esa-sceva/satcom-chunk-collection huggingface dataset:
# Uniaxial Testing of Soil-Cement Composites to Obtain Correlations to Be Used in Numerical Modeling
## 2 The Current State of Knowledge and Basic Data on the Research Project
### Geotechnical Conditions at the Site of the Test Columns  
The choice of the test site where the test columns were carried out was not accidental. The main drawback of deep mixing technology is the risk of obtaining a composite with low strength and stiffness. Such a risk occurs especially when cohesive (fine-grained) soils containing organic parts (i.e., silts) or peaks are mixed. In the location in question, to a depth of approx. 6 m, there are clays and silts in various states (from low plasticity to high plasticity). A typical section is given in Figure 1, taken from a geological survey [37]. The weak layer of peat/organic soil marked red is situated 4-5 m below the ground surface (working platform). Detailed laboratory tests confirmed the content of organic components in the layers defined as silt and silty clays. The fine sands lying below 6 m are in a medium-density state, and despite possible contamination with organic parts in the top part, they are soils with better geotechnical parameters and do not carry the risk of significant settlements under load.  
Basic data about the soils that were subject to improvement by means of various soil mixing techniques are juxtaposed in Table 1. It should be emphasized that the site geotechnical investigation was performed in an unconventional scope for tests carried out for typical construction investments. Apart from drilled boreholes and static soundings (CPT), the soil samples were collected, which were then subjected to oedometric tests. The content of organic matter in samples from various depths was determined, and the effective values of the angle of internal friction and cohesion in the direct shear apparatus were determined.  
Figure 1: Typical geotechnical section at the location of the test columns [37].  
Oedometric tests were then planned for the composite obtained in the process of deep mixing, which allowed us to estimate the scale of the ground improvement. Also, the effective geotechnical parameters (angle of internal friction and cohesion) of the composite were planned and are currently performed to be determined in tests in the triaxial compression apparatus.
## 1 Introduction  
### Background of the Study: EDs and Introduction of the FSED Model  
Hospitals are large, complex institutions that continuously evolve [1, 2]. Along with the growing need to create user sensitive designs and facilities, Emergency Departments (EDs), have been facing numerous challenges such as overcrowding, increasing patient acuity and deficit numbers of experienced staff leading to extended waiting times, negative impact on the overall efficiency and declining patient satisfaction rates, on a global scale [3, 4]. To begin to address crowding, the root of the problem must be determined. There is no single cause and effect scenario; instead, there are many causes depending on the region of the world and the governing healthcare system [5, 6]. Being the most vital and complex zone of any healthcare facility, Hospital Based Emergency Departments (HBED) require instantaneous and inventive interventions to address the growing challenges, as the improper functioning of any ED has the potential to adversely hamper the operation of the entire hospital system, which may hinder the delivery of healthcare services to society [7, 8, 9].  
Among numerous endeavors to find possible solutions to these problems, a new infrastructural model defined as Free Standing Emergency Department (FSED) emerged demonstrating effectiveness in some context. This model has emerged as a promising alternative to the traditional HBED, especially in some regions of the United States (US) (i.e., Texas) and by the Australian healthcare system [10]. The model includes the development of a network of detached centers for emergency care from the main hospital campus for the delivery of emergency services to varied locations with the objective to reduce HBED overcrowding along with ensuring smoother accessibility of these services for the population. Similar to traditional EDs, the FSEDs offer several services like the board-certified emergency physicians, who are capable of treating more emergencies and their activities are available 24 h a day.  
According to the American College of Emergency Physicians [11], FSED can be further characterized based on their location and relationship with the closest hospital:  
1. Satellite Free Standing Emergency Departments (SFSED);
2. Autonomous Free Standing Emergency Departments (AFSED);  
Satellite facilities are generally affiliated with a parent/referral hospital and usually accept Medicaid/public insurance coverage plans while Autonomous FSEDs are privately owned by healthcare professionals [12]. Reports highlight the rapid and accelerating growth of both FSED typologies in the US [13; 14] as well as interest from other countries [10], however, limited information is available on its actual structure, layout and functionality; therefore further research is needed to shed light in this direction.
