esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Baseline Models  
In order to prove that the cellular traffic prediction method proposed in this paper met the requirements of the base station sleep control strategy in terms of accuracy, we chose statistical models, GCN-based deep learning model variants, and transformer variants as baselines for the comparison.  
Autoregressive integrated moving average (ARIMA): ARIMA is a classical time-series analysis and prediction method. It includes three parts, i.e., autoregressive, integrated, and moving averages.  
Support vector regression (SVR): SVR is a regression method based on a support vector machine (SVM), which is used to solve regression problems. Unlike traditional regression methods, SVR focuses on finding a function that minimizes the difference between the predicted value and the true value, and tolerates some errors.  
Gated recurrent unit (GRU): GRU is a recurrent neural network (RNN) structure that is frequently utilized for the analysis of sequence data. Its primary advantage lies in its ability to effectively capture long-term dependencies within the sequence through the introduction of a gating mechanism. Consequently, this enhances the overall performance of the model.  
Temporal graph convolutional network (T-GCN): T-GCN is an artificial neural network model that integrates the graph convolutional network (GCN) and gated recurrent unit (GRU). Its purpose is to effectively capture the spatio-temporal correlation within traffic data and thereby achieve accurate cellular network traffic prediction.  
Spatial-temporal self-attention graph convolutional network (STA-GCN): STA-GCN is a spatial-temporal self-attention graph convolutional network with a good prediction performance and interpretability.
## 2 Materials and Methods
### Research Methods
#### 2.6.2 Carbon Storage Estimation Models  
To compare the estimation accuracy of different models, this study selected three non-parametric models to establish carbon storage estimation models which combined the forest resource inventory data and characteristic variables. Three carbon storage models were used in this research (SVM, bagging, and RF).  
1. Support Vector Machine  
A support vector machine, a supervised learning module related to learning algorithms, is used for data recognition and regression evaluation [36]. There exists certain performance differences in different SVM kernel functions. It maps the linearly inseparable data in the space to the high-dimensional feature space so that the data are separable in the feature space. Generally, three kernel functions are used to complete the multidimensional mapping from input space to feature space [37]. In this research, the 'e1071' package of R 4.2.3 was used to construct the SVR model and choose the RBF (radial basis function) as the kernel function. In order to obtain the best parameters, 10-fold cross-validation was used to optimize the mapping parameter (g) and penalty coefficient (c), with gamma set at 0.2 and C at 0.25.  
2. Bagging  
Bagging is an algorithm that integrates different individuals into a learner. The basis of the bagging algorithm is to obtain different sample subsets via repeated sampling so that the learning tools trained on different sample subsets have a greater difference and a higher generalization performance. The bagging algorithm repeats sampling in each training set after extracting numerous training sets from the sample set of the sample plot using uniform sampling. After that, an experiment is conducted on this part of the sample and a decision tree is established, which is then combined with other decision trees [38]. The bagging function in the 'ipred' package of R 4.2.3 was used to model the samples in this study.  
3. Random Forest  
Random forest is an important learning method for integration, which uses a decision tree as a bagging ensemble and randomly selects attributes in training. Its randomness is mainly reflected in the random selection of samples and the random selection of characteristic variables [39]. Random forest uses the boost strapping algorithm to randomly collect new datasets and put them back to extract 2/3 of samples from the original sample dataset. When used for regression, the mean of all decision tree forecasts is used as the final forecast value [40]. This study used the 'randomforest' package in R 4.2.3 to build the predictive model, and the most important parameters are the number of decision trees that built the sample size (ntree), and the number of random characteristics (mtry). According to 10-fold cross-validation, the optimal parameters ntree was 200, and mtry was 2. The importance of variables can be judged by analyzing the influence of variables on model mean square error and model tree node purity. This research sorted the importance of features using the % IncMSE in R software.
