esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this study, we have presented a united SCF beamformer that is designed to integrate with CPWC. When applied to data from CPWC, the uSCF beamformer calculates from the data before superposition among the angles. Thus, it makes full use of the phase information compared with the spatial beamformer (sSCF) and angular beamformer (aSCF).  
Through the results on imaging data, the uSCF offers significant improvements over CPWC and conventional SCF beamformers (sSCF and aSCF) in both lateral resolution and contrast resolution by simulation, phantom, and in vivo experiments. For simulated images, the CR improvements are over 100%. In experimental images, the maximum improvements of the lateral full width at half maximum (FWHM), CR and gCNR are 35%, 92% and 56%, respectively. In the in vivo experiment, the CR and gCNR of the artery is increased by 197% and 20%. The performance of uSCF also proves, to some extent, the validity of the united beamforming framework in uSCF.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Beamformer** & **Addition Operations** & **Multiplication Operations** \\ \hline DAS & \(MN\) & 0 \\ sSCF & 2\(MN\) & \(N\) \\ aSCF & \(MN\)+\(N\) & 1 \\ uSCF & 2\(MN\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computational complexity for one sample.  
The uSCF also needs further investigation. A proper threshold should be considered to improve the robust of the united approach. Because all the elements in the matrix weight equality in the coherence factor method, the performance of weight factor could also be influenced by very few elements.  
Conceptualization, C.Y.; methodology, C.Y. and Y.J.; investigation, T.J.; writing--original draft preparation, C.Y. and Y.X.; writing--review and editing, C.Y. and Y.C.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported by National Key Technology Research and Development Program of the Ministry of Science and Technology of China (grant no. 2017 YFC0107202), National Natural Science Foundation of China (grant no. 51805529, 11704397), the Funds for Jiangsu Provincial Key Research and Development Plan (grant no. BE2017661), International Cooperation Program of Jiangsu Province (grant no. BZ2016023).  
Our deepest gratitude goes to the anonymous reviewers for their careful work and thoughtful suggestions that have helped improve this paper substantially. The authors would also like to thank the editors.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Numerical Results
#### 3.2.2 Effect of Fins on Thermal Performance at Different Seasons of the Year
When the longitudinal _IAM_ is calculated, \(\theta=\theta_{l}\), where \(\theta_{l}\) is the angle between the CPC collector normal vector and the sun position vector projected in a plane parallel with the trough axis of the collector (while keeping the CPC aperture area normal to the sun rays). When the transverse _IAM_ is calculated, \(\theta=\theta_{t}\), where \(\theta_{t}\) is the angle between the CPC collector normal vector and the sun position vector projected in a plane perpendicular to the trough axis of the collector (while \(\theta_{l}=0\)) [35]. It can be seen in Figure 14 that the longitudinal _IAM_ is similar for all CPC absorbers for all angles \(\theta\). It can also be seen that the transverse _IAM_ decreases for all CPC absorbers when \(\theta\) increases from \(0^{\circ}\) to \(10^{\circ}\), and then increases when \(\theta\) increases from \(10^{\circ}\) to \(30^{\circ}\) for the absorber without fins and when \(\theta\) increases from \(10^{\circ}\) to \(20^{\circ}\) for the absorbers with segmented fins and with continuous fins. This increase in _IAM_ could be related to the combination between direct light and the reflected light from the top section of the reflective surface [37]. Subsequently, the transverse _IAM_ decreases with a further increase in \(\theta\) for all CPC absorbers. It can also be seen that the transverse _IAM_ drops rapidly for \(\theta>45^{\circ}\), which is related to the half-angle of acceptance of \(45^{\circ}\) of the CPCs, as observed for another CPC collector with the same half-angle of acceptance [38]. Transversal _IAM_ has a value of zero for \(\theta\geq 60^{\circ}\) for all CPC absorbers. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**CPC Absorber**} & **Average Power** & **Average Power** & **Average Power** \\  & \(P\) **(W)** & \(P\) **(W)** & \(P\) **(W)** \\  & **1 March 2020** & **15 January 2021** & **9 July 2020** \\  & **(Late Winter)** & **(Early Winter)** & **(Summer)** \\ \hline Without fins & 61.4 & 59.2 & 62.2 \\ With segmented fins & 69.9 & 69.5 & 62.8 \\ With continuous fins & 76.2 & 78.5 & 63.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average power (from 8 a.m. to 5 p.m.) on the surfaces of the different absorbers on late winter, early winter and summer. Figure 14: Longitudinal and transverse _IAM_ as a function of the incidence angle \(\theta\) for the different CPC absorbers.
