esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Series Prediction of Long-Term Sustainability of Grounds Improved by Chemical Grouting
## 2 Methodologies
### Autoregressive Integrated Moving Average (ARIMA) Model  
The general equation of the AR (the \(p\)-th order of autoregressive or number of lags) part of the ARIMA is given by Equation (1).  
\[Y_{t}=\varnothing_{0}+\varnothing_{1}Y_{t-1}+\varnothing_{2}Y_{t-2}+\ldots+ \varnothing_{p}Y_{t-p}+\varepsilon_{t} \tag{1}\]  
The general equation of the MA (the \(q\)-th order of moving average) part of the ARIMA is given by Equation (2).  
\[Y_{t}=\mu+\varepsilon_{1}-\theta_{1}\varepsilon_{t-1}-\theta_{2}\varepsilon_{ t-2}-\ldots-\theta_{q}\varepsilon_{t-q} \tag{2}\]  
For the time series data \(Y_{t}\), the general autoregressive moving average model ARMA (\(p\), \(q\)) is given by Equation (3).  
\[Y_{t}=\varnothing_{0}+\varnothing_{1}Y_{t-1}+\varnothing_{2}Y_{t-2}+\ldots+ \varnothing_{p}Y_{t-p}+\varepsilon_{t}-\theta_{1}\varepsilon_{t-1}-\theta_{2} \varepsilon_{t-2}-\ldots-\theta_{q}\varepsilon_{t-q} \tag{3}\]  
where \(Y_{t}\), \(Y_{t-1}\),..., \(Y_{t-p}\) are the response variable at time \(t\), \(t-1\),..., \(t-p\), respectively; \(\varnothing_{0}\), \(\varnothing_{1}\),..., \(\varnothing_{p}\) and \(\theta_{0}\), \(\theta_{1}\),..., \(\theta_{q}\) are the coefficients to be estimated for Equations (1) and (2) respectively; \(\varepsilon_{t}\) is the error at any time \(t\); \(\varepsilon_{t-1}\), \(\varepsilon_{t-2}\),..., \(\varepsilon_{t-q}\) are the errors in the previous time periods that are incorporated into \(Y_{t}\); and \(\mu\) is the mean.  
Assuming \(L\) as the lag operator (difference) of two consecutive response variables, the number of difference \(d\) is carried until the series becomes stationary, and hence, the \(d\)-th difference is given by \((1-L)^{d}Y_{t}\). Therefore, the general equation for the ARIMA model (\(p\), \(d\), \(q\)) is given by Equation (4).  
\[\varnothing_{p}(L)(1-L)^{d}Y_{t}=\mu+\theta_{q}(L)\varepsilon_{t} \tag{4}\]  
Figure 1 shows the basic flowchart for the working principle of the ARIMA model.
# Comparative Study on Load Monitoring Approaches
## 2 Load Monitoring
### Non-Intrusive Load Monitoring (NILM)
The reported results indicate that the model trained by the deep neural network from a washing machine can be used to detect other appliances, such as a kettle, microwave oven and refrigerator. In sum, the NILM technique is a powerful tool for analyzing energy consumption and identifying energy saving opportunities. It can be used to improve energy efficiency, reduce energy waste, and provide more detailed information about the energy consumption of individual devices. The performance evaluation of NILM requires data sets, without which energy disaggregation could not be done. Indeed, in its operation, NILM uses the energy consumption data of a house (electrical signatures of the different appliances found in a household) stored in a database called the dataset. To date, there are several open access datasets proposed by researchers; these include the PLAID [53], REDD [54], UK-DALE [55], AMPds [56], and BLUED [57] datasets. However, the use of datasets in NILM responds to the choice of inference type. In supervised learning, datasets are used both as training data to train the algorithm and as test data to ensure accuracy in matching trained and tested signatures. In semi-supervised learning, the consumption data of the datasets is partially used, i.e., it is only used when training the algorithm. For non-intrusive load monitoring, datasets are valuable because they typically represent a means of evaluating the performance of algorithms used in NILM. A dataset is a collection of samples collected over time, in which we find voltage, current, active power, reactive power and to a lesser extent power factor and harmonics data [58]. These data collected through their respective signatures are the object of training to accustom the algorithm to the signatures and of testing, when it is a question of making correspondences to ensure the ability of the algorithm to correctly assign the signatures to the devices. All this is made possible by supervised, semi-supervised, or unsupervised learning algorithms, chosen according to the circumstances.
