esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
introduced an adaptive virtual event method to address the excessive dependency on the matching algorithm in the traditional virtual event method, enhancing internal multiple suppression capabilities [19]. Although researchers have improved and modified the virtual event method to address various issues, the traditional virtual event method still requires sequential internal multiple constructions on subsurface interfaces, incurring high computational costs for seismic data derived from complex structures and making primary extraction difficult. To overcome this challenge, we propose an iterative virtual event method. Compared to the traditional virtual event method, the iterative virtual event technique incorporates an iterative calculation process, mitigates the influence of false frequencies generated by data space convolution on predicted multiple models, and significantly improves computational efficiency. When applied to actual onshore post-stack seismic data from the southwest depression of the Tarim Basin, our method effectively suppresses internal multiples that are challenging to eliminate in pre-stack traces, emphasizes the energy of deep effective waves, and further enhances the quality of seismic profiles. The paper is organized as follows. After the introduction, we provide an overview of the study region. Subsequently, we present the theory of virtual events for internal multiple eliminations, followed by synthetic and field data experiments that validate the effectiveness of our approach. Finally, we draw conclusions based on detailed analyses and discussions.
## 2 Basic Theoretical Analysis
### Principle of Lock-In Amplifier and Frequency Error Analysis
Assuming that the amplitude-frequency characteristic of a lowpass filter is \(|H_{LP}(2\pi\Delta f)|\), and the small frequency deviation \(\Delta f\) is in the passband, and ignoring the effect of noise in the measured signal, the output of lowpass filters of two orthogonal channels are:  
Figure 1.5 Figure 1.6Therefore, the measured amplitude of to-be-detected signal can be rewritten as:  
Figure 1.7  
As shown in Equation (8), the actually measured amplitude of signal \(\bar{A}\) has error due to the frequency deviation \(\Delta f\)[28]. Furthermore, taking the IAF as an example for quantitatively analyzing the amplitude measurement error caused by frequency deviation \(\Delta f\), ignoring the noise \(n(t)\), Equations (4) and (5) can be rewritten as :  
Figure 1.8 Figure 1.9 \[r_{c}(t)=B\cos(2\pi f_{1}t) \tag{2a}\]0  
\[r_{c}(t)=B\cos(2\pi f_{1}t) \tag{2a}\]1 \[r_{c}(t)=B\cos(2\pi f_{1}t) \tag{2a}\]2 \[r_{c}(t)=B\cos(2\pi f_{1}t) \tag{2a}\]3  
where the integral length \(T_{1}=\frac{1}{f_{1}}\) is the period of reference signals. Then, the amplitude estimation value with frequency deviation \(\Delta f\) can be obtained [28]:  
\[r_{c}(t)=B\cos(2\pi f_{1}t) \tag{2a}\]4 \[r_{c}(t)=B\cos(2\pi f_{1}t) \tag{2a}\]5 \[r_{c}(t)=B\cos(2\pi f_{1}t) \tag{2a}\]6  
In Equation (10), \(r_{f}=\frac{f-f_{1}}{f_{1}}\) is the relative frequency deviation. Assuming the measured signal's initial phase is \(\phi=0\) and the relative frequency deviation \(r_{f}\) is very small, which is from \(-0.1\) to \(0.1\), the relative amplitude error caused by \(r_{f}\) is shown in Figure 2a. The relative amplitude error is monotonically increasing in this small range. Due to the existence of frequency deviation \(r_{f}\), the relative amplitude error is affected by the initial phase of the measured signal. If the relative frequency deviation \(r_{f}\) is fixed and equals \(0.001\), the relative amplitude error varies periodically with the change of the initial phase of the signal as shown in Figure 2b.
