esa-sceva/satcom-chunk-collection huggingface dataset:
# Regressive Machine Learning for Real-Time Monitoring of Bed-Based Patients
## 3 Results
### Confusion Matrix  
The confusion matrix, shown in Figure 3, represents a six-class classification problem. The classes are Roll right (0), Roll left (1), Drop right (2), Drop left (3), Breathing (4), and Seizure (5). The rows of the matrix represent the actual classes, while the columns represent the predicted classes by the ML model. The diagonal elements represent the number of points for which the predicted label is equal to the true label, while the off-diagonal elements are those that are mislabeled by the classifier. The higher the diagonal values of the confusion matrix the better, indicating many correct predictions.  
In this study, SMOT was employed to balance the class distribution in the dataset. Note that SMOT was applied before the data splitting process, and therefore, the synthetic instances generated by SMOT are included in both the training and testing sets. The data were then split into training and testing sets. However, the distribu  
Figure 3: Confusion matrix heatmap for the model. The x-axis represents the predicted labels and the y-axis represents the actual labels. The color intensity in each cell corresponds to the number of instances, with darker colors indicating higher numbers. The actual numbers are also included in each cell of the heatmap.  
testing set is subject to the randomness of the data splitting process, and therefore, it does not necessarily reflect the exact ratio of the original class distribution. The confusion matrix in Figure 3 represents the performance of the model on the testing set, which includes both real and synthetic instances.  
The model correctly predicted all 39 instances of Roll right (0), as there are no off-diagonal elements in the first row. Similarly, the model correctly predicted all 38 instances of Roll left (1), as there are no off-diagonal elements in the second row. For Drop right (2), the model correctly predicted 39 instances, but misclassified 1 instance as Roll left (1) and 1 instance as Drop left (3). For Drop left (3), the model correctly predicted 47 instances, but misclassified 2 instances as Roll right (0) and 1 instance as Roll left (1). For Breathing (4), the model correctly predicted 34 instances, but misclassified 2 instances as Seizure (5). For Seizure (5), the model correctly predicted 31 instances, but misclassified 5 instances as Breathing (4).  
Overall, the model seems to be doing well in predicting the classes, as the majority of the predictions fall on the diagonal (correct predictions). However, there are some misclassifications, particularly between Breathing (4) and Seizure (5), and to a lesser extent between Drop right (2) and Drop left (3), and Drop left (3) and Roll right (0) and Roll left (1).
# A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)
###### Abstract
The representative ones are divided into two main categories: a class of airspace segmentation and configuration methods based on terminal areas or flight requirements. Existing methodologies include dynamic airspace sector segmentation based on V-maps [15]; whereas [PERSON] et al. [16] proposed similar segmentation based on geometric models, leading to the development of new dynamic airspace configuration algorithms, such as those based on graph theory [17], and dynamic airspace configuration based on genetic dynamic airspace configuration algorithms [18]. Another type of airspace classification method is oriented towards the delineation of airway routes, such as the dy  
Figure 1: Introduction to ADS-B system. namic airspace super sectors (DASS) concept-based airway airspace design and operation scheme [19], as well as the high-volume tube-shape sectors (HTS) prototype concept [20], in addition to the combination of a district airspace and new freeway airspace [21]. In general, the traditional airspace management based on density, traffic, route delineation, and other paradigms are not compatible with the chart system, nor with the actual flight chart display needs to conduct the multitude of required calculations and conversions; thus, it remains unsuitable for the ATC data management framework oriented to ADS-B as a representative data source. It is hoped that the management of flight data and airspace can be directly combined for navigation services, where the most direct guidance for flight services being the chart standard [22]. The present study proposes a discrete digital airspace framework, GeoSOT-AS, for control purposes. The framework conforms to the existing Chinese specification of civil aeronautical charting. It uses a specific space-filling curve approach, has good inheritance between multi-level profiles, and is characterized by low deformation and high practical-ty. The framework is validated by calculations in majority of the Chinese airspace and is identified by a unique code for each unique airspace location, which can also be used as the main key for database management of ATC information. The system performed well in extracting target airspace area data, such as ADS-B data extraction tasks, improving the overall extraction rate by \(\geq\)80% compared to traditional range queries. The GeoSOT-AS grid framework can be used for basic airspace management tasks, such as regional trajectory clustering studies, collision avoidance, airspace planning, and deployment. By matching the framework with the chart, the results of regional analyses can be quickly applied to actual airspace management. As a digital framework for multi-level disaggregation, this framework can carry air traffic data for aviation tasks and provide an effective reference for existing airspace management methods.