## 3 Background
### Security Requirements
#### 3.3.4 Data Integrity  
The information being delivered from one node to another should not be altered in any way. The content received should match what was sent. In communication that goes through various channels, malicious nodes can tamper with the message or send the wrong signals. Such tampering is dangerous because it might result in messages being interpreted differently. To ensure that the network's integrity is intact, digital signatures can be used. The content of the message being passed from one node to the other may save a life or cause damage if modified [62].
# EUR Prediction for Shale Gas Wells Based on the ROA-CatBoost-AM Model
## 3 Analysis of Primary Production Control Factors and EUR Prediction Model
### Correlation Analysis  
A Spearman correlation analysis was conducted on the 16 selected influencing factors (as shown in Figure 4), and mutual information values were calculated (as shown in Figure 5). The correlation coefficient map and mutual information values between the influencing factors were obtained. Features with strong linear correlation (1 correlation coefficient 1 > 0.6) and low mutual information (MI < 0.05) were removed, resulting in eight retained influencing factors:  
Geological factors: true vertical depth, thickness of high-quality reservoirs, and permeability.  
Figure 4: Correlation analysis of influencing factors heatmap.  
Figure 5: Bar chart of mutual information values for influencing factors.  
Engineering factors: drilled length of high-quality reservoirs, length of horizontal section, and stage length.  
Production factors: FBR330 (flowback rate on the 330 th day) and average pumping rate (Table 1).  
Reservoirs with TOC content greater than 2%, a continuous thickness exceeding 15 m, and brittle mineral content above 40% are defined as high-quality reservoirs. Permeability directly reflects the fluid conductivity of the reservoir and is closely related to the efficiency of hydrocarbon flow, making it a key variable influencing EUR. Although porosity and saturation are also important for reservoir characterization, their direct impact on fluid flow is less significant compared to permeability. After removing the redundant features, a Spearman correlation analysis was performed to ensure no strong linear correlations among the retained factors (as shown in Figure 6).
## 3 Results and Discussion
### Experiment on Another Dataset  
Another experiment on the California_Airport dataset is provided. This dataset is collected by an airborne visible/infrared spectral imager from California's Moffett Airport, with a wavelength range of 0.4-2.5 um. Figure 14 shows the pseudo-color image of the experimental data and the true distribution position image of the abnormal targets. The spatial resolution is 3.5 m/pixel. The original hyperspectral image data size is 1924 \(\times\) 753, including 224 bands. After removing some bands in the simulation, 178 bands, including 1-98, 113-114, 129-153, and 172-224, were used. A 192 \(\times\) 48 sized area was selected for detection. The figure includes types of ground objects such as roads, soil, grasslands, airports, etc. Aircraft and maintenance vehicles at the airport are considered abnormal targets.  
The experimental simulation results of each algorithm on the California_Airport dataset are shown in Figure 15. Figure 15a shows the true value of the abnormal objects corresponding to the dataset, and Figure 5b-i shows the abnormal detection results of each comparison algorithm and the algorithm of this manuscript. The AUC values of all the algorithms are shown in Table 5. From Figure 15 and Table 5, it can be seen that LRX and KIFD show relatively good performance. They can detect all abnormal objects. Compared to these two algorithms, RX, WSCF, and the proposed algorithm in this manuscript, will miss some abnormal objects, which are circled in Figure 15i. In addition, there are also some false alarms for the proposed algorithm. These indicate that the proposed algorithm  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Beach\_Plane** & **Saliens\_Synthetic** & **Road\_Vehicle** \\ \hline RX & 0.9852 & 0.9858 & 0.9909 \\ \hline LRX & 0.9971 & 0.9541 & 0.9992 \\ \hline CRD & 0.9943 & 0.9363 & 0.9946 \\ \hline WSCF & 0.9840 & 0.9869 & 0.9988 \\ \hline RPCA-RX & 0.9787 & 0.9925 & 0.9902 \\ \hline LRASR & 0.9621 & 0.9713 & 0.9997 \\ \hline KIFD & 0.9910 & 0.9965 & 0.8908 \\ \hline Ours & 0.9981 & 0.9998 & 0.9997 \\ \hline \hline \end{tabular}
\end{table}
Table 4: AUC values of all algorithms for three experimental sets of data.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithm** & **RX** & **LRX** & **CRD** & **WSCF** & **RPCA-RX** & **LRASR** & **KIFD** & **Ours** \\ \hline AUC & 0.9909 & 0.9992 & 0.9946 & 0.9988 & 0.9902 & 0.9997 & 0.8908 & 0.9997 \\ \hline \hline \end{tabular}
\end{table}
Table 3: AUC values of the Road_Vehicle dataset for all algorithms.
