esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Antioxidant Activity
#### Cytotoxicityicity of MCPE-A, MCPE-B, and MCPE-C in HepG2 Cells
In the present study, the effects of MCPE-A, MCPE-B, and MCPE-C on the antioxidant enzyme activities of T-SOD, CAT, GSH-Px, GSH-Rx, and content of MDA in HepG2 cells were measured for illuminating whether their protection on H\({}_{2}\)O\({}_{2}\)-induced oxidative damage HepG2 cells were associated with the modulation of endogenous antioxidant defense systems. The levels of antioxidant enzymes (T-SOD, CAT, GSH-Px, and GSH-Rx) for all of the groups of HepG2 cells were showed in Figure 9A-D. A significant decrease in the levels of T-SOD, CAT, GSH-Px, and GSH-Rx was observed in the HepG2 cells exposed to H\({}_{2}\)O\({}_{2}\) as compared with normal control (\(p\) < 0.05), and the T-SOD, CAT, GSH-Px, and GSH-Rx activities of HepG2 cells incubated by MCPE-A, MCPE-B, and MCPE-C were significantly higher than that of the H\({}_{2}\)O\({}_{2}\) damaged group  
Figure 8: Damage effect of H\({}_{2}\)O\({}_{2}\) on HepG2 cells (**A**) and protective effects of MCPE-A, MCPE-B, and MCPE-C on H\({}_{2}\)O\({}_{2}\)-induced oxidative damage in HepG2 cells at concentrations of 10.0 \(\upmu\)g/mL, 25.0 \(\upmu\)g/mL, and 50 \(\upmu\)g/mL, respectively; (**B**) All data are presented as the mean \(\pm\) SD of triplicate results. * \(p\) < 0.05 vs. the control group. * \(p\) < 0.05 vs. the H\({}_{2}\)O\({}_{2}\) treated group. ** \(p\) < 0.01 vs. the H\({}_{2}\)O\({}_{2}\) treated group. (\(p\) < 0.05). At the same tested concentrations, the HepG2 cell groups incubated by MCPE-A showed the highest levels of T-SOD, CAT, GSH-Px, and GSH-Rx, and followed by the groups incubated by MCPE-B. Figure 9E indicated that the MDA content (21.38 \(\pm\) 1.12 U/mg prot) of damage group was significantly (\(p\) < 0.05) increased compared with the control group (8.96 \(\pm\) 0.94 U/mg prot). The MDA contents of all protection groups were decreased by MCPE-A, MCPE-B, and MCPE-C pretreatment. At the concentration of 50.0 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, the MDA content of MCPE-A incubated group was 9.73 \(\pm\) 0.82 U/mg prot, which was significantly lower than that of H\({}_{2}\)O\({}_{2}\) treated group (\(p\) < 0.05). The present results indicated that MCPE-A, MCPE-B, and MCPE-C could protect H\({}_{2}\)O\({}_{2}\)-induced HepG2 cells from oxidative stress by enhancing endogenous antioxidant defense systems including the antioxidant enzyme defense system and the GSH system.
# A Novel Wavelet-Based Algorithm for Detection of QRS Complex
## 3 Methodology
### Evaluation of Noise Amount  
The detection of QRS peaks in this study is based on the candidate extremum pairs in d3' or d4' to find the positions of the corresponding candidate QRS peaks in the original ECG signal and to further identify the true QRS peaks. The selection of d3' or d4' is based on their noise amount. A noise evaluation method is proposed to quantify the amount of noise in d3' and d4'. We remove the extreme points which are greater than or smaller than 1.6 times the positive or negative thresholds in d3' and d4'. These removed extreme points represent the components possibly contributed by the QRS peaks, not by the noise. The remaining extreme points are applied to evaluate the amount of noise, which is defined as the sum of the square values of the remaining extreme points. If the noise amount in d3' is smaller than that in d4', the detection of QRS peaks is based on d3'; otherwise it is based on d4'.
