esa-sceva/satcom-chunk-collection huggingface dataset:
## Nomenclature  
\begin{tabular}{l l} \(O\) & centre of the ball \\ \(ou,od\) & inner upper/lower raceway curvature center \\ \(iu,id\) & outer upper/lower raceway curvature center \\ \(iu^{\prime}\) & outer upper curvature center after optimization \\ \(O_{0}\) & centre of the bearing \\ \(o_{i,r}\) & radial distance between curvature center of inner raceway and ball center in the \\ \(o_{i,r}\) & case of a bearing without clearance \\ \(o_{i,a}\) & axial distance between inner (outer) upper raceway center and ball center in the \\ \(o_{i,a}\) & case of a bearing without clearance \\ \(o_{o,r}\) & radial distance between curvature center of outer raceway and ball center in the \\ \(o_{o,a}\) & case of a bearing without clearance \\ \(o_{o,a}\) & axial distance between inner (outer) lower raceway center and ball center in the \\ \(D_{w}\) & rolling element (ball) diameter \\ \(D_{pw}\) & bearing pitch circle diameter \\ \(g_{0}\) & clearance \\ \(\psi\) & angle defining circumferential position of the rolling element (ball) \\ \(\alpha_{0}\) & nominal contact angle \\ \(C\) & contact point \\ \(r\) & raceway curvature radius \\ \(f\) & raceway curvature coefficient \\ \(T\) & transformation matrix \\ \(u,v,w\) & translations of the bearing ring along axes x, y and z \\ \(\varphi_{x},\varphi_{y}\) & rotations of the bearing ring around axes x and y \\ \(x,y,z\) & axes of Cartesian coordinate system \\ \(x^{\prime}\) & axis in the radial direction of the bearing defining vertical plane at angle \(\psi\) \\ \(Z\) & number of rolling elements (ball) \\ \(\delta\) & relative approach of the two opposite raceways \\ \(\delta^{\prime}\) & relative approach of the two opposite raceways after optimization \\ \(\Delta\) & displacement of the centre of the raceway \\ \(\stackrel{{\rightarrow}}{{r}}\) & position vector \\ \(\stackrel{{\rightarrow}}{{e}}\) & unit vector of the contact force \\ \(K\) & contact stiffness \\ \(F,M\) & vectors of forces/tilting moments acting on the bearing \\ \(F_{x},F_{y},F_{z}\) & scalars of radial force/axial force acting on the bearing \\ \(M_{x},M_{y}\) & scalar of overturning moment acting on bearing \\ \(Q\) & contact force acting on the raceway in scalar form \\ \(Q^{\prime}\) & contact force acting on the raceway in scalar form after optimization \\ \(Q_{ci(o)j}\) & basic rated dynamic load bearing \\ \(\lambda,\eta\) & correction coefficient of ball bearing \\ \(L_{10}\) & rated life of bearings \\ \(\gamma\) & \(D_{w}\cos\alpha_{0}\) \\ \(\gamma\) & \(D_{pw}\) \\ \(\gamma\) & \(i,o\) & inner/outer ring \\ \(j\) & subsequent number of contact force \\ \(\psi\) & value of parameter at azimuth (angle) \(\psi\) \\ \end{tabular}
## 3 Results and Discussion
### Characterization of the MWCNTs
#### 3.1.5 Raman Characterization  
A Raman spectrum displays different peaks that undergo alterations with changes in the characteristics of a sample to identify the chemical composition or microstructure constituents of the sample. A Raman spectrum of the MWCNTs is represented in Figure 5. The peak intensity ranging from \(\sim\)1300 to 1350 cm\({}^{-1}\) represents disordered carbon (D band), while peaks ranging from \(\sim\)1580 to 1600 cm\({}^{-1}\) represent graphitic carbon (G band). The D peak for the McNNTs occurred at \(\sim\)1325 cm\({}^{-1}\), while G peaks were observed at \(\sim\)1580 cm\({}^{-1}\). The ratio of ID/IG was calculated to estimate the variation of CNTs' quality and quantity of different defects. The ratio for the CNTs was 0.83; a smaller ID/IG ratio (\(<\)1.0) indicates a higher quality of CNTs [48]. A sample has quite a high number of defects if the intensity of these peaks is proportional [49]. Additionally, the peaks at 10-200 cm\({}^{-1}\) are assigned to the lattice vibrations in crystals. The absorption peaks at 640, 830, 1080, and 1450 cm\({}^{-1}\) are attributed to the \(\upsilon\)(C-S), \(\upsilon\)(C-O-C), \(\upsilon\)(C=5), \(\delta\)(CH\({}_{3}\)), and \(\delta\)(CH\({}_{3}\)) asymmetric, respectively. Furthermore, the absorption peak at 1630 cm\({}^{-1}\) was caused by \(\sim\)C=N group stretching. The absorption band at 1806 cm\({}^{-1}\) is assigned to the C=C group. The carbonyl group was found in 2000 cm\({}^{-1}\). However, the peaks at 3150 and 2570 cm\({}^{-1}\) are assigned to \(\upsilon\) (O-H) and \(\upsilon\) (S-H), respectively.
