esa-sceva/satcom-chunk-collection huggingface dataset:
# Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms
## 2 Dataset and Methods
### Machine Learning Algorithms
#### 2.2.1 The Random Forest Regressor Algorithm  
The random forest regressor algorithm, first created by [PERSON] [50] and then expanded upon by [PERSON] et al. [51], is an ensemble learning technique that generates several decision trees during the training process and calculates the average prediction of these trees for regression problems. Each tree is built with randomly selected samples from the training set (e.g., bootstrap sample), and at each node, the best split is selected from a random subset of features, introducing useful randomness into the model [50]. [PERSON] et al. [51] extended this concept to Extremely Randomized Trees, where the split thresholds for each feature are randomly selected, adding an additional level of randomness. This approach provides a model that is effectively resistant to overfitting, an advantage derived from random feature selection and averaging the predictions from several trees. Although they are capable of modeling complex relationships with high accuracy, random forests can be computationally demanding, especially when composed of many large and deep trees. This method provides improved prediction accuracy and stability in regression tasks due to the combination of decision trees' simplicity and ensembles' power.
# Novel Faulted-Section Location Method for Active Distribution Networks of New-Type Power Systems
## 3 Improved Matrix Algorithm
### Improved Matrix Algorithm Analysis  
The improved matrix algorithm cannot accurately deal with multiple faults with T-connected sections. Specifically, it includes the following two cases:  
1. As shown in Figure 4, multiple faults with the T-connected section occur in (i), and a single fault occurs in (ii). In the two cases, the equipment status information of the \(S_{1}\), \(S_{2}\), and \(S_{3}\) nodes is the same, which is 1, \(-1\), and 1. In matrix \(L\), the results of these sections are all 1. According to the fault criterion, the T-connected section of \(l_{i}=1\) is not faulty when other fault sections are present. Therefore, the algorithm cannot accurately locate whether the T-connection section of \(l_{i}=1\) is faulty in the case of multiple faults.
2. As shown in Figure 5, multiple faults with the T-connected section occur in (i), and a single fault occurs in (ii). In the two cases, the equipment status information of the \(S_{1}\), \(S_{2}\), and \(S_{3}\) nodes is the same, which is 1, 1, and 0. In matrix \(L\), the results of these sections are all 0. According to the fault criterion, the T-connected section of \(l_{i}=0\) is not faulty. Therefore, the algorithm cannot accurately locate whether the T-connected section of \(l_{i}=0\) is faulty in the case of multiple faults.  
Figure 3: The flow chart of the verification.  
In summary, the improved matrix algorithm can accurately locate single and multiple faults in non-T-connected sections without error fault information. However, it cannot accurately judge whether the T-connected sections of \(l_{i}=1\) and \(l_{i}=0\) have failed under multiple faults.
## 3 Results and Discussion
### Contact Angle and Surface Energy Characterization  
The wettability of lignocellulosic hemp fiber in liquid matrices plays a significant role in the fabrication of FRP composites, their overall properties and engineering applications. The fiber-matrix adhesion, interfacial bond and strength depend of the wetting behavior. Wettability, hydrophilicity and hydrophobicity are often analysed by the measure of contact angle formed between the materials (HF/PCL biocomposites) and liquids (glycerol and distilled water) used as well as surface energy levels with these liquids. Therefore, the maximum average contact angles for both glycerol and distilled water taken at 1.05 s for all the 5 samples are presented in Table 4 and the total surface energy results for the 2 liquids are illustrated in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Specimens**} & \multicolumn{2}{c}{**Contact Angle (\({}^{\circ}\))**} \\ \cline{2-3}  & **Glycerol** & **Distilled Water** \\ \hline AR\_00 \({}^{\star}\) & 86.70 & 94.12 \\ AR\_19 & 93.07 & 101.95 \\ AR\_26 & 85.21 & 95.77 \\ AR\_30 & 79.19 & 86.87 \\ AR\_38 & 87.85 & 86.65 \\ \hline \hline \end{tabular} \({}^{\star}\) AR\_00 denotes neat sample, without hemp fiber.  
\end{table}
Table 4: Summary of the contact angle and surface energy results for the 5 samples.  
Figure 8: The scanning electron microscope (SEM) micrographs of the (**a**) hemp fiber and PCL matrix and (**b**) morphological structure of the best fiber element (hemp), showing hollow (lumen).  
