esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Profile of the Volumetric Water Content in a Slope  
During the tests, the hydrological response was monitored by means of VWC (volumetric water content) sensors located at different locations within the slope and crossing the entire soil thickness, allowing the retrieval of volumetric water content profiles at different depths. The VWC sensors in section I in different cases were shown to explain how the capillary barrier effects influence the water infiltration and distribution during the rainfall and drying process, as is shown in Figure 8 (the sensor locations are shown in Figure 4b). Throughout the experiment, the VWC of the toe of slopes increased slowly with time toward a saturated value in response to the saturation process, until failures occurred. It should be noted that the capillary barrier clearly controlled the rate of changes of the VWC in a multi-layer slope, which determined the failure time of slope.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Rainfall Intensity \(I\)** & **Tilt Angle \(\alpha\) (\({}^{\circ}\))** & **Failure Modes** & **Initial Failure Time \(t\) (h)** \\ \hline Case I & 75 mm/h & 0 & Piping slightly & 1.18 h \\ Case II & 75 mm/h & 7 & Piping & 0.84 h \\ Case III & 75 mm/h & 15 & Piping & 0.78 h \\ Case IV & 75 mm/h & 21 & Piping & 0.62 h \\ Case V & 75 mm/h & 0 & Surface slides & 0.95 h \\ Case VI & 75 mm/h & 7 & Surface slides & 0.89 h \\ Case VII & 75 mm/h & 15 & Surface slides & 0.85 h \\ Case VIII & 75 mm/h & 21 & Surface slides & 0.76 h \\ \hline \hline \end{tabular}
\end{table}
Table 4: Failure conditions of physical model experiments.  
Figure 8: _Cont._  
In case I and case V (see Figure 8a,b), the advancement of the wetting front is evident from the time history of the VWC sensors. It shows that the rise of the VWC was rapid, and then became gradual, as the toe of the slope approached a fully saturated condition. When the VWC at point L reached around 0.4, failure occurred at the toe of the slope. In addition, compared with the VWC at point L in the flat group, point L reached the saturated condition later in the multi-layer model, since the capillary barrier prevented the water from infiltrating into the bottom. This made the slope more stable and caused a delay in the failure time, which was 1.2 and 0.92 h in case I and case III, respectively. This capillary barrier effect can also be explained by the VWC histories obtained during the experiments. For example, it took around 26 min for the wetting front to pass through the interface from point B to point C [11].  
Figure 8c,d show that when rainfall was applied, the VWC increased quickly above the interface (point B), and when rainfall was stopped, the soil above the coarse layer (point B) remained wetter than the same location in a single-layer slope. Additionally, for the VWC at the toe of the slope (point L) in the inclined group, the failure occurred earlier (0.78 h), while it was 0.84 h in a single-layer slope, which was contrary to the flat group experimental results.
## 3 Results and Discussion
### Heat Amplification Cycle \"Heat from Cold\" (HeCol)
The boundary adsorption potentials \(\Delta F_{1}\) and \(\Delta F_{2}\) (Table 4, Figure 8) are determined as  
\[\Delta F_{2} =-\text{RT}_{\text{H}}\text{ln}[P(T_{\text{M}})/P(T_{\text{H}})], \tag{3}\] \[\Delta F_{1} =-\text{RT}_{\text{M}}\text{ln}[P(T_{\text{L}})/P(T_{\text{M}})]. \tag{4}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(T_{\text{M}}\)\({}^{\circ}\text{C}\) & \(T_{\text{H}}\)\({}^{\circ}\text{C}\) & \(\Delta F_{2}\), kJ/mol & \(\Delta F_{1}\), kJ/mol & **adsorbent** & \(\Delta w\), g/g & **Period** \\ \hline \multicolumn{8}{c}{Yakutsk} \\ \hline \multirow{4}{*}{3} & 30 & 3.8 & 5.0–7.2 & LiCl/MWCNT & 0.97 & Nov–Feb \\ \cline{2-7}  & 35 & 4.5 & 5.0–7.2 & LiCl/SiO\({}_{2}\) & 0.38 & Nov–Feb \\ \cline{2-7}  & 40 & 5.1 & 6.4–7.2 & MaxSorbIII & 0.14 & Dec–Feb \\ \hline \multirow{4}{*}{10} & 30 & 2.7 & 4.8–8.4 & LiCl/MWCNT & 1.37 & Nov–March \\ \cline{2-7}  & 40 & 4.0 & 4.8–8.4 & LiCl/MWCNT, & 0.67 & Nov–March \\ \cline{2-7}  & 50 & 5.4 & 7.4–8.4 & MaxSorbIII & 0.19 & Dec–Feb \\ \hline \multicolumn{8}{c}{Oimyakon} \\ \hline \multirow{4}{*}{3} & 30 & 3.8 & 6.7–9.0 & LiCl/MWCNT & 0.97 & Nov–March \\ \cline{2-7}  & 35 & 4.5 & 6.7–9.0 & LiCl/SiO\({}_{2}\) & 0.38 & Nov–March \\ \cline{2-7}  & 40 & 5.1 & 8.4–9.0 & MaxSorbIII & 0.14 & Dec–Feb \\ \hline \multirow{4}{*}{10} & 30 & 2.7 & 7.9–10.2 & LiCl/MWCNT & 1.37 & Nov–March \\ \cline{2-7}  & 40 & 4.0 & 7.9–10.2 & LiCl/MWCNT, & 0.67 & Nov–March \\ \cline{2-7}  & 50 & 5.4 & 9.6–10.2 & LiBr/SiO\({}_{2}\) & 0.26 & Dec–Feb \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The boundary conditions, the promising adsorbents, and the mass of exchanged methanol for the HeCol cycle tested. For cycles with the useful heat temperature \(T_{\rm H}\) = 30 \({}^{\circ}\)C, the composite sorbent LiCl/MWCNT appears to be the most promising. LiCl/SiO\({}_{2}\) and MaxSorbIII allow the useful heat at \(T_{\rm H}\) = 35 \({}^{\circ}\)C to be produced. While the LiBr/SiO\({}_{2}\) composite with the highest affinity to methanol vapor can be employed for HeCol cycles with the highest useful heat temperature \(T_{\rm H}\) = 50 \({}^{\circ}\)C (Table 4).
## 4 Experiments and Results
### Experimental Setup
#### 4.1.4 Metrics  
The average success rate of the robot in locating a speaker uttering the keywords, the average distance to the target at the end of tracking and the word error rate (WER) of speech recognition at the end of tracking serve as the evaluation metrics for the performance of the system. The first two correspond to the first challenge in the cocktail party problem, locating the sound of interest, while the latter corresponds to the second challenge, collecting a clear audio signal of the target from a multiple sound sources signal.
# Differential Evolution: A Survey and Analysis
## 3 Parameter Control
### Deterministic Parameter Control  
The parameters are altered using a deterministic rule regardless of the feedback from the evolutionary search, with jitter and dither being two operators that are used in this technique. Dither scales the distance of the vector differentials as the same factor, \(F_{i}\), is applied to all the elementsof a subtracted vector. Jitter multiplies each vector element of the subtracted vector by a different scale factor, \(F_{j}\). The rotation creates jitter using an essentially different procedure than the classic DE's constant mutation with \(F\). However, this approach shows robustness for non-deceiving objective functions [3]. Nonetheless, applied fixed values for each iteration, and \(F\) was created for each individual within the range (0.4, 1), whereas the interval (0.5, 0.7) was selected for \(Cr\)[54; 55].  
Another approach is the composite DE (CoDE) algorithm proposed by [PERSON] et al. In CoDE, a trial vector is selected from a set of groups produced by utilizing diverse DE strategies [56]. The main objective is to arbitrarily merge many trial vector strategies with different parameters at each iteration to construct new trial vectors. These combinations help to successfully solve many problems. [PERSON] et al. used a group of trial vector strategies and a group of control parameter (almost three) to create strategy and parameter candidate pools. The selected strategies are DE/rand/1/bin, DE/rand/2/bin and DE/current-to- rand/1, and the three pair common choices for the control parameter settings were (\(F=1.0\), \(Cr=0.1\)), (\(F=1.0\); \(Cr=0.9\)), and (\(F=0.8\); \(Cr=0.2\)). In each generation, three different strategies are applied, which randomly pick any of the control parameter values. Then, the trial vector is designated the candidate with the better value of fitness. The parameters are chosen based on whether they are frequently implemented with many DEs, and their performances are evaluated. The three pairs of parameter settings that provide diverse effects produce new improved candidates. Furthermore, the different values of the control parameters maintain different levels of search performance.
