esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Models for Local Sedimentary Basin Effect Using a Convolutional Neural Network
###### Abstract  
Although the numerical models can estimate the significant influence of local site conditions on the seismic propagation characteristics near the surface in many studies, they cannot feasibly predict the seismic ground motion amplification in regular engineering practice or earthquake hazard assessment due to the high computational cost and their complex implementation. In this paper, the scattering problem of trapezoidal sedimentary basins, one of the representatives of local complex sites with a relatively small model size, and simplified by practice in this type of study, was selected as the basin model. A series of standard basin models were built to quantify the relationship between the site condition parameters and the site amplification parameters (the peak ground acceleration and the hazard location). In addition, the factors that influence seismic ground motion amplification, such as the basin shape ratio, the soil depth, the basin edge dip angle, the ratio of shear wave velocity between the bedrock and the soil layer, the damping coefficient, and the fundamental frequency, were selected to investigate the sensitivity. A convolutional neural network (CNN) algorithm based on deep learning replacing traditional recursive algorithms was explored to establish a prediction model of basin amplification characteristics. By the Bayesian optimization method, the structural parameters of the CNN predicting model were selected to improve the accuracy of the prediction model. The results show that the optimized CNN models could predict the amplification characteristics of the basin better than the un-optimized CNN models. Three prediction models were established with the site condition parameters as the input parameters and their output parameters were the maximum amplification value of the peak ground acceleration (PCA), the hazard location, and their combination for each basin. To analyze the CNN's prediction ability, each CNN model used about 80% of the data from the seismic model repose results for training and the remaining data (20%) for testing. By comparing the CNN prediction results with the FE simulation results, the accuracy and rationality of each prediction model were studied. The results show that, compared to a single numerical model, the CNN prediction results of the site amplification features could be quickly obtained by inputting the relevant parameters. Compared to recursive class models, the established CNN prediction model can directly establish the relationships among multiple input and multiple output parameters. A comparison of the three kinds of CNN models shows that the prediction accuracy of the joint parameter model was slightly lower than that of the two single-output models.  
site condition; amplification factor; convolutional neural network; site effect +
Footnote †: journal: Applied Sciences
# An Improved Analytical Algorithm on Main Cable System of Suspension Bridge
## 3 Improved Numerical Analysis Method
### The Main Cable System Calculation in Main Span at Finished State
#### 3.1.1 Determination of Cable Force Adjustment at Start Point  
Equation (4) is obtained while ignoring the higher order terms of the Taylor series, which is an approximate expression. Due to strong nonlinear of suspension cable, the iterative methods for determining horizontal and vertical component of cable force adjustment at start point by Equation (4) sometimes fail to converge. Therefore, we need to revise the adjustment amount as the following Equation (15):  
\[\left\{\begin{array}{l}H_{L}=H_{L0}+\alpha\cdot dH\\ V_{L}=V_{L0}+\alpha\cdot dV\end{array}\right. \tag{15}\]  
where, \(H_{L0}\), \(V_{L0}\) are the initial value of horizontal and vertical component of cable force at left start end, respectively; \(\alpha\) is called penalty factor (or Newton-Downhill factor) in the range from 0 to 1.  
Obviously, if the horizontal and vertical component of initial cable force adjustment amount are much less than the value before adjustment, which means that the non-linear property of the cable force adjustment process is not strong, then \(\alpha=1\); otherwise, \(\alpha\) should be chosen between 0.1 and 1. Thus, the iteration can converge with high efficiency. The value of \(\alpha\) is determined based on the above principle, and calculated, as follows:  
\[\left\{\begin{array}{l}\alpha=0.1\frac{\sqrt{H_{10}^{2}+V_{10}^{2}}}{\sqrt{ \left(dH\right)^{2}+\left(dV\right)^{2}}};\ if\ \left|dH\right|>0.1\left|H_{L0}\right|\ or\ \left|dV\right|>0.1\left|V_{L0}\right|\\ \alpha=1;\ if\ \left|dH\right|\leq 0.1\left|H_{L0}\right|\ and\ \left|dV\right|\leq 0.1 \left|V_{L0}\right|\\ \end{array}\right. \tag{16}\]
## 3 Results and Discussion
### Water Holding Capacity and Surface Tension  
WHC can be effective for viscosity, texture and the bulk volume of formulated food products. As can be seen in Table 2, the WHC of the WRP powder obtained from MAE and MAUPE was in the range of 11.72 to 12.43 g/g, which showed proper values for WHC as compared to the WHC of commercial citrus pectin (10.3 g water/g pectin) [36]. According to experimental results (Table 3), the WHC value of pectin from MAE was higher than that  
Figure 3: Watermelon rind powder before (**A**) and after ultrasonic pretreatment (**B**) at 300 W for 20 min.  
obtained by MAUPE, while the only significant difference was observed between samples from MAE and MAUPE when using pretreatment for 20 min at 300 W. The pectin physical structure and chemical composition could be effective for WHC [37]. Therefore, ultrasonic pretreatment (300 W for 20 min) can change the physical structure or chemical composition of pectin, which affects this parameter and reduces the hydrophilic property of the samples.  