## 5 Accuracy of the Proposed Expressions of the Maximum-Rating Constrains
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \(R_{L}\) & \(L_{SH}\) & \(C_{SH}\) & \(L_{o}\) & \(C_{e}\) \\  & [\(\Omega\)] & [\(\mu\)H] & [nF] & [\(\mu\)H] & [pF] \\ \hline
1 & 24.54 & 2.86 & 4.44 & 312.43 & 81.07 \\
2 & 24.54 & 2.86 & 4.44 & 78.11 & 324.30 \\
3 & 24.54 & 2.86 & 4.44 & 39.05 & 648.58 \\
4 & 24.54 & 2.86 & 4.44 & 19.53 & 1297.15 \\
5 & 2.26 & 0.41 & 30.73 & 7.20 & 3115.95 \\
6 & 12.06 & 1.03 & 12.30 & 38.38 & 632.94 \\
7 & 25.27 & 9.20 & 1.38 & 80.45 & 320.20 \\
8 & 27.22 & 36.84 & 0.34 & 86.64 & 298.12 \\
9 & 11.03 & 54.65 & 2.90 & 35.10 & 763.48 \\
10 & 13.54 & 12.45 & 3.18 & 43.10 & 615.12 \\
11 & 20.45 & 4.61 & 3.81 & 65.09 & 397.95 \\
12 & 17.45 & 1.85 & 5.35 & 55.54 & 441.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Circuit components. Figure 8: Phases to find the unknown circuit values. The estimation error related to each parameter sweep (i.e., \(Q_{L}\), \(D\), and \(q\) ) is summarized in Table 7. Furthermore, this statistics (i.e., MAPE the standard deviation, and maximum and minimum values) were calculated based on the percentage errors listed in Table 6, and grouped as shown in Table 7. For instance, the statistics of the \(Q_{L}\) sweep were calculated based on % Er of the amplifiers identified by the IDs 1, 2, 3, and 4. The highest standard deviation \((3.48\%)\) was obtained with the \(Q_{L}\) sweep. In this sweep, the small values of \(Q_{L}\) are critical to calculate the stress conditions reported in this paper. The highest MAPE (\(3.19\%\)) was obtained with the \(D\) sweep. The highest errors are presented for extreme values of \(D\) (i.e., \(D=0.3\) and \(D=0.7\)). Meanwhile, the parameters obtained with the sweep of \(q\) achieved a MAPE of \(2.05\%\), being the smallest error values of the three sweeps. The performance of the designed amplifiers was analyzed based on its \(P_{out}\), input DC power (\(P_{V_{DD}}\)), drain efficiency (\(\eta=P_{out}/P_{V_{DD}}\)), output second-harmonic distortion (\(HD2\)), output third-harmonic distortion (\(HD3\)), and Total Harmonic Distortion (\(THD\)). These resulting Figures of Merit (FoM) were summarized in Table 8. On one hand, the \(P_{V_{DD}}\) and \(P_{out}\) parameters had percentage errors of less than 2% concerning the theoretical value, as long as the loaded quality factor was higher or equal than 10. The simulated and theoretical \(\eta\) values are very similar (i.e., a perceptual error of 0.02% with a standard deviation of 0.01%). On the other hand, the \(HD2\), \(HD3\), and \(THD\) had a mean value of \(-32.85\) dBc, \(-48.46\) dBc, and \(2.75\%\) respectively. Furthermore, the worst error prediction was presented in the study case 4, which was designed with the lowest \(Q_{L}\) value. The performance of the designed amplifiers was evaluated by the power and distortion FoMs, which are related to several PA applications. However, in a specific application context, other FoMs must be addressed. For instance, when the PA is embedded in a transceiver RF front-end, its non-linear FoMs (e.g, 1 dB compression point) must be analyzed.
# Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Celephu, Bhutan
## 2 Materials and Methods
### Accuracy Assessment Methods  
SCP is designed to train the classification model (RF) for machine learning by iteratively improving the input training data set consisting of ROIs (polygons) for each class. The classification can be revised by refining the training area without test data. However, to verify the level of accuracy achieved, the current study used additional testing data consisting of 51 random points created with Google Earth. These points were used to perform the accuracy assessment through an Area Under the Curve (AUC) approach for the RF classification model for both LULC maps.  
In the current study, the sample points used are typically well-known ground truth labels that illustrate the presence or absence of a particular land cover class. The ArcSDM (Spatial Data Modeller) tool in ArcGIS was employed to construct the receiver operating characteristic curve (ROC) curves. A ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) for various classification thresholds (spectral signatures of respective classes). The TPR and FPR are computed using the equations Equation (2) and Equation (3), respectively. The TPR represents the proportion of positive instances correctly classified, while the FPR indicates the proportion of negative instances incorrectly classified as positive. By systematically adjusting the classification threshold, we can observe how the model's TPR and FPR change, resulting in a curve that summarizes its discriminatory power across different operating points. The ROC curve provides a visual representation of the model's trade-off between sensitivity (recall) and specificity. A perfect model would achieve a TPR of 1 and an FPR of 0, resulting in an ROC curve that hugs the top-left corner of the graph. The closer the curve comes to this ideal point, the better the model's performance. The diagonal line from the bottom left to the top right represents the random classifier, indicating an equal chance of correctly classifying positive and negative instances. Any model that falls below this line is considered worse than random.  
The True Positive Rate (TPR), also known as Sensitivity or Recall, is defined as:  
\[\text{TPR}=\text{TP}/(\text{TP}+\text{FN}) \tag{2}\]  
Similarly, the False Positive Rate (FPR) is defined as:  
\[\text{FPR}=\text{FP}/(\text{FP}+\text{TN}) \tag{3}\]  
The area under the ROC curve, commonly referred to as the Area Under the Curve (AUC), quantifies the overall performance of a classification model. A perfect model has an AUC of 1. Higher AUC values indicate better discriminative ability. AUC serves as a reliable metric for comparing different models, as it remains unaffected by class imbalance.
## 2 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & **Milk** & & & **Mozzaella Cheese** \\ \hline
**R.T.** & **Identified Compounds** & **Exp \({}^{\text{a}}\)** & **Ctl \({}^{\text{b}}\)** & \(p\)-Value** & **Exp \({}^{\text{a}}\)** & **Ctl \({}^{\text{b}}\)** & \(p\)-Value** \\ \hline  & **Acids** & **3.32 \(\pm\) 0.08** & **3.62 \(\pm\) 0.08** & **1.15 \(\pm\) 0.11** & **1.28 \(\pm\) 0.11** & **NS** \\
2.01 & Acetic acid & ND & 1.63 \(\pm\) 0.86 & / & ND & ND & \\
4.53 & Butanoic acid & 2.55 \(\pm\) 0.09 & 2.87 \(\pm\) 0.09 & * & ND & 0.30 \(\pm\) 0.05 & / \\
