esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Complexity Recursive Least-Squares Adaptive Algorithm Based on Tensorial Forms
## 5 Conclusions  
This paper proposed a low-complexity adaptive algorithm for the identification of unknown systems based on tensorial decompositions. We used as a starting point the RLS-T adaptive method, and we developed a new algorithm based on the combination between the exponentially weighted RLS and the DCD iterations. The newly RLS-DCD-T method benefits from the low computational requirements of the DCD and provides performance comparable with other established versions of tensorial based RLS methods. The reduction in complexity for the adaptive filter update process is twofold. Firstly, the RLS-DCD-T benefits from the tensorial approach and solves multiple smaller system identification problems instead of a single problem, which is prohibitive for practical applications. Secondly, the usage of the DCD iterations allows for the coefficient updates to be performed using only bit-shifts and additions.  
In future works, we aim to further develop the combination between the least-squares methods and the DCD working within the tensorial decomposition framework. The DCD will be paired with sliding windows versions of the RLS. Moreover, we will develop various methods for increasing the robustness of the RLS-DCD-T in very low SNR conditions, with minimal additions to the arithmetic complexity.  
Conceptualization, C.-L.S., C.E.-I.; Formal analysis, C.A.; Methodology, C.-L.S., C.E.-I.; Software, I.-D.F., C.-L.S. All authors have read and agreed to the published version of the manuscript.  
Figure 5: Performance of the RLS-T and RLS-DCD-T for the identification of the global impulse response \(\mathbf{g}\). The tensor-based filters have \(q_{1}=q_{2}=q_{3}=q_{4}=3\). The input signal is an AR(1) sequence with the pole 0.85, \(N=4\), and \(L=4096\). Between iterations 90001 and 95000, the SNR is set to \(-15\) dB.  
**Funding:** This work was supported by a grant of the Romanian Ministry of Education and Research, CNCS-UEFISCDI, project number PN-III-P1-1.1-TE-2019-0529, within PNCDI III.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting
## 2 Materials and Methods
### Evaluation Method and Metrics  
#### 2.3.1 10-Fold Cross Validation Evaluation Method  
The model is evaluated with 10-fold cross validation method, which is a commonly used evaluation method for assessing machine learning classifiers' efficiency. In such a method, the aim is that all instances of the examined dataset will be used in both states for training set and testing set. The training set and testing set are formed by the initial dataset by dividing it to 10 equal sized sets. In a loop of 10 iterations in each iteration 9 of the equal sized sets are used for forming the training set and the one remaining set is used to form the testing set. To avoid overfitting each instance cannot belong at the same time to both the training and testing set. Instead, during this iterative process at each separate  
\begin{table}
\begin{tabular}{c c} \hline \hline \# & **Prediction and Recommendation Algorithm** \\ \hline
1 & **Input**: \(KB/\)knowledge base \\
2 & //examined instance \\
3 & \(d//\)day of the week \\
4 & \(m//\)historic window size \\
5 & \(l//\)prediction window size \\
6 & \(\mu//\)spatial historic similarity threshold \\
7 & \(\theta//\)spatial prediction similarity threshold \\
8 & \(k//\)recommendation list size \\
9 & **Output**: \(N//\)returned recommendation list \\
10 & **Begin** \\
11 & \(N\gets Null//\)returned recommendation list is empty \\
12 & \(k\gets read()//\)initialize recommendation list size \\
13 & \(i\gets read()//\)read the examined instance from user mobile app \\
14 & \(j\gets read(KB)//\)read the first instance of the \(KB\) \\
15 & **While**\((j\
eq KB(EqF))\)\(\mathbf{Do}//\)traverse \(KB\) \\
16 & **If**\(((d(i)=d(j))\ AND\ (i(m)-j(m)\ \leq\theta)\ AND\ (i(l)-(j(l)\leq\theta))\)**Then** \\
17 & //if current and predicted locations of \(i,\ j\) are similar w.r.t. \(\theta\) similarity for certain day \\
18 & **For**\((n\in[0,\ m-1])\)\(\mathbf{Do}//\)traverse from first to last historic location of the trajectory \\
19 & **If**\((i(n)-j(n)\ \leq\ \mu)\)**Then**//step by step historic comparison \\
20 & \(\theta\leftarrow\ \varphi+1/\)historic similarity flag increases \\
21 & **End If** \\
22 & **End For** \\
23 & **If**\((\varphi=n)\)**Then** \\
24 & \(//\)if historic similarity condition w.r.t. \(\mu\) holds proceed to recommendation list step \\
25 & **If**\((size(N)\leq k)\)**Then**//if size of \(N\) is less than or equal to \(k\) \\
26 & \(N\gets N+j//\)recommendation list is expanded \\
27 & **Else** \\
28 & \(sort(N)//\)sort recommendations in ascending order of similarity \\
29 & \(return(N)//\)return recommendation list and exit \\
30 & **End If** \\
31 & **End If** \\
32 & **End If** \\
33 & **End While** \\
34 & **End** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Prediction and recommendation algorithm of the proposed ride sharing system.  