###### Abstract
This study systematically evaluates and compares ML and deep learning (DL) models for lung cancer prediction, focusing on patient symptoms and lifestyle factors aiming to address the concerns. This research aims to identify the ML model with the best predictive accuracy for lung cancer, using patient features, including lifestyle factors and symptoms, and developing further effective diagnostic technologies that enable early diagnosis and enhance the patient outcomes. Thus, the following research questions are explored to fulfill the aims of the study:  
What are the predictive accuracy, precision, recall, and F-measure of various ML models for lung cancer prediction, based on patient symptoms and lifestyle factors, and which algorithm performs the best across these metrics? 1. How does feature selection affect the ML lung cancer detection accuracy? 2. Do DL methods, such as neural networks, outperform the traditional ML classifiers in lung cancer prediction? 3. This study's findings are expected to clarify the applicability of ML approaches for lung cancer detection, leading to improved screening and early intervention strategies.
# Hybrid Deep Learning-Based Intrusion Detection System for RPL IoT Networks
## 4 Proposed Hybrid Deep Learning-Based IDS (DL-IDS)
### Hybridization of DAE and DANN Models (DAE-DANN)
#### 4.2.1 Deep Autoencoder (DAE)  
The AE is a type of ANN that is usually used in unsupervised machine learning. Figure 3 shows the overall architecture of the AE that is implemented in the proposed model. It consists of an input layer of \(d\) neurons, which represent the number of features. The input layer is then encoded (compressed) by passing it to a hidden layer \(l_{1}\) with a size of \(d/2\) neurons. Another layer with a size of \(l_{1}/2\) neurons is added, which also represents the compressed data representation of the original input. The AE is then trained to reconstruct the inputs from the compressed layer (bottleneck) by having the hidden decoding layers. All layers are followed by dropout hidden layers (not shown in the figure) to prevent the data's overfitting. The encoder maps the input vector \(\mathbf{x}_{i}\) to the hidden representation unit, which represents the latent space of the bottleneck layers, as shown in Equation (5).  
\[\mathbf{l}_{i}=f(x_{i})=\mathbf{g}(W\mathbf{x}_{i}+b_{i}) \tag{5}\]  
where \(\mathbf{W}\) is the weight matrix, \(\mathbf{b}\) is the bias vector, and \(\mathbf{g}\) is the activation function. The well-known activation functions, namely, the Rectified Linear Unit (ReLU) and hyperbolic tangent (tanh), are used interchangeably in the model. Experiments have found that combining these functions produces better results. \(ReLU\) (Equation (6)) is a non-linear activation function, and it is very suitable in MPL with many hidden layers because it is fast and helps to reduce the error gradient issue and state vanishing [27]. The tanh (Equation (7)) function is more like a sigmoid function but differs in the output range of \((-1,1)\), where the range sigmoid is \((0,1)\).  
\[\mathbf{g}(\mathbf{x})=ReLU(\mathbf{x})=\max(0,\mathbf{x}) \tag{6}\]  
\[\mathbf{g}(x)=\tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{7}\]  
where \(x\) is the input to the function.  
The decoder map \(l_{n}\) reconstructs the \(x^{\prime}\) of the same input size \(x\):  
\[lx^{\prime}=\mathbf{g}^{\prime}\big{(}\mathbf{W}\mathbf{l}_{i}+\mathbf{b}_{i}^{\prime}\big{)} \tag{8}\]  
Figure 3: DAE Architecture.  
By using backpropagation of the error in the training process, the autoencoder tries to minimize the average reconstruction error (or loss) between the input and the reconstructed output (Equation (9))  
\[RE\big{(}x,\ x^{\prime}\big{)}=\frac{1}{m}\ \Bigg{\|}\frac{m}{1}\big{(}\mathbf{x}_{i}- \mathbf{x}_{i}^{\prime}\big{)}\Bigg{\|}^{2} \tag{9}\]  
where \(m\) is the number of samples.
