esa-sceva/satcom-chunk-collection huggingface dataset:
# IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
## 7 Simulations and Discussions
### Simulation Result  
Simulations are carried out with parameters defined in Section 7.1. Figures 6-8 illustrate the planning result. If an IPB flies out of the approved airspace, its subsequent data will not be displayed in figures.  
From Figures 6-8, it can be seen that the proposed planning algorithm takes two measures to improve IPCB geometry configuration.  
The first measure is to adjust IPB flight altitudes by changing their masses. It makes most IPBs in the constellation fly at an altitude of around 21 km, where wind velocity is small. This measure lengthens IPBs' flight time in the approved airspace, benefiting from keeping the number of available IPBs in the IPCB.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**IPB** & **Longitude** & **Latitude** & **Flight Altitude** \\ \hline IPB 1 & 107.4\({}^{\circ}\) E & 39.4\({}^{\circ}\) N & 21 km \\ IPB 2 & 108.6\({}^{\circ}\) E & 39.4\({}^{\circ}\) N & 21 km \\ IPB 3 & 108.6\({}^{\circ}\) E & 40.6\({}^{\circ}\) N & 21 km \\ IPB 4 & 107.4\({}^{\circ}\) E & 40.6\({}^{\circ}\) N & 21 km \\ IPB 5 & 107.8\({}^{\circ}\) E & 40\({}^{\circ}\) N & 22 km \\ IPB 6 & 108.2\({}^{\circ}\) E & 40\({}^{\circ}\) N & 22 km \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial geometry configuration of the IPCB.  
Figure 5: Initial geometry configuration, GDOP distribution of the IPCB, and user distribution: (**a**) initial horizontal layout of the IPCB; (**b**) initial flight altitude of the IPCB; (**c**) user distribution in the service area; (**d**) GDOP distribution of the IPCB with initial geometry configuration.  
Figure 8: GDOP of the IPCB in its service duration.  
Figure 6: Flight altitude of the IPCB in its service duration.  
Figure 7: Horizontal trajectory of the IPCB in its service duration.  
The second measure is to make IPBs fly across the quasi-zero wind layer. This measure utilizes the reverse zonal wind direction to change IPBs' movement direction, which can extend IPBs' flight time in the approved airspace and adjust IPBs' horizontal trajectories, thus improving IPCB geometry configuration.
## 1 Introduction
Further, most of the electromagnetic harvesters generate low AC voltage, where rectification is required, and sometimes the required energy for that is not provided by the harvester. Moreover, matching the internal resistance of the energy harvester and the energy-management circuit is required to achieve the maximum power output for the WSN. Several works investigated such challenges; nevertheless, most of them reached only up to 20-80% of efficiency [20] in the case of passive energy management solutions or up to 90% [20] using an active solution with a wide-band input power level, which is not recommended for an autonomous WSN due to their large size and complexity. In this context, few solutions have realized an autonomous solution-based energy harvesting and wireless sensor node for IIoT devices in industry and predictive maintenance. In [20; 21; 22], a sensor network powered through an electromagnetic harvester was developed for the diagnosis of ball bearings; nevertheless, the results showed that the system required 100 mW to measure and transmit the information, which was quite high. Further, in [22], a wireless sensor node was developed for structural health monitoring and had the advantage of low power consumption. Nevertheless, the solution had a limitation in terms of the photovoltaic source, which led to a long charging time of 30 min. In other research work [23], 5 min was required for the wireless sensor node to transmit the information, and the solution was based on the use of a thermoelectric harvester. In this case, the placement of the harvester is crucial and specific to ensure the required energy, which limits its use. To conclude this research, there is always a challenge in ensuring a system with low power consumption and with a continuous energy source, which is the aim of this paper. This paper proposes a passive solution for predictive maintenance through an autonomous WSN powered by a vibration energy harvester with its passive energy management solution. The proposed solution consists of continuous measurement of data to predict failures and to avoid catastrophic damage to the equipment through the converter response. In particular, it enables the continuous measurement of acceleration and alerting through the WSN in case of high acceleration presence, which is considered here at the level of 0.25 g. This paper is structured in mainly four sections. Section 2 presents the system concept of this work for fault detection. Section 3 is devoted to the model and evaluation of the electromagnetic harvester. Section 4 introduces the passive energy management solution and its evaluation. Section 5 describes the selected wireless sensor node and its performance. Finally, the feasibility of powering the wireless sensor node through the developed energy harvester system is discussed.
