esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Comprehensive Ground Surface Damage Forecast Method  
### Displacement and Deformation Forecast  
Currently, the probability integration method (PIM) is widely adopted to forecast the ground surface displacement and deformation due to underground mining [31]. According to its principle, the rock mass overlying the coal seam is taken as a loose discontinuous medium; the mining process is divided into multiple infinitesimals; the overlying rock mass subsidence triggered by each infinitesimal mining process is taken as one random event; and the influences from all infinitesimals upon the displacement and deformation on the ground surface are superposed as the displacement and deformation due to coal mining [32]. By now, the probability integration method has been incorporated in _the Code for Coal Pillar Retention and Coal Mining under Buildings, Water Bodies and Railways and in Main Shaffs and Lanes_ [33] for coal mining guidance in China. Therefore, the mining damage assessment system [34], based on the probability integration principle, was adopted here to forecast the displacement and deformation of ground surface.  
As shown in Figure 2, _sot_ is the coal seam coordinate system where \(\mathbf{s}\) direction is parallel to work face strike, \(t\) direction is parallel to work face dip, and \(\mathbf{xoy}\) is the projection of the coal seam coordinate system on the horizontal plane. If the \(i\)th infinitesimal \(A\) is mined in the coal seam, the values of both subsidence and horizontal displacement at the point \(A\) (\(x_{1}\), \(y_{1}\)) on the ground surface triggered by infinitesimal \(A\) will be shown as Equations (1) and (2) [35; 36]:  
\[W(x_{1},y_{1})=\sum_{i=1}^{n}\iint W_{\max}W_{e}(x_{1},y_{1})dsdt=\!\!\sum_{i= 1}^{n}\iint\frac{W_{\max}}{r^{2}}e^{-\frac{(x_{1}-s)^{2}+(y_{1}-t)^{2}}{r^{2} }}dsdt \tag{1}\]  
\[U(x_{1},y_{1},\varphi)=\sum_{i=1}^{n}\iint\frac{-2\pi bW_{\max}}{r^{2}}\Bigg{\{} [(x_{1}-s)\cos\varphi+(y_{1}-t)\sin\varphi]e^{-\pi\frac{(y_{1}-s)^{2}+(y_{1}- t)^{2}}{r^{2}}}\Bigg{\}}dsdt \tag{2}\]  
where \(W(x_{1},y_{1})\) refers to the quantity of subsidence triggered by underground coal mining at point \(A(x_{1},y_{1})\) on the ground surface; \(U(x_{1},y_{1},\varphi)\) refers to the quantity of horizontal displacement triggered by coal mining at point \(A(x_{1},y_{1})\) on the ground surface; \(\varphi\) is the horizontal displacement direction angle of point \(A\) on the ground surface; \(W_{\max}\) refers to the maximum value of ground surface subsidence whose calculation method is \(W_{\max}=\mathit{m\eta}\cos\alpha;m\) is the mining thickness on the work face; \(\eta\) is the subsidence coefficient; \(\alpha\) is the coal seam dip angle; \(r\) is the major influence radius whose calculation method is \(r=H/\tan\beta\); \(H\) is the coal seam bury depth; and \(\tan\beta\) is the tangent value of major influence angle.