###### Abstract
[PERSON] and [PERSON] [35] adopted a least-square support vector machine (LS-SVM) model to find the EM of weak rocks by considering the Vp, unit weight (r), PLI and tensile strength (Ts). The prediction results showed that the LS-SVM models can be a good substitute for experiments to measure the weak rock EM. [PERSON] and [PERSON] [36] used a support vector regression (SVR) model and a backpropagation neural network (BPNN) model to forecast the EM of reservoir rocks. The prediction results indicated that the former has a better performance than the latter. [PERSON] et al. [37] used an integrated ML model, named the random forest (RF) model, to predict the EM and the uniaxial compressive strength (UCS) of various rocks. Based on the prediction results, the RF model not only achieves a satisfactory accuracy (R\({}^{2}\) = 0.91) for the EM prediction but can also accurately estimate the UCS (R\({}^{2}\) = 0.93). Other similar studies on the rock EM prediction by ML models can be referred to in the literature [38; 39; 40; 41; 42]. To improve the ML model's performance, the metaheuristic optimization algorithms (MOA) are used to select the model hyperparameters. [PERSON] et al. [43] used the imperialism competitive algorithm (ICA) and the particle-swarm optimization (PSO) to optimize an ANN model for predicting the EM of rock materials. The optimization results illustrated that the ICA-ANN has the best prediction accuracy for both the training and testing phases (R\({}^{2}\): 0.952 and 0.955). [PERSON] and [PERSON] [44] combined the cuckoo optimization algorithm (COA) and ANN model to estimate the EM of limestone rocks. The results showed that the COA can obviously improve the prediction performance of the ANN model. Other optimized ML models for the EM prediction can be found in the literature [45; 46; 47; 48; 49; 50]. Among common ML models, the RF model has unique advantages in resisting the overfitting phenomenon, and its combination with MOA can effectively solve the hyperparameter selection problem [51]. In general, MOA can be divided into four groups, i.e., the based evolutionary, based swarm intelligence, based human behavior and based physicochemical groups. Therefore, the aim of this paper is to generate four optimized RF models using four different MOA strategies for predicting the rock EM. An integrated rock database was established using the data-driven technology to train and test the proposed models. In addition, five widely used ML models and one empirical equation were also developed to compare the predictive performance with four hybrid RF models for predicting the rock EM. Finally, a sensitive analysis is carried out to calculate the variable importance.
## 4 Evaluation of Experimental Results
### Optimum Force Calculation for Surface Roughness Optimisation  
Using the parabolas defined in Section 4.1, minimum and maximum points can be defined. As previously shown, the parabolas had minimum points in the case of surface roughness indicators, while they exhibited maximum points when improvement factors were considered. To perform the extremum calculation, the trendline equations are required, which can be displayed in Microsoft Excel through the trendline formatting options, as highlighted in red in Figure 9.  
During the calculation, the optimal burnishing force required to achieve the best surface quality can be determined, meaning the process is optimized regarding surface roughness. In every case, the second-degree polynomial takes the form shown in Equation (6), where \"x\" represents the performance (power), and \"y\" denotes the surface roughness indicator or its improvement factor, written as follows:  
\[y=ax^{2}+bx+c, \tag{6}\]  
Figure 9: Displaying the trendline equation in Microsoft Excel. To determine the extremum, this equation must be differentiated once, resulting in the first-order Equation (7). The curve has a minimum or maximum point where this equation equals zero, so we set it to zero and rearrange for \"\(x\)\", the desired optimal performance, as shown in Equation (8), written as follows:  
\[y^{\prime}=2 ax+b=0, \tag{7}\]  
\[x_{opt}=\frac{-b}{2a}=P_{opt}, \tag{8}\]  
By using Equations (2) and (4), the burnishing force defined as a technological parameter can be calculated from the performance. The following Equation (9) shows the substitution into the power formula, while Equation (10) represents the rearrangement to solve for the force optimum:  
\[P_{opt}=\mu\cdot F_{opt}\cdot v_{r}, \tag{9}\]  
\[F_{opt}=\frac{P_{opt}}{\mu\cdot v_{r}}, \tag{10}\]  
Table 4 contains the calculations related to the surface roughness indicators. Since the x\({}^{2}\) components of the equations are all positive, the calculated extreme values represent the minimum points of the parabolas. However, this distinction is not relevant for the later calculations, since the sought force is the so-called force optimum. These values show the burnishing force at which further increase would be unfavorable in terms of both surface quality and energy efficiency, while decreasing it would improve energy efficiency at the expense of surface quality. This concept of force optimum is illustrated in Figure 10, where the right side shows machining performed with force above the optimum--resulting in increased surface roughness and energy demand--while the left side represents machining with force below the optimum--leading to increased roughness but reduced energy demand. Using the force optimum value, optimizations were performed for given combinations of technological parameters (combinations of feed rate and burnishing speed) for diamond-burnish machining in terms of surface roughness.