9.40 & Hexanoic acid & 3.04 \(\pm\) 0.1 & 3.2 \(\pm\) 0.1 & NS & 1.15 \(\pm\) 0.12 & 1.22 \(\pm\) 0.12 & NS \\
14.31 & Octanoic acid & 2.67 \(\pm\) 0.11 & 3.1 \(\pm\) 0.11 & * & ND & ND & \\
18.81 & Decanoic acid & 1.97 \(\pm\) 0.13 & 2.54 \(\pm\) 0.13 & ** & ND & ND & \\
22.85 & Dodecanoic acid (\({}^{\text{f}}\)) & 1.35 \(\pm\) 0.09 & 2.04 \(\pm\) 0.09 & *** & ND & ND & \\
25.85 & Tetradecanoic acid (\({}^{\text{f}}\)) & 1.48 \(\pm\) 0.04 & 2.11 \(\pm\) 0.04 & *** & ND & ND & \\  & **Aldehydes** & **1.87 \(\pm\) 0.16** & **1.77 \(\pm\) 0.16** & **NS** & **1.82 \(\pm\) 0.08** & **1.79 \(\pm\) 0.08** & **NS** \\
3.10 & Pentanal & 1.36 \(\pm\) 0.59 & ND & / & 1.27 \(\pm\) 0.1 & 1.26 \(\pm\) 0.1 & NS \\
4.95 & Hexanal & 1.80 \(\pm\) 0.75 & ND & / & 1.62 \(\pm\) 0.1 & 1.57 \(\pm\) 0.1 & NS \\
12.73 & Nonanal & 1.46 \(\pm\) 0.08 & 1.77 \(\pm\) 0.08 & ** & 1.10 \(\pm\) 0.08 & 1.24 \(\pm\) 0.08 & NS \\  & **Alcohols** & **1.61 \(\pm\) 0.24** & **ND** & / & **1.76 \(\pm\) 0.04** & **1.62 \(\pm\) 0.04** & * \\
10.72 & 2-Ethyl-1-hexanol (\({}^{\text{f}}\)) & 1.61 \(\pm\) 0.24 & ND & / & ND & ND & \\
1.91 & 1-Propanol (\({}^{\text{f}}\)) & ND & ND & & 1.56 \(\pm\) 0.04 & 1.62 \(\pm\) 0.04 & NS \\
2.70 & 1-Butanol & ND & ND & & 0.85 \(\pm\) 0.34 & ND & / \\
4.32 & 1-Pentanol & ND & ND & & 1.16 \(\pm\) 0.14 & ND & / \\  & **Esters** & **2.28 \(\pm\) 0.51** & **ND** & / & **2.14 \(\pm\) 0.05** & **2.24 \(\pm\) 0.05** & **NS** \\
2.26 & Ethyl acetate & 1.38 \(\pm\) 0.61 & ND & / & 1.37 \(\pm\) 0.09 & 1.33 \(\pm\) 0.09 & NS \\
5.00 & Ethyl butanoate & ND & ND & & 1.42 \(\pm\) 0.06 & 1.48 \(\pm\) 0.06 & NS \\
3.37 & Propyl acetate (\({}^{\text{f}}\)) & 2.23 \(\pm\) 0.7 & ND & / & 1.63 \(\pm\) 0.06 & 1.77 \(\pm\) 0.06 & NS \\
5.20 & Propyl propanoate (\({}^{\text{f}}\)) & ND & ND & & 1.18 \(\pm\) 0.05 & 1.31 \(\pm\) 0.05 & NS \\
7.36 & Propyl butanoate (\({}^{\text{f}}\)) & ND & ND & & 1.48 \(\pm\) 0.02 & 1.60 \(\pm\) 0.02 & *** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Volatile organic compounds in milk (n = 9) and mozzarella cheese (n = 9) samples obtained from buffalo feed total mixed ration with (Exp) or without (Ctl) fresh sorghum.
# An Under-Sampling Array Signal Processing Method Based on Improved Hadamard Matrix
## 2 Array Signal Processing Model Based on Compressive Sensing
### Compression of The Array Signal  
We combine compressed sensing into the array signal in the following. By compressive sampling matrix \(\Phi\), the N-element linear array data \(X\) is compressed by the following formula to obtain the M-element non-uniform linear array data \(Y\):  
\[\mathbf{Y}=\Phi\mathbf{X}=\Phi\mathbf{A}\mathbf{S}+\Phi\mathbf{v}, \tag{7}\]  
where \(\mathbf{X}=\mathbf{X}(t)\) is based on the narrow signal model or \(\mathbf{X}=\mathbf{X}\big{(}f_{g}\big{)}\) is based on the wideband signal model, and \(\mathbf{Y}\) is the received data of the M-element non-uniform linear array after compressing sampling through the \(\Phi\).  
\[\mathbf{Y}=\mathbf{B}\mathbf{S}+\mathbf{V}, \tag{8}\]  
\[\begin{cases}\mathbf{B}=\Phi\mathbf{A}\\ \mathbf{V}=\Phi\mathbf{v}\end{cases}, \tag{9}\]  
where \(\mathbf{B}\) is the new manifold matrix, called the sensing matrix in CS, and \(\mathbf{V}\) is the noise data received by the compressed sampling array. When we need to reconstruct \(\mathbf{S}\), we only need to change sparse matrix \(\mathbf{A}\) in Equation (10) to sensing matrix \(\mathbf{B}\).