# Development of an L-Band SAR Microsatellite Antenna for Earth Observation
## 3 Antenna Gain Estimation
### Effect of Struts  
Support struts block the aperture of a centrally fed reflector and reduce antenna gain. Because the passing waves induce currents on the struts that radiate, the effect of struts can be larger than their area. The struts have circular cross-section of 10 mm diameter and length of 1.308 m and are illuminated by spherical waves from the feed. Induced currents together with the feed illuminate the reflector. Each strut creates a shadow on the field generated by the feed, which decreases the spillover for \(\theta\) larger than zero. A parametric analysis of the effect of number of struts on the radiation pattern is performed, and six main aluminum struts were chosen as they provide good compromise between mechanical stability of the structure and acceptable levels of peak amplitude and SLL for a 10-dB taper feed. Peak losses introduced by the struts were simulated in \(\eta_{struts}=0.7\) dBic with considerate increase in sidelobe levels of 4 dB and axial ratio increase to 2.2 dB. Although sidelobes are above desired levels, they can be improved in the feed assembly by controlling the distance between elements.  
Therefore, Table 6 and Figure 7a,b summarize these contributions affecting the antenna radiation pattern, with total antenna losses estimated in \(\eta_{total}=3\) dBic.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Loss Contributions** & **Co-Pol (dBic)** & **Peak Loss (dB)** & **Peak SLL (dB)** \\ \hline Ideal parabolic & 33.6 & – & – \\ Spillover, taper, blockage & 31.9 & 1.7 & \(-27.4\) \\ Mesh & – & 0.0027 & – \\ Gore shape & 31.3 & 0.6 & \(-19.2\) \\ Struts & 30.6 & 0.7 & \(-15.2\) \\ \hline
**Total** & 30.6 & 3 & \(-15.2\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Illumination losses at 1.275 GHz: effect of antenna elements on the co-polarized gain (Co-Pol), peak gain losses and peak sidelobe levels (Peak SLL).  
Figure 7: Simulated antenna pattern radiation accounting different elements contribution at center frequency: (**a**) co-polarized gain, and (**b**) axial ratio.
## 2 Methodology
### MIC-FCBF Combined Feature Screening
#### 2.1.2 Fast Correlation-Based Filter Algorithm  
The Fast Correlation-Based Filter (FCBF) is an algorithm designed for feature selection, aiming to rapidly and accurately filter out features from high-dimensional feature sets that contribute to classification tasks [37]. This algorithm utilizes the Symmetrical Uncertainty (SU) metric to quantify the correlation between features and performs feature selection by defining relevance and redundancy.  
Let the initial feature set be represented by \(X=\big{(}x_{ij}\big{)}_{N\times M}\), denoting N M-dimensional sample vectors and M N-dimensional feature vectors. To more accurately describe the relevant information of Symmetrical Uncertainty (SU), entropy is employed as a measure for random variables.  
Assuming that two random variables \(X=\{x_{1},x_{2},\ldots,x_{i}\}(i=1,2,\ldots,m)\) have m feature vectors and \(Y=\big{\{}y_{1},y_{2},\ldots,y_{j},\big{\}}(j=1,2,\ldots,n)\) has n feature vectors, then the information entropy \(H(X)\) of random variable X and the conditional entropy \(H(X|Y)\) of random variable X under the condition of variable Y are defined as:  
\[H(X)=-\sum_{i}P(x_{i})\log_{2}P(x_{i}) \tag{3}\]  
\[H(X|Y)=-\sum_{j}P\big{(}y_{j}\big{)}\sum_{i}P\big{(}x_{i}|y_{j}\big{)}\,\log_{ 2}P\big{(}x_{i}|y_{j}\big{)} \tag{4}\]  
\(P(x_{i})\) and \(P\big{(}x_{j}\big{)}\) are the probabilities of random variables X and Y taking the values \(x_{i}\) and \(y_{j}\), respectively; \(P\big{(}x_{i}|y_{j}\big{)}\) is the probability that the random variable X is \(x_{i}\) given that Y = \(y_{j}\).  