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## 2 Performance Evaluation
### Parameters to Evaluate Radiation Performance
#### 2.2.1 Cumulative Effects  
Tests performed for measuring CE are characterized by the total dose reached before unacceptable performance degradation. In mature technologies, ionizing radiation (usually measured in krad(Si) by exposure to a \({}^{60}\)Co source) mainly affects MOS (metal oxide semiconductor) devices, while non-ionizing radiation (measured with the fluence in particles/cm\({}^{2}\)) specially involves bipolar ones. Additionally, bipolar devices (even parasitic structures [23]) can be affected by ionizing radiation at low dose rate (\(\leq\)10 mrad(Si)/s) [24; 25; 26], making also the dose rate an important parameter for TID tests. For some 'More Moore' processes with thinner gate oxide but using thick BOX (buried oxide), ionizing radiation can provoke a back-gate effect [27].
# Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model
## 3 Proposed Methodology
### Real-Time Analysis of Stuck Pipe Trend Based on Inversion of Friction Coefficient  
The friction coefficient during drilling is a comprehensive resistance coefficient, which includes not only the friction between string and wellbore, but also the additional resistance caused by keyway, cuttings bed, mud viscous resistance and other factors. These factors are closely related to stuck pipe phenomenon, so the friction coefficient of string can characterize the stuck condition to a certain extent.  
Four basic parameters are needed to realize the real-time inversion of friction coefficient, including hook tension, torque, WOB and TOB. The hook tension and torque can be measured in real time. WOB and TOB can be measured by downhole measurement tools which is usually stored in downhole tools and read after tripping out. So it is difficult to realize the real-time transmission of WOB and TOB under the existing technical conditions, which leads to the failure of real-time inversion of friction coefficient during drilling. Therefore, the neural network technology is used to realize the real-time calculation of WOB and TOB [23].
# Optimization Design Study of Pipe Curtain Freezing Scheme for Sanya Estuary Submarine Channel
## 3 Three-Dimensional Numerical Modelling
### Establishment of Initial and Boundary Conditions  
#### 3.4.1 Initial Conditions  
The model's initial conditions encompass the specification of the initial temperature of the sandy soil and the determination of the seepage velocity. The initial temperature of the soil layer is set at 18 \({}^{\circ}\)C. The initial conditions for the seepage velocity are ascertained by the upstream and downstream head differences. Thus, the relationship between seepage velocity and head differences can be expressed by the following equation:  
\[V=\frac{\Delta H\times K}{\Delta L}\]  
Figure 3: Schematic diagram of meshing.  
Figure 2: Schematic diagram of the overall model.  
Formulas: \(V\): seepage rate; \(K\): permeability coefficient; \(\Delta H\): head difference; \(\Delta L\): hydraulic path.
## 5 Optimal Path Selection  
After determining the stability boundary, the paths that do not meet the boundary conditions can be eliminated to obtain the feasible path cluster. To choose the optimal path from this viable cluster, the path evaluation cost functions should be designed. The path evaluation cost functions include the comfort cost function, the velocity maintenance function, the lateral offset cost function, and the lane-changing efficiency cost function.
# Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
## 2 Proposed Methods
### Simplex Volume Maximization  
The simplex composed of endmembers has the maximum volume. Based on that, the proposed method makes the final decision on endmembers.  
Suppose that the set of candidate endmembers obtained by spectral clustering is \(\mathbf{Y}=(\mathbf{y}_{1},\mathbf{y}_{2},\ldots,\mathbf{y}_{\mathrm{M}})\in \mathbb{R}^{\mathrm{b}\times\mathrm{M}}\). First, it is necessary to perform PCA on \(\mathbf{Y}\) to reduce thedimension to p \(-\) 1 and obtain a new set denoted by \(\hat{\mathbf{Y}}=(\hat{\mathbf{y}}_{1^{\prime}},\hat{\mathbf{y}}_{2^{\prime}} \ldots,\hat{\mathbf{y}}_{\hat{\mathbf{M}}})\in\mathbb{R}^{(\mathrm{p}-1)\times \mathrm{M}}\). P represents the number of endmembers. The simplex volume is calculated as follows:  
\[\mathrm{V}\Big{(}\hat{\mathbf{y}}_{\mathrm{k}_{1}},\hat{\mathbf{y}}_{\mathrm{k }_{2}},\ldots,\hat{\mathbf{y}}_{\mathrm{k}_{\mathrm{p}}}\Big{)}=\frac{\mathrm{ abs}\Bigg{(}\Bigg{|}\begin{array}{ccc}1&1&\ldots&1\\ \hat{\mathbf{y}}_{\mathrm{k}_{1}}&\hat{\mathbf{y}}_{\mathrm{k}_{2}}&\cdots& \hat{\mathbf{y}}_{\mathrm{k}_{\mathrm{p}}}\end{array}\Bigg{|}\Bigg{)}}{( \mathrm{p}-1)!}. \tag{9}\]  
Iteratively select p vectors from \(\hat{\mathbf{Y}}\) and calculate the corresponding V. The p vectors with the largest V are selected as the final endmembers. It can be formulated as follows:  
\[\mathrm{V}_{-\max}=\underset{\hat{\mathbf{y}}_{\mathrm{k}_{1}},\hat{\mathbf{y }}_{\mathrm{k}_{2}},\ldots,\hat{\mathbf{y}}_{\mathrm{k}_{\mathrm{p}}}}{ \mathrm{argmax}}\mathrm{V}\Big{(}\hat{\mathbf{y}}_{\mathrm{k}_{1}},\hat{ \mathbf{y}}_{\mathrm{k}_{2}},\ldots,\hat{\mathbf{y}}_{\mathrm{k}_{\mathrm{p} }}\Big{)}. \tag{10}\]
## 4 Results
### RQ3: Type of WSN Coverage Area for IR 4.0
#### 4.3.3 Point Coverage  
Point coverage aims to find a target within range using nearby nodes. It is also known as target coverage. It is characterized by consuming less energy in a given zone than in the entire region of the FoI. Only a few targets are covered by individual nodes, while other targets can be detected under other sensor scopes. The primary goal is to select a specific target within the FoI to reduce energy consumption [71].
