esa-sceva/satcom-chunk-collection huggingface dataset:
# Characteristics of Plane Gate Vibration and Holding Force in Closing Process by Experiments
## 3 Results and Discussion
### Characteristics of Holding Force
#### 3.2.2 Comparison of Holding Force in Two Operation Modes  
Figure 9 plots the comparison results of the average of holding force for two operation modes. Overall, holding force in fixed gate opening is greater than the closing process under the same gate opening. With increase of the upstream water depth, difference of holding force between the two operation modes is large in large gate opening and is small in small gate opening. The reason for the situation is that in closing process the cross-section of flow decreased in initial stage, which led to the upstream water level to rise gradually. The water level rising caused the horizontal panel thrust, and the friction force between gate and the track increased, which made holding force less than the fixed gate opening. The higher is the upstream water level, the greater is the difference. However, when the gate dropped down intermittently in the crawl stage, the water level rising was no longer apparent, and the difference was insignificant at small gate opening.  
Next, the dominant frequency of holding force was analyzed for two operation modes. In fixed gate opening, the dominant frequency of holding force is 0.2 Hz and energy spectral frequency is mainly concentrated below 1 Hz. Nevertheless, vertical vibration frequency is mainly concentrated around 5-8 Hz. The frequency of holding force is inconsistent with the vertical vibration frequency and is close to the flow pulsation frequency yet. In closing process, the vertical vibration frequency and the frequency of holding force are consistent. The frequency decreases from 14 to 7 Hz gradually.  
Figure 8: Characteristics of holding force in closing process: (**a**) fluctuation value of holding force; and (**b**) dominant frequency of holding force.  
Figure 9: Average of holding force under the two operation modes.  
Thus, the frequency between the holding force and vibration is not same for two operation modes. The difference may be attributed to the friction force caused by the gate motion state. In fixed gate opening, plane gate is in a state of static friction with the track. The direction of static friction force is changeable with the gate motion tendency. We tentatively put forward that, when the hydrodynamic loads are converted into the steel cable tension, static friction force with variable direction has a certain filtration effect on the loads. Only if the hydrodynamic loads are greater than the maximum static friction force can the loads be converted into the steel cable tension. Otherwise, the loads will be balanced through the static friction force. Thus, frequency of holding force is consistent with the flow pulsation frequency in fixed gate opening. Compared to the fixed gate opening, plane gate is in a state of sliding friction, and the friction force direction, opposite to the gate motion, is constant in closing process. Thus, the frequency of holding force is consistent with vertical vibration frequency in this case.
# Efficient Traffic Flow Guidance Feedback Strategy Considering Drivers' Disobedience in ITS
## 3 Proposed Approach
### New Traffic Flow Guidance Feedback Strategy DWVDFS  
In the basic model of WVDFS (weighted vector directional filters), the vehicle velocity is not considered during the calculation of the vehicle density. However, in our solution, and as mentioned in Section 1, the vehicle velocity is determined to be one of the factors that has an impact on the vehicle density. A dynamic weighted vehicle density strategy (DWVDFS) is thus proposed. Based on the uploaded data, the intelligent driving system could compute the dynamic weighted vehicle density of the route, taking into account a threshold set in advance for the vehicle density. Indeed, if the vehicle density surpasses the threshold, the route is considered congested, and instructions need to be given to drivers. The newly arrived vehicles that are classified as obedient would be given the instruction to change to another route. In WVDFS, a weight factor \(k\) is applied in the calculation of the density. In DWVDFS, the factor \(k\) is replaced by a function containing the vehicles' velocities. The dynamic weighted vehicle density is defined as:  
\[\rho^{d}_{w}=\sum_{i=1}^{N_{w}}\frac{(-\text{log}(v_{i}+1)\times\frac{n_{i}}{L _{w}}+b)}{L_{w}} \tag{3}\]  
where \(\rho^{d}\) refers to the dynamic density, \(L_{w}\) is the length of route \(w\), \(N\) is the total number of vehicles on the route \(w\) at the current time step, \(n_{i}\) stands for the position of \(i\)th vehicle, \(v_{i}\) denotes the velocity of \(i\)th vehicle and \(b\) is a constant, where \(b=5.0\).