## 2 Preparation and Characterization of Magnetic Nanoparticles  
Nanoparticles have been widely applied in bioengineering applications, such as imaging, molecular detection, and drug delivery. They are usually based on ferrous ferric oxide to ensure responsiveness to the applied magnetic field; however, magnetic ferrous ferric oxides have many disadvantages, e.g., agglomeration, oxidation, and biotoxicity. To overcome these deficiencies, magnetic ferrous ferric oxide nanoparticles are often coated with silica, after which they possess higher biocompatibility. Figure 1a shows the process flow of MNPs in this research.  
The Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) core-shell nanoparticles in this study were manufactured as follows. First, 100 mL of 1 mol/L FeCl\({}_{3}\cdot\)6H\({}_{2}\)O solution and 50 mL of 1 mol/L FeCl\({}_{2}\cdot\)4H\({}_{2}\)O solution were mixed in a 250 mL three-neck flask. Next, a water bath was heated to 60 \({}^{\circ}\)C and 0.5 mol/L aqueous ammonia solution was added dropwise while stirring at 800 r/min. As the color of the mixture gradually changed from orange-red to black, ammonia was added to maintain a pH of about 11. The mixture was stirred for 30 min, ultrasonically dispersed for 30 min, and stirred again for 1 h. Tetraethyl orthosilicate (TEOS) was added to the ethanol deionized water ammonia solution for 24 h. Finally, the products were repeatedly washed with deionized water to ensure pH neutrality.  
According to the procedure of the preparation, the MNPs were characterized by Transmission Electron Microscopy (TEM) and X-ray diffraction (XRD). Figure 1b shows the TEM of the prepared MNPs. The size diagram of the prepared MNPs has also been analyzed in Figure 1c. The mean diameter of the Fe3O4@SiO2 is 32.1 nm and the standard deviation is 5.1. The mean thickness of the SiO\({}_{2}\) shell is 7 nm. As can be seen from Figure 2a, the XRD patterns of the prepared Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) and the Fe\({}_{3}\)O\({}_{4}\) samples were analyzed. To evaluate the magnetic properties, the magnetic hysteresis loop of the MNPs was measured by LakeShore 7404 VSM in Figure 2b, and the saturation magnetization is 14.2 emu/g.
## 1 Introduction
The previous reports have focused more on the problems that are created by adding DG to the distribution networks, such as blind protection, sympathetic tripping and failure of relclosing [4; 5; 7; 8]. In [10; 12; 22; 24; 25; 26; 27], the recloser settings and coordination with the other protective devices such as fuses and overcurrent relays were studied. The researchers in one study [14] replaced a new solid dielectric with hydraulic relcosers and showed the benefits of this replacement. Another study [15] introduced the development of an analytical mathematical model to describe the fast dynamic response of opening and closing mechanisms of a power line recloser switch. This paper investigates the impact of reclosers on blinding areas of OC relays with DG penetration and fault impedance in a realistic distribution network data. The proposed investigation shows that using a recloser eliminates the blind areas and improves the reliability. In this way, the blind areas of the case study are detected, and then, reclosers are installed in suitable places. The limitation of the blind areas is determined by overcurrent pickup. The effect of the DG and fault impedance are studied on DGs, feeder and recloser relays. The research data is based on a real-time simulation, actual protection relays and real feeders of a distribution network; this enhances the credibility and validity of the reported findings. Overall, the main contributions of the paper are summarized as follows:  
* Detecting the fault in blinding areas in the presence of DG and fault impedance. * Investigating the effect of DG and fault impedance on DGs, feeder and recloser relays. * Investigating the effect of recloser on protection system and reliability. Section 2 presents the blind areas of an OC relay in the distribution network. The settings of the protective relay, simulation results and reliability of the case study are described in Section 3. Moreover, in this section, the blind points that may occur in the case study are explained.
