esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete
## 2 Research Significance  
As indicated in the introduction part, the estimation of the compressive strength of MSC is important in civil engineering applications. Although miscellaneous experimental investigations have reported this problem, it is difficult to deduce a generalized formulation, taking into account all the parameters that affect compressive strength of MSC, especially in terms of sand's characteristics. The employment of AI approaches could help to look into the (nonlinear or not) relationships between the desired outputs and the corresponding inputs. That way, the influence on the MSC's compressive strength of input variables, particularly sand's characteristics, could be fully achieved and quantified. Moreover, pre-processing data technique such as PCA and optimization technique namely TLBO could be applied to improve the performance of a popular ANFIS model for better accurate prediction of the compressive strength of MSC. Thus, it can be a great approach which could help engineers and/or researchers to decrease cost and time in laboratory experiments for determination of the compressive strength of MSC.
## 2 Materials and Methods
### Real-Time Cell Analysis  
Monitoring of the cell response after exposure to tested substances was conducted using the xCELLigence system (Real-Time Cell Analyser--RTCA, Acea Biosciences Inc., San Diego, CA, USA). The system allows label-free monitoring of cell behaviour (adhesion, proliferation, growth, and morphology) during the whole time of treatment. The basic principle is the measurement of the impedance on gold electrodes at the bottom of microplate wells. The larger the number of cells attached, the higher the impedance. The values were expressed in dimensionless cell index (CI) and recorded in curves each hour throughout the experiment [29; 30; 31]. For this study, the cells were seeded at \(5\times 10^{3}\) cells per well of a three 16-well E-plate (Acea Bioscience, San Diego, CA, USA) and incubated for 20 h. Tested samples were diluted with medium to reach final concentrations of 125-1000 \(\upmu\)g mL\({}^{-1}\) shortly before addition to the cells. Cells without treatment served as a control and their proliferation was considered as 100%. Percentual change in proliferative activity (PA) was calculated using the Formula (3):  
\[\%\text{PA}=\frac{\text{CI sample}}{\text{CI control}}\times 100 \tag{3}\]
# Investigation on Ship Hydroelastic Vibrational Responses in Waves
## 2 Ship Hydroelasticity Theory in Regular and Irregular Waves
### Hydrodynamic Theory in Ship Hydroelasticity
#### 2.3.5 Slamming Wave Force  
The slamming loads acting on ships in regular or irregular waves can be obtained by the momentum impact theory which assumes that the slamming force is determined by the momentum changing rate of the fluid around the ship. Therefore, the sectional slamming force can be expressed as follows [28]:  
\[f_{SL}(x,t)=\frac{dm_{\infty}(x)}{dz}(\frac{dw_{rd}(x,t)}{dt})^{2} \tag{17}\]  
where \(m_{\infty}(x)\) is the sectional infinite frequency added mass in heave mode, \(w_{rd}(x,t)\) is the hull vertical displacement relative to the incident wave elevation which can be expressed as [2]:  
\[w_{rel}(x,t)=\sum_{r=1}^{m}w_{r}(x)p_{r}(t)-\zeta(x,t) \tag{18}\]  
where \(w_{r}(x)\) is the \(r\)-th mode of ship vertical displacement, \(\zeta(x,t)\) is the incident wave elevation. Then the generalized slamming force can be expressed as [29]:  
\[F_{SL}(t)=\int_{L}f_{SL}(x,t)w(x)dx \tag{19}\]  
where \(w(x)\) is vertical normal vector component of \(\mathbf{n}\).