## 3 Results
### Effects of Surface Adsorption Site (NH and NH2 Adsorption Sites)
#### 3.2.1 TMG Adsorption on Si-NH\({}_{2}\) Surface  
The reaction pathway of TMG adsorption on NH\({}_{2}\) passivated Si(100) substrate is shown in Figure 5 (black line). It is a strong exothermic reaction that released 1.33 eV to form the product structure (product 2). The TMG absorbs the silicon surface by consuming two adsorption sites and releasing two by-product molecules. This must be carried out through two successive activation barriers. In the transition states, TS 1-1 and TS 2-1 required the activation barriers of 2.00 and 1.44 eV, respectively. This showed that reactivity was low at TS 1 and then increased at TS 2. When considered at TS 1-1 and TS 2-1 stages, we found that the bonds of Ga-C1 and Ga-C2 were broken with different activation barriers. This allows us to know that the strength of Ga-C1 and Ga-C2 covalent bonds, which are the same type (Ga-C bond), might be differenced or changed during the adsorption. This information is difficult to obtain from experimental characterization techniques such as IR spectroscopy but is obtainable using simulation. This agreed with the first-principle calculations of TMG adsorption on GaN surface reported by [PERSON] in 2015 [47]. The Ga-C bonds within a TMG molecule were reported to be dissociated with the activation barriers of 1.28 and 1.84 eV, respectively. However, the cause of this phenomenon has never been discussed in the publications but is considered in the current work. The difference between  
Figure 5: Comparison of reaction pathways of TMG adsorption on Si–NH\({}_{2}\) and Si\({}_{2}\)–NH surfaces, illustrated by the black and red lines, respectively. The corresponding molecular structures are labeled (a)–(g) for adsorption on the Si–NH\({}_{2}\) surface and (h)–(n) for adsorption on the Si\({}_{2}\)–NH surface, as shown at the bottom of the figure.  
TS 1 and TS 2 may be explained by the CH\({}_{3}\) binding capacity of the Ga atom being reduced upon adsorption at the Si-NH\({}_{2}\) surface site. This can be seen from the breaking distances of the shortened Ga-C1 and Ga-C2 bonds during the TS 1 and TS 2 states, respectively (Figure 5). The breaking distance was substantially reduced from 2.73 A (TS1) to 2.25 A (TS2). The hypothesis for this phenomenon is that the N atom has greater electronegativity than the C atom. When the Ga-C bond is replaced by a Ga-N bond, the Ga electrons transfer to the N region. Therefore, the C atom decreases the Ga electron attraction used to form the Ga-C covalent bond. As a result, the remaining Ga-C bonds in TS 2-1 are weakened. In addition, the structure of PC 2 is more stable than for PC 1, resulting in the reaction tending to continue as PC 2 (consume two-adsorption sites) rather than remaining as PC 1.
## 2 Materials and Methods
### Data Acquisition and Processing  
#### 2.2.1 Sentinel-2 Data  
The Sentinel-2B image data used in this study were obtained from the European Space Agency (ESA, [[https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)]([https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home))) (accessed on 10 May 2021), with data imaged on 29 June, 23 August, and 7 October 2019, and tile numbers T50 SNJ, T50 SNH, T50 SMH, and T50 SMJ, respectively. The images had all less than 5% cloud cover. The SNAP software was used to pre-process the S2 MSIIC data with atmospheric correction, resampling, mosaic, and cropping to generate 13 bands of multispectral images with a spatial resolution of 10 m. The data were mainly used for remote sensing interpretation of rice fields.
# Highly Accurate Deep Learning Models for Estimating Traffic Characteristics from Video Data
## 4 Results Analysis
### Real Application for Video Detection Algorithm in Monthly Crash Prediction  
Video processing was applied to a freeway in southern China for extracting traffic features and building daily and monthly crash prediction models to forecast the number of crashes on every road segment for each direction.  
The freeway is a two-way four-lane road with an overall speed limit of 110 km/h, a tunnel speed limit of 80 km/h, and a speed limit at 2 intervals of 80 km/h.  
Dynamic traffic features such as vehicle speed, traffic volume by direction, and truck-to-car ratio were extracted through video processing, along with weather conditions, historical crash counts, and the effects of holidays and special events, especially for COVID-19 regulation policy, were set as independent variables to predict crash occurrence. A zero-inflated logarithmic link for count time-series (ZILT) model with a negative binomial distribution assumption for crash distribution was built for handling spatio-temporal structure and making a daily prediction. To make a comparison, the same model was also built to make crash predictions without involving dynamic traffic features.  