# A Robot Learning Method with Physiological Interface for Teleoperation Systems
## 3 Method
### Physiological Interface Design  
In the teleoperation system, the human operator manipulates the robot remotely, and the slave movement follows that of the master synchronously. In general, the interaction information between the human operator and the remote environments can be reflected by variability of the muscle activation of the human operator in the process of manipulation.  
In this study, the MYO armband with 200 Hz was used to collect the raw sEMG signal with eight channels [22]. According to the sampled sEMG signal, the sum of sEMG signal can be defined as follows:  
\[u=\sum_{i=0}^{7}\hat{u}_{i},\;i=0,1,\ldots,7. \tag{1}\]  
where \(u\) indicates the amplitude of sEMG signal and \(\hat{u}_{i},i=0,1,\ldots,7\) represents the raw signal.  
The obtained sEMG signal is influenced by noise, therefore, a moving average filter is presented as follows:  
\[\bar{u}=\frac{1}{K}\sum_{k=1}^{K}u \tag{2}\]  
where \(K\) is the moving window size. \(\bar{u}\) is the filtered sEMG signals through the moving average filter.  
Then, an envelope of sEMG signals can be obtained as follows:  
\[\bar{u}=\sum_{j}^{j+M}W_{M-j}\bar{u} \tag{3}\]  
Figure 3: Schematic illustration of the proposed algorithm.  
where \(W_{M-j}\) indicates the weight parameter and \(M\) is an empirical parameter in the experiment.  
According to literature [43; 44], the relationship between the sEMG signals and the indicator of the muscle activation can be presented as follows:  
\[\rho=\frac{e^{\beta}\bar{u}-1}{e^{\beta}-1} \tag{4}\]  
where \(-3\leq\beta\leq 0\) in this study.  
In order to achieve friendly and natural interaction between the human operator and the remote robot, muscle stiffness of human upper limbs is taken into consideration. To this end, muscle stiffness can be presented as a linear relationship according to literatures [22; 39]  
\[\bar{u}=\{a_{max}-\bar{a}_{min}\}\frac{\rho-\rho_{min}}{\rho_{max}-\rho_{min}}+ \bar{a}_{min}, \tag{5}\]  
where \(\bar{a}\in[\bar{a}_{min},\bar{a}_{max}]\) indicate the parameters to guarantee stability of the slave. \(\rho\in[\rho_{min},\rho_{max}]\) represent the muscle activation.
# Optimization of Distribution Network Current Protection for Inverter-Based Distributed Power Access
## 4 Analysis of the Impact of IIDG Access Location on Stage-Based Protection
### Time-Limited Current Interruption Protection Calibration  
A time-limited current flow interruption protection device has been designed to respond rapidly to and isolate fault points that fall outside the current flow interruption protection area. Moreover, it serves as a supplementary component to the current flow disconnect protection, establishing an optimal time delay for the interaction with the current flow disconnect protection, thereby forming an effective protection mechanism. The aforementioned delay mechanism ensures that the protection device will operate in a predetermined sequence in the event of a fault, thus enhancing the stability and reliability of the power system [21].  
The design of the power system enables the rapid and systematic isolation of faults in response to a range of fault scenarios, thereby reducing the impact of faults on system operation. The time-limited current flow interruption rectification value of Protection 2 is given as follows  
\[I^{\prime\prime}_{act\,2}=K^{\prime\prime}_{rel}\,I^{\prime}_{act\,-1} \tag{10}\]  
where \(K_{sen}\) is the sensitivity factor, as follows:  
\[K_{sen}=\frac{I_{k\cdot B\cdot\min}}{I^{\prime\prime}_{act\,2}} \tag{11}\]The impact of an inverter-based distributed generation system (IIDG) connected to the distribution network on the fault current is contingent upon its output power. Theoretical derivations indicate that when the output power of an inverter-based distributed generation system (IIDG) reaches its rated value, the fault current through the protective device will demonstrate a decreasing trend [22]. This suggests that the output power level of an IIDG has a direct impact on the monitoring of fault currents and the response of protective devices.