###### Abstract
Due to their complexity, ES are often difficult to measure [16; 17], especially in the case of peri-urban amenities and cultural ES, which are very challenging to account for [18]. Therefore, there is a strong need to apply multiple approaches, especially when dwelling on social equity in the use of resources and ES in developing countries [5]. Both quantitative and qualitative methods can be used to evaluate the benefits that nature offers to people and to further optimize the opportunity to realistically achieve the SDGs [5]. Usually, ES valuations are based on the distribution of landscape services in the area under investigation [6]. The valuation of the ecosystem services in the given area, moreover, can be economical or non-economical [19; 20; 21]. Among the methods that assign a monetary value to the ES, the contingent valuation method (CVM) estimates the values that people attribute to services, goods, or benefits coming from nature [22; 23; 24]. CVM has been mostly adopted for estimating the non-market or non-use value related to ES [7], turning out to be useful when assessing the ES potential in hypotheses of different scenarios or in a landscape that can change very quickly. In the context of landscape planning with ES, the assessment of the peri-urban landscape status and the evaluation of the related ES provide a basic formulation for planning and defining the future domains for peri-urban infrastructure development [13], trying to overcome the challenges in reaching full incorporation into policy of people's interest in aesthetic values and cultural services. This work is organized as follows: In the first part of the study, a multidisciplinary approach for studying peri-urban landscapes in the rapidly developing city of Gujrat is presented. In the second part of the study, the research tries to highlight the relationship between amenities ensured by green areas and their benefits for local people in the form of ES [25], associating the value that people give to them. This study, therefore, provides a possible application of landscape and ES assessments for integrating the interests of stakeholders, urban planners, citizens, and government in order to implement the SDGs in Pakistan policy-making, aiming to inspire also other countries [26].
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 5 Simulation and Experimental Results
### Computational Complexity Analysis
FC-NN and DNN mitigate some of these issues, but introduce high parameterization costs due to the reliance on fully connected layers. The proposed transfer learning model, particularly in its fine-tuned configuration, provides an optimal balance, leveraging pre-trained features to reduce computational overhead while maintaining high accuracy. By freezing convolutional layers during fine-tuning, the computational burden is significantly reduced, making the approach suitable for real-time UWA communication systems.
## 1 Introduction
Three methods in general have been used to mitigate the effects of the leakage current [30]:  
* Disconnect the AC side and the PV during free wheeling times. * Connect the midpoint of the DC-link capacitors to the neutral line of the utility grid. * Connect the PV negative terminal to the neutral line of the utility grid directly, referred here as CM converters. Transformerless inverters must eliminate, or at least, reduce the leakage current, for example, by including passive damping components or by modifying the modulation strategy [31]. In order to improve efficiency, transformerless topologies have been developed [28; 32; 33; 34; 35]. Generally, the inverters used in RE applications employ the VSI topology due to their low cost, easy control, and mature technology. The leakage current circulates through the physical earth of the grid and parasitic capacitances of each pole of the panel, as illustrated in Figure 3. This current impairs the functioning of the system, injecting harmonics into the grid and produces risks to human health. The paper is structured as follows. In Section 2, the main standards and requirements of panels and inverters are presented. The VDE-AR-N 4105 and VDE 0126-1-1 standards establish the rules for grid-connected systems. An analysis of the common-mode and how it influences in the leakage  
Figure 3: General scheme grid-connected transformerless VSI topology. current is performed in Section 3. Current research also seeks to reduce components number, control complexity and increase the inverters efficiency. Section 4 presents certain topologies that allow comparisons with similar schemes. This section also shows a general comparison of the different converters and their main features; in this way, the reader can differentiate each topology based on the most important elements of each scheme. Also, the discussion of current problems, solutions, and trends in this field of research are described.
# A Tuning Method for Speed Tracking Controller Parameters of Autonomous Vehicles
## 2 Construction of Longitudinal PID Controller for a Vehicle
### PID Controller Construction  
In this study, MATLAB/Simulink(2019b) is used to construct the PID controller. The model diagram of the PID controller is shown in Figure 3.  
In this study, the input to the PID controller is the difference between the target speed and the actual speed, and the output is used to adjust the driving force to regulate the vehicle speed, making it approach the target speed and achieve precise control.
# Energy Harvesting Methods for Transmission Lines: A Comprehensive Review
## 4 State-of-the-Art Energy Harvesting Methods for Transmission Lines
### Solar Energy Harvesting  
Solar EH is a mature technology that is being applied in many areas. Solar energy is harnessed using a photovoltaic (PV) system that converts sunlight into useful electric power. For example, in [40], a temperature monitoring system for overhead transmission lines using two solar panels fixed at the top of the tower was analyzed.  