Crash prediction accuracy is compared with and without involving dynamic traffic data as displayed in Table 4.  
For the model without dynamic traffic features, the predicted results are more variant and R-square is significantly lower than the model with dynamic traffic features. This means that more variances in crash occurrences can be explained by involving dynamic traffic features (obtained through the video analytics presented earlier) as independent variables. Video analytics plays an important role in traffic crash prediction by extracting extra dynamic motion parameters to gain information.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Prediction Accuracy** & **Mean Square Error** \\ \hline Prediction without Dynamic Traffic Data & 78.5\% & 0.96 \\ \hline Prediction with Dynamic Traffic Data & 83.1\% & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction accuracy on different perspective settings (based on Ground Truth data).  
Figure 9: Detection of vehicle fire (blue: fire, red: smoke).
## 4 Sub-Optimal STMD with Clipped Viscous Damping-Stiffness Control Force
### Description of Force Clipping Degree Using the Clipping Coefficient  
A description of the clipping should begin with the introduction of a clipping coefficient, which will allow for an assessment of how much the clipped force differs from the unclipped force. For the purposes of this work, the clipping coefficient will be introduced based on the analysis of the force as a function of displacement.  
Figures 3b and 4b show an ellipse representing the force \(f_{a}\) as a function of the relative displacement \(x_{d}\). For \(k_{a}>0\), this ellipse can be described by:  
\[f_{e1}=c_{a}\omega\sqrt{X_{d}^{2}-x_{d}^{2}}+k_{a}x_{d}, \tag{34}\]  
\[f_{e2}=-c_{a}\omega\sqrt{X_{d}^{2}-x_{d}^{2}}+k_{a}x_{d}, \tag{35}\]  
where \(f_{e1}\) and \(f_{e2}\) denote the parametric representation of the higher and lower branches of the ellipse, respectively.  
The positive root of \(f_{e2}\), that is, the positive displacement at which the force \(f_{e2}\) becomes zero (see point D in Figure 3b) is:  
\[x_{d0}=X_{d}\frac{c_{a}\omega}{\sqrt{k_{a}^{2}+c_{a}^{2}\omega^{2}}}. \tag{36}\]  
Figure 4: Example of viscous damping—negative stiffness force characteristics before and after clipping: (**a**) Force, displacement and velocity versus time; (**b**) force versus displacement; (**c**) force versus velocity. Clipping coefficient: \(\alpha=0.7\).  
Based on (36), the clipping coefficient \(\alpha\) is introduced as follows:  
\[\alpha=\frac{x_{d0}}{X_{d}}=\frac{c_{a}\omega}{\sqrt{k_{a}^{2}+c_{a}^{2}\omega^{2 }}}. \tag{37}\]  
The clipping coefficient ranges from 1 (no clipping, the case of pure viscous damping, \(k_{a}=0\)), through a middle value of 0.5 (moderate clipping, for \(k_{a}=\sqrt{3}c_{a}\omega\)), to 0 (maximal clipping, for \(c_{a}=0\)).  
Figure 5 presents \(\alpha\) as a function of normalized frequency for the sub-optimal STMD with mass ratio \(\mu\) of 1% and 2%, for three selected values of \(K\). Due to their presentation in terms of normalized frequency, the plots are independent of the structural parameters (\(m_{1}\), \(\omega_{1}\)). They depend solely on \(\mu\) and \(K\), which determine the values of \(k_{a}\) and \(c_{a}\). The degree of clipping, described by \(\alpha\), is also independent of the forcing amplitude. Figure 5 indicates small and moderate degree of clipping for the sub-optimal STMD for typical values of \(\mu\) and \(K\) in the considered frequency range. In Figure 5a, the two values of \(\alpha\) are marked, corresponding to the cases of force characteristics in Figures 3 and 4.
