esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Atmospheric Stability  
Atmospheric stability is an important indicator used to characterize atmospheric turbulence, or to describe the magnitude of atmospheric particle diffusion. In other words, it is the degree to which a slight disturbance of the atmosphere in mechanical equilibrium is likely to return to its original state, or significantly change the atmospheric state. The ratings of atmospheric stability can generally be divided into three levels: unstable, neutral, and stable. Atmospheric stability can be broadly classified into mechanical stability and thermal stability, according to the mediator factors that change atmospheric characteristics. A representative classification method of mechanical atmospheric stability is the vertical wind shear coefficient, which is a principle that determines the degree of fluid flow and diffusion by the transfer of momentum according to the difference in wind speed for each  
Figure 3: Concept for calculating the equivalent wind speed.  
Figure 2: **(Left)** Hub height wind speed (HHWS). The power generation is calculated by considering only the single-height wind speed corresponding to the hub height, which is the center of the rotor disk area. **(Right)** Rotor equivalent wind speed (REWS). The power generation is calculated by dividing the rotor disk area into several horizontal bands, and considering each height wind speed difference passing through the divided area, respectively.  
altitude in the z-direction. Thermal stabilities, such as the Richardson number and Monin-Obukhov length, take into account differences in momentum due to wind speed, as well as thermal motion effects due to temperature and heat flux. Table 3 shows the atmospheric stability classification criteria for each factor and representative meteorological phenomena.  
In this study, characteristics changes of HHWS and REWS according to local atmospheric stability near offshore wind farms were analyzed through Richardson number, wind shear coefficient, and turbulence analysis. In addition, after calculating the expected energy production using HHWS and REWS, comparative analysis was performed with the actual power generation, and detailed meteorological cause analysis related to atmospheric stability was also performed at the time-point showing significant difference.
# Thermal Conductivity and Rheology of Graphene Oxide Nanofluids and a Modified Prediction Model
## 3 Results and Discussion
### Thermal Conductivity of Graphene Oxide Nanofluids
#### 3.3.2 Effect of Particle Concentration  
Figure 6 shows the thermal conductivity enhancements of water-based GO nanofluids as function of the particle mass fractions. The results show that the thermal conductivities of nanofluids increase with increasing particle concentration. The mass fraction of nanoparticles strongly affects the thermal conductivity of nanofluids. It is believed that when the nanoparticles are dispersed in the base fluid on a nanometer scale, an increase in the particle concentration increases the number of particles, thereby increasing the area of the contact interface between the particles and the base fluid, which, in turn, enhances the heat transfer of nanofluid. However, when the particle concentration increases to a certain level, the particles agglomerate, the stability of the suspension decreases and the viscosity increases, which finally weakens the heat transfer performance of the nanofluid.  
Figure 5: Thermal conductivity of GO–water nanofluid versus temperature. (**a**): absolute, (**b**): relative to water.  
Figure 6: Thermal conductivity of GO–water nanofluid versus mass fraction at various temperatures.
## 7 Conclusions  
In this work, the purely DE is modeled and analyzed using FEM. The apparent power signal of three-phase squirrel cage induction motor is used to diagnose the fault. Due to the weak signal appearing around the harmonic frequency, the simple use of WPD may not give an accuracy result due to the drawbacks of this method. Therefore, after using the WPD algorithm, the EMD method has been applied to post-process the signal. A combined use of WPD and EMD to the apparent power signal has been presented in this paper. The simulation results show that the purely DE fault can be well diagnosed using the proposed method. It is shown that the FSF based on the harmonic component in the apparent power around \(kf_{r}\) can be considered as an index to detect the fault severity. However, under the influence of load, the use of the amplitude at a frequency around \(2f_{r}\) can give an ideal solution.  
Theoretically, the obtained results are achieved to detect the low levels of purely DE. In practice, the indicator is influenced by noise and the measurement accuracy. Therefore, it is essential to conduct the experiment to verify the FEM simulation results in the future study.  
As for the high-precision rotation system, for example, motorized spindle, even with low levels of DE, can lead to the undesired radial error motion. This can lead to the decrease of machining accuracy for the computer numerical control (CNC) machine tools. Therefore, the diagnosis of low-level eccentricity can help to identify the radial error motion caused by the motor. The simulation result has verified the relationship between the low-level eccentricity and the corresponding apparent power signal. In the next step, our group will conduct the experiment on the motorized spindle. Finally, the relationship modeling between the eccentricity fault of the motorized spindle and radial error motion will be studied.  