# Multifrequency Impedance Method Based on Neural Network for Root Canal Length Measurement
## 2 Material and Methods  
The dual-impedance ratio method selects the impedance ratio of one high frequency and one low frequency as the criteria for locating the apical foramen; however, the level of accuracy is not ideal and the method may be greatly affected by the measurement conditions. The multifrequency impedance method proposed in this paper requires multiple impedance ratios of multiple frequency combinations and utilizes the relationships between them to increase the reliability of the results. A large number of impedance ratios of different frequency combinations are essential for high accuracy of measurement. With the data augmentation of impedance ratios, a nonlinear regression model with the distance between the file and the apical foramen as the prediction result is constructed based on deep learning methods. Neural networks are the most widely used type of deep learning model, which exhibit excellent performance in solving complex nonlinear regression problems [16; 17]. Considering the delicate electrical properties of organisms, deep learning plays a major role in detecting and processing bio-electric signals [18; 19]. In a concrete situation, the structure of the network can be targeted, designed, and optimized, making the model more flexible and efficient. It is crucial to select appropriate features to improve the multifrequency impedance method for root canal length measurement.
## 3 Experiments and Results
### Model Parameter Setting  
In the experiment of TFCF time-frequency image classification of rolling bearing vibration signal, the setting of hyperparameters required by the training network model is as follows. The model is trained through stochastic gradient descent in small batches, and the sample size of small batches is set to 8. Adam algorithm is used to optimize the gradient value of each weight update, and the initial learning rate is set to 0.001. At the same time, L2 regularization is introduced to impose penalty constraints on weight parameters, and the penalty factor is set to 0.0001. The equal interval attenuation strategy is adopted to adjust the learning rate in the training process. The adjustment interval is set as five epochs, the adjustment multiplier gamma is set as 0.5, and other parameters can be seen in Table 2. The kernel size of the two convolution layers in the middle of RCA, RTA, and RFA modules is \(1\times 1\). The corresponding parameter settings in Table 2 refer to the number of output channels of the two convolution layers. In addition, all models are trained and tested using PyTorch deep learning framework and NVIDIA GeForce GTX 1650 GPU.
# Minimum Distance Optimization with Chord Edge Growth for High Girth Non-Binary LDPC Codes
## 1 Introduction
The selection criterion is to ensure that all of the cycles in the current subgraph satisfy the full rank condition (FRC) [15]. The proposed method has the advantage of low complexity. For one thing, the number of cycles to be subjected to the FRC check in the subgraph is effectively reduced, benefiting from the application of the progressive chord edge growth. For another thing, the number of candidate configurations of non-zero entries does not increase exponentially with the increase of the chord edges, due to the FRC constraint. Through such a concise scheme for allocating non-zero elements, a large number of short cycles in the graph that is associated with the code can be cancelled, thereby obtaining an NB-LDPC code with a low error floor. Based on the proposed method, a flexible modification is further developed to construct NB-LDPC codes with an efficient encoding structure. For the efficient encoding structure, good error correction performance can still be provided while using the proposed construction method. Therefore, the contribution of this paper is to develop a construction method for the NB-LDPC codes with a column weight of 2, which can obtain comparable performance and it has a lower complexity than the scheme proposed by [PERSON] et al. [15]. The rest of this paper is organized, as follows. Section 2 gives a brief review of the Hamiltonian representation of NB-LDPC codes and the FRC of cycles and analyzes the method for improving the performance in the error floor region. In Section 3, a method for progressive chord edge growth that was developed for optimizing the minimum distance is presented, and the proposed method is slightly modified to construct easy-to-encode NB-LDPC codes. In Section 4, the complexity analysis of the proposed method and the simulation results of several design examples are provided to verify the proposed design method. Finally, Section 5 provides the conclusions.
