esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning and rs-fMRI to Identify Potential Brain Regions Associated with Autism Severity
## 2 Methodology
### Materials
#### 2.1.2 Preprocessing Pipeline  
Different machines across multiple sites acquired the fMRI data available at ABIDE. Moreover, some sites used different total time acquisition. Thus, some rs-fMRI scans have more frames than others.  
The ABIDE offers 884 preprocessed rs-fMRI scans in four pipeline options:  
* Connectome Computation System (CCS);
* Configurable Pipeline for the Analysis of Connectomes (CPAC);
* Data Processing Assistant for rs-fMRI (DPARSF);
* Neuroimaging Analysis Kit (NIAK).  
These pipelines have different methods and sequences to manage fMRI data, removing noise such as head motion, skull, and magnetic interference. We only used the DPARSF pipeline in this work [26; 38; 39]. The criteria used for choosing DPARSF were analogous to those employed in the atlas definition process. Except for works where the authors create their preprocessing pipeline, DPARSF is the prevailing pipeline in a number of papers [21], reaching meaningful outcomes in ASD classification using rs-fMRI and ML [37; 40; 41; 42].  
The DPARSF final product is a matrix \((X,Y)\), where \(X\) is the number of columns, and \(Y\) is the number of rows. Each table column represents one ROI, according to the chosen atlas, and each table row represents the elapsed time during the scan. The number of rows (\(Y\)) could differ for each fMRI, even using the same atlas. However, the \(X\) value must be the same for all fMRI using the same atlas. For example, in a DPARSF matrix, a value \((X_{i},Y_{j})\) represents the oxygen level of ROI \(i\) at time \(j\).
# Incorporating Setup Effects into the Reliability Analysis of Driven Piles
## 3 Estimation of the Reliability Index of Driven Piles
### Uncertainties of Loads and Resistances  
The mean (or bias factor), coefficient of variation, distribution type, and other factors are used to describe the uncertainty of random variables. The terms normal and lognormal are frequently used to characterize the load and resistance distributions of engineering constructions [30]. The probabilistic features of loads and resistances for driven piles described in Table 2 were employed for this investigation [21; 22; 29; 31].  
Many researchers reported the \(\rho=Q_{\rm D}/Q_{\rm L}\) for bridge constructions and speculated that it varies with bridge span lengths [32; 33]. Meanwhile, [PERSON] et al. [32] adopted a formula to express the relationship between the ratio of \(\rho=Q_{\rm D}/Q_{\rm L}\) and the length of the bridge span, which is,  
\[\frac{Q_{\rm D}}{Q_{\rm L}}=(1+I)(0.0132l) \tag{11}\]  
where \(I\) is the dynamic load factor, and \(l\) is the bridge span length in feet. When the bridge span length \(l\) varies from 10 m to 70 m, the value of \(\rho=Q_{\rm D}/Q_{\rm L}\) virtually spread out from 0.576 to 4.0, according to Equation (11). As a result, for this investigation, values ranging from 0.5 to 4.0 for \(\rho=Q_{\rm D}/Q_{\rm L}\) were chosen.  
The increase factor (\(M_{\rm setup}\)) is re-expressed by using Equations (2) and (6).  
\[M_{\rm setup}=A\log\frac{t}{t_{0}} \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Random** & **Bias Factor, \(\lambda\)** & **Standard** & **Coefficient of** & **Distribution** & **Reference** \\ \cline{2-6}
**Variable** & & **Deviation, \(\sigma\)** & **Variation, COV** & **Distribution** & **Reference** \\ \hline \(R_{0}\) & 1.158 & 0.393 & 0.339 & Log-normal & [PERSON] et al. [21] \\ \hline \(R_{\rm setup}\) & 1.141 & 0.543 & 0.475 & Normal & Yang and Liang [22] \\ \cline{2-6} \(Q_{\rm D}\) & 1.023 & 0.593 & 0.580 & Log-normal & Yang and Liang [31] \\ \hline \(Q_{\rm D}\) & 1.080 & 0.140 & 0.130 & Log-normal & AASHTO [29] \\ \hline \(Q_{\rm L}\) & 1.150 & 0.207 & 0.180 & Log-normal & AASHTO [29] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Probabilistic characteristics of random variables of loads and resistances.  