## Appendix A Step 4--System Dynamics  
System Dynamics modeling is used to obtain time-varying probabilities which allow for the illustration of feedback loops and delays. Both [52; 53] showed that fire accidents are dynamic processes that are complex and SD can be used to analyse them. BN is the starting point where each node of the BN is mapped into an SD model node. The SD perturbation equation with a random term is as follows:  
\[P(fire\,yes)=0.055+Random\,Uniform\,\left(-\frac{defaultChange}{4},\frac{defaultChange }{4}\right)\times\,0.055\] (A4)  
As an example, for scenario 16, the SD behavior is shown in Figure A4. This scenario describes the failure of alarm/notification system due to deficient maintenance.  
Figure A3: Example of Bayesian Network structure.  
The equation that governs the loop for the flow is as follows:  
\[P(deficient\ training\ yes)=Random\ Uniform\ \left(-\frac{defaultChange}{4},\frac{defaultChange}{4} \right)\ P(deficient\ training\ yes)\] (A5)  
Using the Boolean logic, the CPT presented in Table A2 is transformed into an equation. Consider the CPT for the BN node, i.e., 'inefficient timely control' which consists of three parent nodes as follows:  
* 'Deficient training';
* 'Inefficient emergency plan';
* 'Not comply with the instruction'.  
In the SD model, it is characterized by the variables--'inefficient timely control yes' and 'inefficient timely control no' and can be converted into the equation below:  
\[\begin{array}{l}P(ineff\ timely\ control\ no)\\ \  
* FY DY (fire yes, detection yes);
* FY DN (fire yes, detection no);
* FN (fire no).  
The influence of the parent variables is stated through the listed values in the columns on the left of the CPT. The same reasoning is applied to all other nodes in the BN.  
The final ERL variable encompasses the risk value for the specific design solution computed as the sum of the ERLs of every single outcome. The consequence of each sub-scenario (determined via ASET/RSET analysis) is multiplied by the associated path node probability. A consequence is the number of fatalities estimated based on ASET/RSET analysis.  
The mapped SD model is demonstrated in Figure A5. An extended model has been developed to include human and organisational factors and is primarily based on the concept of reliability associated with maintenance practices (Figure A6). This dynamic model is based on the BN and its HOE variables are as follows:  
* Deficient training;
* Inefficient emergency plan;
* Not comply with the instruction;
* No check rules;
* Deficient maintenance;
* Incorrect risk assessment;
* Not following standards;
* Improper safety organization.  
Figure A5: Mapped System Dynamics Model.  
All variables in the cycle are dynamic and their interaction shows an oscillating pattern for the reliability parameter. The dynamic behaviour is simulated by stock and flow variables using Equations (A8) and (A9):  
\[NoC=12+\int RoC(t)dt\] (A8)  
\[RoC(t)=5\;\frac{d}{dt}(ps)\] (A9)
# Physics-Informed Surrogate Modelling in Fire Safety Engineering: A Systematic Review
## 3 Implementing Physics-Informed Surrogate Model
### Data-Driven Objective  
In a conventional data-driven model, the objective is to minimise the error between predicted and actual values for the training dataset. The most common loss functions include Mean Squared Error (MSE) and Mean Absolute Error (MAE). MSE penalises large deviations more heavily, making it suitable for cases where significant errors must be minimised. In contrast, MAE is more robust to noise and outliers, making it advantageous for models incorporating experimental data [26]. A hybrid alternative, referred to as Huberloss, combines MSE for large deviations and MAE for small deviations, offering a balance between robustness and precision [145].  