## 3 Proposed Control Methods
### Snn
With regard to the microgrid control implementation, SNNs offer a lightweight yet effective solution for real-time control. They efficiently manage voltage, frequency, and load dynamics through their simple design and rapid processing capabilities. These attributes make them particularly suitable for deployment on resource-constrained edge devices, where rapid decision-making is critical. Moreover, SNNs enhance microgrid reliability by enabling real-time fault detection and improving stability during islanded operation. However, their ability to handle highly nonlinear, complex, or time-dependent microgrid dynamics remains limited, as they lack the depth required to model intricate system behaviors accurately. To ensure robust performance, SNNs depend heavily on high-quality training data, necessitating careful data collection, preprocessing, and feature engineering to mitigate biases and improve generalization. Proper dataset selection, noise reduction techniques, and balanced representation of operational scenarios are essential steps to maximize their effectiveness in real-world microgrid applications.
## 3 Network Scenario
### Traffic Model  
This study implements the traffic model presented in [52]. The radio data are encapsulated [9] and sent by the RU and processing nodes periodically, within a fixed transmission window, as bursts of Ethernet frames. The frames have a fixed length, and the number of frames forming a data burst is called the burst size. The transmission window length (i.e., the period between the burst sending times) depends on the specific 5G radio numerology applied, where the numerology represents a set of 5G radio transmission parameters [51]. It equals \(66.6\times 2^{-\mu}\)\(\mu\)s, where the numerology is identified with an integer \(\mu\), where \(0\leq\mu\leq 4\)[51,52]. The bit rates of data flows correspond to the amount of radio resources assigned at the RUs to particular network slices, and they may differ between the slices. This work assumes the maximum bit rates of flows available to network slices, corresponding to a whole slice utilization, during the network-planning process.
## 5 Performance Evaluation
### Experiment Setting  
To provide a comprehensive comparison, four common CNNs designed for running on edge devices are adopted, namely AlexNet [34], ResNet110 [35], MobileNet [36] and SqueezeNet [37]. All of these CNNs have a diverse number of layers, memory requirements, and performance. The CNN training and inferencing is based on the Cifar-10 data set [38], which consists of \(32\times 32\) color images divided into ten classes.  
In addition, a simulated distributed system with seven devices with different configurations is set up as shown in Table 1.  
To achieve the performance description of the candidate devices (\(D\) in Algorithm 3), PALEO [39] is adopted, which is an analytical performance model that can efficiently and accurately model the expected scalability and performance under a given deployment assumption. Based on PALEO, the memory requirements and execution time of different DNN layers on each given device are evaluated. In PALEO, the execution time of a single DNN layer consists of the time it takes to receive input from the upper layer, the time it takes for the current layer's computation, and the time it takes to write the output to local memory. Based on this, the energy consumption required to perform a given layer on a specific device is calculated by multiplying device power with execution time.  
The other parameter values in Algorithm 3 are set as follows. The initial size of the partitioning chromosome population is five. In order to ensure fairness in comparing with the basic GA, the population size of deployment chromosomes is set to 50 fixedly, which is the same as population size set in basic GA. In both basic GA and the improved GA, crossover probability is 0.5, mutation probability is 0.01, the maximum iteration number is 200, and the algorithm will be terminated when the optimal fitness value remained unchanged for 50 consecutive generations. In basic GA, the chromosomes are generated according to the structure shown in Section 4.1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Device No.** & **GFLOPS** & **Battery Capacity (J)** &
\begin{tabular}{c} **I/O Bandwidth** \\ **(MBPS)** \\ \end{tabular} \\ \hline
1 & 0.218 & 250 & 140.85 \\
2 & 9.92 & 20 & 1525.63 \\
3 & 0.213 & 500 & 135.89 \\
4 & 13.5 & 10 & 1698.25 \\
5 & 0.247 & 300 & 140.91 \\
6 & 3.62 & 200 & 159.45 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance parameters of edge devices.  
The experiments are executed on a laptop with an AMD Ryzen7 5700U CPU and 16 GB memory in a Pycharm environment. The following results are collected by running a same algorithm ten times as a group.