## 3 Results
### Effects of fermented gold Kiwi Powder on Alcohol Metabolism  
The serum ethanol levels in the mice were measured using an ethanol assay kit to determine whether the FGK could lower the blood ethanol concentration in mice chronically exposed to alcohol (Figure 3a). No serum ethanol was detected in the Sham group (6.5 \(\pm\) 0.3 nmol), and the Con group (48.8 \(\pm\) 0.5 nmol) had a high serum ethanol concentration. Interestingly, the serum ethanol concentration decreased significantly, by 9.8% and 13.3 % in the FGK-M and FGK-H groups, respectively, compared with the Con group (\(p\) < 0.05). In addition, alcohol administration for 2 weeks significantly increased the serum aldehyde concentration in the Con group (Figure 3b), and that increase was reduced in the FGK-L and FGK-M groups, though at an insignificant level (\(p\) > 0.05). Only the FGK-H group showed a significant decrease (\(p\) < 0.05) in serum aldehyde concentration, compared with the Con group.  
Figure 3: Effects of fermented gold kiwi (FGK) on serum alcohol metabolism levels in alcohol-induced mice. (**a**) Ethanol concentration; (**b**) aldehyde concentration. Sham, mice treated with only saline; Con, mice treated with alcohol and saline; AA, mice treated with alcohol and ascorbic acid (25 mg/kg); FGK-L, -M, -H, mice treated with alcohol and FGK at 50, 126, and 250 mg/kg, respectively. Results expression as means \(\pm\) SEM (\(n\) = 8). \({}^{\text{a-d}}\) Different letters indicate significant differences (\(p\) < 0.05) among the groups, as determined by [PERSON]’s analysis.  
Figure 2: Effects of fermented gold kiwi (FGK) on the livers of alcohol-induced mice. (**a**) Histopathological changes; (**b**) steatosis score. Sham, mice treated with only saline; Con, mice treated with alcohol and saline; AA, mice treated with alcohol and ascorbic acid (25 mg/kg); FGK-L, -M, -H, mice treated with alcohol and FGK at 50, 126, and 250 mg/kg, respectively. Results expression as means \(\pm\) SEM (\(n\) = 8). \({}^{\text{a-e}}\) Different letters indicate significant differences (\(p\) < 0.05) among the groups, as determined by [PERSON]’s analysis.  
The effects of alcohol administration on hepatic ADH and ALDH activity were examined next (Figure 4). ADH is involved in the liver's major pathway of ethanol metabolism under normal physiological conditions [16]. The protein expression of ADH decreased in the Con group (Figure 4b), but the pretreatment at the FGK-H level protected against that alcohol-induced ADH decrease. Hepatic ALDH responses to alcohol in mice (Figure 4c) showed a decrease in hepatic ALDH in the Con group compared with the Sham group, and the FGK groups showed a significant increase (\(p<0.05\)).
## 2 Materials and Methods
### Pearson Correlation  
This association method's primary goal is to identify two or more correlated variables [45].  
The Pearson correlation coefficient measures the degree of correlation between two variables in a linear approach. Let \(X\) and \(Y\) be those variables, with measurements given by \(\{x_{1},x_{2},x_{3},...,x_{n}\}\) and \(\{y_{1},y_{2},y_{3},...,y_{n}\}\) and means \(\overline{x}\) and \(\overline{y}\). Then, the Pearson coefficient is given by Equation (3) [42].  
\[\rho(X,Y)=\ \frac{\sum\limits_{i=1}^{n}\ (\ x_{i}-\overline{x}\ )\ (\ y_{i}-\overline{y}\ )}{\left[\ \sum\limits_{i=1}^{n}\ (x_{i}- \overline{x})\ ^{2}\ \sum\limits_{i=1}^{n}\ (y_{i}-\overline{y}\ )^{2}\ \right]^{\frac{1}{2}}} \tag{3}\]  
A Pearson coefficient with the range \(\rho=[-1,1]\) represents the level of correlation when \(\rho\) is positive and correlation is direct, and the negative is the inverse [42].  