## 4 Results and Discussion
### Hybridizing ANFIS Using the LCA Technique  
Utilizing the programming language of MATLAB 2014 (MathWorks, Natick, Massachusetts, USA), the LCA algorithm was coupled with ANFIS. Here, the main role of the metaheuristic algorithms is to adjust the parameters of the MFs [36]. This process is depicted in Figure 6. The FIS is reconstructed each time using new parameters. Note that, Gaussian membership function was used for ANFIS.  
Like other optimization techniques, there are a number of LCA parameters that need to be optimized to make sure the best ensemble is being used [37]. Note that, one thousand iterations were set for each proposed network. Considering the population size, probability of success (POS), and type of formation (TOF) as the variables, three steps were carried out to achieve the most suitable architecture. Also, the RMSE was defined as the objective function, measuring the error of performance in each iteration.  
According to Figure 7a, nine different population sizes of 10, 25, 50, 75, 100, 200, 300, 400, and 500 were tested, where the POS and TOF were set 0.99 and 2, respectively. As is seen, the majority of error reduction has occurred in the first 400 iterations. Finally, the best performance (i.e., lowest RMSE = 0.012741942) was obtained for the LCA-ANFIS with population size = 200.  
Next, five POSs of 0.2, 0.4, 0.6, 0.8, and 0.99 were tested, where the population size and TOF were 200 and 2, respectively. the convergence curves are presented in Figure 7b. The behavior of the models was similar to Figure 7a, and the lowest RMSE was equal to previous value as the best structure was created by POS = 0.99.  
Lastly, the TOF is another influential factor in the LCA algorithm which reflects the basis of the new formation. In this sense, the TOF of 1 and 2 indicate that new formation is based on previous week events and the best formations, respectively. As Figure 7c shows, the ensemble performs more efficiently when the TOF = 2.  
Figure 6: A graphical view of ANFIS optimization using the LCA technique.  
Figure 7: The results of the executed sensitivity analysis based on (**a**) the population size, (**b**) probability of success (POS), and (**c**) type of formation (TOF) of the LCA algorithm.