# A Data-Driven Battery Degradation Estimation Method for Low-Earth-Orbit (LEO) Satellites
## 3 Proposed Method
### Proposed SOC and SOH Estimation Technique
In this study, voltage was modeled using a deep neural network (DNN)-based time-series approach through continuous learning, enabling the inclusion of all factors related to aging and other elements over the satellite's operational period. This comprehensive modeling approach ensures accurate estimation and can be effectively utilized for battery management and monitoring. Voltage estimation for battery SOH prediction is performed as follows. First, a variety of data are collected from the satellite to predict the state of the battery. The selected input features include position (x, y, and z coordinates), quaternion, solar angle, previous battery voltage, the SOC, battery current, mission mode, and remaining mission data time. The output feature is the battery voltage at the current time step. These features represent dynamic data that change over time. The collected data undergo a preprocessing phase, during which redundant sensor values are aggregated, outliers are removed, and the data are normalized to prepare them for model training. Additionally, data synchronization is applied to ensure temporal consistency across diverse input features, thus enhancing the quality of the training data and improving the model's performance. Figure 8: The concept diagram of the proposed SOH estimation technique. The preprocessed data are fed into a deep learning model based on the selected transformer network architecture. This model is designed to learn the temporal correlations within the time-series data of each feature. The transformer network for battery voltage estimation is configured to enable continuous learning, allowing it to adapt to operational variability over the satellite's lifespan. This approach ensures that the model remains robust and accurate as it incorporates the changes and trends associated with the satellite's ongoing operations. Next, the trained model continuously predicts the current battery voltage based on the input features. Specifically, to predict the battery voltage at time \(t_{0}\), the battery voltage at \(t_{0-1}\) and the remaining input features at \(t_{0}\) are used. This process is repeated iteratively to generate the entire time-series voltage dataset up to \(t_{n}\). These predictions provide the values needed to model the battery's \(R_{i}\) and SOH. Under the assumption that satellite mission conditions entail consistent SOC levels (e.g., 90%, 80%, etc. ), virtual input features can be fed into the model to obtain the corresponding voltage time-series data. This approach ensures there are consistent conditions for evaluating and analyzing battery behavior, thereby enhancing the accuracy of degradation modeling. Thus, the generated voltage and current data assumed for the mission timeline were used to estimate \(R_{i}\) via the UKF. This process enables the prediction of battery voltage and the evaluation of the SOH, providing critical support for mission design and satellite operations.
# Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m
###### Abstract  
The safe operation of high-voltage transmission lines ensures the power grid's security. Various foreign objects attached to the transmission lines, such as balloons, kites and nesting birds, can significantly affect the safe and stable operation of high-voltage transmission lines. With the advancement of computer vision technology, periodic automatic inspection of foreign objects is efficient and necessary. Existing detection methods have low accuracy because foreign objects attached to the transmission lines are complex, including occlusions, diverse object types, significant scale variations, and complex backgrounds. In response to the practical needs of the Yunnan Branch of China Southern Power Grid Co., Ltd., this paper proposes an improved YOLOv8m-based model for detecting foreign objects on transmission lines. Experiments are conducted on a dataset collected from Yunnan Power Grid. The proposed model enhances the original YOLOv8m by incorporating a Global Attention Module (GAM) into the backbone to focus on occluded foreign objects, replacing the SPPF module with the SPPCSPC module to augment the model's multiscale feature extraction capability, and introducing the Focal-EIoU loss function to address the issue of high- and low-quality sample imbalances. These improvements accelerate model convergence and enhance detection accuracy. The experimental results demonstrate that our proposed model achieves a 2.7% increase in mAP_0.5, a 4% increase in mAP_0.5.095, and a 6% increase in recall.  
transmission lines; foreign-object detection; YOLO; GAM; SPPCSPC; Focal-EIoU
## 6 Numerical Example
### Results Analysis  
#### 6.3.1 Convergence Analysis of Solution Approach  
Figure 9 shows the convergence processes of the improved MSA algorithm in solving the PSL-SUE and MNL-SUE models, respectively. It shows that the convergence processes are approximately the same, but the PSL-SUE model satisfies the convergence criterion at the 162 th iteration, whereas the traditional MNL-SUE model converges at the 180 th iteration. In Figure 9a, the convergence speed is breakneck before the 25 th iteration. When the iteration times reach 44, the gap value converges to less than 5%. When it reaches the 92 nd iteration, the gap value converges to less than 1%. Moreover, when it reaches the 162 nd iteration, the gap value converges to less than 0.1%. The solution approach in solving the PSL-SUE model has a good convergence of the proposed algorithm.
