esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Recognition Method for Micropores Inside Small Gas Pipelines
## 1 Introduction  
As the important carrier of gas transportation, gas pipelines play a vital role in gas transmission, such as gas and natural gas, thus bringing many conveniences and economic benefits to daily life and industrial production. Gas pipelines are mainly buried underground with intricate pipelines [1], and corrosion, holes, and breakage can occur over the years [2; 3; 4], leading to pipeline leaks. Pipeline leakage not only wastes energy and causes economic loss, but it may also threaten people's life safety in serious cases [5]. Therefore, regular maintenance of gas pipelines is necessary. Existing pipeline leakage identification techniques such as negative pressure wave detection [6], wavelet detection [7], acoustic emission detection [8], pressure point analysis [9], micro-electromagnetic detection [10], the infrared sensor method [11], and other methods can only identify the leakage that has occurred. Early leakages in the inner wall of the pipeline will appear as micropores, the characteristic precursor to leakage, which will gradually become larger and eventually lead to the occurrence of pipeline leaks. However, the above methods are unable to identify these early features of leaks [12; 13; 14]. Therefore, to carry out safety warnings, and prevent the occurrence of large-scale leaks in gas pipelines that lead to economic losses and threats to people's lives, this paper proposes an image recognition method for micropores inside small gas pipelines.  
To address the problem related to difficulties in the early detection of small gas pipeline leaks, a method is proposed for the image recognition for micropores inside small gaspipelines. First, we use the head camera of an inspection robot to collect micropore images inside the small gas pipeline. Then, the number of images is expanded to build a dataset, and the dataset is labeled with micropores to produce label data. Second, the BiFPN [15] is introduced into the Neck of the YOLOv5s algorithm to enhance the fusion of different scale feature information fusion to improve the recognition precision of leaking pores. Finally, a small target detection layer [16] is constructed in the Head of the YOLOv5s algorithm to identify micropores of tiny size and improve the detection rate of the network model for micropores of tiny size. The method in this paper can solve the problem that traditional methods cannot realize by visualizing early leakage characteristics such as micropores, avoiding the high economic cost and high manpower investment related to large-area excavation, and preventing the danger caused by a large leakage in the pipeline to realize the early safety investigation of pipeline leakage.
## 3 Corrosion Evaluation  
Before applying the CP system, a visual inspection was performed to evaluate the severity of corrosion. As shown in Figure 2, the bridge columns exhibited numerous  
Figure 1: Entire view of the bridge before the CP application (**a**) and top view with column numbers (**b**).  
corrosion-induced cracks, which caused concrete deterioration and localized damage to the reinforcing bars. In addition, after obtaining specimens from the piers at various locations, the chloride content as a function of depth was evaluated in the laboratory, as shown in Figure 3. The chloride content was measured at different heights: basement (approximately 0 m), middle (approximately 1.5 m), and top (approximately 3 m). The basement part was mostly submerged in saline water; the middle part was submerged 1-2 times during the month; the top part was not submerged, and the presence of chloride was mostly due to water splashes. The surface chloride content was high in the basement but decreased significantly at higher heights. In addition, the deeper the location, the lower the chloride content tended to be. These findings make sense because the chloride content is mostly due to splashes in the middle and top sections, and chloride penetration starts from the outer shell. Notably, the chloride content near the rebar location exceeds the critical threshold of 6.34 kg/m\({}^{3}\) for many columns, based on an internal report, which indirectly indicates that corrosion has already been initiated. Figure 4 shows the corrosion potential as a function of pier location. In general, the lower the potential, the greater the possibility of rebar corrosion. The lower sections have a very low potential of less than \(-\)350 mV/CSE, indicating that severe corrosion is expected in those sections. The top section maintains the novel condition with a potential higher than \(-\)100 mV/CSE, which means that the possibility of corrosion in that section is low due to less invasion of chloride. Visual inspection and chloride content/potential measurements indicated that the corrosion deterioration was in the acceleration phase, which is the rationale for the need for CP system application.  
Figure 2: Examples of damaged/cracked piers caused by chloride. (**a**) P2C4 (west direction); (**b**) P5C1 (east direction).