Figure 13: The comparison results between the simple use of WPD and the proposed method around the frequency of \(2f_{r}\): (**a**) WPD method, (**b**) proposed method (under 33% of rated load with 2% of purely DE).  
**Author Contributions:** [PERSON] and [PERSON] proposed the idea; [PERSON] carried out the modeling of the induction motor; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] drafted the paper; [PERSON] and [PERSON] revised the paper.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 61273205) and The APC was funded by the National Natural Science Foundation of China (Grant No. 61273205).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Seismic Periodic Noise Attenuation Based on Sparse Representation Using a Noise Dictionary
## 2 Method  
Periodic noise is recurring in raw seismic data, and its amplitude is constant within the recording time [6]. Based on these characteristics, we can differentiate and estimate periodic noise from ambient noise. Because ambient noise, such as periodic noise, white Gaussian noise and other non-stationary [1; 18] random noise, is ubiquitous in seismic records before the first arrivals, we can extract noise features and construct the noise dictionary from it. The algorithm is outlined in the flow skeleton (Figure 1).  
Figure 1: Flow skeleton of the proposed method.
## 4 Discussion
In addition, a review reported that the performance of deep learning-based segmentation approaches has been deteriorated by low image quality such as low contrast objects [43]. The image quality of treatment planning CT images used in this study may be affected by respiratory motion. Even so, in our results, the proposed prediction approach of CTRs reached ICCs of 0.943 (IN) and 0.904 (EX), possibly because the LoG filter for all treatment planning CT images enhanced the edges and reduced image noise. Nevertheless, the image quality of the treatment planning CT images should be improved continuously using other filters, which could reduce motion artifacts [44]. There are several limitations in this study. First, unbalanced training datasets were employed, including 73 solid, 31 part-solid and 11 pure GGO tumors. This may be the reason for the imbalance in performance for the different tumor types and could affect the segmentation accuracy of the pure GGO and part-solid tumors. This could influence the performance of the predicted CTR. Therefore, training the model on a larger number of cases, especially more part-solid tumors, is needed to improve the accuracy for the prediction of CTRs. Although the image augmentation technique with image flipping, rotation and rescaling was applied to increase the training data, more pure GGO and part-solid cases will be collected from Kyushu University Hospital or open-source datasets with prognostic data. In addition, advanced augmentation techniques including generative adversarial networks [45] can be applied to adjust the number of unbalanced data. Second, the DSCs for three types of tumors obtained with our DLS model were still below 0.9, and especially, were lower than 0.8 for part-solid tumors. The performance can be improved by using novel networks, for instance morphological vision transformers [46]. Finally, the 38 cases in the internal test dataset underwent SBRT, but the 49 external test cases received radiation therapy or chemo-radiotherapy. Since the patients in this study received different types of radiotherapy, the times to progression for the KM analysis may have been affected by the treatment methods. Therefore, we should use data from patients who underwent the same type of radiotherapy in future work.
## 3 Materials and Methods
### Feature Selection
Search algorithms are required for feature selection since they allow for feature searching. The attribute evaluator uses a single attribute, or a combination of attributes retrieved by the search method as input to determine its worth. The search process can begin with no or all attributes, or at any point in the feature space. It comes to a halt when any remaining features are added or removed, resulting in a drop in evaluation. By traversing the area from one end to the other and noting the order in which attributes are selected, it can also generate a ranked list of attributes. The phases of the correlation-based feature selection method in selecting principal components are shown in Figure 5.  
Best First with Forward search direction is one of the most extensively used search methods in the CFS attribute evaluation. Forward search begins with an empty feature set and gradually adds features in subsequent cycles. A stop criterion is implemented to prevent the best first search from searching the whole attribute subset search area. If five consecutive fully extended subsets demonstrate no enhancement over the present best subset, the search will be terminated. The dataset is made up of PC attributes that are supplied to the CFS function. CFS computes class-attribute and attribute-attribute correlations before searching the attribute subset space. The subset selected during the search with the highest merit (as determined by Equation (1)) is utilized to reduce the dimensionality of the original data. After that, the reduced dataset can be fed into a machine learning algorithm for training and validation. By performing the processes as illustrated in Figure 5, the CFS method evaluated a total of 98 subsets. With the best subset merit value of 0.335, the CFS method selected six transformed components out of 18, which accounted for just 33.33% of the entire feature dimension. The optimal components 1, 3, 6, 8, 13, and 16 were included from Table 2 to assess the machine learning model's performance.