# Universal Behavior of the Image Resolution for Different Scanning Trajectories
###### Abstract  
This study examines the characteristics of various scanning trajectories or patterns under the influence of scanning parameters in order to develop a theory to define their corresponding image resolutions. The lack of an accurate estimation of pixel size for a specified set of scanning parameters and their connection is a key challenge with existing scanning methods. Thus, this research aimed to propose a novel approach to estimate the pixel size of different scanning techniques. The findings showed that there is a link between pixel size and a frequency ratio \(N_{P}\), which is the ratio of two waveform frequencies that regulates the density of the scanning pattern. A theory has been developed in this study to explain the relationship between scanning parameters and scanning density or pixel size, which was not previously considered. This unique theory permitted the a priori estimate of the image resolution using a particular set of scanning parameters, including the scan time, frequencies, frequency ratio, and their amplitudes. This paper presents a novel and systematic approach for estimating the pixel size of various scanning trajectories, offering the user additional flexibility in adjusting the scanning time or frequency to achieve the desired resolution. Our findings also reveal that in order to achieve a high-quality image with high signal-to-noise and low error, the scanning trajectory must be able to generate a fairly uniform or regular pattern with a small pixel size.  
bidirectional Cartesian (B.C.); biomedical imaging; radial Lissajous (R.L.); image resolution; scanning trajectory; sinusoidal Lissajous (S.L.); triangular Lissajous (T.L.)
## 3 Results  
### Electrical and Optical Characteristics of NTAPP  
Figure 1b shows the voltage and current signal applied to the plasma jet source. The discharge voltage was 2.0 kV, and the frequency was 90 kHz with a 15% duty ratio. And the root means square (RMS) voltage and current were V\({}_{\text{rms}}\) = 0.69 kV and I\({}_{\text{rms}}\) = 69.1 mA. The discharge power was calculated as 2.38 J/s. Figure 1c shows the OES of our NTAPP in the wavelength range between 200-1000 nm. The main peaks were the second positive system N\({}_{2}\)(C-B) in the near UV region (300-440 nm band) and N\({}_{2}\) (B-A) first positive system (390-500 nm). Atomic oxygen (777 nm), NO-\(\beta\) (200-300 nm), and NO-\(\gamma\) (260-320 nm) system were also detected weakly in the OES spectrum. Figure 1d shows the temperature according to the gas flow rate and the distance from the end of a quartz tube. Temperature decreases with increasing the distance and with increasing the gas flow rate. Temperature was 37.7 \({}^{\circ}\)C when the distance was 15 mm, and the gas flow rate was 1.5 ppm. We used this condition for further studies since this temperature is adequate for biological applications. In this condition, the pressure of the discharge gas hardly effects on the sample as shown in Figure 1e. Since 1 mm agarose gel film is very thin and flexible, it is deformed with long treatment. However, it did not cause a loss of sealing between the cuvette and the gel film as shown in Figure 2.
# An Early Investigation of the HHL Quantum Linear Solver for Scientific Applications
## 5 Scientific Applications and Evaluation
### Heat Transfer Problem in Climate Projection
#### 5.2.1 Settings of the Numerical Experiments  
In this section, we examine the two-dimensional (2-D) heat diffusion equation in [73]  
\[\frac{\partial T}{\partial t}=D\
abla^{2}T+F \tag{5}\]  
where \(T\) represents the temperature at a given 2-D point and time, \(D\) is the heat transfer coefficient, and \(F\) is the forcing term consisting of arbitrary boundary and initial conditions. Equation (5) is a linear partial differential equation. We discretize Equation (5) in space and time into a system of ordinary differential equations using the finite difference method,  
\[AT=F, \tag{6}\]  
where \(A\) is the resulting coefficient matrix. Take the square lattices with a lateral size of three grid points and five grid points, and the resulting dimension of \(A\) is \(9\times 9\) and \(25\times 25\), respectively. Such configurations require 4 qubits and 5 qubits to represent the RHS vectors (\(F\) term in Equation (6)) in both linear systems, respectively. Let \(A^{(heat,I)}\) be the coefficient matrix generated from \(l\) number of grid points; the entry values are  
\[A^{(heat,I)}_{pq}=\begin{cases}1+4r,&p=q\\ -r&p=q\pm 1\text{ or }\text{ or }p=q\pm\text{ }l\\ 0,&\text{otherwise}\end{cases}\]  
where \(p\) and \(q\) denote the index of the entries of \(A\), and \(r\) is 0.00016 in the three-point case and 0.00064 in the five-point case.