## 4 Results
### Comparative Experiment
#### 4.4.2 Comparison on the ISIC2018 Data Set
image feature information, reduce the loss of feature information, correctly process the image edge information, and make the segmentation results from more clear contour, more detailed edges, and higher segmentation accuracy. Table 7 gives an objective comparison of this network with other reference networks on the ISIC2018 data set. The accuracy of this network is only 0.0003 lower than that of the first MS RED network, ranking second, and other indexes are also slightly lower. The similarity coefficients of Spe and Dice reach the best, but the overall performance is still better than that of most existing models. FAT-Net adds an encoder based on Transformers to the traditional CNN encoder to capture deep-level information and global context information, making the sensitivity of the network reach the highest. The Literature MS RED network uses multi-residual decoding and fusion modules to extract features; the multi-scale residual decoding and fusion module adaptively fuses multi-scale information. At the same time, a new multi-resolution and multi-channel feature fusion module is proposed. So that the network can extract more feature information or obtain better segmentation results. Overall, the performance of the proposed network is good on the whole; it can effectively segment skin lesion images, provide a new method for medical diagnosis, and improve diagnostic efficiency. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Network** & **Acc** & **Sen** & **Spe** & **Dice** & **Jac** \\ \hline
[48] & CPF-Net & 0.9602 & 0.8942 & 0.9617 & 0.8963 & 0.8210 \\
[54] & CU-Net & 0.9503 & 0.8967 & 0.9623 & 0.8847 & 0.8214 \\
[51] & FAT-Net & 0.9578 & 0.9100 & 0.9699 & 0.8903 & 0.8202 \\
[45] & MS RED & 0.9619 & 0.9049 & - & 0.8999 & 0.8345 \\ Ours & MSREA-N & 0.9616 & 0.8942 & 0.9729 & 0.9284 & 0.8148 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Objective comparison of different networks on ISIC2018 data set. Figure 10: Different network segmentation results on ISIC2018 data set. Images (**a–f**) represent 6 different images of skin diseases. From left to right, the images are Image, mask, U-Net segmentation results, CE-Net segmentation results, DeepLabv3+ segmentation results, CPF-Net segmentation results and the segmentation results of our algorithm. Details of each network partition are shown in red circles.
# Effects of Caprolactam Wastewater on Algal Growth and Nutrients Removal by _Arthrospira platensis_
## 3 Results and Discussion
### Investigation of Factors Inhibiting the Growth of _A. platensis_
#### 3.2.1 Effects of Organic Matter in WCP on the Growth of _A. platensis_  
Figure 4A shows the effects of CPL on the biomass concentration of _A. platensis_. After 4 d of cultivation, a biomass concentration of 0.53 g/L was obtained in the control. Similar biomass concentrations were also observed in the cultures with 100, 300, 500, and 1000 mg/L of CPL addition, thereby indicating that CPL concentrations as high as 1000 mg/L had no negative effect on the growth of _A. platensis_.  
CHN at a concentration as high as 1200 mg/L had no negative effect on the growth of _A. platensis_ (\(p>0.05\)), as demonstrated by the similar biomass concentration compared with the control (Figure 4B). Higher CHN concentrations (1800 mg/L and 2000 mg/L) significantly (\(p<0.05\)) inhibited the growth of _A. platensis_ by 9.6% and 13.5%, respectively. CHL at a concentration as high as 600 mg/L had no negative effect on the growth of _A. platensis_ (\(p>0.05\)). Further increases in the CHL concentration to 800 mg/L and 1000 mg/L significantly (\(p<0.05\); Figure 4C) inhibited the growth of _A. platensis_ by 7.3% and 11.9%, respectively. CHO at a concentration as high as 250 mg/L had no adverse effects on the growth of _A. platensis_ (\(p>0.05\)), whereas an increase in the CHO concentration significantly (\(p<0.05\)) inhibited the growth of _A. platensis_ (Figure 4D).  