The measurement method of relevant information between random variables \(X\) and \(Y\) is defined as mutual information, then the mutual information between the two is expressed as follows:  
\[I[X;Y]=H(X)-H(X|Y) \tag{5}\]  
By combining Equations (3) and (5), SU can be expressed as follows:  
\[L_{SU}(X;Y)=2\frac{I(X;Y)}{H(X)+H(Y)} \tag{6}\]  
As can be seen from the SU definition, the value range is (0, 1), and the increase in this value means that the correlation between the two variables increases.  
To ensure that the initial feature space has the maximum correlation between the target feature and the aging category while minimizing redundancy with other features, define the aging category as variable C. Calculate the correlation \(L_{SU}(v_{i};C)\) between each feature \(v_{i}\) in the original feature space and the aging category C. Set a threshold m. If \(L_{SU}(v_{i};C)\) < m, then eliminate feature \(v_{i}\), retaining all features that meet this condition [38]. After the initial screening, the remaining set of features undergoes a secondary selection. In this step, the correlation between feature \(v_{i}\) and each of the remaining features \(v_{j}\) is computed. If \(L_{SU}(v_{i};v_{j})>L_{SU}(v_{j};C)\), the feature \(v_{j}\) is removed. Following this selection process, the features are arranged in a specific order from best to least, resulting in a detailed feature ranking.
## 6 Simulation and Case Study
### Simulation of Multiple Temporal Stages of Frequency Control
#### 6.1.4 Summary  
The control of system frequency provided by PEIs under VSG control is shown in Figure 10. The VSG algorithm enables IBG units to perform multiple temporal frequency control after a frequent event.  
Figure 10: Multiple temporal frequency control under VSG algorithm.  
Figure 9: Simulation of load forecast gap in TFR.  
Correspondingly, the multiple temporal stages of frequency control provided by VSG emulation strategies are summarized in Table 3.
# Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm
## 2 Hardware Structures for Linear Operation
### Photonic Recurrent Neural Network (PRNN)  
The recurrent neural network is also known as the reservoir calculation. The information transmits forward and backward to form a loop structure. The reservoir is mainly composed of an input layer, middle layer and output layer. Only the output weights are trained in the RNN. For photonic storage pool network, there are two structures: one is the parallel scheme, and the other is the serial scheme, as shown in Figure 6 Different from the previous two neural networks, the reservoir network is mainly used for dimension transformation of data.
# An Analysis of Spatial Variation in Human Impact on Forest Ecological Functions
## 2 Materials and Methods  
### Study Area  
The Yellow River (located between 95\({}^{\circ}\)53\({}^{\prime}\)-119\({}^{\circ}\)05\({}^{\prime}\) E and 32\({}^{\circ}\)10\({}^{\prime}\)-41\({}^{\circ}\)50\({}^{\prime}\) N) originates in the northern foothills of Bayan Har Mountain. It traverses the Qinghai-Tibet Plateau, the Inner Mongolia Plateau, the Loess Plateau and the North China Plain form west to east, with a total length of 5464 km [25]. The Yellow River basin (YRB) covers a total area of 795,000 km\({}^{2}\), which exhibits almost all the typical climatic zones and topographic landscapes of northern China. Based on the vegetation hierarchy system formulated by the National Climate Division and Vegetation Map of China, and referring to the multi-year average temperature and precipitation and information of soil zoning, the YRB is divided into eight physical geographic regions [26]. As shown in Figure 1, I is the sub-rigid semi-arid meadow steppe region in Qinghai Plateau; II is the sub-frigid semi-humid scrub and meadow region in the Zoige Plateau and surrounding mountains; III is the temperate humid and sub-humid alpine deep valley coniferous forest region in West Sichuan and East Tibet Plateau; IV is coniferous forest region and grassland region in the temperate semi-arid alpine basin in Qilian Qingdong Plateau; V is the warm temperate steppe and desert-grassland region in the Yellow River loop and Ordos Plateau; VI is the warm temperate semi-arid grassland region in the northwest of Loess Plateau and semi-humid forest region in the southeast; VII is the warm temperate humid and sub-humid forest region in the mountains and plains of lower Yellow River; and VIII is the region of warm temperate semi-humid saline meadow of the Yellow River delta [26].  