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 3 Proposed Methodology  
This section outlines the architecture and learning framework of the RASST, as illustrated in Figure 1. It integrates rotationally aware patch embeddings, ARC, and MSFF to tackle the challenges of oriented object detection. Additionally, the PGL framework is introduced, employing RAW and GC losses to enhance the utilization of unlabeled data. The proposed methodology highlights robustness to object orientations, scalability across varying object scales, and efficiency in semi-supervised learning.  
Figure 1 illustrates how the RASST framework incorporates a rotationally aware patch embedding mechanism integrated with a ViT to encode orientation information. The ARC module dynamically aligns convolutional kernels with local object orientations, while the PGL framework refines pseudo-labels using RAW and GC losses. MSFF enhances the model ability to detect objects at varying scales, delivering robust performance across diverse aerial imagery scenarios.
## Methods
### Experimental Principle  
In order to prevent the adverse effects of extraneous electromagnetic fields while transmitting the recorded signal (from 1 to 3 in Figure 1), shielded cables were utilized as the electrode leads. When the signal is recorded, the shield is usually grounded, thus avoiding the effects of extraneous interferences on the signal in the inner core wire. Although the shielded cables protect the signals in the core from extraneous electromagnetic fields, a distributed resistance and capacitance often exists between the shield and the core wire, thus affecting the quality of recorded biopotential signals that are generally characterized by low amplitude and high impedance [45]. Therefore, this study implemented a shielded drive technology, in which the signal in the core drives the signal in the shield, as shown in Figure 2D. When the output of the amplifier is connected to the shield, which means that the shield is connected to a low internal resistance voltage source, the effect of the shielded cable against interference remains unchanged. However, since the voltage of the core is equal to the voltage of the shield, according to [PERSON]'s law [46], the current in the core automatically becomes zero which then nullifies the resistance and capacitance that once existed between the shield and the core. Hence, the biopotential signal passing through the shielded cable is no longer affected by the distributed resistance and capacitance.  
During the experiments, a total of four electrode channels were simultaneously used to record the ECG, EOG, and EMG signals across all 20 of the recruited subjects. Meanwhile, the first electrode channel denoted as Channel 1 was set as the control channel, thus it was not implemented on the proposed shielded drive circuit. In addition, we implemented three different shielded drive techniques on the other channels (Channel 2, Channel 3 and Channel 4). As shown in Figure 2A, the first channel adopted the conventional method that has no shield (herein referred to as Method 1). In Figure 2B, the second channel connected the outer shields of both electrode cables to the buffered ground signal of the ADS1299 EEG-FE, as shown in Figure 3 (referred to as Method 2). Figure 2C shows that the outer shields of the electrode cables of the third channel were connected to the buffered BIAS_SHD signal (Figure 3) generated by the integrated circuit of ADS1299 EEG-FE (referred to as Method 3). In Figure 2D, the signal of the inner wire was fed back to the shield using a low impedance output of an amplifier (herein referred to as Method 4). And thus, the voltage of the shielded cable with respect to the shield became zero. Furthermore, the four channels shared the same driven-right-leg (DRL) electrode placed on the earlobe, as the ground and the DRL signal were generated from the integrated  
Figure 2: Schematic diagram of the different shielded drive methods investigated in this study.  
circuit of the ADS1299 EEG-FE kit. The DRL circuit sensed the common-mode of a selected set of electrodes and created a negative feedback loop by driving the body with an inverted common-mode signal [47].
# RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems
## 4 Results and Analysis
### RSSI-Only vs. RSSI-and-Device-Pose Localization Results (NN)  
Both the RSSI-only and RSSI-and-device-pose neural networks were equally sized (125 neurons), and both were trained for the number of epochs where the RSSI-only network achieved a cross-entropy loss goal of 0.15 (from Figure 10, this occurred at 136 epochs). Both neural networks were trained and tested 100 times, with the entire collected dataset (57,489 vectors) being randomly split into 70% training and 30% testing for each run. Both neural networks were presented with the same training and testing vector dataset (with device-pose information appended for the RSSI-and-device-pose neural network vectors from the device-pose identification neural network, refer to Figure 4), during each of the 100 runs. The total accuracy for both networks was taken over these 100 randomized runs and is given in Figure 12. In Figure 13, the percentage point improvement in true-negative  
Figure 10: RSSI-only neural network’s \(\mathit{Loss}_{\text{CE\_TR}}\) (left) and 5-sample moving average \(\mathit{Loss}_{\text{CE\_TR}}-\mathit{Loss}_{\text{CE\_VAL}}\) (right). Note: \(\mathit{Loss}_{\text{CE\_TR}}\) is shown for epochs > 20 due to scale.  