The WHC value of watermelon pectin, with or without pretreatment, was higher than the WHC value of guar gum (4.8 g/g) and carboxymethyl cellulose (10.0 g/g) [10] used in cosmetic products as a thickener and to reduce the phenomenon of water loss [11]. Because pectin is classified as a thickening agent and has a WHC value, watermelon pectin can be suggested for uses in cosmetic products. There is a large demand in the food and cosmetic industries to obtain products with better foam properties [12]. Easy and fast foam formation and foam stability are desirable for cosmetic products [7]. Anionic surfactants are more popular for cosmetic applications, which may be due to the hair charge being negative, and consequently, anionic detergents absorb the positively charged oils of hair. As a result, the surfactant can be washed off easily. The viscosity and foaming capability of the cosmetic product (shampoo) is a desirability factor, and it is generally accepted that the higher the foaming obtained, the better the quality of the shampoo [13]. Therefore, due to its anionic composition, the thickening and stabilizing effect, and the foaming properties of watermelon pectin make it a suitable candidate for cosmetic uses. The surface tension of deionized water--the reference--was measured at 71.0 mN/m. As shown in Table 3, the values of surface tension of the WRP solution, 1% (\(w/v\)), were less than 71.0 mN/m, indicating that WRP can reduce the surface tension of the solution. This parameter is one of the most important physical properties of biopolymers for foam formation: the lower the surface tension, the greater the ability to form a foam [38; 39; 40]. The surface tension values of the WRP extracted by MAE, at 45.75 \(\pm\) 0.21 mN/m, was higher than the value from MAUPE, which ranged from 44.2 \(\pm\) 0.20 to 45.5 \(\pm\) 0.14 mN/m. However, the surface tension value of the WRP solution (1% \(w/v\)) was less than that of commercial pectin (50.8 mN/m) derived from a citrus pectin solution (1% (\(w/v\))), reported by [PERSON] et al. [41].
## 3 Materials and Methods  
### Databases Selection and Description  
The three publicly available databases used include photos collected under controlled shooting conditions where the individuals posed with specific facial expressions. These are:  
* KDEF consists of 4900 images divided equally into seven facial emotions viewed from five shooting angles. The participants are males and females in equal parts, between the ages of 20 and 30, who do not wear glasses and jewelry and do not have a beard or unstache. The images are 567 \(\times\) 762 pixels with 24-bit color values in jpeg format [31];
* JAFFE consists of 213 frontal faces of 10 females expressing seven different emotions. The images are 256 \(\times\) 256 pixels 8-bit grayscale in tiff format [32]. This database was employed to examine how the algorithms perform in small sets and low-resolution images;
* RaFD consists of 8040 images divided equally into eight facial emotions viewed from five shooting angles. The individuals are Caucasian adults, both men (30%) and women (28%), Caucasian children, both boys (6%) and girls (9%), and Moroccan Dutch males (27%). The images are 681 \(\times\) 1024 pixels with 24-bit color values in jpeg format [33].  
The KDEF and JAFFE databases contain the same seven facial emotions: anger, disgust, fear, happy, neutral, sad, and surprise, while the Radboud Faces Database has one more class, disgust. The details of each database are shown in Table 1:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Database** & **Files** & **Classes** & **Poses** & **Color** &
\begin{tabular}{c} **Pixels** \\ **(Width \(\times\) Height)** \\ \end{tabular} & **Format** \\ \hline KDEF & 4900 & 7 & 5 & true color & 562 \(\times\) 762 & jpeg \\ JAFFE & 213 & 7 & 1 & grayscale & 256 \(\times\) 256 & tiff \\ RaFD & 8040 & 8 & 5 & true color & 681 \(\times\) 1024 & jpeg \\ \hline \hline \end{tabular}
\end{table}
Table 1: Databases number of files, classes, poses, colors, dimensions of images, and format.  