## 5 Methods
### Feature Extraction  
The impedance spectra form a continuous course. As a result, the differences in impedance values for neighboring frequencies are rather small and there is a strong correlation. Therefore, to reduce the dimensionality of the feature vector, the principle component analysis (PCA) is applied. PCA is a statistical technique that allows you to combine many variables into a few principal components. Its aim is to combine the variance of the data from many individual variables into few principal components. These principal components are stochastically independent of each other. As shown in Figure 22, this creates a new coordinate system whose axes each map the direction of maximum variance and are orthogonal to each other. The essential tool of this procedure is the diagonalization of a symmetric matrix with the help of an orthogonal matrix.  
When using the principle component transformation, the question arises about how many components should finally be taken over. One approach is to specify the number of components directly. Another approach is the definition of the portion of the explained variance. This portion can be determined by the eigenvalues of the principal components. Table 1 shows the explained variance depending on the number of principal components when applying the principal component transformation to the training data. As can be seen, the assumption that there is a strong variance between the measurement frequencies is confirmed. Already three principal components are sufficient to describe 90% of the variance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No. Princ. Comp.** & **Explained Variance** \\ \hline
1 & 46\% \\
2 & 76\% \\
3 & 90\% \\
4 & 95\% \\
5 & 97\% \\
7 & 98\% \\
9 & 99\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Explained variance depending on number of principle components.  
Figure 22: Illustration of the principal component transformation.
# Contactless Ultrasonic Wavefield Imaging to Visualize Near-Surface Damage in Concrete Elements
## 2 Materials and Methods
### Ultrasonic Wavefield Data Collection  
The contactless scanning ultrasound measurement hardware to collect ultrasonic surface wave data from the concrete sample is shown in Figure 1. The hardware consists of an air-coupled transmitter (NCG50-D50, Ultran Group, State College, PA, USA) with a center frequency of 43 kHz to generate ultrasonic surface waves in the concrete sample, an eight-channel MEMS microphone array to collect the corresponding surface wave responses across eight lateral spatial points simultaneously, and a mechanical scanning frame to position and move the MEMS microphone array across a predefined scanning grid. The ultrasonic transmitter was excited by a 3-cycle sinusoidal tone burst signal with a center frequency of 43 kHz. Note that the MEMS microphone array was developed by the authors; details about the development can be found in [18]. The lift-off distance and incident angle were respectively set to 37.5 mm and 10\({}^{\circ}\) for the air-coupled transmitter and 12.5 mm and 10\({}^{\circ}\) for the MEMS microphone array. Ultrasonic surface wave signals were collected across predefined 101 \(\times\) 8 spatial grid points over a 250 mm \(\times\) 49 mm scan area on the top surface of the concrete sample, as indicated in Figure 1. The spacing between two adjacent grid points (spatial sampling interval) was 2.5 mm along the horizontal axis and 7 mm along the vertical axis of the scanned area. Given the spatial sampling intervals, the Nyquist wavenumbers are 200 m\({}^{-1}\) and 71 m\({}^{-1}\) for the horizontal and vertical directions, respectively. Hence, the smallest wavelengths that can be detected are 5 mm and 14 mm along the horizontal and vertical axes respectively. A multifunction data acquisition device (NI USB-6366, National Instruments Inc., Austin, TX, USA) was used to generate an input signal and digitize the measured surface wave signals. To achieve high-voltage excitation, the input signal generated by the multifunction device was amplified with 60 dB gain using a power amplifier (1000S04, E&I, Ltd., Rochester, NY, USA) before being fed into the air-coupled transmitter. The temporal sampling rate was 2 MS/s, and the signals were repetition time-averaged 25 times.