loop an instance can be part of either the training set, or the testing set. Note that since all the instances are used during the evaluation this is not a random process.
# Impulse Signal Detection for Bearing Fault Diagnosis via Residual-Variational Mode Decomposition
Accordingly, prior information was necessary for this method, and the impulse signal evaluation criterion was too crude. On the basis of the above descriptions, some problems remain to be solved, such as VMD without an intelligent algorithm and a suitable criterion for impulse signals. A novel method called residual-VMD (RVMD) is proposed in the current study to fill the above gaps. RVDM is designed to have two stages. In the first stage, the balance parameter is set as 1000 following previous studies [26; 29]. The modal number is initialized as one and increases gradually until the relationship correlation between the input and decomposition results reaches the threshold. A new kurtosis, which is redefined on the basis of the number of elements whose values are above the root mean square (RMS) of the squared data, is applied to rebuild EK which can effectively evaluate the ratio of the periodic impulses in each mode. By keeping the mode with the largest new EK, and its neighbors, the VMD has two modes in the second stage. An iteration process is also applied to determine the optimal balance parameter. After obtaining the optimal balance parameter, the VMD is re-run with it. The sub-mode with the larger new EK is analyzed by envelope analysis technology to observe the fault feature. The effectiveness of the proposed method is examined by simulation and experiment signals. The comparison results among three other methods indicate the superiority of the proposed method. The rest of the study is organized as follows. The proposed method is described in Section 2. The results of the numerical experiment, case study, and comparison are organized in Section 3. Finally, a concise summary is discussed in Section 4.
# Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models
## 8 Discussion
These extreme cases may indicate structural changes occurring in the signals during the one-minute interval, and a more dynamic model may be more appropriate. The estimated confidence intervals for RAST3 and RAST4 constructed by both methods were similar for all datasets; however, the confidence limits were more in agreement when the half-life estimates were smaller (<10). The CIs for RMST4 were wider for similar half-life estimates, indicating higher variability in the RMST4 estimates. The non-seizure channels also had higher variability, leading to exclusion of some CIs because of negative confidence limits. It appears larger half-life estimates yield much wider CIs. This may be due to a higher covariance between the ARCH and GARCH parameters as their difference grows and/or their sum approaches unity. The models estimated were based on one-minute segments of data, assuming that they were stationary, which may not be the case. However, in the cases of RAST3 and RAST4, it appears that the models were consistent across different segments for the pre-seizure and awake data, both in the segments used in the analyses and when randomly selecting starting points. This may indicate that these signals are ARMA-GARCH processes for these particular states. The differences in the estimates for the sleep and sleep to awake data may also be indicative of an underlying ARMA-GARCH structure, but they may have different lag structures. The non-seizure signals may also be ARMA-GARCH processes with different lag structures, especially the cases where the ARCH parameter was less than 0.01. All of the signals may also show different volatility properties with shorter segments, indicating possible regime changes. The volatility half-life as a characteristic of epileptic EEG channels may be related to high frequency oscillations (HFOs), which may be possible characteristics in epileptic EEG channels ([48]. HFOs and their relationship to seizure onset zones have been studied using machine learning and deep learning methods [48; 49; 50; 52; 70]. These studies have found various accuracy, sensitivity and specificity in identifying under different conditions, but do pose another potential tool in studying EEGs.
## 2 Methodology
### Multiple Converter Configuration Model (C-CM)
#### 2.6.3 DC/DC Converter  
A power converter is an electronic system or equipment that aims to convert electrical energy between two different formats: continuous current from alternating current or direct current to direct current [19]. The initial converter concept can be extended to include aspects, such as efficiency, reversibility, degree of ideality, reliability, volume, or technology, to name the most important. Figure 7 shows the different parts that make up the powertrain of the multiple converter configuration as well as the electrical inputs and outputs that are involved in said system.  