## 3 Results and Discussion
### Adsorption and Desorption Experiments
#### 3.2.7 Analysis of Real Sample  
The columns with microspheres prepared as a solid phase were applied to the preconcentration and determination of trace cadmium ions in natural water samples. Tap water samples were collected from our research laboratory. Spring water samples were collected from the Baicheng county of Aksu in Xinjiang, China. River water samples were collected from the Aksu River in Xinjiang, China. The enrichment factor reached 20 times by the columns. For the analysis of natural water samples, the standard addition method was used; the results are given in Table 4. The recovery of the added 1 and 5 \(\mu\)g L\({}^{-1}\) Cd(II) in tap water, spring water and river water exceeded 96%, indicating that Cd-I-MA-co-AN can be applied to preconcentrations of Cd(II) from natural water.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Metal Ion**} & \multicolumn{2}{c}{**Distribution Coefficient D**} & \multicolumn{2}{c}{**Selectivity Coefficient \(\alpha\)**} & \multirow{2}{*}{**Relative Selectivity**} \\ \cline{2-2} \cline{4-6}  & **Cd-I-MA-_co_-AN** & & & **Cd-I-MA-_co_-AN** & & **NI-MA-_co_-AN** & **Coefficient k** \\ \hline Cd(II) & 3.58 & 1.18 & & & \\ Cu(II) & 0.236 & 0.294 & 15.2 & 4.01 & 3.79 \\ Mn(II) & 0.873 & 0.975 & 4.10 & 1.21 & 3.39 \\ Ni(II) & 0.389 & 0.500 & 9.20 & 2.36 & 3.90 \\ Pb(II) & 1.19 & 1.30 & 3.01 & 0.908 & 3.31 \\ \hline \end{tabular}
\end{table}
Table 3: Adsorption selectivity parameters of Cd-I-MA-_co_-AN and NI-MA-_co_-AN.  
Figure 9: Freundlich isotherm model.
# A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models
## 1 Introduction
### Related Works
The ETSI ISG RIS aims to identify and address some technical challenges that need to be adequately solved before the RIS can be adopted in future communication standards. The propagation channel in IS-aided communication systems at the THz band has not been sufficiently explored in the literature, despite the IS applications have being able to mitigate the signal restrictions caused by the frequency selectivity of the THz communication channel. Most existing surveys and review papers have focused on summarizing various possible applications of IS in wireless communication [1; 2; 3; 43; 28; 44; 45; 46]. This paper emphasizes ISs operating at the THz band with the potential to impact THz communication channel modeling. Based on this research, it is possible to clearly understand the IS design and the progress in the IS technology. Moreover, this paper presents a meaningful overview of the fundamentals, design principles (hardware and control), and selectivity of THz channels, which can be modified by the ISs.
# Bitumen and Bitumen Modification: A Review on Latest Advances
## 2. Bitumen Polymers
### Natural and Synthetic Rubber
#### 2.4.2 Synthetic Rubber  
Synthetic rubber is a man-made rubber which is produced in manufacturing plants by synthesizing it from petroleum and other minerals. Synthetic rubber is basically a polymer or an artificial polymer. It has the property of undergoing elastic stretch ability or deformation under stress, but can also return to its previous size without permanent deformation. Styrene butadiene rubber (SBR), polyisoprene, and polybutadiene, polychloroprene, and tire rubber are some of the synthetic rubbers commonly used today. From the mentioned categories, synthetic rubber obtained from end-of-life tires (ELTs) are common for producing rubberized bitumen binders for both paving and industrial application. To date many studies have investigated synthetic rubbers from different points of view. From these studies, it can be concluded that many factors can influence the performance and mechanical properties of rubberized binders including: the size and content of rubber [88; 89; 90], the chemical structure [91; 92], particles surface properties (ambient granulating or cryogenically crushed tire rubber) [93], blend production method and temperature [94; 95].  
Among the aforementioned factors, probably the most important one is the optimum percentage of rubber in the binder. Many research works have been conducted comparing the performance and mechanical properties of mixtures containing different amounts of rubber. However, it greatly depends on the application method (dry or wet), when it is used in asphalt mixtures and the characteristics of the base binder. Regarding rubberized bitumen, ASTM D6114 defines asphalt rubber (rubberized bitumen) as material consisting of the virgin binder and a minimum of 15% crumb rubber (by the weight of total blend) [96]. In addition, many studies concluded that while a low rubber content, around 4% (to the weight of bitumen), has almost no effect, or at least no significant effects on the performance and mechanical properties of the binder, more than 20% was found to be unsuitable.