When two variables are highly correlated, one can be redundant. The Pearson correlation works only for linear relations and results in incorrectly measured correlations for nonlinear systems. When classifying with binary outputs, it is possible to identify using Pearson coefficients how an attribute correlates with the target class [42].  
Additionally, one can perform a correlation statistical significance test using the \(p_{value}\) coefficient, such as a test of the probability that the correlation coefficient \(\rho\) is a wrong hypothesis; for example, as a convention from the literature, if \(p_{value}>0.05\), it is unreliable. The alternatives for such a determination include statistical tests, such as the \(t_{value}\), variance analysis (ANOVA), and \(1_{tailed}\) or \(2_{tailed}\) tests [48].
# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 4 Definitions and Problem Statement
### VEPO-S2S Model  
We propose a novel trajectory prediction model VEPO-S2S based on the Seq2 Seq model, and the structure is shown in Figure 5. As the figure shows, VEPO-S2S consists of the Multi-level Vessel Trajectory Representation Module (Multi-Rep) and the Feature Fusion and Decoding Module (FFDM). The Multi-Rep is targeted to acquire trajectory information and Multi-level Vessel Characteristics, encoding them with several encoders. The FFDM is designed to select and merge the above information and characteristics from Multi-Rep for prediction.  
Figure 4: The trajectory cluster result. Different colors represent different clusters, and the black labels indicate cluster centers.  
Figure 5: The structure of VEPO-S2S comprises the Multi-level Vessel Trajectory Representation Module (Multi-Rep) and the Feature Fusion and Decoding Module (FFDM). The Multi-Rep is designed to obtain trajectory information and Multi-level Vessel Characteristics, applying distinct encoders for encoding. The FFDM is targeted to select and integrate the above characteristics from Multi-Rep for prediction.
## 2 Materials and Methods
### Mathematical Modeling  
Several simple regression models were used to evaluate the relationship between the net ecosystem exchange of carbon dioxide components (\(GEE\) and \(R_{eco}\)) and environmental factors (_WTL_, \(T_{0}\), PAR and plant phytomass). The selected models described the influence of individual environmental factors or the combined impact of their complex [46; 47; 48]. The following \(GEE\) models have been considered:  
\[GEE=\Big{(}c_{1}Ph_{V}{}^{2}+c_{2}Ph_{V}+c_{3}\Big{)}a\frac{\text{PAR}}{k_{1}+ \text{PAR}} \tag{1}\]  
\[GEE=c_{4}aPh_{V}\frac{\text{PAR}}{k_{2}+\text{PAR}} \tag{2}\]  
\[GEE=GEE_{\text{max}}\frac{\text{PAR}}{k_{3}+\text{PAR}}+c_{5}aPh_{V} \tag{3}\]  
\[GEE=GEE_{\text{max}}\frac{\text{PAR}}{k_{4}+\text{PAR}}(1-\exp(c_{6}Ph_{V})) \tag{4}\]  
\[GEE=GEE_{\text{max}}\frac{\text{PAR}}{k_{5}+\text{PAR}}(1-\exp(c_{7}Ph_{V})) \exp\Big{(}d_{1}WTL^{2}+d_{2}WTL\Big{)} \tag{5}\]  
where \(GEE\): gross ecosystem exchange (mgC m\({}^{-2}\) h\({}^{-1}\)); \(c_{1}\) (m\({}^{2}\) g\({}^{-1}\) h\({}^{-1}\)), \(c_{3}\) (g m\({}^{-2}\) h\({}^{-1}\)), \(c_{2}\), \(c_{4}\), \(c_{5}\) (h\({}^{-1}\)), \(c_{6}\), \(c_{7}\) (m\({}^{2}\)/g): parameters of the relationship between GEE and phytomass _PhV_; _a_: scaling factor equal 450 (mgC/g); \(k_{1}\)\(\div\)\(k_{5}\): half-saturation constant (such as the value of PAR at which GEE reaches a value equal to half of the maximum); \(d_{1}\) (cm\({}^{-2}\)) and \(d_{2}\) (cm\({}^{-1}\)): GEE and WTL coupling parameters; PAR: photosynthetically active radiation (\(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\)), _PhV_: specific aboveground phytomass of vascular plants (g/m\({}^{2}\)); and \(GEE_{\text{max}}\): maximum potential rate of gross photosynthesis (mgC m\({}^{-2}\) h\({}^{-1}\)), _WTL_: water table level (cm; negative values correspond to WTL above the soil surface, positive values correspond to the below).