# A Deep Reinforcement Learning-Based Decision-Making Approach for Routing Problems
## 3 Methodology  
### Mathematical Framework  
A routing instance \(s\) contains a set of \(N\) order-invariant customer nodes \(X=[x_{1};\cdots;x_{N}]\), spatially distributed in the Euclidean space. \(x_{i}\) denotes the 2-dimensional coordinate of node \(i\) in TSP, whereas in CVRP, it encapsulates both the coordinate and the associated demand. The goal of the DRL-based routing model is to construct a permutation \(\Pi=(\pi_{1},\cdots,\pi_{N})\) that defines the traversal sequence, where \(\pi_{t}\) represents the node to be visited at time \(t\). Starting from an arbitrary depot \(x_{\pi_{1}}\), the objective of the routing problem is to minimize the total length \(L(\Pi|X)\) while adhering to two constraints: (1) each customer node must be visited exactly once; and (2) each salesman departs from the single depot, visits all customer nodes, and ultimately returns to the depot.  
\[L(\Pi|s)=\left\|x_{\pi_{N}}-x_{\pi_{1}}\right\|_{2}+\sum_{t=2}^{N}\left\|x_{\pi_{t} }-x_{\pi_{t-1}}\right\|_{2}. \tag{1}\]  
We formulate the routing problem as a sequential decision-making process within an encoder-decoder framework, leveraging a constructive strategy to derive the optimal route \(\Pi\). Given an instance as input, the encoder extracts and represents features by embedding raw node attributes through \(L\) attention layers. During decoding, the decoder iteratively processes \(N\) time steps to construct a complete traversal route. At each time step \(t\), a compatibility layer determines the action \(\pi_{t}\), selecting the next node to visit based on the current state \(s_{t}\), which encapsulates both instance-specific and dynamic visitation information. The reward is defined as the negative of the tour length \(R=L(\Pi|s)\). The objective is to learn a policy, parameterized by \(\theta\), that optimizes the sequential decision-making process to maximize the cumulative reward. The probability of the entire action sequence can be factorized using the chain rule as follows:  
\[p_{\theta}(\Pi|s)=\prod_{t=1}^{N}p_{\theta}(\pi_{t}|s_{t},\pi_{1:t-1}). \tag{2}\]
_applied sciences_  
_Article_  
**Correlation between Scoliosis Flexibility Degree on Preoperative Imaging with Postoperative Curve Correction and Mechanical Complications**  
**[PERSON] \({}^{1,*}\)\({}^{\copyright}\)**, [PERSON] \({}^{2,3}\), [PERSON] \({}^{2,3}\) and [PERSON] \({}^{2,3}\)**  
\({}^{1}\) Independent Researcher, Sao Paulo 01316-010, Brazil  
\({}^{2}\) Department of Orthopedic Surgery, University of Sao Paulo, Sao Paulo 05403-010, Brazil;  
[EMAIL_ADDRESS] (R.M.M.); [EMAIL_ADDRESS] (A.F.C.); [EMAIL_ADDRESS] (O.B.L.)  