\(M_{\rm setup}\) estimation is dependent on parameters \(A\) and \(\log\left(t/t_{0}\right)\), as shown in Equation (12). [PERSON] and [PERSON] [22; 31] summarized databases that contained both static and dynamic load test results of driven piles in clay and sand, with the value of \(A\) ranging from 0.1 to 1.0. Furthermore, time \(t\) following EOD varied between 1 and 100 days in the majority of cases, with \(\log(t/t_{0})\) with \(t_{0}=1\) ranging between 0 and 2. As a result of the analysis of \(A\) and \(\log(t/t_{0})\), the increase factor (\(M_{\rm setup}\)) for this study was determined to be between 0 and 2.
## 2 The BEAM Toolbox and the Land Surface Temperature Processor
### The LST Processor as a BEAM Plug-In
#### 2.2.3 Application to Sample Images  
As a demonstration of the potential of the SEN4 LT product for climate studies, a monthly LST composite over the Iberian Peninsula for July 2009 is shown in Figure 6. The LST is generated with the SW algorithm using MERIS/AATSR imagery. This figure clearly shows the different LST patterns over Northern and Southern Spain, with lower temperatures in the North and higher temperatures in the South.  
Figure 6: Monthly LST for July 2009 over the Iberian Peninsula.  
Figure 7 shows an image showing the LST (derived from the SW algorithm) for all of Europe over a period of one month (July 2003). The spatial resolution is 3 km. The colour bar and histogram are in the lower left corner. During that summer month, many cloud-free samples for almost of Europe were available, except for Ireland and a few small areas over Eastern Europe.
# A Sub-6 GHz MIMO Antenna Array for 5G Wireless Terminals
## 2 Antenna Geometry
### MIMO Configuration  
The array design achieved utilizing the corporate feed technique is extended to the MIMO configuration of two ports as shown in Figure 6. The arrays are placed with a 90-degree shift with respect to each other in order to achieve pattern diversity and good isolation among antenna arrays, which can cause issues when extending arrays to MIMO. The dimensions of the MIMO antenna are outlined in Table 3.
## 4 Discussion  
We verified the accuracy of elemarkTM by confirming the correlation between the whole blood lipid measurement values of three lipid markers (TC, TG, HDL) from elemarkTM and the serum lipid values of the same three lipid markers from existing automated hospital equipment. elemarkTM satisfied the acceptance criteria for the CE approval; 95% of the individual blood-testing parameter result fell within \(\pm\)10 mg/dL of the appropriate reference result at HDL concentration <100 mg/dL and within \(\pm\)15% of the reference result at TC and TG concentrations \(\geq\)100 mg/dL [11].  
We used a smartphone-based, easy-to-use, intuitive user interface (Figure 2) [12,13]. Smartphone-based point-of-care blood tests have several advantages in terms of computation, communication, usability, and cost-effectiveness [14,15]. In addition, data generated from point-of-care devices can be easily shared with caregivers and healthcare professionals, helping to manage chronic disease in patients. Because the device is always connected to the network, data points cannot be lost  
Figure 3: Regression and difference charts between the proposed device (elemarkTM) and the reference device (AU5800). (**A**) Total cholesterol (TC) regression chart, (**B**) percent difference chart with \(\pm\)15% accuracy standard, (**C**) triglyceride (TG) regression chart, (**D**) percent difference chart with \(\pm\)15% accuracy standard, (**E**) high density lipoprotein (HDL) regression chart, (**F**) percent difference chart with \(\pm\)10% accuracy standard.  
and the generated data can be automatically saved and configured for later analysis. Future machine learning-based analysis will allow us to predict cholesterol levels and calculate a patient's risk of metabolic disease [16; 17].  
Our study has several limitations. We used venous blood for the elemarkTM test. Capillary whole blood results are known to be different from venous whole blood results [18]. Future studies showing actual use cases should use capillary whole blood. We are also aware that the recruited participants were mainly from the elderly population (\(70.8\pm 10.8\) years of age). However, we believe that our current study of method comparison results is independent of age distribution. Rather, our results showed that elderly participants at high risk for cardiovascular disease produced accurate cholesterol diagnostics.  
Conceptualization, K.Y., J.C., I.-U.S. and Y.-A.C.; methodology, K.Y.; software, K.Y.; validation, K.Y., I.-U.S. and Y.-A.C.; formal analysis, K.Y.; investigation, K.Y., I.-U.S. and Y.-A.C.; resources, J.C.; data curation, I.-U.S. and Y.-A.C.; writing--original draft preparation, K.Y.; writing--review and editing, K.Y., I.-U.S. and Y.-A.C.; visualization, K.Y.; supervision, I.-U.S. and Y.-A.C.; project administration, J.C.; funding acquisition, J.C. This study was funded by BBB Inc.  