The YRB is taken as the study area for this paper based on the following considerations: first, the YRB is an ecological corridor connecting the east and west of China, and it holds paramount importance in wind-breaking, sand-fixing, and ecological protection. There has been a consistent concern about its direct impact on the evolution trend in ecological security and environmental quality in the medium to long term [27; 28]. And the Chinese government positioned the ecological protection and high-quality development of the YRB as a prime national strategy since 2019 [29]. Second, the YRB has been one of the regions most seriously affected by human activities in the world [30]. Covering 448 counties in nine provinces, the YRB plays a crucial role in China's economic development and food security [31]. And considering that stepped social and economic development levels are observed across the upper, middle, and lower reaches of the YRB, the findings of this study will be potentially universal to other countries and regions.
## 4 SLR Methodology
### Planning the SLR
#### 4.1.2 Research Questions  
Considering the urge to conduct an SLR of the exploited methods of providing explainability for AI/ML systems and their evaluations in different application domains and tasks, several RQs were formulated. Primarily, the questions were defined to investigate the prevailing approaches towards making AI/ML models explainable. This included the probe of explainable models by design, different structures of the generated explanation, and the significant application domains and tasks utilising the XAI methods. Furthermore, the means of validating the explainable models were also considered, followed by the open issues and future research directions. For convenience, the RQs for conducting this SLR are outlined as follows:  
Figure 4: SLR methodology stages following the guidelines from [PERSON] and [PERSON] [38].  
* _RQ1:_ What are the application domains and tasks in which XAI is being explored and exploited?
* _RQ1.1:_ What are the XAI methods that have been used in the identified application domains and tasks?
* _RQ1.2:_ What are the different forms of providing explanations?
* _RQ1.3:_ What are the evaluation metrics for XAI methods used in different application domains and tasks?
# Determining Optimal Power Flow Solutions Using New Adaptive Gaussian TLBO Method
## 4 Numerical Results
### IEEE 57-Bus Test System
#### 4.3.2 Case 10: Minimizing the Fuel Cost While Improving Voltage Profile
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**Algorithms**} & \multicolumn{2}{c}{**Fuel Cost**} & \multicolumn{2}{c}{**V.D. (p.u.)**} & \multicolumn{2}{c}{**Power Losses (MW)**} \\ \hline AGTLBO & 1.901 & **41,678.310** & 1.618 & 15.064 \\ TLBO & 1.928 & 41,679.891 & 1.651 & 15.153 \\ ICBO (Improved Colliding Bodies Optimization) [11] & - & 41,697.330 & 1.317 & 15.547 \\ MPSO (Modified Particle Swarm Optimization) [16] & 1.944 & 41,678.676 & 1.340 & 15.127 \\ MFO (Multi-Flame Optimization) [16] & 2.004 & 41,686.412 & 1.294 & 15.611 \\ DSA (Differential Search Algorithm) [22] & - & 41,686.820 & 1.083 & - \\ LTLBO (Levy Teaching–Learning-Based Optimization) [28] & - & 41,679.545 & - & 15.159 \\ MICA (Modified Imperialist Competitive Algorithm) [45] & - & 41,683.048 & 1.449 & 15.266 \\ ARCBBO (Adaptive Real Coded Biogeography-Based Optimization) [51] & - & 41,686.000 & - & 15.377 \\ EADDE (Evolving Ant Direction Differential Evolution) [63] & - & 41,713.620 & 1.098 & 16.090 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._Figure 4: Single line diagram of IEEE 57-bus test system. \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{5}{c}{**Algorithms**} \\ \multirow{2}{*}{**Control Variables Settings**} & **EADDE (Evolving Ant Direction Differential Evolution)** & **MICA (Modified Imperialist Competitive Algorithm)** & **MICA-TLA (Modified Imperialist Competitive Algorithm)** & **AGTLBO** \\ \hline \(P_{C1}\) (MW) & 153.820 & 163.830 & 152.900 & 142.857 \\ \(P_{C2}\) (MW) & 83.670 & 99.993 & 99.805 & 88.694 \\ \(P_{C3}\) (MW) & 71.560 & 42.111 & 42.718 & 45.094 \\ \(P_{C6}\)(MW) & 54.790 & 29.671 & 37.046 & 71.816 \\ \(P_{C8}\) (MW) & 506.750 & 467.689 & 474.497 & 459.886 \\ \(P_{C9}\) (MW) & 79.930 & 100.000 & 77.787 & 96.653 \\ \(P_{C12}\) (MW) & 319.600 & 367.810 & 385.955 & 361.617 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Optimal control variables of IEEE 57-bus test system for Case 10.