## 4 Experiment and Analysis
### Baselines  
To determine the effectiveness of CDML, a comprehensive comparative study was conducted. This study evaluates the CDML along with several excellent baseline models. These models can be categorized as follows:  
(i) Neural Network based methods: RAEVN, MuIT, MISA, MoNIG:  
(ii) Translation based methods: MCTN, CMJRT:  
(iii) Multitask-learning-based methods: Self-MM.  
RAVEN [21]: The recurrent attended variation embedding network utilizes information in the nonverbal modality to supplement the semantic information represented by the words.  
MCTN [39]: The Multimodal Cyclic Translation Network is based on Seg2 seq, which requires only unimodal input data and robust multimodal joint representation learning methods in the testing phase.  
MuIT [11]: The Multimodal Transformer provides a mechanism for fusing cross-modal information by directly focusing on the lower-order features of other modalities, fusing multimodal information.  
MISA [40]: Modality-Invariant and -Specific Representations decompose each modality into modality-invariant and -specific representations and then fuse them to predict affective states.  
Self-MM [29]: The Self-Supervised Multitask Multimodal sentiment analysis network generates corresponding modal labels for each modality by self-supervision.  
MoNIG [41]: The mixture of Normal-Inverse Gamma distributions algorithm ensures the reliability of the final output by capturing both modality-specific and fused uncertainties.  
CMJRT [42]: The Cross-Modal Joint Representation Transformer enables effective information integration and processing across modalities by leveraging hierarchical interactions. Additionally, the passing of joint representations allows for a comprehensive understanding of the interplay between different modalities.
## 5 HUVEC Model Applications
### Cardiovascular and Metabolic Diseases
#### 5.1.3 Hypercholesterolemia  
Hypercholesterolemia is considered one of the main risk factors for the development of atherosclerosis [2; 6; 119; 127; 128]. The atherogenic endothelial phenotype has higher permeability to circulating LDL. Oxidized LDL accumulate in the subendothelial space and initiate a local inflammatory response [2; 3; 6; 119; 128].  
HUVEC models have been useful for the identification of new biological markers associated with hyperlipidemic-induced damage, attempting to achieve early diagnosis [6; 128; 129; 130]. HUVEC models have also been useful to identify therapeutic targets, such as microRNA 338-3p. In pathological conditions, microRNA 338-3p induces apoptosis. It decreases EC viability by binding to the BAMBI mRNA (BMP and activin receptor membrane-bound inhibitor), a pseudoreceptor that acts as a TGF-\(\beta\) signaling pathway inhibitor [6].
# A Power-Line Communication System Governed by Loop Resonance for Photovoltaic Plant Monitoring
## 2 Materials and Methods
### Coil Saturation  
Most of the configurations presented before make use of coils (or transformers) in series with the DC current path of the PV installation. These components show a serious limitation related to the saturation of the core material under high DC currents that make them stop behaving as coils or transformers for the communications signals. Increasing currents in modern solar modules will make these configurations ineffective at high irradiances. To overcome this drawback for our blocking coil we have developed an anti-saturation coil composed of two equal bindings in series winded in opposite directions that lead to a zero flux inside the core for DC currents, avoiding its saturation. To allow inductive behavior for AC signals, a capacitor C is placed in shunt configuration with one of the bindings as shown in Figure 1 (L1). Network analysis of this configuration shows a device impedance: \(Z=j\frac{\omega L(1-K)(\omega^{2}LC(1+K)-2)}{\omega^{2}LC-1}\) (where C is the capacitance of the capacitor attached to the coil, L is the auto-induction coefficient of each of the two bindings and K is the coupling coefficient between them). For high enough frequency AC signals where \(\omega^{2}LC(1+K)\gg 2\)_and_\(\omega^{2}LC\gg 1\), Z can be expressed as \(Z=j\omega L(1-K)(1+K)\) and the device shows the inductive behavior desired with an equivalent autoinduction coefficient equal to \(L_{equ}=L(1-K^{2})\).  
Figure 1: PLC circuitry for transmission and reception.
## 2 Proposed Scheme
### On Content Owner Side  
On the content owner side, the original 3D mesh model is first preprocessed and divided into several groups. In each group, the first vertex is denoted as reference and then the closest pair prediction is proposed to predict rest vertices using the reference vertices. By doing so, the original 3D mesh model can be compressed to vacate the spare room before model encryption. Finally, the content owner encrypts the processed model and uploads it to the cloud.