The authors acknowledge [PERSON] for her technical support.  
K.Y. and J.C. work for BBB Inc.
# Proxy Re-Encryption Scheme for Decentralized Storage Networks
## 3 Preliminaries
### Outsourcing Attack and Proof-of-Replication  
In a permission-less decentralized storage network, whoever joins the network can provide resources and make incoming attacks. The resource providers may make arbitrary attacks to reduce their costs and increase the margin. One of the most critical is the outsourcing attack. The solution to prevent outsourcing attacks is proof-of-replication [1].  
The data must have redundancy stored in a permission-less decentralized storage network. An individual resource provider who deleted the local copy of data to save the storage cost makes an outsourcing attack. When the request to retrieve data comes, the resource provider can fetch the content from another provider and send it back to the requester. The data must be preprocessed into the replication format with the unique key to prevent the outsourcing attack. The cost of replication should be more expensive and time-costly than honestly storing the data. The replicated data are hard for another to utilize, as the replication key is unique. Proof-of-replication ensures the dedicating unique physical store for the data.
# Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics
## Materials and Methods
### Multivariate Data Analysis
#### 2.5.3 Discrimination Models  
As the eventual purpose of this study was to implement classification of wheat varieties, three models, including LDA, support vector machine (SVM), and extreme learning machine (ELM), were employed. These discriminant models from diverse fields (traditional and neural network algorithms) take effect on the basis of dissimilar principles, such as linear and nonlinear data processing or high-dimensional and low-dimensional space transformation, or otherwise. Their performance was evaluated using the classification accuracy and the proportion measured according the Equation (2).  
\[\text{Accuracy}=\frac{\text{The correct number of predicted labels}}{\text{The number of actual labels}}\ast 100\% \tag{2}\]  
LDA is a classical pattern recognition algorithm, which is usually recognized as a simplified version of SVM. As previously mentioned, LDA ensures the maximized separability of samples in the projection space. The key of LDA is to compute the right projected direction and establish the appropriate linear discriminant function.  
SVM maps the original data into a higher dimensional space and constructs a separating hyperplane to maximize samples' spacing distance. Based on structural risk minimization, it introduces kernel functions to effectively deal with nonlinear data or linear inseparable data [27; 28]. An appropriate kernel function can improve the model's performance. Radial basis function (RBF) is used popularly in the spectroscopic analysis field. To get the satisfactory performance, penalty coefficient \(c\) and the kernel parameter \(g\) should be determined in advance. In this study, a grid-search procedure was employed to select optimal \(c\) and \(g\) by using an internal toolkit of a library for support vector machine-libsvm ([[https://www.csie.ntu.edu.tw/~](https://www.csie.ntu.edu.tw/~)]([https://www.csie.ntu.edu.tw/~](https://www.csie.ntu.edu.tw/~)){}\(\{\)}cjlin/libsvm/). Here, the searching range of \(c\) was set from \(2^{-5}\) to \(2^{15}\) and \(g\) was set from \(2^{3}\) to \(2^{-15}\).  
ELM is a kind of feed-forward neural network with a single-hidden layer, which has generally been applied in multiclass discrimination. It outputs the weight by only adjusting the parameters of hidden layer and possesses powerful generalization [29]. By setting the number of hidden nodes from 1 to 3000 by steps of 100, the discrimination accuracy of training set possessing homologous incremental nodes can be successively calculated. The optimal number of hidden nodes, \(n\), can be determined according to the optimal model performance.