\({}^{3}\) Spine Surgery, AACD (Associacao de Assistencia a Ciriano Deficiente), Sao Paulo 04027-000, Brazil  
\({}^{*}\) Correspondence: [PERSON]_ADDRESS]  
**Abstract:** (1) Background: In the preoperative planning stage of scoliosis surgery, it is routine to use radiographs obtained with and without traction to observe the curve flexibility in order to estimate curve correction, but its association with mechanical complications is not completely understood. (2) Methods: Retrospective cohort study of all patients undergoing infantile, congenital, neuromuscular or idiopathic adolescent scoliosis correction surgery at a single institution between 2015 and 2019, with a minimum follow-up of 24 months. Associations between qualitative variables were tested with the chi-square test. The association between qualitative and quantitative variables were tested with the Mann-Whitney test, and correlations between quantitative variables was tested with Spearman's correlation. (3) Results: A total of 330 patients, 88 males and 242 females, with a mean age of 16.98 years at surgery, were included. The mean value of preoperative main curves, its flexibility and postoperative value were 54.44 degrees, 21.73 degrees and 18.08 degrees, respectively. (4) Conclusions: Preoperative spinal X-ray examination with traction or bending films is a reasonable option for assessing scoliotic curve flexibility, and patients with neuromuscular scoliosis who are not ambulatory can be informed of the increased risks of late mechanical complications.  
**Keywords:** scoliosis; neuromuscular scoliosis; mechanical complications
## 3 Data Modeling Methodology
### Hybrid Differential Evolution and Gray Wolf Optimization Algorithm (DE-GWO-SVR)
Step 4 When the population iteration is completed and the fitness function value tends to be stable and convergent, the position of \(\alpha\) wolf in the current parent population is assigned to C and gamma in SVR, and SVR modeling is conducted on the test set through these parameters to predict the DO content in the perch aquaculture water body, and the model performance is evaluated and analyzed. In this paper, we use the coefficient of determination (R\({}^{2}\)), Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for correlation analysis and model evaluation, which are calculated as follows:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\widehat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}- \overline{y}_{i})^{2}} \tag{15}\]  
\[MSE=\frac{\sum_{i=1}^{n}(y_{i}-\widehat{y}_{i})^{2}}{n} \tag{16}\]  
\[MAE=\frac{\sum_{i=1}^{n}|y_{i}-\widehat{y}_{i}|}{n} \tag{17}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(y_{i}-\widehat{y}_{i})^{2}}{n}} \tag{18}\]  
where \(\widehat{y}_{i}\) is the predicted value, \(y_{i}\) is the observed value, \(\overline{y}_{i}\) is the mean of the observations, and \(n\) is the total number of predicted samples. The prediction model of R\({}^{2}\) lies between \([0,1]\), the closer to \(1\) the better the model predicts, while the closer MSE, MAE, and RMSE are to, \(0\) the better the model is. The models in this experiment are all run in a computer with Windows 11, CPU model R7-5800H, and graphics card model RTX3060. Coding in Matlab2022a with Python3.9 integrated in the environment of Anaconda3, and the machine learning framework is Scikit-based on the Python-3.9 learn framework.
# Geometric Parameter Self-Calibration Based on Projection Feature Matching for X-Ray Nanotomography
## 2 Materials and Methods
### Geometric Parameter Calibration Process
#### 2.2.1 Projection Image Preprocessing  
Firstly, the mirror projection images are linearly transformed to normalize the gray value of the projected image. The normalized projected image is then denoised by a non-local average denoising algorithm to reduce the influence of noise on the algorithm. Finally, histogram equalization is applied to enhance the contrast. The preprocessing of projection images improves image quality and contrast, which lays the foundation for more accurate projection feature extraction and matching.
## 2 Method Description
### Ordinary Kriging (OK) Model  
The Kriging method is a full interpolation method. The approach can generate the most likely response value and variation at the projected location depending on the presumptions upon which it is based. The formula for its interpolation is Equation (1), where \(c\) is the coefficient that needs to be solved for.  