In an ideal converter the input power is equal to the output power; that is to say it has an efficiency of 100% (\(\eta_{conv}=1\)). There are no energy losses. If 100% efficiency is considered for the battery converter in this work, the output power of the converter is expressed as:  
\[P_{b\_conv}=P_{bo}^{T}\]  
Figure 6: UC electrical model.  
where \(P_{bo}^{T}\) is the total power delivered by the battery pack. In real life Equation (7) is not fulfilled since there are energy losses in the converter components [19]. In a real case the efficiency of a converter is less than 100%. In this case the input and output powers are related as follows:  
\[P_{b\_conv}=\eta_{b\_conv}P_{bo}^{T} \tag{8}\]  
where \(P_{b\_conv}\) is the output power of the battery pack converters and \(P_{bo}^{T}\) is the total power delivered by the B pack, and \(\eta_{b}\) is the efficiency of the converter connected to the batteries with \(0<\eta_{b\_conv}<1\). For the UC converter the output power of the converter is given by:  
\[P_{uc\_conv}=\eta_{uc\_conv}P_{uo}^{T} \tag{9}\]  
where \(P_{uc\_conv}\) is the output power of the ultracapacitor bank converters, \(P_{uo}^{T}\) is the total power delivered by the UC bank, and \(\eta_{uc\_conv}\) is the efficiency of the converter connected to the batteries with \(0<\eta_{b\_conv}<1\). Figure 2 shows that the output voltage in both converters is equal to the load voltage since they are connected in parallel, that is:  
\[V_{load}=V_{b\_conv}=V_{uc\_conv} \tag{10}\]  
where \(V_{b\_conv}\) is the voltage that is at the output of the battery converter, \(V_{uc\_conv}\) is the voltage that is at the output of the UC converter, and \(V_{load}\) is the voltage of the charge or motor.
# Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec
## 3 The Effect of Index Distribution
### Vector Quantization of G.723.1  
When being encoded, the speech signal is first passed through the high-pass filtering, frame segmentation and other processing processes. Then, the Levinson-Durbin algorithm is used to calculate 4 sub-frames of each input speech frame. It gets 4 sets of 10 th order linear predictive coding (LPC) coefficients. The synthesis filter of LPC is defined as Equation (1) [3].  
\[A_{i}(z)=\frac{1}{1-\sum\limits_{j=1}^{10}a_{ij}z^{-j}},0\leq i\leq 3 \tag{1}\]  
where \(a_{ij}\) is the \(j\)-th order LPC prediction coefficient, and \(i\) is a subframe index with values of 0, 1, 2, and 3.  
In order to facilitate quantization, LPC coefficient is usually converted into line spectrum pair (LSP) coefficient which also known as line spectrum frequency (LSF). The LSP coefficients are divided into two 3-dimensionalityal vectors and one 4-dimensionalityal vector. The three split vectors are respectively recorded as \(f_{1}\), \(f_{2}\), and \(f_{3}\), and the corresponding codebooks are \(L_{1}\), \(L_{2}\), and \(L_{3}\). Each codebook has a codeword space of size \(2^{8}=256\). According to the minimum error criterion, each split vector selects the optimal codeword from corresponding codebook. The optimal codeword is the codeword index, which is the quantized result.
# Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis
## 2 Basic Theory  
### CEEMD  
To solve the mode aliasing problem caused by EMD, EEMD uses the characteristics that white noise can be evenly distributed in the spectrum. Adding white noise to EMD improves the mode aliasing phenomenon; however, the residual white noise also brings reconstruction error problems.  
Inspired by EEMD, adding white noise to equalize the noise in the original signal, [PERSON] [15] proposed a new time-frequency analysis method CEEMD. The CEEMD method adds a pair of positive and negative white noise to the original signal for EMD decomposition to achieve the purpose of eliminating residual white noise in the reconstructed signal. In addition, the added white noise improves the mode aliasing problem, and the number of CEEMD noise addition is lower, which improves the calculation efficiency.  
The CEEMD method is as follows:  
1. Two new signals are formed by adding white noise with a certain standard deviation and equal length and opposite signs to the original signal. \[\begin{split} S_{+i}&=S(t)+n_{+i}\\ S_{-i}&=S(t)+n_{-i}\end{split}\] (1) where \(S_{+i}\) is the sum of the original signal and the positive white noise signal, \(S(t)\) is the original signal, \(n_{+i}\) is the positive white noise; \(S_{-i}\) is the sum of the original signal and the negative white noise signal, and \(n_{-i}\) is the negative white noise.
