esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Test Results  
### Steady-State Ground Resistance Values, \(R_{DC}\)  
Before the impulse tests were carried out on these ground electrodes, measurements on all the ground electrodes (i-iii) were first conducted under steady-state conditions using a fall-of-potential (FoP) method to obtain the ground resistance values (\(R_{DC}\)). Table 2 summarises the \(R_{DC}\) values of all the ground electrodes used. The table indicates that the addition of EM substantially reduced the \(R_{DC}\) by more than 40%, as evidenced in the comparison between ground electrodes (i) and (ii). The appreciable fall in \(R_{DC}\) from (i) GEN to (ii) GEM is thought to be due to the high conductivity of the EM mixture, as well as the increased conductive area resulting from the placement of EM slurry on and under the copper strips or around the vertical rod electrode, which improved the \(R_{DC}\). A steady conduction pattern of ground electrode (ii) GEM can also be seen from the close \(R_{DC}\) values between (ii) GEM and (iii) GEM_10L, where the addition of 10 L of water content did not affect the \(R_{DC}\) value. As more water was poured into the soil, close \(R_{DC}\) values between (ii) and all the ground electrodes (iii) were seen. The highly conductive nature of ground electrode (ii) GEM shows that the current flowed steadily, suggesting that any changes to the soil or ground electrode may not significantly change the \(R_{DC}\). This aligns with the findings presented in the standards [22], in which, for the grounding systems with low \(R_{DC}\) values, variations in some parameters (water and salt content) will not significantly change the \(R_{DC}\) values.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Conditions**} & \multicolumn{4}{c}{**Soil Resistivity Profile**} \\ \cline{2-5}  & \(\mathbf{\rho_{1}\) (\(\Omega\)m)} & \(\mathbf{\rho_{2}\) (\(\Omega\)m)} & \(\mathbf{h_{1}\) (m)} & \(\mathbf{h_{2}\) (m)} \\ \hline Before excavation and installation work & 202.15 & 341.12 & 2.71 & Infinite \\ After the addition of EM and Water Content (WC) & 207.79 & 289.31 & 2.95 & Infinite \\ \hline \hline \end{tabular}
\end{table}
Table 1: Two-layer soil resistivity profile using CDEGS.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Ground Electrodes**} & \multicolumn{1}{c}{**Measured Ground Resistance Value (\(\Omega\))**} \\ \hline GEN & 39.7 \\ GEM & 22.2 \\ GEM\_10L & 22.5 \\ GEM\_20L & 22.8 \\ GEM\_50L & 23.1 \\ GEM\_100L & 22.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measured ground resistance values, \(R_{DC}\).
###### Abstract  
Fine-scale population distribution information is crucial for applications in urban public safety, planning, and management. However, when using machine learning methods for population spatialization, issues such as data overfitting and limited interpretability need to be addressed. This study introduced a combined approach using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP) to estimate population spatialization at various grid scales and interpret the key influencing factors, then we applied accuracy evaluation metrics and landscape ecology indices to identify the optimal grid scale. The results showed that the XGBoost model outperformed the WorldPop dataset in accuracy across all grid scales, with determination coefficients (R\({}^{2}\)) consistently exceeding 0.83. The SHAP analysis revealed that the primary influencing factors were the address, access, and dwelling characteristics of points of interest (POIs). The influence of these factors showed regional variations, with urban centers having a strong positive effect, while the negative influence increased with the distance to suburban areas. The population density estimates across different grid scales consistently exhibited a spatial gradient pattern of decreasing density from the urban center toward suburban areas. Based on comprehensive evaluations of accuracy and spatial heterogeneity, the 100 m grid was identified as the optimal scale for Shanghai's population spatialization. The proposed XGBoost-SHAP population spatialization method demonstrates high reliability and generalizability, effectively explaining the heterogeneity of population distribution. This approach not only provides critical decision-making support for urban planning but also serves as a methodological reference for high-resolution population spatialization studies in other cities.  
population spatialization; XGBoost; SHAP; grid scale; feature variable Article
# Differential Evolution and Fuzzy-Logic-Based Predictive Algorithm for V2G Charging Stations
###### Abstract  
This work proposes a differential evolution algorithm to control a vehicle-to-grid (V2G) system based on photovoltaic generation and energy cost curves, and constraints associated with the power converters' operation, battery charging strategy, and initial budgets. The algorithm is designed to trade off the batteries' state of charge and the profits gained from selling energy to the grid. To achieve this balance, a fuzzy controller is employed and acts based on forecasts of the photovoltaic generation and the cost of electricity, within prediction windows of 120 min, adapting the batteries' rate of charging or discharging. Simulation results show that for different curves and different initial budgets, the target state of charge is reached at the end of the time horizon. By evaluating the proposed scheme under different scenarios, the algorithm's performance is proven to be suitable for future practical deployment.  