\[\hat{y}(x)=\sum_{i=1}^{n}c_{i}y(x_{i}) \tag{1}\]  
The subsequent deduction is then based on the assumption of the distribution of \(y\). Equation (2) is a common expression for the distribution:  
\[\begin{array}{l}y(x)=f(x)\beta+z(x)\\ f(x)=(f_{1}(x),\cdots,f_{n^{\prime}}(x))\end{array} \tag{2}\]  
where \(f(x)\) is the basis function (ordinary Kriging (OK) uses a basis function of 1 (zero polynomial); when \(f(x)\) is any other basis function, it is called universal Kriging), \(\beta\) is the coefficient, \(n^{\prime}\) is the number of terms in the basis function, and \(z(x)\) is generally assumed to obey a normal distribution, N(0, \(\sigma^{2}\)). By minimizing the following expectations in Equation (3), the interpolation process, which is a Gaussian process, gives the most likely response values and variances by solving the linear system of equations based on the Lagrange Multiplier Method.  
\[\text{min}Cov((y(x)-\hat{y}(x)),(y(x)-\hat{y}(x))) \tag{3}\]  
Its solution, which can be consulted through the literature [52], takes the following form as Equation (4):  
\[\left\{\begin{array}{l}\hat{y}(x)=f(x)^{T}\beta^{*}+r^{T}(x)\gamma^{*}\\ s^{2}(x)=\sigma^{2}(1+u^{T}(F^{T}R^{-1}F)^{-1}u-r^{T}(x)R^{-1}r(x))\\ \\ \left\{\begin{array}{l}\beta^{*}=(F^{T}R^{-1}F)^{-1}F^{T}R^{-1}Y\\ R\gamma^{*}=Y-F\beta^{*}\\ u=F^{T}R^{-1}r(x)-f(x)\\ \sigma^{2}=\frac{1}{m}(Y-F\beta^{*})^{T}R^{-1}(Y-F\beta^{*})\\ \end{array}\right.\\ \\ \left\{\begin{array}{l}r(x)=[R(x_{1},x),\cdots,R(x_{n},x)]^{T}\\ R(x)=\left[\begin{array}{ccc}R(x_{1},x_{1})&\cdots&R(x_{1},x_{n})\\ \vdots&\ddots&\vdots\\ R(x_{n},x_{1})&\cdots&R(x_{n},x_{n})\\ \end{array}\right]\\ \\ F=\left[f(x_{1}),\cdots,f(x_{n})\right]^{T}\\ Y=\left[y(x_{1}),\cdots,y(x_{n})\right]\end{array}\right. \tag{4}\]  
where \(x\) is the point that needs to be interpolated, \(x_{i}\) (\(i\) = 1, \(\ldots\), \(n\)) is the known point, \(R(x_{i},x_{j})\) is the function that creates the correlation coefficients, \(R(x)\) is the matrix that only contains the correlation coefficients of the known points, and \(r(x)\) is the vector made up of  
Figure 1: Flowchart of NSGA-II.  
the correlation coefficients of the point that needs to be interpolated and all of the known points. Based on the above, multiple OK models can be built from a known data set and multiple objective functions can be developed. Then, the NSGA-II method can be used to find the Pareto set.
# Microseismic Signal Denoising and Separation Based on Fully Convolutional Encoder-Decoder Network
## 1 Introduction  
During monitoring and data acquisition processes, microseismic signals are often corrupted by various types of noise due to the uncontrollable sources, conditions, and complicated environmental situations. Possible noise could be electrical, construction, mechanical, or traffic noises. Spectral filtering is commonly used for improving the signal-to-noise ratio (SNR) of the microseismic signal. However, it is ineffective to suppress the noise that has a similar frequency band with a microseismic signal. Moreover, it can distort the signal [1] and/or generate artifacts before impulsive arrivals [2].  
In order to alleviate this limitation, many methods have been proposed to suppress the noise in seismic/microseismic data, including Short Time Fourier Transform (STFT) [3], the Continuous Wavelet Transform (CWT) [4, 5], 5-transform [6], the Radon Transform [7, 8, 9], the Wave-Packet Transform (WPT) [10, 11], Empirical Mode Decomposition (EMD) [12, 13, 14], Fuzzy methods [15], singular spectrum analysis [16], sparse transform-based denoising [17], mathematical morphology-based denoising approach [18], and the non-local means (NLM) algorithm [19]. Further, some hybrid methods were proposed, which combine the advantages of two or more denoising methods [20]. Signal denoising performance can be improved through two ways: a more effective sparse representation of the dataand a more flexible and powerful mapping function. Although these methods mentioned above can properly suppress noise components in the data, it is still tricky to choose the optimal mapping function between the noisy signal and the estimated signal.  