## 3 Results and Discussion
### Effect of Extraction Conditions on Sea Buckthorn Leaves
Currently, it is difficult to find analogous studies to compare the efficiency of water extraction of sea buckthorn leaves remaining after berry harvesting using PCSL extraction. As shown in Table 2, although the L-2 extract yielded the highest concentration of bioactive compounds during the long extraction, the L-4 and L-5 extracts had similar amounts of proanthocyanidins and TPC. Therefore, the L-4 extract, obtained by PCSL extraction with 20 cycles of 2 min each, was selected for further studies. Although the shorter extraction does not result in the maximum concentration of bioactive compounds, the shortened extraction time and the use of an environmentally friendly solvent make \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Extraction Cycles** & **Cycle Duration, min** & \
\begin{tabular}{c} **Proanthocyanidins,** \\ **gcc/L** \\ \end{tabular} &
\begin{tabular}{c} **TPC, ggAg/L** \\ \end{tabular} \\ \hline L-1 & 20 & 10 & 2.47 \(\pm\) 0.10 a & 8.39 \(\pm\) 0.19 a \\ L-3 & 20 & 5 & 3.46 \(\pm\) 0.08 b & 8.81 \(\pm\) 0.11 b \\ L-4 & 20 & 2 & 4.44 \(\pm\) 0.06 d & 9.85 \(\pm\) 0.02 d \\ L-2 & 60 & 10 & 5.51 \(\pm\) 0.08 e & 12.42 \(\pm\) 0.25 e \\ L-6 & 60 & 5 & 3.92 \(\pm\) 0.11 c & 9.30 \(\pm\) 0.10 c \\ L-5 & 60 & 2 & 4.36 \(\pm\) 0.06 d & 9.98 \(\pm\) 0.06 d \\ \hline \hline \end{tabular}  
* Data values are expressed as means with the standard deviation (\(n=3\)). Different lowercase letters a–e in the column show that values are significantly different (\(p<0.05\)).  
\end{table} 
Table 2: Extraction conditions and content of biologically active compounds in sea buckthorn leaf aqueous extracts. it superior to extracts reported in the literature using non-sustainable solvents. This demonstrates a trade-off between extraction efficiency and process sustainability.
# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 4 Integration of Co-Simulation Components
### Design Challenges and Solutions of PTV Vissim--CARLA Integration
#### 4.1.3 Synchronization of Traffic Flow  
Traffic flow synchronization between CARLA and PTV Vissim is achieved through the _DrivingSimulator.all_ file, which facilitates bidirectional communication. This functionality, originally written in C++, is accessed via Python using the ctypes library, enabling seamless integration with the CARLA API. The synchronization process ensures that vehicles and their speeds, positions, and lane placements in PTV Vissim are accurately reflected in CARLA's 3D environment.
# Prognostics and Health Management Based on Next-Generation Technologies: A Literature Review
## 3 Research History of PHM
### PHM Supported by New-Generation Technology  
[PERSON] et al. provide an overview of the application of deep learning to PHM and show the differences between the existing approaches [10]. [PERSON] et al. propose a predictive health manager for PHM based on the Internet of Things (IoT) and cloud computing technologies to supervise and control geographically dispersed and important machines [11]. [PERSON] et al. proposed fuzzy classification and a neural network based on an on-orbit payload health status monitoring method to address the problem that on-orbit payloads of space vehicles are demanding to maintain promptly and cannot be inspected manually [12]. [PERSON] et al. proposed the emerging technology of a digital twin (DT) that realizes physical-virtual fusion in order to improve the accuracy and efficiency of PHM for complex devices [13]. [PERSON] et al. used a data-driven approach with a multilayer perceptual neural network algorithm for the prediction of the remaining service life of the PHM system for a flight engine and found that the method had good accuracy [14]. [PERSON] et al. proposed a multi-feature deep convolutional migration learning network based on multiple features, which was used on a PHM system to obtain higher-accuracy prediction by extracting multiple features from the raw vibration signals of the bearings [15]. [PERSON] et al. proposed a virtual data generation method for developing a fault analysis tool for fault PHM for RF modules [16]. [PERSON] et al. proposed a deep unsupervised learning-based supervised learning meta-learning method (Autoencoder) for the detection of anomalous vibrations to improve the accuracy of PHM [17]. A summary of the classic papers among these is shown in Table 1.
## 4 Media Access Control
### Local Sensing Configuration
#### 4.1.1 Sensing Mode  
The mode of spectrum sensing controls the acquisition of knowledge about spectral condition. Because the spectral area exhibits more changeable characteristics than conventional wired transmission media, spectrum sensing can either take place on demand of transmissions or be scheduled to repeat periodically [23, 27, 39]; these are known as reactive and proactive (or periodic) modes, respectively.  
As described in [40], nodes in the reactive mode only sense the spectrum once they need to transmit data. If an available channel is located, the medium is accessed based on either carrier sense multiple access with collision avoidance (CSMA/CA) or synchronous mechanism. The reactive mode is power friendly as it minimizes power consumption by performing the least possible number of sensing [23]. Also, as relatively little time is taken up by spectrum sensing, more time can be dedicated to data transmission, thereby helping to maintain the overall throughput.  