# Loop Closure Detection Based on Multi-Scale Deep Feature Fusion
## 4 Parameter Training  
### Training Method  
Since SPP in the feature fusion layer is a special maxing pooling layer, it has no parameters to be trained. Here we only need to train the parameters of the convolution network in the feature extraction layer. The algorithm in this paper consists of two branches with the same structure in the feature extraction layer and the feature fusion layer. For the loop closure detection problem, its positive sample indicates the same location, and the negative sample indicates non-loop closure. Loop closure always happens at different locations and with very few times, which means a large number of classes and small labeled samples. Taking these into consideration, this paper uses the Siamese [32] model to train.  
The Siamese network is mainly used in the field of face recognition can solve the classification problem with small sample data well. Figure 4 shows the training model.  
Contrastive loss function is used here. For the \(i\)-th pair of samples, we assume that the feature vectors of the SoftMax layer are \(f_{i1}\), \(f_{i2}\) respectively, and the contrastive loss function is:  
\[L=\frac{1}{2M}\sum\
olimits_{i=1}^{M}[y_{i}d_{i}^{2}+(1-y_{i})\max\left(m\arg in -d_{i},0\right)^{2}]\, \tag{6}\]  
where \(M\) is the number of sample pairs; \(d_{i}=\parallel f_{i1}-f_{i2}\parallel_{2}\) is the Euclidean distance of the two samples in the feature space, and \(y_{i}\) is the label of the \(i\)-th pair of samples. \(y=1\) is the positive sample, which means that the two images are similar and form a loop closure; \(y=0\) represents a negative sample, which means that the similarity of the two pictures is small and does not form a loop closure. The threshold \(margin\) is set to 1 in the experiment. When \(y=1\) and the loss function is \(L=\frac{1}{2M}\sum\
olimits_{i=1}^{M}y_{i}d_{i}^{2}\), if their Euclidean distance d in the feature space is large, the current training model is not good and its loss value increases. When \(y=0\) and the loss function is \(L=\frac{1}{2M}\sum\
olimits_{i=1}^{M}[\max\left(m\arg in-d_{i},0\right)^{2}]\), if their Euclidean distance in the feature space is small, the loss value of the model will become large. The contrastive loss function can express the matching degree of paired samples, and it can be used for the model training.
## 4 Principles of Fuel Cell
### Types of Fuel Cell
#### 4.1.5 Alkaline Fuel Cells (AFC)  
AFCs use an aqueous solution of potassium hydroxide (KOH) as the electrolyte to conduct ions between electrodes. Since the electrolyte is alkaline, the ion conduction mechanism is different from PEMFCs. The ion carried by the alkaline electrolyte is a hydroxide ion (OH), which affects several other aspects of the FC.  
The half reactions are as follows:  
Oxidation:  
\[\mathrm{H_{2}}+2\mathrm{\;OH^{-}}\to 2\mathrm{H_{2}O}+2\mathrm{e^{-}} \tag{12}\]  
Reduction:  
\[\mathrm{O2}\ +\ 4\mathrm{e^{-}}\ +2\mathrm{H_{2}O}\ \to 4\mathrm{\;OH^{-}} \tag{13}\]  
Water is required at the cathode by the oxygen reduction, thus water management is an important issue that is sometimes resolved by using waterproof electrodes and keeping the water in the electrolyte. The cathode reaction uses water from the electrolyte, whereas the anode reaction rejects its produced water. The excess water (2 mol per reaction) is evaporated outside the stack. AFCs can operate over  
Figure 3: Basic structure of fuel cell based electric vehicle [47].  
a wide range of pressures and temperatures, from 0.22 to 4.5 MPa and 80 to 230 \({}^{\circ}\)C, respectively. High-temperature AFCs also make use of a highly concentrated electrolyte-- highly concentrated enough that the ion transport mechanism changes from an aqueous solution to molten salt [48].