## 1 Introduction
### A Technique to Evaluate Angular Misalignments of a Small Cylinder
First, we obtain the arc-profile of a workpiece at a \(Y\)-position of \(y_{1}\), and detect the \(X\)-coordinate of the peak (\(x_{\text{peak1}}\)) in the obtained arc-profile through fitting a circle based on the least squares method. In the same manner, we detect the \(X\)-coordinate of the peak (\(x_{\text{peak2}}\)) in the arc-profile obtained at a \(Y\)-position of \(y_{2}\). From the obtained peak coordinates, \(\theta_{Z}\) can be calculated by the following equation:  
\[\theta_{z}=\tan\left(\frac{x_{\text{peak2}}-x_{\text{peak1}}}{y_{2}-y_{1}}\right) \tag{18}\]  
Figure 9: Evaluation of the angular misalignment \(\theta_{X}\) of a small cylinder about the \(X\)-axis: (**a**) before the rotation of the holder unit about the \(Z\)-axis; (**b**) after 90\({}^{\circ}\) rotation of the holder unit about the \(Z\)-axis. It should be noted that the angular misalignment of the workpiece about the \(X\)-axis (\(\varphi_{X}\)) could affect the evaluation of \(\theta_{Z}\). From the geometric relationship, the error \(e_{\theta Z}\) in measurement of \(\theta_{Z}\) due to \(\varphi_{X}\) can be expressed by the following equation:  
\[e_{\theta Z}=\theta_{Z}-\tan^{-1}(\cos\varphi_{X}\tan\theta_{Z}) \tag{19}\]  
In the proposed method, the evaluation of \(\theta_{Z}\) is carried out after the measurement and alignment of \(\theta_{X}\). On the assumption that \(\theta_{X}\) is aligned to be less than \(0.1^{\circ},e_{\theta Z}\) becomes \(2.66\times 10^{-6\circ}\) under the condition of \(\theta_{Z}=0.1^{\circ}\); the influence of \(\theta_{X}\) can thus be treated as small enough to be neglected.
# Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes
## 3 Methodology
### Win Probability Estimation Based on CNN Classification  
CNN specializes in image classification, and its accuracy has recently surpassed that of humans in various image recognition applications. With regard to the behavioral patterns in sports matches, we represent the odds and scores of matches as candlesticks in order to understand the fluctuations in time series. However, the time series transformed into 2D images by GAF is a highly in-depth knowledge graph, and humans cannot easily observe the features and patterns on this graph given their unknown style. Therefore, we exploit the classification capability of CNN to recognize the implied patterns from these image-based time series.  
Figure 5 shows our CNN architecture, which originates from the most popular, small, and easy architecture, LeNet-5 [56], and is enhanced by incorporating the concepts of rectified linear units (ReLU), max-pooling, and dropouts that have been proposed in recent years. After some testing and fine tuning, the adjusted architecture has been proven to work well.  
Figure 4: Candlesticks of OHLC time series encoded by GAF into four channels of 10 \(\times\) 10 images, which are used as input for CNN classifier.  
As shown in Figure 5, the CNN comprises two sets of convolutional, activation, and max-pooling layers followed by a dropout, fully connected layer, activation, another dropout, and a softmax classifier. The detailed structure and hyperparameters of each layer are described follows:
## 3 Assessing the Effectiveness of the Proposed IDA Procedure  
The effectiveness of the IDA procedure proposed in Section 2.1 will be assessed by comparing it with two approaches: the standard procedure that refers to the elastic period and the procedure that refers to literature values of the elongated period calculated according to Equation (1) or to Equation (2). First, the _IM_ and _DM_ values are compared through different percentiles. Subsequently, the dispersion achieved by both the _IM_ and the _DM_ is analyzed by comparing the standard deviations and the coefficient of variation of the set of IDA curves corresponding to each direction of analysis.  
Figure 6: Evolution of the inelastic smooth peak period \(T_{inSP}\) in the \(x\)-direction (**a**) and in the \(y\)-direction (**b**). The curves refer to the earthquakes considered in Table 3.  
The intensity measure will be given by the \(\xi=5\%\) damped spectral acceleration at the structure's first-mode period, that is, \(IM=S_{a}(T_{1},5\%)\). The value of \(T_{1}\) will be considered either as the elastic fundamental period \(T_{el}\) (standard IDA procedure) or as the elongated inelastic period \(T_{in}\), estimated following [PERSON] et al. (2018) or [PERSON] and [PERSON] (2019) or calculated as proposed in this paper (alternative IDA procedure).  
According to Equation (6), the maximum interstory drift ratio of the building, referred to as \(ID_{max}\), was adopted as the indicator of the maximum demand on the structure (the \(DM\) parameter). All analyses performed herein were referenced to the two-story building displayed in Figure 3 and to the set of spectrum-consistent earthquakes described in Table 2.