The databases differ in terms of (a) number of images, (b) image characteristics (resolution, color depth, format), and (c) the number of classes (for RaFD). RaFD and KDEF have a larger number of images, which supports models' training, but the classification is challenging due to the different shooting angles. Figure 1 shows a sample depicting happiness from each database.
## 3 Network Background
### Sixth-Generation Cellular Networks
#### 3.2.1 Diverse Mobile Communication Technologies  
To enable global internet access, 6G cellular networks will seamlessly integrate satellite, UAV, and maritime communications. These technologies will complement terrestrial  
Figure 1: Fifth-Generation connectivity and its applications across various sectors.  
networks, significantly extending 6G coverage [1]. However, despite being access network technologies, their expansion requires robust support from the core network.
## 5 Conclusions  
This work proposes a simple real-road test method to evaluate the SCR system's operation states. We conducted a heavy-duty diesel vehicle real-road driving emission test and verified the proposed test procedure. The main conclusions are summarised as follows.  
The SCR's de-NOx efficiency experiences three operation states according to the exhaust temperature. In state I, SCR works and injects urea, resulting in high de-NOx efficiency (>90%). State III occurs during the cold start and has the lowest de-NOx efficiency (<50%) because SCR injects no urea and no catalytic conversion happens due to the low exhaust temperature. State II is a transition stage and has a certain conversion efficiency (50-90%) caused by the ammonia storage. During state II, the SCR de-NOx efficiency is  
Figure 7: Effect of exhaust temperature on conversion efficiency.  
linearly related to exhaust temperature; the slope of the conversion efficiency K declines while the temperature range Tt increases as the average exhaust temperature rises.  
The different SCR three-state behaviors induced by ammonia storage can be used to judge whether the SCR works normally. The SCR works and injects urea in state I, and no urea injection occurs in state II. The method developed in this study is simple and easy to apply to different SCR operation states, and it is effective and repeatable. In the future, further simple and rapid test procedures based on this SCR state method may be applied to spot emission inspections.  
Conceptualization, Y.L.; methodology, Y.L. and T.Q.; software, J.Y.; formal analysis, C.L.; investigation, Y.L. and J.Y.; resources, D.G.; data curation, C.L.; writing--original draft preparation, Y.L.; writing--review and editing, G.P.; supervision, T.Q.; project administration, D.G.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Beijing Vehicle Emissions Management Center] grant number [40005015202204].  
The authors declare no conflict of interest.
# Smartphone Accelerometer for Gait Assessment: Validity and Reliability in Healthy Adults
###### Abstract  
Smartphone-based accelerometers offer a cost-effective and portable alternative to traditional gait analysis systems, with high reliability in measuring key parameters such as walking speed, cadence, and distance. This study assessed their validity compared to the GAITRite system, a widely recognized gold-standard tool, using data from 30 healthy adults walking at 3 self-selected speeds: preferred, slow, and fast. The results demonstrated a high degree of agreement between the two systems, with intraclass correlation coefficients (ICCs) ranging from 0.778 to 0.999. Although the findings emphasize the potential of smartphone accelerometers for clinical and real-world applications, certain limitations were noted, including participant homogeneity and minor discrepancies at extreme walking speeds. To address these limitations, incorporating data from additional sensors, such as gyroscopes and magnetometers, may enhance the accuracy and reliability of spatial parameter estimation. Overall, the findings support the use of smartphone accelerometers as a promising tool for advancing gait monitoring technologies, particularly in the contexts of telerehabilitation and mobility assessments.  
gait analysis; smartphone accelerometer; wearable technology; intraclass correlation; applied ergonomics
# Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer
## 4 Model Introduction and Application
### Transformer Model
#### 4.2.3 Feed Forward Networks and Normalization Layer  
As shown in Figure 13, the feedforward neural network consists of two linear transformation layers and a nonlinear activation function (ReLU). FFN accepts the signal from the multi-head attention layer, and maps the input vector to a higher dimensional space through two linear transformation layers, followed by a nonlinear transformation through the activation function to finally obtain a new output, which ensures that the model can learn more complex features. The calculation process of FFN can be expressed as Equation (12):  
\[\text{Y}=\text{ReLU}(\text{x}W_{1}+\text{b}_{1})W_{2}+\text{b}_{2} \tag{12}\]where the input vector is x; the output vector is Y; W1 and W2 are the weight matrix of the first and second linear transformations, and b1 and b2 are the bias vector.  