Proactive sensing, on the other hand, is conducted periodically in addition to, or in place of, the on-demand sensing [39]. This model, in contrast, pays extra overhead to gain an historical record of sensing data, which can further help formulate information about the spectrum characteristics. Such a practice can lead to accelerated processes of finding a white space [23] and more accurate detection, which could contribute to 30% less disruptions [41]. However, although applications that are sensitive to delays incurred by channel switching would benefit from the feature, the system in energy-stringent circumstances could suffer from a shortened lifetime. The choice between these two mechanisms thus depends on the constraints of specific applications.
# A Simple Model for Estimating the Hydraulic Conductivity of Unsaturated Soil
## 4 Discussion  
In the present study, based on the analysis of the soil-water interaction mechanisms over a wide suction range, a simple model for estimating the hydraulic conductivity of unsaturated soil has been proposed. The results have demonstrated that the proposed model has a good ability to estimate the hydraulic conductivity of different unsaturated soils with satisfactory accuracy. Compared with existing approaches, the proposed model has a simpler form composed of three straight lines. Furthermore, all parameters of the proposed model can be conveniently calibrated by the measured SWCC and saturated hydraulic conductivity, and it is unnecessary to calibrate the scaling parameter by the measured unsaturated hydraulic conductivity curve as the existing approaches [30]. Therefore, the proposed model can more conveniently estimate the hydraulic conductivity of unsaturated soil over a wide suction range, which will facilitate the application of unsaturated seepage in practice.  
The estimation accuracy of the proposed model is associated with the determination of the air-entry value and residual suction based on the measured SWCC. The parameters calibration approach in this study mainly focuses on the unimodal SWCC, which cannot determine the residual suction of the bimodal SWCC. Additionally, this study does not consider the influence of hydraulic hysteresis on the hydraulic conductivity. Further studies on these aspects are needed in the future.
# Stability Analysis of Lane-Keeping Assistance System for Trucks under Crosswind Conditions
## 2 Materials and Methods
### LKAS Control Strategy  
#### 2.2.1 LQR Controller  
The main objective of the LQR controller is to determine the optimal control gains to achieve optimal control of the system [20]. The discrete state-space equation is obtained by applying the midpoint and forward Euler methods to discretize the state and control variables:  
\[X(k+1)=A_{T}X(k)+B_{T}U(k), \tag{13}\]  
where \(A_{T}=\left(E-\frac{AT}{2}\right)^{-1}\left(E+\frac{AT}{2}\right)^{-1}\), \(B_{T}=BT\), \(E\) is the identity matrix, \(X(k)\) is the state variable of the system at time \(k\), and \(U(k)\) is the control variable required by the system at time \(k\).  
To improve the control performance, a target function is assumed:  
\[J=\sum_{k}^{\infty}\left(X_{k}^{T}QX_{k}+U_{k}^{T}YU_{k}\right), \tag{14}\]  
where \(X_{k}\) is the system state variable, \(U_{k}\) is the system control variable, and \(Q\) and \(Y\) are the weighted matrix coefficients for the system state and control variables, respectively. The problem now is to find the control law such that the cost function (14) is minimized under the constraint conditions.  
The feedback controller for the LQR controller is:  
\[U(k)=-KX(k), \tag{15}\]  
where \(K=\left(Y+B_{T}^{T}PB_{T}^{-1}\right)^{-1}B_{T}^{T}PA_{T}\) is the gain matrix of the feedback controller, in which \(P\) is the positive definite solution to the Riccati equation. The Riccati equation is as follows:  
\[P=Q-A_{T}^{T}PB_{T}\left(Y+B_{T}^{T}PB_{T}\right)^{-1}B_{T}^{T}PA_{T}+A_{T}^{T }PA_{T}. \tag{16}\]  
By incorporating the combined deviation matrix, \(e_{r}\), of the lateral position and heading angle deviations, \(\delta_{f}\), into the system state variable and the front wheel steering angle into the control variable, the LQR control law based on the front wheel steering angle is derived as:  
\[\left\{\begin{array}{l}\delta_{f}(k)=-Ke_{r}(k)\\ K=\left[\begin{array}{cccc}k_{1}&k_{2}&k_{3}&k_{4}\\ e_{r}&\dot{e}_{d}&\dot{e}_{\varphi}&\dot{e}_{\varphi}\end{array}\right]\end{array} \right., \tag{17}\]  
where \(K\) is the gain matrix of the LQR controller and \(e_{r}\) is the combined deviation matrix.