## 3 Results and Discussion
### Effect of pH on the Efficiency of Dye Sorption  
Figure 2 shows the sorption efficiency of BR46 BV10 dyes on BBeF and BBiF sorbents as a function of the initial pH of the dye solution. In the case of BR46, the tests were performed in the pH range of 2-8, as discolouration of the solution was observed at an initial pH > 8. The study shows that an increase in the removal efficiency of Basic Red 46 was observed with increasing pH. The efficiency of sorption of BR46 pigment on both bark types increased with increasing pH = 5. A further increase in pH did not lead to an increase in the sorption efficiency of BR46 (Figure 2a).  
Figure 1: FTIR spectra for birch bark and beech bark.  
The functional groups of the sorbent from beech bark and birch bark were protonated at low pH. This resulted in the sorbent acquiring a positive charge. The positively charged surface of the sorbent repelled molecules of the cationic dye Basic Red 46 electrostatically, which had a negative effect on sorption, explaining the lowest sorption efficiency of BR46 at pH 2.  
A different process was observed with the second tested dye, BV10. It was also a cationic dye, but the highest sorption efficiency was observed at pH 3 (Figure 2b). As with BR46, the sorption efficiency of BV10 was compared for both sorbents.  
Basic Red 46 and Basic Violet 10 are cationic dyes, but the effect of initial pH on the sorption efficiency of the two dyes was different. These differences may be due to the different chemical structure of the dyes.  
One feature of Basic Violet 10 is the presence of an acidic carboxyl group, which could lead to electrostatic attraction of the functional carboxyl group (COO\({}^{-}\)) of BV10 by the positively charged surface of the sorbent at low pH.  
On the other hand, the lower sorption of the BV10 dye at higher pH could be due to the negative charge of the sorbent and the mutual repulsion of the dye molecules and the sorbent surface.  
The pH\({}_{\text{PZC}}\) values determined using the \"drift\" method for birch bark and beech bark were, respectively, pH\({}_{\text{PZC}}\) = 5.55 and pH\({}_{\text{PZC}}\) = 5.77 (Figure 3). The values obtained indicate the slightly acidic nature of the sorbents tested. This is probably due to the fact that these materials have a larger number of acidic functional groups (e.g., carboxyl, sulfonic acid) compared to basic groups (e.g., amino). The acidic nature of bark-based materials indicates potentially good sorption properties compared to cationic sorbates (including BR46 and BV10).  
Figure 3: (a,b) pH\({}_{\text{PZC}}\) of the tested sorbents determined with the “drift” method.  
Figure 2: The influence of pH on the sorption efficiency of dyes (a) BR46 and (b) BV10.
## 4 Results
### Simulation Result Limitations
#### 4.4.2 Limitation on the Benchmark of Compared Approaches  
This paper focuses on the comparison between TFD-OLSR, a mesh-like approach, and CBL-OLSR, a backbone-like approach. However, some evaluations performed in a scenario involving two communicating nodes seven hops apart but also a reactive protocol, dynamic source routing (DSR), and a geographic routing protocol (GRP) confirmed that TFD-OLSR and CBL-OLSR still achieved a higher delivery rate (Figure 13a) and a lower end-to-end delay (Figure 13b), respectively. The end-to-end delay for DSR exceeds 1 s every time.  
Figure 12: Mean number of IP hops and packet delivery ratio for the monodirectional packet flow application and two communicating nodes approximately 6 hops apart.