Recently, deep learning methods have been rapidly developed to overcome the drawbacks of the existing signal denoising methods. The deep neural network can learn extremely complicated mapping functions through the training, which has been proved to be a powerful tool for signal processing [21; 22; 23; 24; 25]. Inspired by the success of the encoder-decoder network for image/signal processing, a deep fully-convolutional encoder-decoder network is adopted (CNN-denoiser) for microseismic signals denoising. Given a noisy input signal, the CNN-denoiser can produce two masks (sparse representations): one for estimating signal and the other for estimating noise. Microseismic data recorded in the Micang Mountain tunnel in China are used for network training, validation, and testing. The method has also been tested with other noisy signals recorded in different actual projects, and semisynthetic signals (i.e., generated by superimposing microseismic signals and real noise) are also used to evaluate the proposed method in comparison to existing methods.
## 2 Materials and Methods
### Validation Directional Forest Canopy Reflectance Estimation  
We computed the directional reflectance of a forest canopy based on the weighted linear combination of the reflectance of their four forest components as [1,5]:  
\[R=R_{T}k_{T}+R_{G}k_{G}+R_{ZT}k_{ZT}+R_{ZG}k_{ZG} \tag{4}\]  
where \(R_{T}\), \(R_{G}\), \(R_{ZT}\), and \(R_{ZG}\), are the reflectance values of the sunlit overstory, sunlit forest background, shaded overstory, and shaded forest background, respectively. The \(k_{T}\), \(k_{G}\), \(k_{ZT}\), and \(k_{ZG}\) are the proportions of the four forest components at a specific observation geometry configuration. We investigated the spatial variations of forest canopy directional reflectance in the forest plot WPA-P2 in both red and rear-infrared bands with a fixed sun position (i.e., SZA= 37\({}^{\circ}\), and solar azimuthal angle, SAA = 150\({}^{\circ}\)) and varied observation directions ranging from -60\({}^{\circ}\) to 60\({}^{\circ}\). First, we selected 9 pixels (27 in total) for low density, medium density, and high density in both red (i.e., 660 nm) and near-infrared (i.e., 850 nm), respectively. Then, we obtained the reflectance values (i.e., \(R_{T}\), \(R_{G}\), \(R_{ZT}\), and \(R_{ZG}\)) of the four forest components by solving the 27 equations based on the ALS-determined weighted factor (i.e., \(k_{T}\), \(k_{G}\), \(k_{ZT}\) and \(k_{ZG}\)) using the least square method [54]. In addition, we compared the ALS-based directional forest canopy reflectance values in red and near-infrared bands with the simulated ones using the DART model.
## 2 Related Works
Packet duplication is a technique to improve reliability by transmitting the same packet over multiple redundant parallel links [23]. Although it is the same as using multiple redundant parallel links, our proposal can increase attack resistance to a greater degree than this study because it sends encoded information. Frequency hopping is a technique used in Bluetooth. By periodically changing the frequency used for communication, attack resistance is increased. In [24], an adaptive frequency hopping method which changes to a new frequency only when the packet drop rate of the channel beyond a given threshold is proposed. The adaptive frequency hopping can increase the network reliability by 10-20%. We have always used multiple RATs, but we would like to consider selecting the RATs by scheduling. The novelty of our proposal is to communicate information encoded by shortened code using multiple RATs. Redundancy by RATs and error correction capability can simultaneously improve wiretap resistance and attack resistance. By using shortened codes, the dependability can be further enhanced because the error correction capability between the receiver and the cyberterrorist can make a difference. To do this, the distance between the sender and the receiver is shared securely using UWB.