# Pilot-Scale Fenton-like System for Wastewater Treatment Using Iron Mud Carbon Catalyst
## 2 Materials and Methods
### Quenching Experiments  
Through quenching experiments, the types and contribution levels of reactive oxygen species in the Fenton-like oxidation system can be determined. In this experiment, methyl phenyl sulfoxide (PMSO), tert-butanol (TBA), trichloromethane (chloroform), and furfuryl alcohol were used to identify Fe(VI), \({}^{\bullet}\)OH, O\({}_{2}\)\({}^{\bullet-}\), and \({}^{1}\)O\({}_{2}\). PMSO can capture high-valence metals (k = 10\({}^{1}\) M\({}^{-1}\)\(\cdot\)s\({}^{-1}\)) [17]. TBA has high reactivity towards \({}^{\bullet}\)OH and can act as a \({}^{\bullet}\)OH catcher (k = (3.8-7.6) \(\times\) 10\({}^{8}\) M\({}^{-1}\)\(\cdot\)s\({}^{-1}\)) [18]. Trichloromethane can act as an O\({}_{2}\)\({}^{\bullet-}\) catcher (k = 3.0 \(\times\) 10\({}^{10}\) M\({}^{-1}\)\(\cdot\)s\({}^{-1}\)) [19]. Furthermore, furfural can capture \({}^{\bullet}\)OH and \({}^{1}\)O\({}_{2}\) (k\({}^{\bullet}\)OH = 5.0 \(\times\) 10\({}^{9}\) M\({}^{-1}\)\(\cdot\)s\({}^{-1}\), \(\text{k}_{\text{i}_{\text{O}_{2}}}\) = 1.5 \(\times\) 10\({}^{8}\) M\({}^{-1}\)\(\cdot\)s\({}^{-1}\)) [20].
# A New Approach to Fall Detection Based on the Human Torso Motion Model
###### Abstract  
This paper presents a new approach for fall detection based on two features and their motion characteristics extracted from the human torso. The 3D positions of the hip center joint and the shoulder center joint in depth images are used to build a fall detection model named the human torso motion model (HTMM). Person's torso angle and centroid height are imported as key features in HTMM. Once a person comes into the scene, the positions of these two joints are fetched to calculate the person's torso angle. Whenever the angle is larger than a given threshold, the changing rates of the torso angle and the centroid height are recorded frame by frame in a given period of time. A fall can be identified when the above two changing rates reach the thresholds. By using the new feature, falls can be accurately and effectively distinguished from other fall-like activities, which are very difficult for other computer vision-based approaches to differentiate. Experiment results show that our approach achieved a detection accuracy of 97.5%, 98% true positive rate (TPR) and 97% true negative rate (TNR). Furthermore, the approach is time efficient and robust because of only calculating the changing rate of gravity and centroid height.  
full detection; human torso motion model; torso angle; centroid height; computer vision; depth image; Kinect +
Footnote †: journal: Applied  
_Applied_
# What Lies Beneath One's Feet? Terrain Classification Using Inertial Data of Human Walk
## 2 Materials and Methods
### Classification
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Feature** & **Domain** & **Sensor** & **Features** & **Equation** \\ \hline
**Mean** & T & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\frac{\sum_{i=1}^{n}x_{i}}{\#}\) \\
**Standard Deviation** & T & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\sqrt{\frac{\sum_{i=1}(x_{i}-\mu)}{2n}}\) \\
**Median** & T & \(A_{xyz}\), \(G_{xyz}\) & 6 & \([(n+1)/2]\) th value \\
**Maximum** & T & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\max(x)\) \\
**Minimum** & T & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\min(x)\) \\
**Max-Min Diff** & T & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\max(x)-\min(x)\) \\
**Root Mean Square** & T & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\sqrt{\frac{\#}{n}\frac{1}{n}\sum_{i=1}^{n}|x_{i}|^{2}}\) \\
**Signal Magnitude Area** & T & \(A_{x}\), \(G_{x}\) & 2 & \(\frac{\#}{n}(\sum_{i=1}^{n}|x_{i}|+\sum_{i=1}^{n}|y_{i}|+\sum_{i=1}^{n}|z_{i}|)\) \\
**Index of maximum** & T & \(A_{xyz}\), \(G_{xyz}\) & 6 & Index of maximum value in stride \\
**Index of minimum** & T & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(110\)\(\frac{\sqrt{\sum_{i=1}|x_{i}|^{2}}}{\sum_{i=1}^{n}|x_{i}|^{2}}\) \\
**Power** & T & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\sum_{i=1}^{n}(P_{i})\text{log}_{2}(P_{i})\) where \(P_{i}=\frac{\frac{x_{i}}{\min(x)}}{\sum_{i=1}^{n}\frac{x_{i}}{\max(x_{i})}}\) \\
**Energy** & T & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\frac{\sum_{i=1}^{n}(P_{i})\text{log}_{2}(P_{i})}{\sum_{i=1}^{n}\frac{x_{i}}{ \max(x_{i})}}\) \\
**Skewness** & T & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\frac{E(x-\mu)^{3}}{\sigma^{3}}\) \\
**Kurtosis** & T & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\frac{E(x-\mu)^{4}}{\sigma^{4}}\) \\
**Interquartile Range** & T & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\frac{\#}{Q_{3}}-Q_{1}\) where \(Q_{1}=\frac{\#}{4}\) and \(Q_{3}=\frac{3n}{4}\) \\
**Mean Absolute Deviation** & T & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\frac{1}{n}\sum_{i=1}^{n}(x_{i}-\mu)\) \\
**Jerk** & T & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\frac{1}{(n-1)}\sum_{i=0}^{n-2}x_{i}\) \\
**Zero Crossing Rate** & T & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\frac{\sum_{i=1}^{n}|x(i)|-|x(i-1)|}\) \\
**Mean** & F & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\frac{1}{n}\sum_{i=1}^{n}|X_{k}|\) \\
**Maximum** & F & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\max(X_{k}|_{i=1}^{n})\) \\
**Magnitude** & F & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(84\)\(\frac{\max(2\)\(\mu\)\(\mid X_{k}\)}{\sum_{i=1}^{n}|X_{k}|^{2}}\) \\
**Band Power of Signal** & F & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\frac{1}{N}\sum_{k=1}^{n}|X_{k}|^{2}\) \\
**Energy** & F & \(A_{xyz}\), \(G_{xyz}\) & 6 & \(\frac{\sum_{i=1}^{n}|X_{k}|^{2}}{\sum_{i=1}^{n}|X_{k}|^{2}}\) \\
**Nine Coefficients of FFT** & F & \(A_{xyz}\), \(G_{xyz}\) & 9\(\times\)6 & \(|X_{k}|_{i=2}^{10}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: List of hand-crafted features extracted from 6D accelerations and angular velocities for each stride. The features set consists of a total of 194 tempo-spectral features, of which 110 features were computed from the time domain (T) and 84 features were computed from the frequency domain (F).
# DNN Beamforming for LEO Satellite Communication at Sub-THz Bands
## 1 Introduction
The shorter wavelength allows the design of very small antennas that can be densely packaged as massive phased arrays of antenna elements. However, as we move to higher frequency bands above 100 GHz, the use of phase shifters for beamforming for a wideband system at sub-THz is less recommended due to inefficiency, non-linearity and beam squinting effects [24; 25]. Currently, the design of phased array antenna mostly rely on use of phase shifters for analog beamforming of the receiver of the massive array of elements that will cause beam squinting problem especially for large bandwidth communication system [26]. For a wideband system, this could degrade the performance significantly. The phase shifters are also not efficient for attaining wide scanning angles [26; 27]. The alternative option to phase shift beamforming is the use of true-time delay (TTD) analog beamforming though its implementation is costly. Furthermore, a very large number of antenna elements employed at the receiver will significantly increase the cost of implementation with such analog processing of antenna array output signals. To resolve these issues, one option is to implement digital beamforming that will require some additional components in the front end design of wideband receiver. However, thanks to the breakthrough advancements in digital integrated circuits and technology, it is now becoming more affordable and with digital processing, it is a lot easier to put into practice [28; 29]. In this work, to address the aforementioned problems, we propose to implement the beamforming algorithm of the massive number of receiver antenna elements using a deep neural network (DNN). The output of individual array elements are fed into a deep neural network through an analog to digital converter (ADC) and the DNN beamformer is trained to mimic the actual output voltage generated by a TTD beamformer as the angle of arrival (AoA) changes at the receiver due to rapid movement of the LEO satellite. The numerical results show that DNN beamforming algorithm is able to match the voltage output of a TTD beamformer with a very low mean square error for a wideband signal. Such implementation of the signal processing algorithm will reduce the cost of receiver and various nonidealities caused by the phase shifter beamformer as we employ a massive array of antenna elements for the receiver design at sub-THz bands. To the best knowledge of the authors, the proposed beamforming algorithm using the novel deep learning algorithm for wideband LEO satellite applications at sub-THz bands is for the first time to be published.