It is observed that almost all the contact angles from glycerol are less than that of distilled water, and significantly less than 90\({}^{\circ}\). This indicates that glycerol exhibited higher relative wettability, hydrophilicity and adhesion properties with the samples. Consequently, lower total surface energy of 64 mN/m on the surfaces of the samples with glycerol was obtained when compared with 72 mN/m using distilled water. The discrepancy in these values has been similarly reported with water and diiodomethane recorded total surface energies of 72.8 and 50.8 mN/m, respectively [35]. Also, the samples experimentally demonstrated their higher hydrophobic nature with water. It shows that glycerol spread over larger area with smaller contact angles. This phenomenon further shows lower hydrophobicity of most of these samples using glycerol, when compared to distilled water.
###### Abstract  
Cervical laminoplasty is a useful for treatment for cervical myelopathy. However, this procedure has limitations for kyphotic cervical alignments. We used the finite element (FE) analysis and investigated the biomechanical changes in intact and laminoplasty models with lordosis, straight, and kyphosis cervical alignments. A three-dimensional FE model of the cervical spine (C2-C7) with ligaments was created from computer tomography. The model was modified with the following cobb angles (a) intact-lordotic model (intact-L; C2-C7 angle: \(-10^{\circ}\)), (b) intact-straight model (intact-S; C2-C7 angle: \(0^{\circ}\)), and (c) intact-kyphotic model (intact-K; C2-C7 angle: \(10^{\circ}\)). The C3-C6 lamino-plasty was conducted on the three intact models, represented by the laminoplasty-lordosis model (LM-L), laminoplasty-straight model (LM-S), and laminoplasty-kyphosis model (LM-K), respectively. Pure moment with compressive follower load of 100 N to represent the weight of the head/cranium and cervical muscle stabilization was applied to these models and the range of motion (ROM), annular stress, nucleus stress and facet forces were analyzed. ROM of intact-K and LM-K increased when compared to the other models. The LM-K had the highest mobility with 324% increase in ROM observed under extension, compared to LM-L. In addition, the annular stresses and nucleus stresses in intact-K and LM-K were higher compared to the other models. The maximum increase in annular stresses was about 309% in LM-K compared to the LM-L, observed at the C3-C4 segment. However, the facet contact forces were lower in the intact-K and LM-K, compared to the other models. Cases with cervical kyphosis alignment are at a disadvantage compared to cases with lordosis or straight alignment and should be treated with caution.  
finite element method; alignment; stress; laminoplasty; kyphosis Article
## 1 Introduction
The objective function method obtains results by creating an objective function and using an optimization algorithm; genetic algorithm [32] is one of the popular choices due to its parallel computation and global search capabilities. However, its main drawback is the potential convergence to local optima during numerical computation [33]. In contrast, probabilistic direct search algorithms such as Differential Evolution (DE) [34] and Particle Swarm Optimization (PSO) [35; 36; 37] are more suitable for complex design problems. To obtain optimal mechanism parameters, it is essential to employ scientifically sound methods. The response surface methodology, proposed by [PERSON] and [PERSON] [38], combines statistical and mathematical techniques to analyze the relationship between explanatory variables and one or more responses, or to maximize the response by adjusting individual variables. In addition, this approach requires relatively few experimental runs, thus improving optimization efficiency. Response surface methodology has higher efficiency and fitting accuracy compared to genetic and neural network algorithms [39]. In summary, the existing methods still have some difficulties in achieving multi-parameter multi-objective optimization, and new methods need to be further explored and proposed to better serve the engineering application needs of complex parallel robots. In order to improve the stiffness of the parallel mechanism and increase the workspace, this paper addresses the optimization design of a novel 6-DOF redundant parallel posture adjustment mechanism with three links and four joints for solar wing docking proposed in our early work [40]. To reduce the number of calculations and improve the efficiency of the analysis, a combination of the one-factor method and the response surface method is used. Section 2 carries out the mechanism description and establishes the mechanism coordinate system. Section 3 analyzes the sensitivity of various link parameters on the posture adjustment range of the parallel mechanism based on volume. The single-factor method was utilized to analyze the effect of parameters such as moving and fixed platform parameters, the drive trajectory of kinematic pairs, and angles between kinematic pairs on the posture adjustment range. Section 4 establishes the performance index. In Section 5, the parameter optimization design was carried out by response surface methodology. Firstly, the Plackett-Burman test was used to identify parameters with significant effects on performance metrics. Secondly, the steepest climb test was used to determine the optimal range of significant parameters. Then RSM was used to establish regression models for the parameters and to identify the optimal parameter combination for the mechanism. Finally, the workspaces before and after optimization are compared, which provides a basis for the practical application of the redundant parallel mechanism.