(TN) rates is depicted as an empirical cumulative density function (ECDF) over all the runs. For each zone class, the TN improvement was obtained by subtracting the rate of the RSSI-only neural network from the rate of the RSSI-and-device-pose neural network (i.e., \(TN_{RSSI-and-device-pose}-TN_{RSSI-Only}\)).  
From Figure 12 (also summarized in Table 4), the overall accuracy of the RSSI-only neural network was quite high (min-max range: 70.5-72.3%), but when device pose data was added, a consistent accuracy increase of between 8.8 and 11.8 percentage points was achieved (average improvement: 10.72 percentage points). The spread of the normalized histogram is also slightly narrower, implying better precision. Interestingly, the TN rate improvement (Figure 13) shows that the ZB zone had the largest percentage point improvements. This matches the expectations from the KS test results in Section 4.1 where the ZB zone had the highest number of type II errors.
## 4 Analysis of Experimental Results
### The Relationship Between Facular Area and Shooting Distance  
Through the above experimental analysis, it can be known that UV image shooting distance greatly affects the size of the facular area. Detecting the discharge of the insulator by UV imager, the shooting distance is limited. Therefore, studying the influence of the shooting distance on the facular area is of great significance to the UV imaging method for detecting the leakage state of electrical equipment. In this experiment, the insulator leakage UV video was taken at 5 m, 6 m, 7 m, 8 m, and 9 m away from the insulator. The voltage of the insulator is maintained at 10 kV. From the videos shot at different distances, a 30 s video with stable discharge is selected for analysis. The relationship between different detection distances  
Figure 14 is a fitting curve of facular area under a voltage of 10 kV. The relationship between the facular area and the shooting distance satisfies an exponential function. Its expression is Equation (10). The experimental results obtained by data fitting the specific values of the parameters in the expression are shown in Table 6.  
In this paper, the parameter b in Equation (10) is called the facular area attenuation index, and parameter a is called the change amplitude of the facular area. The relationship between facular area and shoot distance under 8 kV voltage across the insulator also satisfies the exponential function (see Figure 15). Under different voltages, the attenuation index of the facular area is the same, but the change amplitude of the facular area is different. The function fitting curve with a voltage of 8 kV is shown below:  
In [1], the fitting curve equation of facular and shoot distance is obtained through experiments, and it is also an exponential function with an attenuation index of about \(-2\), which is similar to the one in this experiment. The following conclusions can be obtained by analyzing the above experimental data. The detection distance has a greater impact on the spot area. The spot area is real-time, and the statistical parameters of the facular area within a period of time can be obtained through image processing and statistics. The total number and average value of the facular area in a period of time can characterize the changing trend of the facular spot. The sum and average of the facular area in a period of time can characterize the changing trend of the facular area. The changing amplitude of the facular area in the attenuation function is affected by many conditions, such as the insulator pollution class, the environmental humidity, and the voltage across the insulator.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **10 kV** \\ \hline a & \(8.06\times 10^{9}\) \\ b & \(-2.269\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Facular area parameters.  
Figure 14: Fitting curve of facular area.
## 4 Experimental Results and Analysis
### Introduction of Evaluation Index
Conversely, a lower SSE value suggests that the model's overall prediction deviations are minimal, providing an alternative view of the model's predictive accuracy. By thoroughly examining these assessment metrics, the MDFFE model's effectiveness in forecasting crude oil and hot-rolled coil futures prices can be precisely gauged. This provides a robust foundation for refining and adapting the model, as well as for its practical use in scenarios such as financial market transactions and investment strategies. For instance, investors, financial institutions, and other related entities can leverage these metrics to assess the dependability of the model's predictions, enabling them to make more informed decisions in futures trading, asset allocation, and risk management, thereby enhancing operational efficiency and performance in financial markets.