In this study, the CHN, CHL, and CHO concentrations in the 50% mixed WCP were 82.3, 16.4, and 23.0 mg/L, respectively, and no CPL was detected (Table 1). The minimum concentrations of CHN, CHL, and CHO, which had significant effects on _A. platensis_ growth, were 1800, 800, and 500 mg/L, respectively (Figure 4). The CHN, CHL, and CHO concentrations in the 50.0% mixed WCP were far lower than those that inhibited the growth of _A. platensis_, and will further decreased after dilution. However, _A. platensis_ growth was inhibited by 12.5% and 50.0% WCP (Figure 2). Thus, CHN, CHL, and CHO could not  
Figure 4: Effects of organic matters on the growth of _Arthospira platensis_. (**A**) Caprolactam (CPL), (**B**) cyclohexanone (CHN), (**C**) cyclohexanol (CHL), (**D**) cyclohexanoneixime (CHO). Error bars represent the standard deviation (\(n=3\)). Data with different letters indicate significant differences (\(p<0.05\)) between the control and treatment.  
explain the inhibitory effect of WCP on _A. platensis_ growth. In other words, the growth of _A. platensis_ may be inhibited by other factors in WCP.
## 3 Results
### Case 3--Effect of Running Speed on Track Dynamics  
The running speed has a significant influence on track dynamics. Figure 13 indicates the relationship between the vertical rail accelerations and running speed. The same trend as in Cases 1 and 2 is clearly evident.  
Figure 12: Effects of harmonic order on dynamics in rail-supporting structures: (**a**) displacement versus order and (**b**) von Mises equivalent stress versus order.  
Figure 13: Time histories of vertical vibration acceleration vs. speed at points A to E.  
The dynamic response of the rail in Case 3 is displayed in Figure 14. Figure 14a shows that when the running speed is 200 km/h, the vertical rail accelerations at positions A, B, C, D, and E are 42.76, 41.21, 39.91, 38.05, and 39.53 m/s\({}^{2}\), respectively. When the running speed increases to 350 km/h, the vertical rail accelerations become 130.41, 125.65, 121.01, 120.22, and 120.81 m/s\({}^{2}\), respectively. It can be concluded that an increase in running speed produces a proportional increase in the vertical rail acceleration.  
Figure 14b shows that the vibration displacements of the rail at positions A, B, C, D, and E increase from 0.545, 0.544, 0.541, 0.539, and 0.539 mm at 200 km/h to 0.5645, 0.5635, 0.5605, 0.5585, and 0.5584 mm at 350 km/h, respectively.  
By contrast, Figure 14c indicates that the von Mises stresses at different positions in the rail increase slowly as the speed increases from 200 to 350 km/h. The simulation results show that running speed has a greater influence on the vertical rail vibration acceleration than on the vibration displacement and von Mises stress.  
The corresponding dynamic responses of the track substructure are shown in Figure 15. The vibration displacements of the concrete slab layer, CA layer, and support layer are, respectively, approximately 0.00436, 0.00190, and 0.00094 mm at 200 km/h. When the speed increases to 350 km/h, the vibration displacements increase to 0.00551, 0.00260, and 0.00137 mm, increases of 26.38%, 36.84%, and 45.28%, respectively, over the corresponding values at 200 km/h. The von Mises equivalent stresses of the concrete slab layer, CA layer, and support layer increase from 0.83321, 0.13159, and 0.10736 MPa  
Figure 14: Effects of running speed on rail dynamics: (**a**) vibration acceleration versus running speed; (**b**) vibration displacement versus running speed and (**c**) von Mises equivalent stress versus running speed.  
at 200 km/h to approximately 0.96711, 0.17375, and 0.15748 MPa at 350 km/h, enhancements of 16.07%, 32.04%, and 46.68%, respectively. These results indicate that the von Mises equivalent stress and vibration displacement in the track substructure increase in proportion to the increase in running speed.
## 2 Materials and Methods
### Policy Gradient-Based Deep Reinforcement Learning
#### 2.3.4 Reward-to-Go Method  
In the \"reward-to-go\" policy gradient approach, the concept revolves around the estimation of the \(Q\)-values by considering the cumulative rewards starting from the current timestep. The central idea behind this method is to emphasize the contribution of future rewards in the \(Q\)-value estimation, thus, allowing the agent to focus on the long-term impact of its actions.  
Let \(Q^{\pi}(s_{t},a_{t})\) represent the expected cumulative reward following the policy \(\pi\) after taking action \(a_{t}\) at state \(s_{t}\), as follows:  
\[Q^{\pi}(s_{t},a_{t})=E\bigg{[}\sum_{t^{\prime}=t}^{T}\gamma^{t^{\prime}-t}*r (s_{t^{\prime}},a_{t^{\prime}})\bigg{|}s_{t},a_{t},\pi\bigg{]} \tag{10}\]  
where \(\gamma\) is the discount factor, \(r(s_{t},a_{t})\) represents the reward at timestep \(t\), and \(T\) is the final timestep.  