# Joint Cell Association and User Scheduling in Carrier Aggregated Heterogeneous Networks
## 4 Numerical Analysis  
In this section, simulation results are demonstrated to evaluate the performances of the proposed multi-flow based joint cell association schemes on the traffic load balancing and the network utility problems.
###### Abstract
However, due to adiabatic heating and the temperature increase that occurs during HP processing [8; 9], the current industrial practice, mainly in the meat industry, includes treatment in which the pressure-transmitting fluid (i.e., water) lays within the range of 4-8 \({}^{\circ}\)C to prevent the potential quality deterioration caused by a temperature increase in the treated products. Moreover, several studies have reported that the inactivation of microorganisms is promoted under low temperatures due to changes in the physical state of the cell membrane [28]. However, most of the research conducted on the effect of HP treatment on the quality deterioration of fish products concerns treatments at high pressures combined with an ambient temperature, and limited work has been performed in low [17; 29] or ultra-low [30] temperatures. The European sea bass (_Dicentarchus labrax_) is one of the most important cultured species in the Mediterranean, in addition to the gilthead sea bream (_Sparus aurata_) [31]. European sea bass are mainly consumed as fresh fish, with little activity observed in processing the fish, i.e., packing, marinating, or smoking, at least in the most important EU producers (e.g., Greece, Spain, Italy, and Croatia). European sea bass production in the EU has been rising in past years, posting a record year in 2019 with 86,149 t and a total value of EUR 491 million [32]. Sea bass is sold as both a whole fish and as fish fillets, with the latter being traded in amounts of almost 10% of the total annual production. A European sea bass fillet is a ready-to-cook product of significant commercial interest, and its shelf life plays a crucial role in the potential to distribute it to destinations far away from the production/filleting plant. Thus, by combining mild to medium processing conditions (i.e., pressure and temperature), this work aims to systematically investigate the effect of HP processing European sea bass fillets on the reduction in the initial microbial load and the estimation of the shelf life during chilled storage (0 \(\pm\) 0.2 \({}^{\circ}\)C), which is the common practice for the transportation and storage of fresh fish.
## 2 Materials and Methods
### Meteorological Data Monitoring  
Sensors were not applicable. The climatic data were obtained from the LARI weather station, installed 3 km from the site (Figure 5).  
Figure 4: Yellow sticky traps (2 traps/ha) with ammonium bicarbonate in small bag (10 g).  
Figure 3: Output of the OliveFlyNet application interface showing details about information stored in the geodatabase system.  
The Hasbaya AWS of DIAM at LARI uses the Evapotranspirationiration iMETOS 3.3 by Pessl Instruments ([[https://metos.at/](https://metos.at/)]([https://metos.at/](https://metos.at/)) accessed on 25 May 2022) and is located at latitude 33.4009732\({}^{\circ}\), longitude 35.6/772606\({}^{\circ}\), and altitude 873 m (1 km from the site). It started recording data in mid-December 2013. The iMETOS 3.3 is powered by a rechargeable battery and a solar panel. It is versatile, with the possibility to configure and connect many different sensors--over 600 sensors can be connected through the intelligent sensor bus system. The data logger has a built-in UMTS/CDMA modern for direct communication (GPRS) with the Field Climate Web Platform. The system may store up to 8 MB of logged data. The data are regularly uploaded to the Field Climate Web Platform, with the possibility of a 15 min recording frequency. The reference evapotranspiration is a daily calculated parameter. The Hasbaya AWS is equipped with sensors to measure the following parameters: precipitation (mm), air temperature (\({}^{\circ}\)C) (dew point temperature is then estimated), relative humidity (%), wind speed (m/s), solar radiation (W/m2), wind direction (degrees), atmospheric pressure (kPa), reference evapotranspiration (ETO mm by day) (calculated through FAO Perman-Montieth equation). Thus, meteorological data were recorded during the whole experiment, at an interval of 15 min (24/7). The major parameters recorded were the temperature (average, maximum, minimum), the relative humidity (average, maximum, minimum), the wind speed, and the precipitation, which were recorded from the beginning of the experiment, on 22 July, until the end of the experiment, on 10 November.