## 2 Experimental Work  
The polished surface of p-Si wafer (100) obtained from Sigma Aldrich was used as a substrate. The substrate was treated with HF:H\({}_{2}\)O to etch the top oxide layer from Si. It was then cleaned in ultrasonic bath with acetone and isopropanol for 5 min in each. A uniform solution of PFB was prepared in chloroform at 20 mg/mL with the help of a magnetic stirrer at room temperature (300 K).  
The solution was spin coated on p-Si substrate at 3000 rpm for 20 s. Then, the sample was allowed to dry at 60 \({}^{\circ}\)C by using a hot plate for 1 h under nitrogen gas flow. A 90 nm-thick layer of silver (Ag) electrode was thermally deposited on PFB via a vacuum thermal evaporator at \(1.5\times 10^{-5}\) mbar to make the sandwich-type Ag/PFB/p-Si heterojunction device. The current-voltage (\(I\)-\(V\)) characteristics were performed to extract different device parameters. Additionally, morphology of the PFB thin film was investigated by using an atomic force microscope (AFM) NanoScope-IIIa SPM from Dimension TM 3100, Digital Instruments Veeco Metrology Group, which was operated in noncontact mode to avoid any damage to the film. The scan area was 2 \(\upmu\)m \(\times\) 2 \(\upmu\)m while the scan rate was kept as low as 0.86 Hz to see detailed surface features. Scanning electron microscopy (SEM) JEOL JSM-840 was also carried out to correlate with the AFM images. A two-dimensional SEM image on 2.5 \(\upmu\)m \(\times\) 2.5 \(\upmu\)m scale was recorded at 50,000\(\times\) magnification operated at 15 kV. The spectroscopic studies of PFB were carried out via ultraviolet and visible (UV-vis) UV/Vis/NIR spectrophotometer LAMBDA 950 PerkinElmer and Fourier transform infrared (FT-IR) IR Tracer-100 FTIR Spectrometer (Shimadzu). The crystallinity of PFB was investigated by using X-ray diffraction (XRD) with a Philips X'Pert X-ray powder diffractometer with a copper-target (Cu anode 2000 W) high-intensity X-ray tube operated at 40 kV and 35 mA.  
Electrical characterization of the fabricated heterojunction was carried out using an MDC CSM/Win system. HP4140B pA and HP4275A C-V meters were connected with the MDC MDC811-150 instrument [24]. Figure 1a,b show the molecular structure of PFB and schematic diagram of the fabricated Ag/PFB/p-Si heterojunction device.
## 4 Dynamic BLINs Based on Response Surface Method
### BLINs Considering Elastic Deformation  
Based on the concept of BLINs [8,27], in the initial installation state, considering the supporting foundation as rigid (fixed without deformation) and elastic (supported by elastic boundary conditions), respectively, the rigid BLINs matrix \(A_{r}\) and the elastic BLINs matrix \(A_{e}\) were calculated (unit: kN/mm).  
\[A_{r}=\left[\begin{array}{ccc}0.59&-1.56&1.85\\ -1.56&5.79&6.78\\ 1.85&-5.67&27.65\\ \end{array}\right] \tag{3}\]  
\[A_{e}=\left[\begin{array}{ccc}0.64&-1.16&1.22\\ -1.16&6.76&-10.65\\ 0.78&-3.71&37.75\\ \end{array}\right] \tag{4}\]  
Figure 6: Deflection curves and equivalent line.  
By comparing Equations (3) and (4), it is evident that \(A_{r}\) is a symmetric matrix, whereas \(A_{e}\) is an asymmetric matrix. This asymmetry arises from the dynamic changes in the vertical positions of all the three bearings caused by the elastic deformation of the raft frame. Compared to rigid support, considering the elasticity of the support foundation leads to an increase in the BLICs of all the bearings relative to their own positions, especially for the thrust bearing. When the shafting is elastically supported, the load of the thrust bearing increases by 37.75 kN for every 1 mm of elevation, which is significantly higher than 27.65 kN under rigid conditions. Moreover, when the front stern bearing is raised by 1 mm, the loads of both the rear stern bearing and thrust bearing decrease, which is obviously distinct from rigid support.  
