\), in the class of potential games according to the definition introduced by [PERSON] and [PERSON] [30]. Indeed, a potential function is given by:_  
\[P(t,x)=\sum_{i=1}^{n}u_{i}(t,x)-\frac{\phi}{2}\sum_{i=1}^{n}\sum_{j=1}^{n}g_{ ij}x_{i}x_{j}. \tag{30}\]  
_[PERSON] and [PERSON] have proven that, in general, the solutions of the problem_  
\[\max_{x\in A(t)}P(t,x)\]  
_form a subset of the solution set of the Nash game. Because both problems have a unique solution under the condition \(\phi\rho(G)<1\), it follows that the two problems share the same solution._
## 5 Use Cases
### Analysis Using Graph Neural Networks
#### 5.7.1 Network Architecture  
The implemented architecture combines enhanced graph convolutional networks (GCNs) [36] with attention mechanisms:  
* Multi-scale convolutions: three-layer GCN with an expanding-contracting structure.
* Feature transformation: parallel node feature processing through multi-layer perceptrons.
* Attention integration: edge attention mechanisms for relationship weighting.
* Residual connections: skip connections between consecutive layers.
* Batch normalization: layer-specific normalization for training stability.
# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 2 Related Work in IoT-Based Sensing for PA
Combining aerial and ground-based sensing has only been investigated to a limited extent in [26; 30]. In summary, most related works propose or investigate either the ground-based [10; 13; 32; 33] or aerial [25; 34; 35] sensing approach. However, no related work has investigated or proposed a hybrid sensing approach for improving the accuracy of aerial data while reducing the cost of and effort involved in ground-based sensing. Some related research proposed the use of UAVs together with ground sensors, but the UAV serves a communication role (e.g., it is a data mule) [23; 32] or performs actuations [10]. Some publications discuss both aerial and ground-based sensing; however, the aerial and ground data are not integrated for data quality improvement [7; 8; 27]. Among the related works, only two publications have investigated the integration of UAV and ground-based sensing approaches [26; 30]. However, the focus of these two publications is more on data processing and data analysis algorithms. Therefore, it can be noticed that the integration of aerial and ground-based sensing approaches into a HSP to improve the data quality and reduce the data collection cost is an existing gap in the literature. In this paper, we address these gaps and we focus on how a HSP can improve the data quality and address some of the existing limitations of aerial and ground-based sensing approaches.
## 5 Conclusions  
We propose a PV output forecasting model, MS2E, considering the incident angle and spectral changes owing to atmospheric parameters and cloud conditions. Simulation results were close to the measurement value when considering these three factors. The Si module was almost unaffected by the solar spectral changes. On the other hand, the IMM module was greatly affected by the solar spectral changes. The solar spectrum was greatly affected by atmospheric parameters and cloud conditions, which result in estimation error. Without the atmospheric parameters and cloud conditions, the decreasing in the photocurrent of the bottom cell cannot be explained in summer. Above all, it is necessary to consider the solar spectrum change in highly accurate PV output forecasting. Regardless of the season, PR was decreased when considering incident angle dependence. The annual effective insolation rates were 99.69% for tracking Si module, 98.75% for fixed Si module, and 98.19% for the fixed IMM module. Without considering the incident angle modifier, there is a risk of overestimating PV output. Using the PV output forecasting model developed in this study, it is possible to simulate PV output potential with various structures at any location when considering the solar spectrum in the local environment. Conceptualization: Y.O., and K.A. ; methodology: H.T., H.S., Y.O., and K.A. ; validation: H.T., H.S., T.T., Y.O., and K.A. ; formal analysis: H.T., H.S., and Y.O. ; data curation, H.T., H.S., and Y.O. ; writing--original draft preparation, H.T and Y.O. ; writing--review and editing: H.T., Y.O., and K.A. ; supervision: K.N. All authors have read and agreed to the published version of the manuscript. This research was funded by the New Energy and Industrial Technology Development Organization (NEDO), and a grant for Scientific Research on Priority Areas from the University of Miyazaki. This work was supported in part by New Energy and Industrial Technology Development Organization (NEDO). \begin{table}
\begin{tabular}{c c c c} \hline  & **Annual Irradiance** & **Annual Irradiance with** & **Effective Insolation** \\  & **without Incident Angle** & **Incident Angle** & **Rate** \\  & **Modifier (kWh/m2)** & **Modifier (kWh/m2)** & **Ratio** \\ \hline Fixed Si & 1606 & 1586 & 98.75\% \\ Tracking Si & 1923 & 1917 & 99.69\% \\ Fixed IMM & 1606 & 1577 & 98.19\% \\ \hline \end{tabular}
\end{table} 
Table 2: Influence of the incident angle modifier on annual total solar irradiance. **Conflicts of Interest:** The authors declare that there are no conflicts of interest.