## 3 Results and Discussion
### Artificial Neural Network Prediction Analysis
#### 3.2.4 ANN Model Training and Simulation
The values of average error for head coefficient and relative theoretical efficiency are found to be 0.0008 and 0.0002%, respectively, and are well within the range of acceptable limits. Figure 26: Regression analysis with training data, test data and all data sets. The deviation between actual and predicted values is due to the generalized fitness of the network during the training process, i.e., during the learning process, if the noisy or fluctuating data are picked up by the model, then they will affect the developed model negatively. This results in the overfitting observed for some of the predicted values. Hence, it is concluded that ANN can be employed to predict the output parameters of a centrifugal fan equipped with an optimized hub using the limited number of CFD simulation results. Through this approach, a huge amount of manual effort, as well as a significant amount of time, can be saved. Figure 28: Comparison between the relative theoretical efficiency values obtained through CFD analyses and ANN. Figure 27: Comparison between the head coefficient values obtained through CFD analyses and ANN.
## 3 Results
Concentrations of 20S proteasome in the serum and urine of patients with a primary tumour were higher than for patients with a recurrent tumour (\(p=0.0258\) for serum, \(p=0.0384\) for urine). Values of \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Proteasome 20S**} & \multicolumn{3}{c}{**UCH-L1**} \\  & \multicolumn{3}{c}{**Concentration**} & \multicolumn{3}{c}{**Concentration**} \\  & \multicolumn{3}{c}{**[\(\mu\)g/mL]**} & \multicolumn{3}{c}{**[ng/mL]**} \\ \hline  & **Range** & **Median** & \(p\)**-Value** & **Range** & **Median** & \(p\)**-Value** \\ \hline  & \multicolumn{3}{c}{Primary/recurrent} \\ \hline Primary (35) & 7.78–36.42 & 19.81 & 0.0258 & 1.47–22.61 & 6.3 & 0.0621 \\ Recurrent (47) & 3.75–32.91 & 15.92 & 1.54–11.51 & 5.5 & (NS) \\ \hline  & \multicolumn{3}{c}{Multiplicity} \\ \hline Single (46) & 3.75–36.42 & 18.08 & 0.6458 & 1.54–11.32 & 5.36 \\ Multiply (36) & 5.64–32.91 & 18.94 & (NS) & 1.47–22.61 & 6.7 & 0.0329 \\ \hline  & \multicolumn{3}{c}{Stage} \\ \hline Non-muscle & \multicolumn{3}{c}{} \\ invasive (51) & 3.75–32.91 & 16.27 & 0.0264 & 1.47–11.51 & 4.99 & 0.0001 \\ Muscle invasive (31) (T2 + T3 = T4) & 5.64–36.42 & 23.54 & 2.74–22.61 & 6.95 & \\ \hline  & \multicolumn{3}{c}{Grade} \\ \hline Low-grade (34) & 3.75–28.99 & 15.06 & 0.0036 & 1.47–9.85 & 4.65 & 0.0001 \\ High-grade (48) & 5.64–36.42 & 20.07 & 2.74–22.61 & 6.62 & 0.0001 \\ \hline  & \multicolumn{3}{c}{Size (mm)} \\ \hline \(<\)30 (49) & 6.19–30.56 & 16.62 & 0.0668 & 1.54–22.61 & 5.6 & 0.1761 \\ \(>\)30 (33) & 3.75–36.42 & 19.68 & (NS) & 1.47–12.34 & 6.41 & (NS) \\ \hline \hline \end{tabular} NS—no statistically significant values.  
\end{table} 
Table 3: Diagnostic characteristics of serum 20S proteasome and UCH-L1 concentration ratio compared with various clinicopathological parameters. UCH-L1 concentration were significantly elevated in the serum of patients with multiple tumours, compared with patients with a single tumour (\(p\) = 0.0329), and in the urine of patients with tumour size greater than 30 mm (\(p\) = 0.0184). There were no statistically significant correlations between serum and urinary UCH-L1 concentrations and other clinicopathological parameters.