The main disadvantage of solar panels is that, due to the stochastic nature of weather conditions, a stable energy supply cannot be guaranteed [41] because, in many places, it is possible to reliably generate solar power for less than 8 h a day [42]. Further, weather conditions, dust, sand, dirt, ice, or snow can significantly impact the availability of solar energy, increasing maintenance costs [43]. Since a constant electrical supply is not feasible [44], combining solar EH with an ultracapacitor or a rechargeable battery is necessary to provide a stable supply to the sensor system [11; 45]. Due to the drawbacks mentioned above, recent developments point toward using hybrid solutions that combine solar EH with other sources, such as electromagnetic or RF. For example, in [41; 46], a harvester combining solar and magnetic field EH is proposed for WSNs intended for smart grid applications, where the magnetic field harvester is based on a multi-turn coil surrounding a magnetic core. In [47], a hybrid solar-RF energy harvesting system combining a solar panel and an antenna is proposed.
## 3 Results
### Characterization of Methanol Extract CFs  
For molecular characterization, we selected the fractions that showed the highest SI, and antitumor and antioxidant activities, without inducing hemolysis (fractions CF3 and CF4). We identified the major compounds loliolide, cinnamic acid, methyl dihydrojasmonate, and cryptophycin 29, which had been previously reported to show anticancer, antibacterial, or antioxidant activities (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Collective** & **Yield (\%)** & **PBMC** & **L5178Y-R** & **SI a** & **MCF-7** & **SI a** \\
**Fraction** & **IC\({}_{\text{50}}\) (ug/mL)** & **IC\({}_{\text{50}}\) (ug/mL)** & **IC\({}_{\text{50}}\) (ug/mL)** & **IC\({}_{\text{50}}\) (ug/mL)** & **IC\({}_{\text{50}}\) (ug/mL)** & **SI a** \\ \hline
**CF1** & 47.8 & 14,053 \(\pm\) 0.1233 & 413.8 \(\pm\) 1.69 & 33.96 & \(>\)500 & \(>\)28.1 \\
**CF2** & 0.6 & N/A & N/A & N/A & N/A \\
**CF3** & 9.2 & 392.8 \(\pm\) 0.1598 & 93.22 \(\pm\) 1.69 & 5.12 & 126.4 \(\pm\) 1.12 & 3.1 \\
**CF4** & 4.6 & 349.1 \(\pm\) 0.8389 & 76.69 \(\pm\) 1.63 & 4.55 & 76.8 \(\pm\) 1.09 & 4.54 \\
**CF5** & 25.4 & 211.9 \(\pm\) 1.5967 & 127.1 \(\pm\) 1.51 & 1.67 & 100.9 \(\pm\) 1.16 & 2.1 \\
**CF6** & 5.2 & 0 & 486.2 \(\pm\) 2.29 & — & 298.3 \(\pm\) 1.18 & — \\ \hline \hline \end{tabular}  
* SI values represent the IC\({}_{50}\) of the PBMC/IC\({}_{50}\) of L5178Y and MCF-7 cells respectively.  
\end{table}
Table 1: Yield and IC\({}_{50}\) of _Ch. sorokiniana_ CFs against PBMC, L5178Y-R, and MCF-7 cells after 48 h exposure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Extract and CFs** & **DPPH (IC\({}_{\text{50}}\))** & **Activity a** \\ \hline _Ch. sorokiniana_ & 11.96 \(\pm\) 1.528 & High \\ CF1 & 4.852 \(\pm\) 0.678 & High \\ CF3 & 3.52 \(\pm\) 1.257 & High \\ CF4 & 4.452 \(\pm\) 0.592 & High \\ CF5 & 7.222 \(\pm\) 0.671 & High \\ CF6 & 5.835 \(\pm\) 1.036 & High \\ Ascorbic acid (50 ug/mL) & 7.21 \(\pm\) 1.074 & High \\ \hline \hline \end{tabular}  
* Weak = IC\({}_{50}\)\(>\) 1000 ug/mL; moderate = IC\({}_{50}\) ranging from 200 to 1000 ug/mL; and high = IC\({}_{50}\)\(<\) 200 ug/mL. Data represent the means \(\pm\) SD (\(p\) < 0.05) of the IC\({}_{50}\) (ug/mL) for each evaluated CF.  
\end{table}
Table 2: Antioxidant activity of _Ch. sorokiniana_ crude methanol extract and collective fractions (CFs).
# Research on Road Pattern Recognition of a Vision-Guided Robot Based on Improved-YOLOv8
## 5 Research on Pavement Recognition Based on Improved YOLO v8
### Research on Improvement of YOLO v8 Pavement Recognition Model Based on the C2f-ODConv Module  
Dynamic convolution means that in the convolution process, the weight of the convolution kernel is not fixed but can be dynamically adjusted according to the different input data. The advantage of dynamic convolution is that it can make the convolution kernel better adapt to the characteristics of the input data and improve the performance of the convolution network.  