V2G applications; differential evolution algorithm; fuzzy controller; charging optimization Article
# A Novel Reconfigurable Vector-Processed Interleaving Algorithm for a DVB-RCS2 Turbo Encoder
## 5 Portability and Architectural Considerations
### Adaptation to FPGA Platforms  
The phased structure of the algorithm (Load \(\rightarrow\) Execute \(\rightarrow\) Store) aligns naturally with hardware description paradigms:  
* Each phase can be pipelined in a hardware design, allowing concurrent execution and reduced latency [38].
* Data access regularity makes it feasible to construct dedicated memory controllers and buffer interfaces using standard RTL or HLS tools [39, 40].
* The deterministic operation flow makes the algorithm highly suitable for static scheduling and resource sharing--both essential in FPGA-based designs [41].
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 5 Conclusions  
In this paper, the Bloch Spherical Quantum Genetic Algorithm model fused with the least-squares principle for a single robot and the Bloch Spherical Quantum Bee Colony Algorithm for multi-robots are proposed to seek the optimal path. From a progressive point of view, consider the least-squares method and quantum optimization; the discrete grid division approach deals with the unpredictable evacuation problem caused by emergencies. In the case of uncertain building conditions, a fusion least-squares method is established for studying the three-dimensional path planning problem. The principle of the quantum model and quantum optimization algorithm based on a Bloch sphere is designed. On the basis of verifying the validity of the algorithm, the algorithm comparison is carried out, and the running time and fitness of the algorithm are compared, as well as the influence of stability.  
Experiments show that the rasterization of building floors is more convenient for finding the shortest safe path. Compared with ACA, GA and ABC, the methods in this paper can effectively deal with the problems of slow convergence and long execution time caused by the traditional algorithms. The solution to these problems can enable humans to quickly evacuate from dangerous areas when an emergency occurs, thereby improving the safety factor for human activities in the building.  
Future research will focus on improved qubit encoding, which can make the food source more evenly traversable throughout the solution space to improve the search efficiency of the algorithm.  
Conceptualization, Z.D. and H.L.; methodology, Z.D.; software, Z.D.; validation, Z.D. and H.L.; formal analysis, Z.D.; investigation, H.L.; resources, Z.D.; data curation, H.L.; writing--original draft preparation, Z.D.; writing--review and editing, H.L.; visualization, H.L.; supervision, Z.D.; project administration, H.L.; funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science Foundation of Harbin Commerce University, grant number 2023-KY7 WF-0983.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Data Preparation  
SRT data underwent four processes before it could fit into the 1D-CNN model. The process started with handling categorical data, pre-processing the data using min-max scaler--famously called normalization--, reshaping the scaled data, and splitting the data into training and testing sets.  
The categorical variables were identified in the data collection phase, which are the screen and plugging sign. As the group in screen variable is nominal, the one-hot encoding method was used to change the data from the character form to numeric form. For example, if the screen column has three categorical groups: premium, wire wrap, and metal mesh, three new columns were created with the value of 0 and 1. A premium column with a value of 1 means the observation used a premium screen while 0 refers to the other screens, as in wire wrap or metal mesh. As for the plugging sign, the group can be converted into a number using a label encoder without creating a new column. A value of 0 represents screens that do not plug, while 1 is assigned when the screen plugging occurs for respective observations.  
Next, normalization was performed to transform variables within the range of 0 and 1. The normalized data have a mean of zero and a standard deviation of one. Using unnormalized data to train the neural network can lead to an unstable or slow learning process [42]. The min-max scaler for normalization was computed as follows:  
\[\hat{x}=\frac{x_{i}-\min(x)}{\max(x)-\min(x)} \tag{3}\]  
In Equation (3), \(x_{i}\) is the value of the variable for the \(i^{th}\) observation, \(\min(x)\) is the minimum value of variable, and \(\max(x)\) is the maximum value of the variable.  