# Some Key Aspects in the Mechanics of Stress Transfer Between SRG and Masony
## 3 Discussion of Results
### Load Responses
#### 3.2.2 Influence of the Type of Matrix  
In this Section, load responses of masonry blocks strengthened with cement-based matrix SRG are shown and compared with those of masonry blocks strengthened with lime-based matrix SRG (Figure 7). The matrix width for all the specimens compared in this Section was equal to 50 mm, i.e., it was the same as the width of the fiber sheet. The light green curves represent the masonry blocks strengthened with lime-based matrix SRG (herein called together as DS_315_50_LD_LM_ALL).  
For masonry blocks strengthened with cement-based matrix SRG, the initial branch was linear, followed by a non-linear branch with some drops of the load. The first substantial drop of the load corresponded to the onset of the interfacial crack. When the interfacial crack propagated, the load remained almost constant while the global slip increased. The load-carrying capacity was quite consistent among specimens strengthened with cement-based matrix SRG, and the average values of the peak load and load-carrying capacity were \(\overline{P^{*}}\) = 9.1 kN and \(\overline{P_{crit}}\) = 7.7 kN, respectively.  
Compared with specimens with the same matrix width but strengthened with lime-based matrix SRG, \(\overline{P_{crit}}\) of cement-based matrix specimens increased by 33% (7.7 kN versus 5.8 kN), and the average peak load \(\overline{P^{*}}\) by 20% (9.1 kN versus 7.6 kN). This result was expected as the cement-based matrix has higher mechanical properties (including fracture energy). Indeed, as the matrix between fibers fractures in the interlaminar failure, the load-carrying capacity of the SRG-masonry joint depends on the properties of the matrix itself.
## 3 Software Architecture and Measurement Algorithm
### Measurement Algorithm  
The goal of the algorithm is to measure the time-frequency characteristics of the interference received by the LUME-1 satellite. The window of first-order stationarity of the interference is the shortest time window long enough to include a sufficiently varied selection of samples to form a local mean close to the mean envelope in that region. For the stationarity window length, the LME has a low coefficient of variation, and it indicates the length over which an interleaver has to spread information to obtain average interference behaviour.  
The In-Phase Quadrature (IQ) samples \(x[l]\) are acquired from the ADC at an sample frequency \(f_{s}\). A Discrete Fourier Transform (DFT) with \(M\) frequencies is calculated every \(M\) samples of \(x[l]\), forming the time frequency representation \(X_{k}[n]\) of the signal. The frequency bin is \(k\in\{0,1,\ldots,M-1\}\), and the time index \(n\) denotes that the DFT is applied to \(\{x[nM],x[nM+1],\ldots,x[(n+1)M-1]\}\). To estimate the time variability, the magnitude of the DFT, \(z_{k}[n]=\left|X_{k}[n]\right|\), is used [26].  
The LME statistics are estimated for each frequency bin. For simplicity, \(z_{k}[n]\) is defined as the time series of the values of the magnitude of the DFT for a particular frequency bin \(k\) and \(n\in\{0,1,\ldots,T-1\}\), with \(T\) being the total number of DFTs in a single measurement.  
The first moment of a distribution is the mean \(m_{1}\). In the discrete domain, the mean of series \(z_{k}[n]\) with \(T\) samples is calculated as the time average  
\[m_{1k}=\frac{1}{T}\sum_{n=0}^{T-1}z_{k}[n]. \tag{1}\]  
The second moment (\(m_{2}\)) can be seen as the average power of the signal. This can be calculated as  
\[m_{2k}=\frac{1}{T}\sum_{n=0}^{T-1}z_{k}^{2}[n]. \tag{2}\]  
In order to measure the stationarity of the signals in the traditional way, it would be necessary to acquire all IQ samples and process them on the ground. However, the downlink data throughput is limited, as described in Section 2, so an on-board processing method was developed to reduce the data to be downloaded.  
A simple method to measure stationarity that generates little data is to compare local statistics using different time windows. In this article, we analyse the first-order stationarity by measuring the variability of the local mean of the envelope (LME) for different time windows. Short-time windows generally create highly variable local means for stochastic signals. For longer windows, the variability decreases and approaches the mean of the region. If the signal is non-stationary in the region where the data is collected, then no window size smaller than the entire data set gives a LME with small variability. The  
The values of \(m_{2k}^{T_{i}}\) for different \(T_{i}\) depend on the specific signal analysed (see Appendix A). The algorithm implemented provides: (1) an overall mean \(m_{1k}\), (2) overall average power \(m_{2k}\), and (3) measurements of variability of the local mean for different time windows \(m_{2k}^{T_{i}}\).