The normalization layer helps alleviate the problems of vanishing and exploding gradients, and usually follows the output of each sub-layer (such as multi-head self-attention mechanisms and feedforward neural networks).
# Galile Acid Induces S and G2 Phase Arrest and Apoptosis in Human Ovarian Cancer Cells In Vitro
## 3 Results
### GA Promotes Cell Apoptosis in Both OC Cell Lines  
To explore GA-mediated inhibition of OC cell viability via cell apoptosis, we exposed both OC cells lines to GA treatment (0, 5, 10, 20 \(\upmu\)M) for 48 h and assessed nuclear morphology alterations using Hoechst 3342 DNA staining. We demonstrated marked chromatin condensation and nucleus shrinkage with GA exposure in both OC cell lines (Figure 2a). To further confirm these results, we performed flow cytometry assays via Annexin V/PI staining. Based on our results, GA exposure markedly elevated apoptotic cell population in both OC cell lines and it was dose dependent (Figure 2b). The total apoptotic rate rose to 21.42% with 20 \(\upmu\)M GA in OVCAR-3 cells vs. 5.34% in control cells (Figure 2c) (\(p<\) 0.01). Moreover, relative to controls, the early and late apoptotic rates of the OVCAR-3 cell line, with 20 \(\upmu\)M GA exposure, were also significantly high at 11.36% and 10.06% (\(p<\) 0.01) respectively. As with the viability results, the suppressive role of GA on A2780/CP70 cells was lower than on OVCAR-3 cells. Similarly, GA exposure also induced less apoptosis on A2780/CP70 cells than OVCAR-3 cells. The total apoptotic rate rose to 17.69% with 20 \(\upmu\)M GA exposure in A2780/CP70 cells vs. 8.01% in control cells (Figure 2d) (\(p<\) 0.01). From Figure 2b, the fraction of dead but non-apoptotic (PI+ only) cells also increase in a dose-dependent manner for OVCAR-3 cells. This indicates some non-apoptotic effects induced by GA on OVCAR-3 cells. It is reported that Rhein derivative 4a RHA also causes non-apoptotic effects on ovarian cancer cells by endoplasmic reticulum stress [18]. The non-apoptotic mechanism induced by GA on OVCAR-3 cells is still unclear, and further research is needed.  
Figure 1: GA suppresses OC cell proliferation. (**a**) Structure of GA. (**b**) GA inhibited OVCAR-3 cell viability. (**c**) GA inhibited the A2780/CP70 cell viability.
## 3 Robust Clustering with Local Contextual Information (RC_LCI)
### Anisotropic Weighting Scheme  
In the anisotropic weighting scheme, each pixel in the neighborhood has a different effect on its central pixel \(x\). That is, a pixel similar to \(x\) has a larger weight and vice versa. Thus, the weight of each  
Figure 1: The composition of the robust clustering with local contextual information (RC_LCI) model: (**a**) original image and corresponding histogram; (**b**) noise-removed image and corresponding histogram; (**c**) bias field correction image and corresponding histogram; and (**d**) estimated bias field and segmentation result.  
pixel in the neighborhood and the corresponding gray value can be used to update the current central pixel and then generate a new central pixel value.  
Assuming that a pixel within its 8-neighborhood \(N_{x}\) is \(y\), then \(y\in N_{x}\). The gray mean square error between pixel \(y\) and another pixel \(y^{\prime}\) in the 8-neighborhood is first calculated to obtain the difference of the gray value for all pixels in the 8-neighborhood:  
\[\sigma_{xy}=\left[\frac{\sum_{y^{\prime}\in N_{x}\backslash\{y\}}\left(I_{y }-I_{y}\right)^{2}}{n_{x}-1}\right]^{\frac{1}{2}}, \tag{8}\]  
where \(I_{y}\) and \(I_{y^{\prime}}\) represent the corresponding gray value, and \(n_{x}\) shows the number of pixels within the neighborhood \(N_{x}\) (i.e., \(n_{x}=8\)). \(\sigma_{xy}\) can be used to generate the weight of each pixel in the neighborhood:  
\[\gamma_{xy}=\exp\left[-\left(\sigma_{xy}-\frac{\sum_{y\in N_{x}}\sigma_{xy}}{ n_{x}}\right)\right]. \tag{9}\]  
Finally, these weights are normalized as  
\[\eta_{xy}=\frac{\gamma_{xy}}{\sum_{y\in N_{x}}\gamma_{xy}}. \tag{10}\]  
Therefore, \(x\) is updated according to similar pixels in the neighborhood and corresponding weights:  
\[I_{w}(x)=\sum_{y\in N_{x}}I_{y}\times\eta_{xy}. \tag{11}\]  
The gray value \(I_{w}(x)\) can be used to update all \(x\) in the original image.