2. EMD decomposition of \(S_{+i}\) and \(S_{-i}\) to obtain \(IMF_{+(i,j)}\) and \(IMF_{-(i,j)}\), then the first-order \(IMF\) is \[IMF_{1}=\frac{1}{2N}\sum_{i=1}^{N}\left(IMF_{+(i,1)}+IMF_{-(i,1)}\right)\] (2)
3. Repeat the above steps to add Gaussian white noise \(2N\) times.
4. Finally, the decomposition results of multiple component combinations are obtained: \[IMF_{j}=\frac{1}{2N}\sum_{i=1}^{N}\left(IMF_{+(i,j)}+IMF_{-(i,j)}\right)\] (3) where \(N\) is the number of pairs added with white noise, \(IMF_{j}\) is the \(j\)-th component obtained by CEEMD decomposition, and \(IMF_{(i,j)}\) is the \(i\)-th IMF component of the \(j\)-th signal.  
The decomposition process of CEEMD is shown in Figure 1.  
Figure 1: Complementary ensemble empirical mode decomposition (CEEMD) flowchart.
## 3 Result and Analysis
### Stress at Failure
#### 3.4.2 BS Standard  
The prismatic CS increased by 200 to 575 % (Figure 10). When the CG was modified with 0.16 % of the polymer after 28 days of testing, the prismatic CS of CG Sand #1 and Sand #5 raised by 200% and by 240% (Figure 10b). To predict the prismatic CS of CG at various w/c, curing ages, polymer amounts, and sand grain sizes, a linear equation (Equation (8)) and a nonlinear equation (Equation (9)) were developed. The nonlinear technique (NLR) predicts the prismatic CS of CG more accurately than the linear technique (LR) based on statistical assessment tools, as shown in Figure 11.  
\[\sigma_{\rm pc}=-11.2\ *\ \frac{\rm w}{\rm c}+0.93*\rm t-2.39*\rm d_{10}+231* \rm P+13.4 \tag{9}\]  
\[\sigma_{\rm pc}=5.68(\frac{\rm t^{0.22}}{\rm w/c^{1.23}d_{10}{}^{0.11}})+84.9 (\frac{\rm t^{0.27}P^{1.47}}{\rm w/c^{0.28}d_{10}{}^{0.17}}) \tag{10}\]  
Figure 9: Measured and predicted relationship for the cylindrical CS of CG (**a**) LR model, and (**b**) NLR model.  
Figure 11: Measured and predicted relationship for the Prismatic CS of CG (**a**) LR model, (**b**) NLR model, and (**c**) residual error.  
Figure 10: Typical variation of prismatic CS of CG modified with water reducer polymer-polymer at (**a**) one d and (**b**) 28 d of curing.
## 1 Introduction
Ecosystem models, while addressing nonlinear and dynamic aspects of vegetation growth under water deficiency, require numerous parameter settings, which may introduce potential uncertainties. Machine learning approaches, particularly those based on decision tree models, circumvent the need for predetermined functional forms between driving factors and responses. They efficiently manage multiple input variables and pinpoint key influencing factors, thus gaining popularity for analyzing and predicting vegetation dynamics [20; 21; 22]. LightGBM, a distributed gradient boosting framework based on decision tree algorithms, stands out for its enhanced efficiency and speed compared to other tree-based models [23]. To analyze the response of different vegetation indices to water resource variations, this study utilizes LightGBM to construct regression models of environmental factors and vegetation indices, with integrating the Shapley Additive Explanations (SHAP) method via TreeExplainer for model interpretation. Traditional machine learning models often lack transparency in explaining individual predictions or decisions. The SHAP approach, leveraging TreeExplainer, overcomes this by offering local explanations for model predictions. It can also identify nonlinear interactions between features [24]. By calculating the marginal contribution of each feature to the model output, SHAP can enhance the interpretability of the machine learning models used in this study. This study first compares the different response of satellite-derived SIF (GOSIF product) and NDVI (MODIS NDVI product), kNDVI to long-term changes in water resources, over China's subtropical areas with high vegetation coverage. Then we explore their interactions with GPP changes during short-term extreme drought events. Generally, our research aims: (1) To compare the sensitivity of SIF, NDVI and kNDVI in detecting the impact of water stress on vegetation growth; (2) To analyze the primary environmental factors influencing vegetation growth using the LightGBM and SHAP method; (3) To evaluate the ability of SIF, NDVI and kNDVI to capture GPP changes in subtropical vegetation under drought stress. By contrasting the performance of SIF, NDVI and kNDVI in long-term responses to changes in water resources and short-term extreme drought events, we aim to clarify the respective strengths and limitations of these indices in drought monitoring and vegetation ecological analysis.