ODConv [44] uses a multidimensional attention mechanism to learn complementary attention along the four dimensions of the kernel space through a parallel strategy, which extends the dynamic properties of one dimension in CondConv and considers the dynamics of the null domain, input channel, output channel, and other dimensions at the same time; thus, it is called a full-dimensional dynamic convolution. In this paper, ODConv is used to replace the convolution inside the Bottleneck in the C2f module of the YOLO v8 road recognition model to form a new C2f-ODConv module, as shown in Figure 5.  
Figure 5: C2f-ODConv module structure.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Parameters** & **GFLOPS** & **Toplac-val** & **Infer-Time** & **FPS** \\ \hline YOLO v8 & 36,226,645 & 99.1 & 0.825 & 2 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of road pattern recognition based on YOLO v8l.  
On this basis, the improved YOLO v8 pavement recognition model based on the C2f-ODConv module is defined as Yolo v8n-C, as shown in Figure 6.  
Research on Improvement of YOLO v8 pavement Recognition Model Based on the AWD Adaptive Weight Downsampling Module  
Traditional downsampling methods, such as convolution with MaxPooling and a step size of 2, lose the relative importance of elements. Influenced by the idea of RFAConv [45], the AWD (adaptive weight downsampling) downsampling module was designed. As shown in Figure 7, AWD fully considers the relationship between elements when downsampling. The upper branch fuses the attention mechanism, uses the average pooling method to obtain the importance of the element, and finally obtains the weight of the element through the softmax operation. Meanwhile, the following branches use group convolution [46] to reduce the number of parameters and calculations. Finally, multiplication and summation operations are performed to obtain the final downsampling results.  
On this basis, the AWD downsampling module is used to replace some Conv modules in the YOLO v8 pavement recognition model constructed above, and the improved YOLO v8 pavement recognition model based on the AWD adaptive weight downsampling module is obtained, which is defined as Yolo v8n-A, as shown in Figure 8.  
Figure 6: Improved YOLO v8 pavement recognition model based on the C2f-ODConv module.
# TYCOS: A Specialized Dataset for Typical Components of Satellites
###### Abstract  
The successful detection of key components within satellites is a crucial prerequisite for executing on-orbit capture missions. Due to the inherent data-driven functionality, deep learning-based component detection algorithms rely heavily on the scale and quality of the dataset for their accuracy and robustness. Nevertheless, existing satellite image datasets exhibit several deficiencies, such as the lack of satellite motion states, extreme illuminations, or occlusion of critical components, which severely hinder the performance of detection algorithms. In this work, we bridge the gap via the release of a novel dataset tailored for the detection of key components of satellites. Unlike the conventional datasets composed of synthetic images, the proposed Typical Components of Satellites (TYCOS) dataset comprises authentic photos captured in a simulated space environment. It encompasses three types of satellite, three types of key components, three types of illumination, and three types of motion state. Meanwhile, scenarios with occlusion in front of the satellite are also taken into consideration. On the basis of TYCOS, several state-of-the-art detection methods are employed in rigorous experiments followed by a comprehensive analysis, which further enhances the development of space scene perception and satellite safety.  