Defining the vertical position of a bearing as the height relative to the equivalent line of the raft frame surface, the BLINs matrix \(A_{e}^{\prime}\) in the initial installation state was calculated, as shown in Equation (5).  
\[A_{e}^{\prime}=\begin{bmatrix}0.45&-1.77&2.05\\ -1.80&4.66&-6.13\\ 2.00&-5.98&18.92\\ \end{bmatrix} \tag{5}\]  
The raft frame will achieve a so-called leveled state when the deflection curve of the raft frame surface is approximately a horizontal straight line, after regulation of the airbag vibration isolators' pressure. The calculated BLINs matrix \(A_{e}^{\prime\prime}\) in the leveled state is shown in Equation (6).  
\[A_{e}^{\prime\prime}=\begin{bmatrix}0.87&-0.94&3.16\\ -0.99&2.72&-5.58\\ 3.04&-5.39&22.92\\ \end{bmatrix} \tag{6}\]  
Comparing Equations (5) and (6) with (4), it can be seen that measuring the position of each bearing using the equivalent line of the raft frame surface can effectively reduce the influence of elastic deformation on the asymmetry of the BLINs matrix, but the BLINs will change dynamically according to the deformation state of the raft frame surface.
###### Abstract  
This paper presents an optimized 3-coil inductive wireless power transfer (WPT) system at 13.56 MHz and 40.68 MHz to show and compare the specific absorption rate (SAR) effects on human tissue. This work also substantiates the effects of perfect alignment, lateral and/or angular misalignments on the power transfer efficiency (PTE) of the proposed WPT system. Additionally, the impacts of different tissue composition, input power and coil shape on the SAR are analyzed. The distance between the external and implantable coils is 10 mm. The results have been verified through simulations and measurements. The simulated results show that the SAR of the system at 40.68 MHz had crossed the limit designated by the Federal Communications Commission and hence, it is unsafe and causes tissue damage. Measurement results of the system in air medium show that the optimized printed circuit board coils at 13.56 MHz achieved a PTE of 41.7% whereas PTE waned to 18.2% and 15.4% at 10 mm of lateral misalignment and 60\({}^{\circ}\) of angular misalignment respectively. The PTE of a combination of 10 mm lateral misalignment and 60\({}^{\circ}\) angular misalignment is 21%. To analyze in a real-environment, a boneless pork sample with 10 mm of thickness is placed as a medium between the external and implantable coils. At perfect alignment, the PTE through pork sample is 30.8%. A RF power generator operating at 13.56 MHz provides 1 W input power to the external coil and the power delivered to load through the air and tissue mediums are 347 mW and 266 mW respectively.  
implantable medical devices; 3-coil inductive link; power transfer efficiency; air medium and tissue medium; specific absorption rate +
Footnote †: journal: Applied Physics Letters
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 3 Results and Discussion
### Applications and Implications
#### 3.3.2 Predictive Insights for Infrastructure Development  
In the context of infrastructure development, these models can aid in the design of sewer systems, treatment plants, and stormwater management systems. Understanding how variations in these factors can impact water quality allows for a more sustainable and effective design. For example, sewer systems could be designed with capacities that align with the peak sewage output times identified by the models, and stormwater systems could be built to cope with peak runoff scenarios [15; 60; 61].
## 3 Results
### Verification
#### 3.1.3 Air Demand and Velocity Profiles  
As can be seen in Figure 4, the results of numerical simulation were compared to the prototype observation. The variation trend of air demand with gate opening obtained by numerical simulation was the same as that obtained by prototype observation. However, the results based on the Schiller and Naumann drag model were closer to the prototype data for large gate opening percentage, while the results based on the symmetric drag model were closer to the prototype data for small gate opening percentage.  