## Materials and Methods
### Design of Simulation Experiments  
Four simulation experiments were designed to validate our new method HRePML. In the first simulation study, our goal was to explore the new method's performance on the statistical power, mean square error (MSE), and running time. We generated a set of genotype data consisting of 500 individuals and 10,000 markers, which was based on the _Arabidopsis thaliana_ dataset [36]. Eight quantitative trait nucleotides (QTNs) were simulated with heritability of 0.01, 0.03, 0.03, 0.05, 0.08, 0.01, 0.05, and 0.05, respectively. Their positions and true effects are described in Table 1. The total genetic heritability is \(h_{T}^{2}=\sigma_{G}^{2}/(\sigma_{G}^{2}+\sigma_{e}^{2})=0.01\times 2+0.03 \times 2+0.05\times 3+0.08=0.31\), and the residual variance is \(\sigma_{e}^{2}=10.0\). Then, the total genetic variance \(\sigma_{G}^{2}\) can be obtained, as well as the genetic variance of each QTN \(\sigma_{gl}^{2}(i=1,\cdots,8)\). The population mean is set to 10.0. The phenotype is generated by the model \(y=\mu+\sum\limits_{i=1}^{8}\mathbf{x}_{i}\beta_{i}+\epsilon\), where \(\epsilon\sim MVN(0,10.0\times\mathbf{I}_{n})\). The experiment is repeated 1000 times. The statistical power for each QTN is defined as the percentage of the number of detected QTN to the number of repetitions. We used the logarithm of the odds (LOD = 3.0) as the criterion for detecting QTN [32, 43, 44]. Mean squared error was calculated as \(MSE_{k}=\frac{1}{5}\sum\limits_{i=1}^{S}\left(\hat{\beta}_{ki}-\beta_{k} \right)^{2}\), where \(k=1,\cdots,8\), \(S\) was the number of detected QTN \(k\) among 1000 repetitions, \(\hat{\beta}_{ki}\) was the effect estimation of QTN \(k\) in the ith repeat, and \(\beta_{k}\) was the \(k\)th QTN's true effect. In the second simulation study, we aimed at exploring the influence of polygenic background on HRePML. The polygenic effects were introduced with multivariate normal distribution \(MVN(0,\sigma_{pg}^{2}\mathbf{K})\), where the polygenic variance \(\sigma_{pg}^{2}\) was set to 2.0, genetic relatedness matrix \(\mathbf{K}\) was calculated as \(\mathbf{G}^{\mathrm{T}}\mathbf{G}/M\), \(\mathbf{G}\) was the genotype matrix, and \(M\) was the number of markers. Other parameters were set to the same as the first simulation study. The position and true effect of each QTN are listed in Table S1. Based on the model \(y=\mu+\sum\limits_{i=1}^{8}\mathbf{x}_{i}\beta_{i}+\mathbf{u}+\epsilon\), where \(\mathbf{u}\sim MVN(0,2.0\times\mathbf{K})\), the phenotypes are simulated.
# Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities
## 2 Materials and Methods
### Features Extraction
#### 2.4.3 The Root Mean Square of the Successive Differences (\(RMSSD\))  
The correlation of heart rate variability (HRV) values can be used to diagnose AF [35]. The root mean square of the successive differences (\(RMSSD\)) is one of the calculations of HRV that can be calculated based on fluctuating ECG signals with limited time intervals. The \(RMSSD\) formula is shown in (3), where the number of \(RR\) intervals is \(N\). \(RMSSD\) value of AF is higher than that of NSR [36].  
\[RMSSD=\sqrt{\frac{1}{N-1}\left(\sum_{i-1}^{N-1}\left(\left(RR\right)_{i+1}- \left(RR\right)_{i}\right)^{2}\right)} \tag{3}\]2.4.4. The Square of Average Variation (\(SAV\))  
The difference between any data and the median is a statistical measure of the number of outliers generating asymmetric data [33]. The sum of variations of the \(RR\) interval from the median of \(RR\) intervals within the segment was averaged and magnified by squaring. The square of average variation (\(SAV\)), as shown in (4), is presented as a new measurement technique in this research to diagnose AF.  
\[SAV=\left(\frac{\sum_{i-1}^{N-1}\bigl{(}\left(RR\right)_{i}-median\bigl{(}RR_ {seg}\bigr{)}\bigr{)}}{N-1}\right)^{2} \tag{4}\]
# Design of a Laparoscopic Robot System Based on Spherical Magnetic Field
## 4. **Mathematic Modeling**
### Statics Analysis  
The force acting on the rotor can be divided into four parts: the magnetic force \(F_{m}\) from the stator, the gravity \(mg\), the support force \(F_{n}\), as well as the friction force \(f\) from the abdominal wall.  
When the position and gesture of the rotor change slowly, every moment of the whole moving process can be considered as a quasi-stationary state. According to [PERSON]'s law of motion, we have:  
\[F_{m}-F_{n}-f-mg=0 \tag{38}\]  
As mentioned in the control principle part, the position of the rotor is controlled by the orientation of the stator. The posture control of the rotor consists of two DOFs, the rotation along the precession angle \(\mathbf{\alpha}\), and the deflection along the nutation angle \(\beta\).  
To simplify the control model and reduce computation, the rotation of the rotor is realized by the self-rotation of the stator. The deflection control of the rotor is realized by changing the electric current of electromagnets.  
To achieve gravity compensation of the rotor we have:  
\[\left|F_{z}\right|\geq\left|mg\right| \tag{39}\]  
where \(F_{z}\) is the attractive force from the stator to the rotor along the vertical axis \(Z\).  