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 3 FIR Prototype Filter Design
### Optimization Based Methods
#### 3.3.1 Ababneh's Method  
In Reference [131], the authors propose designing the FIR filter by the particle swarm optimization (PSO) algorithm. For each particle \(i\), the position \(\mathbf{P}_{i}\) and the velocity \(\mathbf{V}_{i}\) are shown as  
\[\mathbf{P}_{i}=(p_{i,1},p_{i,2},\cdots,p_{i,D}), \tag{56a}\] \[\mathbf{V}_{i}=(v_{i,1},v_{i,2},\cdots,v_{i,D}),\] (56b) \[v_{i,d+1}=\mu v_{i,d}+c_{1}\beta_{1}(pb_{i,d}-p_{i,d})+c_{2}\beta_{2}(gb_{d}-p_{ i,d}),\] (56c) \[p_{i,d+1}=p_{i,d}+v_{i,d+1}, \tag{56d}\]  
where \(D\) is the iteration of velocity and position, \(\mu\) is the weighting function, \(c_{1}\) and \(c_{2}\) are the acceleration constants, \(\beta_{1}\) and \(\beta_{2}\) are random numbers in the range \([0,1]\). In addition, the _pbest_\(pb_{i,d}\) denotes the personal best of the \(i_{th}\) particle vector at the \(d_{th}\) dimension. The _gbest_\(gb_{d}\) denotes the group best at the \(d_{th}\) dimension. During each iteration, the position and the velocity are calculated by the Equations (56c) and (56d), respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Comments** & **Pros and Cons** \\ \hline [PERSON]’s method [125] & Connecting Hamming window and Blackman window by a parameter \(\lambda\). & **Pros:** Best first side-lobe level and spectrum efficiency compared to Hamming and Blackman windows. \\  & & **Cons:** Large transition bandwidth. \\ \hline [PERSON]’s method [126] & Product of Hamming window and Gaussian window. & **Pros:** Excellent performance of stopband attenuation compared to Hamming and Gaussian windows. \\  & & **Cons:** Under the same parameter setting, the width of main-lobe will increase. \\ \hline [PERSON]’s method [127] & Design a four-semester raised cosine window by optimizing window parameters. & **Pros:** Narrower main-lobe under similar conditions compared to Hamming window. **Cons:** Although with improved performance of stopband attenuation, the performance gain is inconspicuous. \\ \hline [PERSON]’s method [129] & A window function combining tangent hyperbolic function and weighted cosine series using an adjustable parameter \(\gamma\). & **Pros:** Higher side-lobe roll-off ratio under the same main-lobe width compared to Gaussian window. **Cons:** Need to constantly adjust the parameters \(\gamma\). \\ \hline [PERSON]’s method [130] & The parameters of a four-term generalized cosine window are optimized on the basis of a given objective function. & **Pros:** Best performance of signal-to-overall-interference ratio compared to [PERSON] and Blackman windows. \\  & & **Cons:** High algorithm complexity. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of windowing based methods.
# A Bimodal EMG/FMG System Using Machine Learning Techniques for Gesture Recognition Optimization
## 2 Materials and Methods
### EMG and FMG Signals
Two of these sensors were applied, one for each muscle group under study: the flexor and extensor forearm. The choice of FSR sensors is justified by their ability to detect variations in force from an initial/resting state rather than providing an accurate measurement of the applied force. This property is essential for FMG systems in gesture recognition, where the goal is not necessarily to quantify the exact force being applied but to identify if there is any force being applied and how that force changes over time. The FSR 402, in particular, was chosen for its active area (14.7 mm diameter) and minimum actuation force (0.1 N), which were considered suitable for the application in question.