In order to reveal the difference between the drag models, several typical sections were selected. Water velocity, air velocity, and air volume fraction \(\alpha_{a}\) on the center line of these sections were extracted. As the water velocity from different drag models showed little discrepancy, only one profile for water velocity was plotted on every subfigure. In Figure 5, the profiles of air velocity in the location where \(\alpha_{a}<1\) was close to the water velocity. This indicates that in the calculation of this paper, whether the Schiller and Naumann model or the Symmetric model was adopted, the distribution of air velocity in the water body would eventually be consistent with the profile of water velocity. However, near the air-water interface where \(\alpha_{a}\) transforms from \(\alpha_{a}<1\) to \(\alpha_{a}=1\), the difference of the air velocity profile calculated from two models appeared. The air velocity calculated by the Symmetric model was obviously distorted here, and the large gradient resulted in a suddenly decrease of air velocity. The results of the symmetric drag model are generally smaller than those of the Schiller and Naumann models. Nevertheless, it is necessary to provide a more proper drag model since it has a great influence on the calculation of air demand of a spillway tunnel.  
Figure 4: Air demand comparison between prototype observation and numerical simulation.
# Effect of Environmental Parameters on Structural Health Status Assessment Using OMA Techniques
## 7 Data Analysis with Applied OMA Methods
### Application of the Peak-Picking Method
In conclusion, the peak-picking method proves efficient for the low-order modes, which are those most easily excited and therefore more easily detected. For the higher order modes, which are harder to excite, the signal-to-noise ratio becomes unfavorable; the signal starts to be buried in the PSD noise and the output can hardly be used for damage detection. Nevertheless, peak-picking can be useful in a preliminary analysis to understand the overall structural behavior.
# Analysis and Prediction of Carsharing Demand Based on Data Mining Methods
## 4 Results and Comparison
### Comparison
#### 4.2.2 Comparison  
Other station sets are predicted in the same way as above. The demand level of each station set is different, which is why the predicted error value will also be different. The final prediction error analysis of GBDT and ARIMA is shown in Table 7.  
Establishing ARIMA to predict users' demands is feasible. However, the model still has the following problems:  
1. The prediction error of user demand is still large, which will lead to unreliability in actual operation.
2. The ARIMA prediction model established in this study only highlights the role of time factor in prediction and does not consider the influence of external factors. When great changes take place in the outside world, great deviations will always occur.  
RMSE and MAE are the evaluation indexes of machine learning. The formulas are shown in (19) and (20). According to the calculation formula, a small value corresponds to improved results. Then, when the value of MAE exceeds 2.5, the value is high, indicating that the prediction error is large. Therefore, the result is not of great value to operators.  
The historical data of all stations from 4 to 10 September 2018 are used for verification to compare the advantages and disadvantages of the established ARIMA and GBDT. The comparison results of the prediction accuracy of the two models are shown in Table 8.  
Figure 14: Prediction comparison chart of station collection 1.  
Figure 15: Error graph on station collection 1.  
The comparison results in Table 8 indicate that the absolute value of each error item (maximum error, minimum error, average absolute error, and root mean square error) of GDBT is smaller than that of ARIMA. This result occurred because ARIMA only uses the influence of historical time series on the future. It does not consider the influence of other related factors. However, the GBDT not only considers its own historical value but also considers weather and the demand situation of adjacent stations, among other factors, thus improving the prediction effect of the model better. Thus, the GDBT model has higher prediction accuracy. So, it has a better reference value for user demand prediction in practical application.
## 4 Results and Discussion
### Electricity Energy System
#### 4.2.2 Challenges and Limitations  
Despite these clear benefits, several challenges persist. The environmental footprint of panel and Li-ion battery production visible in Figures 8-10 reveals substantial non-renewable energy inputs in the manufacturing, refining, and transportation of raw materials. Li-ion batteries, in particular, demand metals like lithium, cobalt, and nickel, whose  
Figure 8: CED of solar photovoltaic electricity at DEI Agra.  
extraction and processing can lead to resource depletion, particulate emissions, and environmental hazards [37; 38]. Additionally, the challenge of end-of-life management adds another layer of complexity. As the number of PV systems increases, the volume of end-of-life solar panels will rise significantly, posing a waste management challenge [37; 39]. Research indicates that improper disposal of these panels could lead to leaching of hazardous substances, undermining their environmental benefits and potentially causing soil and water contamination [39]. Effective recycling processes for PV panels are critical to mitigate these risks. In that regard, solvent-based delamination offers up to 95% material recovery under lab conditions, though scalability remains a challenge [32; 40]. Emerging solvent systems may reduce environmental burden, but results vary by process. For Li-ion batteries, hydrometallurgical \"direct recycling\" can lower global warming potential by up to 60% and recover key materials like lithium and cobalt, contributing to circular economy goals [41; 42]. However, the recycling of PV panels is hampered by the lack of widespread infrastructure and technologies capable of efficiently handling and processing these materials [43].  