In the \"reward-to-go\" policy gradient method, we estimate \(Q^{\pi}(s_{t},a_{t})\) using the discounted sum of rewards starting from timestep \(t\), as follows:  
\[Q_{t}=\sum_{t^{\prime}=t}^{T}\gamma^{t^{\prime}-t}*r(s_{t^{\prime}},a_{t^{\prime }}) \tag{11}\]  
In contrast, the standard policy gradient method typically uses the total discounted reward for the entire trajectory to compute \(Q\)-values, which may not fully capture the long-term consequences of individual actions. The \"reward-to-go\" method offers advantages over conventional policy gradient approaches, encouraging the agent to select actions that not only yield immediate rewards but also maximize long-term gains. Additionally, the \"reward-to-go\" method is more sensitive to the temporal structure of the problem, potentially leading to faster convergence and better performances.
## 2 Verification of Calculation Models and Methods  
### Calculation Model  
As shown in Figure 1, a certain subsea tunnel adopts a three-hole arrangement of \"Main tunnel + Service tunnel\". The simplified calculation model established in this study is shown in Figure 2. The main tunnel section is a multicenter circular shape with an excavation area of approximately 162.7 m\({}^{2}\). The service tunnel has a circular cross-section with an excavation diameter of 7.7 m.  
In this study, numerical software is used to simulate the tunnel seepage field. An isotropic seepage model is employed in the numerical model, with saturation set to 1.0. The fluid is assumed to have no tensile strength, and a specific hydrostatic pressure is applied to the seabed surface based on the seawater depth. The left, right, and lower boundaries of the model are set as impermeable boundaries. Additionally, the pore water pressure at the tunnel excavation boundary is assumed to be set to 0 following tunnel excavation. Throughtrial calculations, the model dimensions were determined as 500 m \(\times\) 200 m \(\times\) 1 m, taking into account both solution accuracy and computational efficiency.
# SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection
## 2 Related Work
[37] combined semantic information from the shallowest features and fused them with deeper features to enhance the detection rate of small objects. [PERSON] et al. [38] combined shallow information and simultaneously improved the loss function to increase the training weights of small targets. [PERSON] et al. [39] used deconvolution layers to fuse shallow and deep features, further enhancing the detection of small objects. However, introducing shallow features often introduces significant background noise for small objects. [PERSON] et al. [40] addressed this issue by introducing a balancing factor to weigh thefusion of shallow and deep features. Still, this approach relies on the detection task's a priori knowledge, limiting its robustness across different tasks. [PERSON] et al. [41] utilized a two-stage Faster R-CNN approach, employing deconvolution to upsample each candidate region from the previous stage, enlarging the feature map size and improving small object detection. [PERSON] et al. [42] employed deconvolution layers to scale up in-depth features and fuse them with shallow features, completing the detection process jointly. Nevertheless, deconvolution-based operations introduce additional parameters. To mitigate this, [PERSON] et al. [43] employed expanded convolution operations as a parameter-efficient alternative, reducing parameters while maintaining the same perceptual field. However, expanded convolution may lead to loss of local information. To address this limitation, [PERSON] et al. [44] introduced a pixel attention mechanism for local information fusion, compensating for the drawbacks of expanded convolution and improving small object detection. Nevertheless, upsampling operations remain meaningful only if small objects are still distinguishable in the deep features. Up-sampling does not recover the lost feature information if small objects are \"lost\" due to downsampling in deep features. If small objects are \"lost\" due to downsampling in deep features, upsampling does not recover the lost feature information. Therefore, some works [42] combine the introduction of shallow features with a deep feature upsampling process to leverage complementary advantages. However, as the feature map scale increases, computational complexity also rises, leading to increased time consumption during the detection process, despite the enhanced small object detection capability. To mitigate this issue, this research paper introduces an end-to-end algorithm designed for detecting tiny objects within remote sensing images. The proposed approach leverages a YOLOv8 architecture as its foundation. The algorithm incorporates the efficient path aggregation network SPANet and SPD-Conv modules, enabling stable, quick, and accurate detection of tiny objects in images with complex backgrounds.