## 3 Results
### LA-Treated PDiff Caco-2 Cells  
#### 3.2.1 Differential Gene Expression  
We identified 4225 differentially expressed genes (adjusted \(p\) < 0.05) between untreated and LA-treated PDiff Caco-2 cells. Of these, 2080 genes were upregulated and 2145 genes were downregulated in LA-treated cells compared to untreated cells (Table S7). As in 13-HPODE-treated cells, genes involved in lipid uptake and metabolism such as _FABP1_ and _HMGCS2_ were also induced upon LA treatment. _APOL6_, which has a role in lipid transport in the cytosol, was also upregulated. On the contrary, other genes of lipid metabolism were downregulated including _MGLL_, which hydrolyzes monoacylglycerolglycerol to fatty acid and glycerol, and _OLR1_, which promotes internalization of oxidized LDL. _PCK2_ involved in glucose production was upregulated as well. _DDIT4_ and _CHAC1_, which have a role in stress and unfolded protein response, were upregulated. _CEBPB_, which have shown to inhibit proliferation, promote differentiation, and mediate acute phase response [44], and _IHH_, that regulates proliferation and promotes differentiation [50], were also upregulated. On the other hand, similar to 13-HPODE-treated cells, _RGCC_, _CCNI/N2_, _PIM1_, and _CYTOR_ were downregulated in LA-treated cells, which may indicate reduced proliferative potential and enhanced differentiation of PDiff cells by both treatments. While some small molecule transporters such as _SLC7A11_, an anionic amino acid transporter, were upregulated; others were downregulated such as _SLC20A1_, a sodium-phosphate symporter, suggesting altered membrane transport of substrates by LA. Reduced expression of _CXCL1_ and _CCL20_ chemotactic factors was also seen indicating anti-inflammatory effect of LA. The top 50 DEGs upon LA treatment are shown in Figure 3.
## 3 Data and Methodology
### Array Techniques
#### 3.3.1 The Frequency-Wavenumber (F-K) Analysis  
F-K analysis is one of the most commonly used techniques for estimating Rayleigh wave dispersion curves. It has some advantages over other techniques, such as the ability to identify the direction of ambient vibration source and the recognition of the different modes presented in the wavefield [47]. The latter is composed of a superposition of several propagated waves. The F-K technique allows estimating the velocity and the direction of approach (back-azimuth) of these waves [48].  
The F-K technique is based on two fundamental assumptions: the first is that the process is stationary in time. The second is that the process is stationary in the horizontal plane and that the propagation of the wavefront is only in the vertical direction. The stationary aspect of propagated seismic waves allows the power spectral density function of the frequency wavenumber to be calculated, which contains information about the power as a function of frequency and velocity vectors of the propagated wave. There are two methods for calculating the power spectral density: the maximum likelihood method (MLM) or the high-resolution method [40; 41] and the beamforming method (BFM) [42]. The BFM technique is used in this study, as it is less sensitive to errors than the MLM technique [41].  
For all the array techniques, only the vertical component of the records was analyzed, as it is the one required for the estimation of the Rayleigh wave dispersion curves. The Sesarray software package [34] was used to perform the F-K analysis. First, the coordinates of each of the seven sensors were introduced in the WARANGPS software in order to calculate the array transfer function and the theoretical wavenumber limits (K\({}_{\text{min}}\) and K\({}_{\text{max}}\)). Then, the signals were loaded into the Geopsy software and the BFM method was applied. The signals were divided into several windows of frequency-dependent lengths (50 periods). After that, an anti-triggering algorithm was used for the window selection. The processing requires the input of the grid step and grid size parameters. The grid size corresponds to the K\({}_{\text{max}}\) value, which is related to the aliasing limit. The grid step determines the maximum resolution and was chosen as K\({}_{\text{min}}/2\). Once all these parameters were introduced, the final processing was launched and the dispersion curve was obtained. The processing was the same for the data of the 11 arrays.