Subsequently, the scaling data needed to be reshaped into three dimensions, representing the row, column, and channel. Lastly, the reshaped data were divided into two sets using a 70:30 ratio for the training and testing set.
## 5 Performance when Using CCIS in AAPS Sky Mesh Network Applications
The performance in terms of eye pattern and BER of the inter-platform communication system is conducted by making a simulation for the proposed model given in Figure 10.  
The design represents a transmitter side in the SS of SS1, while the receiver is shown in SS2. The transmission and channel parts, as shown in Figure 14a, include the following:  
* A PRBG;
* NRZ pulse generator for information signal;
* Optical source (LED, CW laser, and CCIS) to make the encoder of SAC-OCDMA
* using FRS codes;
* An MZM modulator;
* An FSO channel. The avalanche photodiode (APD) is used in the complementary subtraction detection technique and the LPF is used in the receiver part of SS2 propagation, as shown in Figure 14b. The parameters of the FSO system are set according to typical industry values in order to simulate a real-world environment as closely as possible. Table 5 indicates the parameters that are used in the FSO channel [25; 26]. Figure 13: Proposed sky mesh network using AAPS. Figure 15 depicts the eye openings for CW laser, LED optical sources, and CCIS-based AAPS-OCDMA systems at 1.25 Gbps data rate with a total of 16 subscribers at 0 dBm received power. The results show that the eye-opening in the CCIS design is quite wide compared to the pure LED-based system, and the eye-opening is less with pure laser. Also, the distance is the main factor that limits performance regardless of the type of optical source. The maximum distance that the proposed system can propagate with BER less than is 6, 38, and 60 km, respectively, when LED, CCIS, and CW laser sources are used. The simulation experiments showed that the CCIS design significantly improves system performance because the combined light source (CW laser + LED) increases the effective transmission power of the first system in proportion to the angle of permittivity of the laser beam received. The possibility of using the FSO system in the sky mesh network based on the CCIS-OCDMA system leads us to our conclusion. \begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Data rate & 1.25 Gbps \\  & 6 km when using LED source \\ Propagation range (\(R\)) & 60 km when using CW laser source \\  & 38 km when using CCIS \\ Transmitter aperture diameter (\(d_{Tx}\)) & 17 cm \\ Receiver aperture diameter (\(d_{Rx}\)) & 22 cm \\ Beam divergence angle (\(\theta\)) & 1 mrad \\ Attenuation for weather conditions (\(\beta\)) & 0.14 dB/km \\ \hline \end{tabular}
\end{table} 
Table 5: FSO link parameters based on the CCIS design [25; 26]. Figure 14: Proposed FSO model based on CCIS design used in AAPS: (**a**) transmitter, (**b**) receiver.
# A Compact Wearable Textile Antenna for NB-IoT and ISM Band Patient Tracking Applications
## 2 Antenna Design Methodology
### Radiation Pattern Measurements  
Figure 17a-f compares simulated and measured radiation patterns of the gain in the H-plane and E-plane. The simulated and measured peak gains are demonstrated in Table 3 (boresight direction (\(\theta\) = 0\({}^{\circ}\))). In Table 3, measured gain values are smaller than the simulated ones because the actual human body is larger than the cuboid phantom. At 1.8 GHz, according to Figure 17a,b, radiation patterns of the antenna in both the H (\(\phi\) = 90\({}^{\circ}\)) and E (\(\phi\) = 90\({}^{\circ}\)) planes are nearly omnidirectional, and at 2.45 GHz, as shown in Figure 17c,d, most of the radiation is in boresight direction. However, at 5.8 GHz, the antenna shows a directive pattern (boresight), as shown in Figure 17e,f. Additionally, simulated 3D radiation patterns of the wearable textile antenna are shown in Figure 18a-c at 1.8, 2.45, and 5.8 GHz, respectively. Figure 18 clearly shows that the proposed textile antenna efficiently radiates outside the body at all three frequency bands (NB-IoT 1.8 GHz, RFID ISM 2.45 GHz, and 5.8 GHz), demonstrating its feasibility for stable wireless communication outside the body.  