## 2 Rock Tunnel Deformation Monitoring Scheme
### Protection Cover of the Sensor  
A specific cover was designed and implemented to protect the sensors from high-impact collisions during the drill and blasting construction process. The cover is designed with a triangular shape using a 304 stainless steel plate with 2 mm thickness (Figure 2a). The cover size is designed according to the size of the sensor. The protection cover has rubber sheets on the outside surfaces, designed to buffer the impact of flying rock slag after the on-site excavation in the rock tunnel (Figure 2b).  
The cover protection's length, width, and height are designed to have enough safety space based on the sensor sizes (100 \(\times\) 100 \(\times\) 60 mm). As a result, the installed cover can cover each sensor from the top and the front side towards the excavating tunnel face. In addition to the design, the laser beam can shoot through the gap of the cover truss bar to the target point on the tunnel linings (Figure 3). To have the cover firmly fixed on the primary tunnel lining structure, three bolt holes are distributed along the arc-welded stainless-steel plate. During the deployment of the protection cover, three steel expansion bolts are used to connect the tunnel lining and the cover. Overall, this protective cover can ensure that the monitoring sensor is not damaged during the rock tunnel construction process.  
Figure 1: Sensor locations for monitoring deformations in: (**a**) typical tunnel transverse section; (**b**) tunnel face.
# On-Orbit Experimental Result of a Non-Deployable 430-MHz-Band Antenna Using a 1U CubeSat Structure
## 3 BIRDS-4 Project  
The Joint Global Multi-National Birds or BIRDS project is a multinational CubeSat project led by the Kyushu Institute of Technology in Japan. The students must design, develop, and operate CubeSats within their master or doctor courses. The BIRDS-4 project is the 4 th generation, and the mission statement of BIRDS-4 is building Paraguay's first satellite while improving the standardized bus system for future missions and give continuity to the satellite development of Japan and the Philippines, and previous missions from BIRDS-1, 2 and 3 [10]. The BIRDS-4 project began in November 2018 and the flight model was deployed in March 2021. Figure 2 shows the flight models of the BIRDS-4 satellites.
## 2 Materials and Methods
### Non-Parametric Signal Analysis
#### 2.3.2 Frequency Domain Analysis  
In the first instance, to determine the global frequency variability the fast Fourier transform (FFT) was implemented, which highlighted the fundamental frequencies of the signals.  
In the second instance, given the limitations of the use of the FFT, the short-time Fourier transform (STFT) was implemented to obtain a better resolution of the power spectrum. Equation (1) defines the discrete short-time Fourier transform.  
\[X(m,\omega)=\sum_{m=-\infty}^{\infty}x[n]\omega[n-m]e^{-jwn} \tag{1}\]where \(X(m,\omega)\) is the STFT of the signal, \(x[n]\) is the input signal in the time domain to be analyzed, \(w[n-m]\) is a scrolled window centered on \(n=m,e^{-i\omega n}\) is the exponential term of the Fourier transform, \(m\) represents time, and \(\omega\) represents the frequency.  
This technique allows the observation of the power spectrum and the characteristics of the frequency behavior of the signal. It is important to note that this analysis is limited due to the linear and non-stationary nature of the signal [7]. The reason for implementing the STFT is due to its advantage in its use for analysis of non-stationary signals, where the frequency characteristics are changing over time, compared to other techniques used such as the fast Fourier transform (FFT). Python version 3.12.4 was used to implement the STFT, using libraries such as Numpy and SciPy. The short-time Fourier transform (STFT) implementation with a Hamming window of 25 samples was a carefully justified choice based on the signal characteristics and the purpose of the analysis. The sampling period (\(\mathrm{T_{s}}\)) was defined as \(\mathrm{T_{s}}=1/\mathrm{f_{s}}\), where \(\mathrm{f_{s}}\) is the sampling frequency of the system. For a typical sampling of \(\mathrm{f_{s}}=1000\) Hz, the sampling period would be as \(\mathrm{T_{s}}=1\) ms, which corresponds to a total window width of 25 ms. The Hamming window was selected because of its ability to reduce discontinuities at the edges of the windows, thus minimizing the effect of spectral leakage. Additionally, the 90% overlap between windows ensures continuity in the temporal analysis, improving the fidelity of the resulting spectrogram.