## 2 Methodology
### Grid-Side Converter Control  
As shown in Figure 2, the primary objective of the outer loop is to maintain the DC-link voltage \(V_{\text{DC}}\) at its reference value \(V_{\text{DC,ref}}\).  
\[e_{\text{e}_{\text{e}_{\text{e}_{\text{e}_{\text{e}_{\text{e}_{\text{e}_{ \text{e}_{\text{e}_{\text{e}_{\text{e}_{\text{e}_{\text{e}_{\text{e}_{ \text{e}}}}}}}}}}}}}}}(t)=V_{\text{DC,ref}}-V_{\text{DC}}(t) \tag{2}\]  
This error is processed through a Proportional-Integral (PI) controller to generate the reference d-axis current \(i_{d\_ref}\):  
Figure 2: Control diagram of a grid-connected and load-side inverter system.  
\[i_{d\_ref}(t)=K_{p\_-v}e_{v_{d_{c}}}(t)+K_{i\_-v}\int e_{v_{d_{c}}}(\tau)d\tau \tag{3}\]  
where  
* \(K_{p\_v}\) is the proportional gain.
* \(K_{i\_v}\) is the integral gain of the DC voltage controller. The current control loop is implemented in the synchronous rotating dq-frame synchronized to the fundamental frequency. The inverter output current \(i_{cv}\) is measured and transformed into the dq-frame components \(i_{d\_cv}\) and \(i_{q\_-cv}\). The \(d\)-axis and \(q\)-axis control objectives are as follows:
* \(D\)-axis: track \(i_{d\_ref}\) to regulate active power (and indirectly DC-link voltage).
* \(Q\)-axis: track \(i_{q\_-ref}\) (typically set to zero to achieve unity power factor or adjusted for reactive power exchange). The control errors are as follows: \[\begin{array}{l}e_{i_{d}}(t)=i_{d\_ref}-i_{d\_cv}\\ e_{i_{q}}(t)=i_{q\_ref}-i_{q\_-cv}\end{array}\] (4) Each error signal is processed through independent PI controllers as follows: For the \(d\)-axis \[v_{d\_cv}(t)=K_{p\_-i}e_{i_{d}}(t)+K_{i\_-i}\int e_{i_{d}}(\tau)d\tau+v_{d\_ -ff}\] (5) For the \(q\)-axis \[v_{q\_-cv}(t)=K_{p\_-i}e_{i_{q}}(t)+K_{i\_-i}\int e_{i_{q}}(\tau)d\tau+v_{q\_ -ff}\] (6) where
* \(K_{p\_-i}\) and \(K_{i\_-i}\) are the proportional and integral gains of the current controllers.
* \(v_{d\_ff}=\omega Li_{q\_-cv}\) is the feedforward voltage term to compensate for the coupling between d and q axes.
* \(v_{q\_-ff}=-\omega Li_{d\_-cv}\) is the corresponding feedforward term for the \(q\)-axis. Thus, the decoupled control ensures dynamic and independent regulation of both current components. The grid-side converter (GSC) and the customer-side inverter (CSI) operate in synchronous rotating reference frames. The GSC uses phase-locked loop (PLL) synchronization. A synchronous reference frame PLL operates on the grid side. The structure provides enhanced tracking performance when dealing with harmonic-rich and distorted grid conditions through improved filtering and reduced phase error. The d-axis points towards the voltage vector at the point of common coupling (PCC) using the filtered grid voltage (V\({}_{\text{g}}\)). The system enables precise synchronization for multi-frequency operation, which allows independent control of fundamental and harmonic components through resonant controllers in the \(d/q\) frame. The CSI is a grid-forming inverter that outputs proper fundamental voltage because end-user loads receive sinusoidal waveforms without harmonic distortion.