## 2 Theoretical Background  
### Direct Symbolic Integration Approach  
In the case of some real function \(f(b)\) of the stationary input random variable \(b\) having probability density function \(p(b)\), classical definitions of the basic probabilistic moments and coefficients were introduced [53; 54; 55; 56], for example:  
* the expectation: \[E[f(b)]=\int\limits_{-\infty}^{+\infty}f(b)\cdot p(b)db\] (1)
* the variance: \[Var(f(b))=\int\limits_{-\infty}^{+\infty}(f(b)-E[f(b)])^{2}\cdot p(b)db\] (2)
* the \(m\)th central probabilistic moment (for \(m>2\)): \[\mu_{m}(f(b))=\int\limits_{-\infty}^{+\infty}(f(b)-E[f(b)])^{m}\cdot p(b)db\] (3)  
The Gaussian probability density function is considered further, so that:  
\[p(b)=\frac{1}{\sigma(b)\cdot\sqrt{2\cdot\pi}}\exp\left\{-\frac{(b-E[b])^{2}}{ 2\cdot\sigma^{2}(b)}\right\}\ \ (b\in\Re) \tag{4}\]  
where \(\sigma(b)\equiv\sigma\) stands for the standard deviation of \(b\) substituted further by a product of the expected value \(E[b]=b^{0}\) and the input coefficient of variation \(\alpha(b)\equiv\alpha\).  
The integral definitions proposed in Equations (1)-(3) are very rarely used with infinite limits. Random variables are usually analyzed as truncated having some lower and upper bounds of the probability density function driven by the physical meaning of the specific parameter or just the experimental works. Therefore, the integration process is limited here using the well-known three-sigma rule recognized as very efficient in various computational experiments [29; 57]. Once the response function represents a reference exact dependence \(f(b)\), we obtain a solution of the probabilistic analytical method (AM), but when it is only an approximation \(f_{\mathrm{a}}(b)\)--of the semi-analytical method (SAM) [30; 58].  
Due to a large number of symbolic calculations in Section 3, in order to reduce their computation time, the semi-analytical method was approximated by determining the integrals using a numerical quadrature technique--the midpoint Riemann sum method [59]. The number of its subintervals was selected experimentally (see Section 3.4).  
Probabilistic characteristics calculated on the basis of the formulas provided in this subsection can only be discrete. Their values were determined each time for the input coefficients of variation \(\alpha_{\mathrm{i}}\in\{0.0125,\,0.025,\,\ldots,\,a_{\mathrm{max}}\}\) with an increment of \(\Delta\alpha=1/80\), where for \(\alpha_{\mathrm{max}}=0.30\), there were 24 calculation points.  
In this work, the results of the probabilistic analytical method were taken as exact values. The approximate solutions of the semi-analytical method were compared to them in order to test the criteria for selecting the response function.
# Towards Energy-Efficient Mobile Ad Optimization: An App Developer Perspective
## 4 Overview of the Proposed Work
### Static Model  
To acquire the energy consumption of mobile ads in app, existing work [5] uses static modeling. The model provides information regarding the ads energy utilization before the development phase. Before app implementation, the developer includes some static variables of ads such as size \(s\), type \(t\), and refresh rate \(r\). Therefore, these values of a variable are taken as an input to the static model result in high-level energy estimation of ads in the mobile app. In order to build efficient estimation through the static model, we conduct several numerical experiments. In these experiments, we considered static ad configuration, varying one configuration and keeping the others the same, then calculating the average energy consumption of mobile ads over application. In order to find accurate average energy consumption of ads, we repeat this numerical experiment several times. Thus, finally we conclude that energy consumption of ads is linear when \(r\) when \(s\) and \(t\) are kept fixed. We formulate the relation as:  
\[E=\beta-\alpha*(r-30) \tag{1}\]  
In the above Equation (1), \(\alpha\) is a coefficient, \(\beta\) presents the power consumption (average) and the lowest refresh rate \(r\) is 30.