satellite dataset; key component detection; deep learning; data simulation +
Footnote †: journal: Applied sciences
# A Normalization Strategy for Weakly Supervised 3D Hand Pose Estimation
## 4 Experiments
### Evaluation of Normalization Strategy
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**w/ norm**} & \multicolumn{3}{c}{**w/o norm**} \\ \cline{2-7}
**Datasets** & \(\mathbf{e}_{\ell}\) & \(\mathbf{e}_{\text{CPS}}\) & \(\mathbf{e}_{\text{ARB}}\) & \(\mathbf{e}_{\ell}\) & \(\mathbf{e}_{\text{CPS}}\) & \(\mathbf{e}_{\text{ARB}}\) \\ \hline STB & 8.43 & 0.0001 & 0.036 & 71.05 & 0.245 & 0.818 \\ RHD-A & 9.89 & 0.0169 & 0.006 & 33.75 & 0.079 & 0.062 \\ RHD-B & 10.10 & 0.0162 & 0.007 & 31.51 & 0.067 & 0.053 \\ RHD-C & 8.69 & 0.0083 & 0.007 & 22.73 & 0.122 & 0.044 \\ RHD-D & 8.08 & 0.0057 & 0.007 & 21.03 & 0.118 & 0.035 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of errors in static biomechanical features. The model VPose3D employed for this analysis was trained on both non-normalized and normalized datasets. The comparison encompasses biomechanical features \(\left\{\ell,\phi_{\text{ARB}},\phi_{\text{CPS}}\right\}\), with the errors \(\left\{\mathbf{e}_{\ell},\mathbf{e}_{\text{CPS}},\mathbf{e}_{\text{ARB}}\right\}\) computed by evaluating the averaged Euclidean distance between the predicted biomechanical features and their corresponding ground truth values. Figure 4: Illustration of the distribution of static biomechanical features in non-normalized dataset, normalized dataset, and the testing dataset. The visualization is achieved using principal component analysis (PCA). A random selection of 500 samples from each dataset was conducted, where each sample encompasses static features \(\left\{\ell,\phi_{\text{ARB}},\phi_{\text{CPS}}\right\}\in\mathbb{R}^{27}\). Figure 6 presents qualitative results illustrating the impact of the normalization strategy. The estimated poses (green line) on both the STB and RHD datasets show a closer alignment with the ground truth (GT) poses (red line) compared to the model trained without the normalized dataset (blue line). Figure 6: Qualitative results of poses generated by the models trained on normalized dataset, the models trained without normalized dataset, and GT poses. (**a**) Results from the STB dataset. (**b**) Results from the RHD-A dataset. (**c**) Results from the RHD-B dataset.
# Numerical Analysis of a Spiral Tube Damping Busbar to Suppress VFTO in 1000 kV GIS
## 4 Inductance Effect on Suppressing VFTO  
When VFTO passes through the inductance, the wavefront is smoothed, its steepness is reduced, and the amplitude is decreased. The inductance \(L_{i}\) increases the round-trip time of the travelling wave, which in turn leads to a reduction in the high-frequency components of the VFTO. The travelling wave leads to a higher voltage on the inductance of the small circuit, which causes the parallel resistance \(R_{i}\) to absorb the travelling wave energy, increase the consumption of active power, and reduce the amplitude of the VFTO.  
The inductance value of the damping busbar is an important factor for an obvious VFTO suppression effect. The limitation on the steepness of the transients is a result of the inductance effect of the damping busbar. Figure 8 describes the influence of the inductance value on VFTO suppression at the load side during transient phenomena.  
The more considerable value of the equivalent inductance of the damping busbar leads to an obvious suppression effect. Table 4 illustrates the transient characteristics of the VFTO waveform under an opening operation for different values of equivalent inductance of the damping busbar.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**VFTO Characteristic Parameters**} & \multicolumn{2}{c}{**Simulation Results**} \\ \cline{2-3}  & **Before Installing the** & **After Installing the** \\  & **Damping Busbar** & **Damping Busbar** \\ \hline Rise time [ns] & 10 & 30 \\ Average breakdown steepness [kV/ns] & 71.53 & 48.725 \\ Maximum amplitude of VFTO [p.u.] & 1.554 & 1.13 \\ Average voltage overshoot coefficient & 0.487 & 0.244 \\ Delay time [\(\upmu\)s] & 24.4 & 47.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The transient characteristics of VFTO under an opening operation at the load side before and after installing the damping busbar.  
Figure 7: Very fast transient overvoltage (VFTO) and VFTC waveforms: VFTO at the source side (**a**) before installing the damping busbar and (**b**) after installing it; VFTO at the load side (**c**) before installing the damping busbar and (**d**) after installing it; VFTC (**e**) before installing the damping busbar and (**f**) after installing it.  
It can be observed that a higher damping effect was achieved by increasing the equivalent inductance of the damping busbar. Furthermore, increasing the inductance value can damp the VFT waveform's front. It is also helpful for absorbing the sharp spikes of the VFT since surge arresters do not act quickly enough to prevent steep-fronted switching transients. Therefore, installing an additional spiral coil with the damping busbar is highly valuable to attenuating the effects of VFTs. For this reason, a spiral coil of resistive litz wire was designed to be installed in the helical groove and connected in series with the damping busbar.