## 4 Discussion
### Comparison to BP Neural Network  
Table 4 shows the tested compressive strength results from XU, G.Q. [37], predicted compressive strength by BP neural network model and predicted compressive strength by the CCSDOT model. The BP neural network model used 61 groups of data and the CCSDOT model only used 10 groups of data.  
We put the proportions of the 10 groups of concrete and the measured compressive strength at each age of group 6 into the model, then the 12 parameters can be calculated as shown in Table 5.  
Substitute the results in Table 5 into Equation (8) and we get \(ss_{x}\) as listed in Table 6  
In practical engineering, the use of fly ash and limestone flour is unfavorable to the early age compressive strength development of concrete compared to cement. Thus, \(ss_{FA}\) and \(S_{28 LF}\) are less than 0. And limestone flour is more unfavorable to the early age compressive strength development of concrete compared to fly ash, which is conformed to reality. And the use of fly ash and limestone flour is beneficial to the long-term compressive strength development of concrete compared to cement. Therefore, \(\lambda_{FA}\) and \(\lambda_{LF}\) are greater than \(\lambda_{C}\). Furthermore, \(S_{3}\) and \(S_{7}\) can be calculated based on Table 5 and Equation (2).  
Table 7 shows the relative error of the predicted results by the BP neural network and CCSDOT model. The performance of CCSDOT is better than BP at any age of concrete. The CCSDOT model is more accurate when compared to the BP neural network model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(ss_{C}\) & 1 \\ \(ss_{SL}\) & 0 \\ \(ss_{FA}\) & \(-\)0.0158 \\ \(ss_{LF}\) & \(-\)0.8599 \\ \hline \hline \end{tabular}
\end{table}
Table 6: 28-day compressive strength contribution coefficient of each gelled material.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{8}{c}{**Compressive Strength (MPa)**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Tested**} & \multicolumn{2}{c}{**BP Neural Network**} & \multicolumn{2}{c}{**CCSDOT Model**} \\ \cline{2-9}  & **3 d** & **7 d** & **28 d** & **3 d** & **7 d** & **28 d** & **3 d** & **7 d** & **28 d** \\ \hline
6 & 16.9 & 22.0 & 30.3 & 16.99 & 21.36 & 31.07 & 16.94 & 22.26 & 30.96 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Compressive strength of green concrete containing fly ash and limestone flour. BP: back propagation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Value (MPa)** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(S_{C}\) & 43.03 & \(i_{C}\) & 0.4742 & \(\lambda_{C}\) & 0.1585 \\ \(S_{SL}\) & 0 & \(i_{SL}\) & 0 & \(\lambda_{SL}\) & 0 \\ \(S_{FA}\) & \(-\)0.68 & \(i_{FA}\) & 1.3229 & \(\lambda_{FA}\) & 0.9454 \\ \(S_{LF}\) & \(-\)37.00 & \(i_{LF}\) & 1.6029 & \(\lambda_{LF}\) & 0.4746 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimal parameters of CCSDOT model.