# The Influence of Laser Linewidth on the Brillouin Shift Frequency Accuracy of BOTDR
In this paper, how the BFS accuracy is determined by the linewidth of the laser source is theoretically analyzed and experimentally verified in a frequency-scanning BOTDR (FS-BOTDR). The broadening effect of BGS was analyzed, allowing for both the pulse width of the probe light and the frequency-scanning process in an FS-BOTDR. Then, the SNR of BOTDR with coherent heterodyne detection was calculated, treating the phase fluctuation as the primary noise source. The BFS accuracy was finally simulated numerically taking into account both the BGS and the SNR synthetically. We used five lasers with respective linewidths of 1.05 MHz, 101 kHz, 10.2 kHz, 3.1 kHz, and 98 Hz to perform temperature measurement in the BOTDR sensing system and verify the numerical simulation. The results of this research will be helpful to choose the laser linewidth for the BOTDR.
## 3 Results
### Estimation of Operating Costs and Emissions  
The estimation of operating costs was based on a time horizon of 12 years, which can be assumed to be the useful compactor truck lifespan. For the ICE-HPS, costs are represented by the diesel fuel consumption during garbage collection operations. Through the BSFC engine map, it is possible to obtain the specific fuel consumption (SFC) according to the engine speed and the mechanical power required for operating driven machine components. The overall fuel consumption was obtained based on the timing required by each task of the collection scenario and projected over the vehicle lifespan, considering 260 workdays per year. Figure 20 shows the regression analysis from a 5-year moving average of the diesel fuel price and 2-year moving average of the electricity price in Italy. Figure 19: Estimate of the battery pack state of health (SOH) as a function of the number of cycles. Costs related to the electricity required by the e-HPS were estimated based on the forecasted price of electricity that is demanded for the daily recharge of the battery, by means of a regression analysis of a 2-year moving average (Figure 20). Costs also include batteries, the electric motor, the inverter, the BMS, and the battery charger, in addition to the system installation and maintenance tasks. From the results obtained in Section 3.3, it is reasonable to expect to replace the battery pack twice within the vehicle lifespan. Costs related to all e-HPS components are listed in Table 2.  
Table 3 shows the daily consumption for both electricity and diesel fuel. According to [PERSON] (2011) [46], it is possible to consider, for general motor-driven systems, that 95% of total costs are related to electricity costs and the remaining 5% to installation, maintenance, and other related activities. For the prototype system, the installation costs are relevant because of the time required for the realization of the e-HPS, that is, up to 480 working hours. Therefore, it was assumed that maintenance costs constitute all of the remaining 5% of the total costs. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Model** & **Costs (€)** \\ \hline Battery & Samsung 2900 mAh INR18650-29E & 3360 \\ Motor & Miromax HFM-10K/48 V & 1000 \\ Inverter & Sevcon Gen4 48 V & 1500 \\ BMS & Emus G1 & 1500 \\ Battery charger & Zivan NG9 & 2000 \\ Installation & - & 14,400 \\ Maintenance & - & 864 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Costs related to the e-HPS components. Figure 20: Regression analysis from a 5-year moving average of the diesel fuel price and 2-year moving average of the electricity price in Italy. Source: Authors’ elaboration. Considering both systems, it is possible to determine the lifespan cost saving [47], which was obtained as the difference of total costs related to the most expensive system and the most economical one, taking into account all of the components and activities described in this section.
# Simplified Model Predicts Binder Behavior in Sand Mold Printing
## 1 Introduction
### Simplified Model  
The simulation of binder droplets in ANSYS Fluent presents challenges in complexity and computational time. To address these challenges, we propose a geometric approach where the binder droplet is modeled as a circular shape rather than an actual droplet, and the dynamic mesh feature is utilized to transform the initial boundary condition into a wall at a specified time \(\Delta t\). This method eliminates the need to simulate actual droplets, resulting in significant savings in computational resources and time. By adopting this simplified approach, we can efficiently predict the diffusion and penetration behavior of binder droplets with different materials and volumes in porous media of varying porosities. The successful implementation of this simplified model empowers researchers and engineers to expedite the simulation of binder behavior, thereby facilitating process optimization and advancing the understanding of binder jetting technology in the additive manufacturing field.