## 4 Case Study  
In this study, we obtained real data from the plastic industry, including the manufacture of scuba gear, freediving equipment, snorkeling devices, spearfishing equipment, and swimming accessories. The plastic components and products were fabricated using IMMs and utilized for the final product assembly. We considered nine IMMs that operate solely on electric power and can be remotely controlled to produce plastic products, such as fins, masks, and thermoplastic components. Each IMM was equipped with an electrical absorption monitoring board that recorded the energy consumption every hour and transmitted the measured values to a structured query language (SQL) database, as shown in Figure 3.  
From the SQL database, we collected a dataset comprising 119 daily time-series data points from 1 January to 30 April 2023. Each time series in the dataset pertains to the 24 h consumption of kWh for IMM no. 1. The time series exhibited various characteristics influenced by the specific production schedule of the IMM for each day. Some days may experience the IMM being idle or under maintenance, resulting in reduced consumption levels compared to periods when it is operational for production. The energy profile varies according to the current IMM production schedule.  
Our research aims to establish virtual energy profiles for each cluster by grouping the hourly energy consumption patterns of each IMM in the plant. These profiles can subsequently be utilized for the accurate forecasting of energy consumption, even when changes occur in the time step (e.g., from every hour to every minute). This information is critical for effective demand-side management, resource planning, and capital budgeting. To achieve this, mixture model algorithms, such as B-spline and polynomial regression-based autocorrelated time-series clustering, have been proposed and applied to extract temporal components and classify daily load curves into distinct clusters based on their consumption patterns.  
Our study employed clustering algorithms coded in MATLAB R2023a on a computing device equipped with 16 GB of memory and a 2.6 GHz Intel Core i7 processor. The clustering algorithms implemented in this study converged within a few iterations and required less than 300 s for the complete series data of four-month hourly consumption data.
## 1 Introduction
### Challenges in Multiple PD Source Classification  
The identification of PD via AI encounters several significant challenges: (1) extracting pertinent features from PD datasets; (2) implementing robust pattern recognition techniques for PD identification; and (3) distinguishing simultaneous sources of PD in HV systems [1]. Accurate diagnostics are based on the clear identification of multiple sources of PD in intricate systems [2; 3]. Real-world systems frequently exhibit multiple concurrent defects, producing overlapping PD signals and complicating source discernment [3; 4; 5]. The analysis of insulation with multiple PD sources is challenging due to overlaps of time, frequency, and phase signals, which obscure key diagnostic features. The variability in PRPD patterns further complicates the identification of phase-specific discharges. Although PRPD is often used for phase alignment with AC voltages [6; 7], dealing with signal overlaps requires advanced methodologies. Such overlaps generate complex patterns that complicate differentiation via PRPD and PRPS techniques, leading to ambiguous phase data that hinder PD categorization [2; 8; 9; 10]. Thus, utilities are in pursuit of automated approaches to distinguish and ascertain PDs from distinct sources using pulse waveforms [11].
## 2 Weather Data Super-Resolution Algorithm Based on CBAM-SRResNet
### Mixed Attention Mechanism
#### 2.2.2 Spatial Attention Mechanism  
The spatial attention mechanism [39] is designed for processing spatial data, enabling the model to dynamically focus on the different regions of an image to better capture important features. By incorporating attention before the convolution operation, the model can assign weights to the input feature map according to the saliency of each position. This selective focus on task-relevant regions enhances the model's representation and generalization capabilities. Its structure is shown in Figure 6.  
As shown in Figure 6, the input weather feature map undergoes average pooling and max pooling. The pooled results are then concatenated along the channel dimension. Finally, convolution is applied to the concatenated results, followed by an activation function. The calculation is given in Equation (8).  
\[M_{S}(F)=\sigma(*([AP(F),MP(F)])) \tag{8}\]  
where \(*\) is represented by a convolution operation, \(AP\) is represented by average pooling, \(MP\) is represented by maximum pooling, \(F\) is represented by the input feature map, and \(\sigma\) is represented by mapping weight.