Figure 9: Endpoint environmental impact analysis (ReCiPe 2016) for solar photovoltaic electricity at DEI Agra.
# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### Virtual Tutors and Chatbots
#### 3.2.3 Examples of Applications  
Several applications demonstrate the integration of MLLMs into virtual tutors and chatbots. Educational platforms are increasingly incorporating advanced MLLMs to provide personalized learning experiences. For instance, LOVA3's capabilities can be leveraged to develop virtual tutors that not only answer student queries but also pose relevant questions and assess student responses, thereby simulating a more interactive and engaging learning environment.  
Language learning assistants can benefit from MLLMs that handle multiple modalities. By integrating LOVA3's approach, these assistants can incorporate visual prompts and assessments, enhancing language acquisition through multimodal interactions.  
In STEM education, virtual tutors equipped with MLLMs like LOVA3 can assist students in subjects like mathematics and science by providing detailed explanations,posing challenging problems, and assessing student solutions. This mirrors the human tutoring process more closely and can lead to better learning outcomes.
## 4 Experiments  
To verify the performance of ERF-IMCS, the experiment has been implemented on a real mobile robot and tested intensively in Beihang University, as shown in Figure 6. The maximum linear speed of the mobile robot is approximate 0.5 m/s. The robot is equipped with a monocular camera which can provide the RGB image at the resolution of 640 \(\times\) 480. The vision-based topological navigation system is processed on an Nvidia Jetson TX2 with 256 core-Pascal GPUs.  
Figure 6: The mobile robot used to perform ERF-IMCS experiment.  
The plan of the experimental environment is shown in Figure 7. The experimental environment is about 800 m\({}^{2}\) in size. During the testing stage, we firstly steer the robot through the indoor experimental environment and record the image frames and odometry information in which the keyframes is extracted to construct the topological nodes via CNN-based image matching. In the map building, the travel distance of the robot between each keyframe is set to be relatively small. In this way, the topological nodes and the links between them in indoor environments can be determined more robustly through visual feature matching without the need for geometry information of the environment. For the topological map, we build a database containing 826 images that are uniformly resized to 224 \(\times\) 224 pixels. The path length traversed by the robot in topological mapping is about 120 m. In Figure 7, a resulting topological map is presented in which the blue dots represent the nodes with distinctive visual features in topological map and the links exhibit topological relationships between nodes. Additionally, several keyframe images from the experimental environment are displayed.
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 2 Literature Review
### Applications of Predictive Control in Robotics  
In [19], experts present a comprehensive analysis of linear MPC and Nonlinear Predictive Control (NMPC). The study emphasizes that NMPC requires addressing a nonlinear non-convex problem (NPL), presenting computational challenges in cost and reliability. The authors detail Sequential Quadratic Programming (SQP) methods, which extend Newton-type methods to achieve Karush-Kuhn-Tucker (KKT) condition convergence. This approach must ensure rapid convergence while managing ill-conditioning and extreme nonlinearities.  
Research presented in [20] acknowledges the highly nonlinear nature of robot models and proposes multiple control approaches, including fuzzy and neural control. The study develops a PID-NMPC-based robot with an optimal time trajectory using convex optimization. Their methodology employs a two-step design: initial system linearization followed by MPC application, comparing results with optimal Linear Quadratic (LQ) control.  
In [21], researchers explore predictive control as an optimal method for robot planning and obstacle avoidance. They introduce a model-free predictive controller capable of operation without prior kinematic and dynamic knowledge. The controller utilizes MPC to predict system states and generate optimal control signals over a defined prediction horizon.  
MPC is used in both linear and nonlinear systems, with the latter applied to a robot manipulator, enabling its use in robot path planning and obstacle avoidance. It should be noted that MPC requires an accurate model of the system, making it essential to use the robot's dynamic model with the greatest detail. This document presents an MPC applied to a 3-DoF Scorbot robot.