# Transitioning to Society 5.0 in Africa: Tools to Support ICT Infrastructure Sharing
## 1 Introduction
### Motivation  
Currently each service provider operates its own base station network in Nigeria. This means that many urban areas have a proliferation of mobile base stations and masts as shown in Figure 1. If colocation is adhered to, there will be a potential reduction in operational/expenditure cost and reduction in CO2 emissions. As there are over 53,000 BTSs in Nigeria, this reduction could be significant. The presence of so many BTSs in a residential environment as shown in Figure 2 is causing concern [9; 10]. The case of Nigeria is problematic as the BTSs are powered with diesel fueled electricity generators, posing a threat to the environment. Collapsing masts as shown in Figures 3 and 4 have been reported in different urban cities in Nigeria, killing several people and injuring others and destroying properties; moreover, the fumes and noise from generators were also reported as problematic [11]. Infrastructure sharing has become one of the most viable options for reducing the total number of BTSs/ masts and addressing the associated environmental issues [4].
# Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing
## 6 Conclusions  
In this paper, we proposed the SWIM ant colony optimization task scheduling algorithm based on load balancing (ACTS-LB). By improved the traditional ant colony optimization algorithm, the hardware performance quality index and load standard deviation function of resource nodes in SWIM were comprehensively adopted to improve the pheromone updating strategy of the algorithm. The ACTS-LB algorithm improved the defect that pheromone updating can easily fall into the local optimum and ensures that the system's load balancing requirements were maximized while the scheduling task was effectively completed. The experimental simulation using NS-3 showed that the ACTS-LB algorithm performs better than the classic min-min algorithm, ACO algorithm and PSO algorithm, as it can maintain SWIM in a balanced load state and improved the efficiency of system task scheduling and execution. However, in this paper, we presented the ACTS-LB algorithm, which was tested only in a simulated SWIM environment. The algorithm had not yet been tested in an actual SWIM application environment and the experimental results were not convincing. Next, in order to further verify the validity of the proposed ACTS-LB algorithm, we will try to simulate the algorithm in a real SWIM environment.  
[PERSON] contributed to presenting the ACTS-LB algorithm, performed evaluating experiments and was the main contributor to the manuscript; [PERSON] contributed to proposing the evaluating methodology and helped in the article processing.  
This work was funded by the National Natural Science Foundation for Youth of China (61601467). The Major Program of Natural Science Foundation of Tianjin (17 CZDJC30900) and the Fundamental Research Funds for the Central Universities of China (3122018D007).  
The authors declare no conflict of interest.
## 4 Experimental Results
### Metrics of Evaluation  
The evaluation of FBP models is mainly based on three metrics: Pearson correlation (PC), mean absolute error (MAE), and root-mean-square error (RMSE). PC ranges from \(1\) to \(-1\), where \(1\) indicates a perfect positive linear correlation, \(0\) denotes no correlation, and \(-1\) represents a perfect negative correlation. MAE and RMSE assess the model's efficiency, with values close to zero indicating a good performance. Given a set of n tests in samples, we have the following:  
\[PC=\frac{\sum_{i=1}^{n}\left(y_{t}^{i}-\overline{y}\right)\left(y_{p}^{i}- \overline{p}\right)}{\sqrt{\sum_{i=1}^{n}\left(y_{t}^{i}-\overline{y}\right)^{2 }}\sqrt{\sum_{i=1}^{n}\left(\left(y_{p}^{i}-\overline{p}\right)^{2}\right)}} \tag{8}\]  
\[MAE=\frac{1}{n}{\sum_{i=1}^{n}\left|y_{p}^{i}-y_{t}^{i}\right|} \tag{9}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}\left(y_{p}^{i}-y_{t}^{i}\right) ^{2}}} \tag{10}\]  
where the ground-truth beauty score is represented by \(y_{t}^{i}\), and the estimated score for the lth image is written as \(y_{p}^{i}\). Meanwhile, \(\overline{y}\) is the average of all of the beauty scores in the ground truth, and \(\overline{p}\) is the average of the scores that were predicted by the model. Additionally, a better performance is shown by higher PC values and lower MAE and RMSE values.