Beyond standard loss functions, regularisation techniques enhance model stability and generalizability. L1 regularisation (Lasso) [146] promotes sparsity by reducing the impact of less significant coefficients, while L2 regularisation (Ridge) [147] discourages large parameter values, mitigating overfitting. These techniques are particularly valuable in physics-based surrogate models, where model interpretability and generalisation to unseen data are crucial.  
Physics-Informed Objective (Loss-Constrained Technique--LCT)  
Physics-informed objectives integrate known physical relationships into the loss function, ensuring model predictions adhere to fundamental laws. This is achieved by introducing penalty terms that constrain deviations from governing equations, such as partial differential equations (PDEs), boundary conditions, and initial conditions.  
Equation (2). Heat conduction equation  
\[\frac{\partial T}{\partial t}=\alpha\cdot\
abla^{2}T \tag{2}\]  
Equation (3). Loss term for heat conduction  
\[Loss_{PDE}=\left|\frac{\partial T}{\partial t}-\alpha\cdot\
abla^{2}T\right| \tag{3}\]  
In cases where enforcing strict PDE constraints is not a primary objective, soft constraints can be applied. Instead of directly penalising deviations from physical equations, a weighted combination of data-driven and physics-based loss terms can be used. A penalty multiplier adjusts the relative importance of each term, allowing flexibility in how strictly physical laws are enforced [14].
## 3 Materials and Methods
### Definition of Relevant Use Cases
External HMI (eHMI) elucidates ASBs' communication and interaction capabilities with other road users (ORU), encompassing VRUs like pedestrians, cyclists, and wheelchair users, as well as human drivers of manually operated vehicles [60]. The external interaction is progressively being emphasized. As depicted in Figure 6, the ASB's eHMI information display and notification mechanisms currently fall into three categories. The emphasis on user experience has increased from communicating information solely through lights to installing displays at the front-end/rear-end/body-end of ASB for text, expressions, and other signals. Therefore, all cases where there is no display of information and carriers on the outside of the vehicle are filtered out because, in this case, the user cannot interact substantially with the SAV. Moreover, it is worthwhile to investigate how to avoid information overload while captivating users' attention and allowing them to trust ASB through appropriate interaction. * **Collaborative construction of internal HMI:** The absence of a driver will increase the demand for reliable and easily accessible information, which gives the passenger access to non-driving-related activities (NDRA). As shown in Figure 7, the interior HMIs from the 15 selected ASBs are analyzed in terms of the different interior layouts and display carriers. Displays and physical controls dominate the ASB for the internal HMI, with an application on the user's mobile device serving as a secondary means. ASBs have essentially fixed routes and locations, and an app is used to obtain information about scheduled stops in the case of on-demand routes. It is important to exclude cases where only an app serves as the interface for human-vehicle interaction. In such instances, users do not engage with the vehicle in any physical sense, and therefore, we consider this to be beyond the scope of our research. Additionally, due to the layout and seating position of the ASB, the internal display has progressively evolved from a single primary screen to multiple screens of diverse sizes and positions [8]. The seating arrangement of ASBs differs from that of conventional buses; the majority have a circular layout, and passengers face various directions during operation, necessitating multiple displays to convey information. The resulting five generic displays obtained by the display-based approach are depicted in the lower row of Figure 8, which illustrates the complete process of implementing this result. Furthermore, as shown in Figure 4, the layout-based method is tailored explicitly for iHMI. Based on the analysis mentioned above, passenger seating arrangements  
Figure 6: Configurations and classifications for external HMI in selected Autonomous Shuttle Buses (ASBs). within the vehicle can be categorized into the following three configurations: semi-circular C-shaped layout, front and back rows of seats facing each other, and all seats uniformly facing forward.