## 5 Discussion
The RCSP algorithm preserves the core computational framework of the CSP while incorporating a covariance matrix regularization strategy. Although this introduces additional matrix summation operations \(O((J+i)\bullet T^{2})\), the eigendecomposition remains the computational bottleneck, resulting in marginally increased training time while maintaining comparable testing time to the CSP. The FBCSP algorithm employs a serial multi-sub-band processing mechanism, leading to computational complexity that scales linearly with the number of sub-bands \(\Omega\)\(O(\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{ \mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{ \mathsf{ \mathsf{  }}}}}}}}}}}}}}} \mathrm{T})\), significantly increasing the computational burden. Building upon the FBCSP framework, the FBRCSP algorithm integrates regularization strategies. While maintaining the same order of computational complexity, it requires cross-validation to optimize multiple hyper-parameters, consequently further extending the training duration. Notably, the OFBRCSP algorithm incorporates Laplacian spatial filtering as a preprocessing step, introducing computational complexity closely related to channel neighborhoods \(E\)\(O(\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{ \mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{ \mathsf{ \mathsf{ \mathsf{   }}}}}}}}}}}}}}} } \mathrm{E})\), which results in the highest time consumption during the testing phase. The proposed WFBRCSP algorithm innovatively introduces a mutual  
to a significant increase in computation time. Therefore, although the improvement in classification accuracy is small, the computational efficiency advantage of the Fisher score makes it more effective in practical applications. As exhibited in Figure 7, compared to other methods, the WFBRCSP method has the highest AUC value of 0.96, with the steepest ROC curve that is closest to the top-left corner. The WFBRCSP model exhibits the highest true-positive rate and the lowest false-positive rate among the methods exhibited. The experimental results exhibit that adjusting subject weights based on inter-subject correlation when processing EEG signals across subjects helps to improve the model's generalization ability. As exhibited in Table 3, the comparison of the main contributions of this paper with existing related work reveals key differences and innovations.
# Mobility-Enabled Edge Server Selection for Multi-User Composite Services
## 7 Conclusions  
In this paper, we focused on multi-user edge server selection in terms of minimizing the total waiting time of users in mobile edge computing. This problem was formally modeled, and we took the proximity constraint and capacity constraint into consideration to calculate the total waiting time. The MESP algorithm was adopted to achieve the objective of time-aware multi-user edge server selection. The simulation experiments showed that our MESP algorithm-based edge server selection method outperformed traditional methods in terms of time latency. However, the application of the proposed method was restricted. The mobile path and the speed of all mobile users should be precisely predicted. Moreover, the proposed algorithm selected edge servers only in terms of time consumption, and the energy consumption was not taken into consideration. In the future, we will select edge servers considering both time and energy consumption. We will also seek other algorithms to select the edge server for mobile users with higher effectiveness or efficiency.  
**Author Contributions:** Conceptualization, W.Z. and Y.Z.; methodology, W.Z. and K.P.; software, Q.W.; validation, W.Z., Y.Z., and K.P.; formal analysis, Y.Z. and Q.W.; investigation, W.Z.; resources, W.Z.; data curation, Q.W.; writing, original draft preparation, W.Z. and K.P.; writing, review and editing, W.Z., Y.Z., Q.W., and K.P.; visualization, Y.Z.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z.  
**Funding:** This research was funded by the National Natural Science Foundation of China, Grant Number 61872002, the Anhui Key Research and Development Plan, Grant Number 201904a05020091, and the Natural Science Foundation of Anhui Province of China, Grant Number 1808085 MF197.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Predicting Multi-Gene Mutation Based on Lung Cancer CT Images and Mut-SeResNet
## 5 Discussion
### Comparison with Recent Studies and Methods
Literature E mainly consisted of two different 3D CNNs that used the softmax activation function to obtain the final disease probability. It finally achieved 82.89% accuracy. Literature F built a 3D convolutional neural network (3D CNN) model based on the processed images. By improving the baseline model, it eventually achieved 65% accuracy. Literature G proposed the Otsu method combined with morphological operations for segmentation, using 6306 CT images from the LIDC-IDRI database for the experiment, and achieved 84.31% accuracy, with sensitivity and specificity results of 91.69% and 73.16%, respectively. The Mut-SeResNet model in this paper predicted gene mutations in both EGFR and KRAS by learning the features of genes. The multi-task influence and promote each other. Although imaging detection cannot replace biopsy, it can be used as a complement to biopsy methods. When compared to existing depth models and imaging omics models, the model proposed in this paper achieved higher prediction accuracy and better prediction performance. The main advantage of predicting gene mutations based on images, compared to pathological biopsies, is to provide an alternative solution when patients cannot receive biopsies at high frequencies. Our proposed prediction model can be tracked repeatedly throughout the treatment process, thus continuously learning and continuously improving the accuracy.