To achieve the deflection control of the rotor, the input electric current of the stator need to be change to make the status of the controlling magnetic field corresponds to the target position of the rotor. The related status of the controlling magnetic field can be obtained by combining Equation (32) and the following equation set:  
\[\left\{\begin{array}{c}T_{x}=mgl\\ T_{y}=0\end{array}\right. \tag{40}\]  
In Equation (40), \(T_{x}\) is the magnetic torque upon the rotor along the direction of axis \(x\), and \(T_{y}\) is the magnetic torque upon the rotor along the direction of axis \(y\). \(l\) is the force arm of gravity.  
Combining Equations (36), (37), (39), and (40), the relationship between the combination of input electric current \(\overrightarrow{I}(\mathbf{I_{1}I_{2}I_{3}I_{4}I_{5}})\) and the deflection angle \(\mathbf{\alpha}(\overrightarrow{I})\) can be obtained.  
In the orientation control of the rotor, because there are five input electric currents, except a constraint condition provided by the gravity compensation of the rotor, there are multiple solutions. Therefore, in the actual orientation control of the rotor, some input electric current need to be selected previously.
## 1 Introduction
We first proposed a centralized scheduling algorithm based on cross-entropy (CE) and a corresponding power allocation algorithm. Since centralized algorithms involve numerous calculations, the time delay could be intolerable in a large-scale network. An alternative method to decrease the time delay is to conduct resource allocation in a decentralized way, where calculations are distributed to small cells. For this reason, we also propose modified algorithms that can be used in a decentralized mode. The rest of this paper is organized as follows. In Section 2, we present the considered system model. Then, the mathematical formulation of the discussed problem is described in Section 3. Section 4 proposes a centralized strategy of resource allocation, which includes a CE-based scheduling  
Figure 1: Illustration of a HetNet. algorithm and a power allocation algorithm. Section 5 modifies the proposed algorithms, so that they can be utilized in a decentralized system. Simulation results and relevant analysis are shown in Section 6. At last, we conclude our work in Section 7.
# Faster MDNet for Visual Object Tracking
## 3 Method
### Channel Attention Module  
#### 3.2.1 Local Cross-Channel Interactions  
The input feature map is \(\mathcal{F}\in\mathbb{R}^{W\times H\times C}\), where \(W\), \(H\), and \(C\) represent the width, height, and channel, respectively. The aggregated feature \(\mathbf{y}\in\mathbb{R}^{C}\) can be computed as:  
\[\mathbf{y}=g(\mathcal{F}) \tag{1}\]  
where \(g(\mathcal{F})=\frac{1}{WH}\sum_{i=1,j=1}^{W,H}\mathcal{F}_{i,j}\) is channel-wise global average pooling.  
In the case of \(\mathbf{y}\in\mathbb{R}^{C}\) without dimensionality reduction, channel attention can be calculated as follows:  
\[\omega=\sigma(\mathbf{Wy}) \tag{2}\]  
Figure 1: Network structure of the proposed method. It consists of feature extraction, a channel attention module, domain adaptation components, spatial pyramid pooling, and target classification.  
where \(\sigma\) is a sigmoid function and \(\mathbf{W}\) is a band matrix that has \(C\times k\) parameters, i.e.,  
\[\left[\begin{array}{cccccccc}w^{1,1}&\cdots&w^{1,k}&0&0&\cdots&\cdots&0\\ 0&w^{2,2}&\cdots&w^{2,k+1}&0&\cdots&\cdots&0\\ \vdots&\vdots&\vdots&\vdots&\ddots&\vdots&\vdots&\vdots\\ 0&\cdots&0&0&\cdots&w^{C,C-k+1}&\cdots&w^{C,C}\end{array}\right] \tag{3}\]  
For the previously mentioned matrix, the weight of \(y_{i}\) can be computed by the interactions of \(y_{i}\) and \(k\) channels adjacent to \(y_{i}\) as:  
\[\omega_{i}=\sigma\left(\sum_{j=1}^{k}w^{j}y_{i}^{j}\right),y_{i}^{j}\in\Omega_ {i}^{k} \tag{4}\]  
We implement this strategy by a fast 1D convolution whose kernel size is \(k\):  
\[\omega=\sigma(\text{Conv1D}_{k}(\mathbf{y})) \tag{5}\]  
The output feature map \(Y\) of our channel attention module can be computed as:  
\[Y=\omega\mathcal{F} \tag{6}\]  
This channel attention module of our work, which only involves \(k\) parameters, maintains tracking efficiency.