Subsequently, the radiation performance of the proposed antenna is measured in different bending scenarios, as shown in Figure 19a-f. When the antenna is bent in a 45 mm and 35 mm radius, the antenna radiated in a boresight direction. Similarly, when it is bent in a 25 mm radius, a slight tilt in the radiation pattern in the E-plane is observed, as shown in Figure 19a-f. Peak gain values in different bending scenarios are shown in Table 4.  
Figure 17: Comparison of simulated and measured radiation patterns of the proposed antenna (**a**) H-plane at 1.8 GHz (**b**) E-plane at 1.8 GHz (**c**) H-plane at 2.45 GHz (**d**) E-plane at 2.45 GHz (**e**) H-plane at 5.8 GHz (**f**) E-plane at 5.8 GHz.  
Figure 19: Comparison of measured radiation patterns in different bending scenarios (at 25 mm, 35 mm, and 45 mm) (**a**) H-plane at 1.8 GHz (**b**) E-plane at 1.8 GHz (**c**) H-plane at 2.45 GHz (**d**) E-plane at 2.45 GHz (**e**) H-plane at 5.8 GHz.  
Figure 18: 3D radiation patterns of the wearable textile antenna: (**a**) 1.8 GHz, (**b**) 2.45 GHz, (**c**) 5.8 GHz.
## 3 Results and Discussion
### Stability Analysis  
Figure 6a shows the storage stabilities of immobilized and free Lac. With an increasing storage time, the relative activities of free and immobilized Lac decreased, but free Lac was more rapidly inactivated than immobilized Lac. Free Lac lost 70% of its activity after incubation at 4 \({}^{\circ}\)C for 30 days, whereas immobilized Lac lost less than 40% of its activity, demonstrating that it had a higher stability in refrigerated storage than free Lac. It is well-known that storage stability is an important advantage of immobilized enzymes for application in bioprocesses. Figure 6b presents the thermal stability of immobilized Lac. It can be seen that the thermal stability of immobilized Lac on the \(\text{P}p\text{PD}/\text{Fe}_{3}\text{O}_{4}\) nanocomposite was improved to a certain degree. Enzyme immobilization could greatly reduce the costs of using enzymes in practical applications. Immobilized enzymes often have other drawbacks such as low rates of enzyme recovery and recycling [12; 30; 31; 32; 33]. However, it has been proven that the use of magnetic carriers can dramatically improve the efficiency of enzyme recovery and recycling [34; 35; 36]. Thus, the magnetically separable Lac-\(\text{P}p\text{PD}/\text{Fe}_{3}\text{O}_{4}\) nanocomposite may contribute to enzyme recovery and reduce costs. As shown in Figure 6d, Lac immobilized on magnetically separable Lac-\(\text{P}p\text{PD}/\text{Fe}_{3}\text{O}_{4}\) nanocomposite is a promising candidate for the development of a \(\text{P}p\text{PD}/\text{Fe}_{3}\text{O}_{4}\) nanocomposite.  
Figure 4: Schematic illustration of Lac immobilization on \(\text{P}p\text{PD}/\text{Fe}_{3}\text{O}_{4}\) nanocomposite.  
Figure 5: Effects of (**a**) \(\text{pH}\) and (**b**) temperature on Lac activity.  
P_p_PD nanofibers could be efficiently separated from the reactants using a magnet. After each reaction, the immobilized enzyme was harvested and the recovery of enzyme activity was determined. With an increasing number of use and recovery cycles, the activity of the immobilized Lac gradually decreased owing to inactivation and product loss during each run (Figure 6c). However, more than 75% of its initial activity was retained after eight cycles. The activity of immobilized Lac was clearly stabilized compared with that of free Lac. Similar results have been reported for other nanobiocatalysts [37; 38].  
The above results showed that the immobilized Lac had a higher stability than its free counterpart. The improved storage and operational stabilities of the immobilized Lac can be attributed to the covalent bonding between the Lac and P_p_PD nanofibers, which improves the enzyme's stability against conformational denaturation under extreme conditions.
# Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements
## 3 TTP-GPR for Coal Seam Thickness Prediction
### The GP Algorithm  
GP is a kind of machine learning method that was developed on the basis of the combination of Bayesian theory and statistical learning theory, which has a greater advantage in dealing with small samples and nonlinear problems. Meanwhile, it has to be able to calculate the confidence level for predicted values, so that the output results have the meaning of probability.  
Gaussian process regression belongs to the supervised learning methods. Its training process can better train and learn the training data, and it contains fewer parameters. For the data \(\text{D}=\{(x_{i},z_{i})\}_{i=1}^{n}\), the input vectors are \(\text{x}_{i}\in R^{d}\) and the outputs are \(\text{z}_{i}\in R\). In a finite set of data \(\text{D}\), \(f(\text{x}_{i})\) can be a set of random variables, and its joint distribution of arbitrary dimension and finite variables obey the Gaussian distribution. The Gaussian distribution can be expressed by its mean function \(\text{m}(\text{x})\) and covariance function \(k(x,x^{\prime})\). The covariance matrix embodies the intrinsic relation of different data points in the Gaussian process algorithm. The Gaussian process is defined as \(f(x)\sim GP(m(x),k(x,x^{\prime}))\).  
It is assumed that joint distribution of training points X with y and test points \(x_{*}\) with \(z_{*}\) is jointly normal under a Gaussian process:  
\[\left[\begin{array}{c}z\\ z_{*}\end{array}\right]\sim N\Bigg{(}0,\left[\begin{array}{cc}\text{K}( \text{X},\text{X})+\sigma_{\text{n}}^{2}I&\text{K}(\text{X},\text{X}_{*})\\ \text{K}(\text{X}_{*},\text{X})&\text{K}(\text{X}_{*},\text{X}_{*})\end{array} \right]\Bigg{)} \tag{14}\]  
Figure 6: Form of data transformation.  
where K is the covariance matrix that is created by the chosen kernel function, \(\sigma_{n}^{2}\) is the observation noise, and \(I\) is the identity matrix. In general, the Gaussian kernel function (RBF) is chosen. The conditional probability \(p(z_{*}|z)\) follows a Gaussian distribution:  
\[z_{*}|z\sim N\bigg{(}\text{K}(\text{X}_{*},\text{X})\Big{(}\text{K}(\text{X}, \text{X})+\sigma_{\text{n}}^{2}I\Big{)}^{-1}\text{z},\text{K}(\text{X}_{*}, \text{X}_{*})-\text{ K}(\text{X}_{*},\text{X})\Big{(}\text{K}(\text{X},\text{X })+\sigma_{\text{n}}^{2}I\Big{)}^{-1}\text{K}(\text{X},\text{X}_{*})\bigg{)} \tag{15}\]  
The best estimate for \(z_{*}\) is the mean of this distribution:  
\[z_{*}=\text{K}(\text{X}_{*},\text{X})\Big{(}\text{K}(\text{X},\text{X})+\sigma _{\text{n}}^{2}I\Big{)}^{-1}\text{z} \tag{16}\]  
The Gaussian process regression algorithm [10] assumes that its prior information distribution satisfies the Gaussian distribution in predicting data, and its probability density among data points depends on the kernel function. The kernel function describes the internal relations of data points in the Gaussian process model. It is usually selected by human experience, which may cause the relations between points to be inaccurate. As a result, it cannot accurately model the spatial distribution of the coal seam.
# A New Sensor System for Accurate 3D Surface Measurements and Modeling of Underwater Objects
## 2 Materials and Methods
### New Sensor System
#### 2.1.3 Geometric Modeling  
To obtain optimal geometric 3D sensor modeling, an extensive theoretical geometrical analysis of the optical sensor components (cameras, lenses, and glass cover) and the additional important conditions and features was performed.  
Furthermore, refraction of the vision rays at the interfaces between air and glass and glass and water has to be considered in the geometric camera modeling. This was done using an extended pinhole model (see [42; 43]). Simulations revealed that the systematic measurement error of our stereo system with the actual geometric parameters was weak over the complete measurement distance (2.0 m \(\pm\) 0.4 m), even if a simple pinhole model including distortion correction was used.  
As a result of the camera model analysis, an extended pinhole camera model was selected as a 3D sensor model, leading to minimal systematic measurement errors. A detailed description of the geometric property analysis is given in [43].