## 4 Methodology
### Data Acquisition
#### 4.2.1 Physico-Chemical Parameters  
The data was acquired from the Google Earth Engine, which comprised S2-MSI L1C images for extracting water quality parameters. The SRTM data is used for the creation of DEM. The S2-MSI L1C contains Top of Atmosphere (TOA) images factored by a value of 10,000. These images were observed for the monsoon season, i.e., June to September of 2018 to 2022 for the Rawal stream network. The different band compositions of the images were used to acquire physical parameters for the stream network using the adapted equations for calculating the TDS, pH, EC, SDD, DO, Tur and chl-\(\alpha\) that are mentioned in Table 3. The equation (given in Table 3) shows the Tur and chl-\(\alpha\) calculation, which are based on bands 3, 4 and 8a that gave a Root Mean Square Error (RMSE) of 7.65 NTU and 10.15 mg/L, respectively, when observed with the ground truth values obtained for the study area. The equation for pH shows an RMSE of 3.36 using bands 11, 3 and 4. EC is based on band 11 with an RMSE of 228.7 mS/cm. DO and TDS are based on bands 8a and 1 with an RMSE of 2.82 mg/L and 111.92 mg/L, respectively. Lastly, bands 2 and 4 are used to calculate SDD with an RMSE of 0.22 m. Figure 3 shows a sample of the parameters extracted for July 2020.  
Figure 2: Study area map for the Rawal watershed located in Islamabad, Pakistan.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Type of Data** & **Sources** \\ \hline \multirow{8}{*}{Physiochemical Parameters} & TDS & \\  & pH & \\  & EC & SRTM \\  & SDD & DEM \\  & DO & SZ-MSI LIC [35] \\  & Tur & \\  & chl-\(\alpha\) & \\ \hline \multirow{8}{*}{Hydrological and \(\;\
# Tracked Wall-Climbing Robot for Calibration of Large Vertical Metal Tanks
## 2 Design of Tracked Wall-climbing Robot (TWCR)
### Design of Capacity Calibration System  
The TWCR, based on the ORLM, is developed primarily for the capacity calibration of vertical metal tanks. In the ORLM, a trolley that carries a scale is supposed to move up and down vertically  
Figure 5: The position of the lowest magnetic unit which is disengaging with the surface.  
Figure 6: Driving, transmission, and steering mechanism (1—track, 2—driving wheel, 3—pillow block, 4—electromagnetic particle clutch, 5—baseboard, 6—level gear reducer, 7—motor).  
and stop at a number of pre-assigned positions, allowing the operator to record the reference offsets by reading the scale through the optical device. Although the TWCR is designed to replace the manually-lifted trolley, a scale is still required. A secondary 25 W AC gear motor with a reduction ratio of 1:72, which adjusts the position of the scale through a ball screw, is also installed on the baseboard. In practice, the tracked locomotion mechanism moves the robot close to the target location, and adjustments by the scale-moving mechanism obtain the precise position.  
To avoid collision between the fire control pipes/ribs arranged above the tank surface and the towering scale, a collapsible scale, tensioned by an elastic band to maintain its straightness, was developed. As can be seen in Figure 7, the upper and lower sections of the scale are connected by a shaft, and an elastic band is used to assist the scale in recovering from a collapsed state. No new structure is introduced, and the scale-moving mechanism is used to fulfill the collapsing function. When the scale approaches one side of the limit, the upper part of the scale is stopped by a bar installed on the outer frame of the robot and the lower part of the scale continues moving forward. This results in the scale folding, as shown in Figure 8, allowing the robot to pass any restricted places.  
Considering that the surface of the metal tanks could be significantly rusted, and that the rust could scrap against the permanent magnetic tracks and stain them, which will increase the measured offsets, as well as decrease the adhesion force, a shovel-like rust remover can be installed on the front of the robot. However, one side effect of this rust remover is that it could increase resistance to movement. A rotating mechanism driven by a small motor can lift the rust remover to avoid the problem, as shown in Figure 9.  
Figure 8: Collapsing process of scale (**a**) the stand state (**b**) the folded state (1—upper section, 2—shaft, 3—lower section, 4—base, 5—stopping bar, 6—ball screw).  
Figure 7: The mechanical structure of scale (**a**) the stand state; (**b**) the folded state (1—upper section, 2—shaft, 3—lower section, 4—elastic band, 5—fixing bolt).