# Understanding the Fracture Behaviors of Metallic Glasses--An Overview
## 3 Fracture Mechanism of MGs
### Discontinuous Stress/Strain Field  
For polycrystalline alloys with work hardening effect, the deformation and stress/strain field can be approximately continuous. For MGs, however, due to the shear softening effect [33; 34; 96], the viscous flow in MGs at room temperature will localize and form shear bands [16; 34]. Since the shear transformations and shear band motions can be considered as thermal activated events [97], the position and the moment of shear band formation are random. After formation, the shear band can slide at a relatively lower stress [16; 27; 34], with a strain rate much faster than the external load [98; 69]. During further loading, the shear bands delaminate and cause rapid fracture of the material [38]. As a result, the spatial and temporal distribution of strain/stress field in MGs during the fracture process will be discontinuous. The global stress/strain will also change discontinuously, observed as the serrations  
Figure 7: Fractograph morphology of a brittle Mg-Cu-Ag-Y MG after tension fatigue fracture. (**a**) The overall appearance of the fractograph. (**b**) Enlarged image of the crack initiation site. (**c**) The boundary between the regular fatigue striations and the fast fracture region. (**d**) The morphology at the end of the fractograph. (**e**) Enlarge image of the regular fatigue striations. (**f**) Enlarged image of the fast fracture region (**g**) Enlarge image of the droplet feature at the end of the fractograph.  
on the loading curves [13; 25; 99]. The strain in the shear bands is highly localized, while the other places are still elastic.
## 4 Analysis of Results
### Reliability, Validity and Fit Analysis
#### 4.2.2 Validity  
Convergent Validity  
As shown in Table 7, the combined reliability of perceived ease of use is 0.9397, and the average extraction of variance is 0.7578; the combined reliability of perceived usefulness is 0.9410, and the average extraction of variance is 0.7623; the combined reliability of attitude to use is 0.9492, the average variance extraction is 0.7579; the combined reliability of behavioral intention is 0.9598, and the average variance extraction is 0.8270; the combined reliability of transaction security is 0.9590, and the average variance extraction is 0.7990; The combined reliability values of all facets were greater than 0.7, and the average variance extraction values were greater than 0.5, indicating that the model had convergent validity.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Construct**} & \multirow{2}{*}{**Indicator**} & \multicolumn{2}{c}{**Standardized Factor**} & \multicolumn{2}{c}{**Unstandardized Factor Loading**} & \multirow{2}{*}{_p Value_} & \multirow{2}{*}{**GFI**} & \multirow{2}{*}{**CFI**} & \multirow{2}{*}{**RMSE**} \\  & & & & & & & \\ \hline \multirow{4}{*}{BI} & BI1 & 0.857 & 1 & *** & & \\  & BI2 & 0.921 & 1.053 & *** & & & \\  & BI3 & 0.912 & 1.035 & *** & 0.902 & 0.958 & 0.222 \\  & BI4 & 0.923 & 1.054 & *** & & & \\  & BI5 & 0.932 & 1.050 & *** & & & \\ \hline \hline \end{tabular}  
* *** \(p\) < 0.001. Source: calculated by this research.  
\end{table}
Table 4: CFA analysis for behavioral intention to use.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Construct**} & \multirow{2}{*}{**Indicator**} & \multicolumn{2}{c}{**Standardized Factor**} & \multicolumn{2}{c}{**Unstandardized Factor Loading**} & \multirow{2}{*}{_p Value_} & \multirow{2}{*}{**GFI**} & \multirow{2}{*}{**CFI**} & \multirow{2}{*}{**RMSE**} \\  & & & & & & & & \\ \hline \multirow{4}{*}{TS} & TS1 & 0.924 & 1 & *** & & & \\  & TS2 & 0.933 & 1 & *** & & & \\  & TS3 & 0.953 & 1.008 & *** & & & \\  & TS4 & 0.940 & 1.008 & *** & 0.911 & 0.964 & 0.179 \\  & TS5 & 0.953 & 0.992 & *** & & & \\  & TS6 & 0.608 & 0.602 & *** & & & \\ \hline \hline \end{tabular}  
* *** \(p\) < 0.001. Source: calculated by this research.  
\end{table}
Table 5: CFA of perceived usefulness.  
The combined reliability (CR) and average extraction of variance (AVE) values of each aspect are listed in Table 7.
## 5 Application Example
### Doujiagou Tunnel  
The central mileage of Lonely Gully Tunnel on the Baotou-Lanzhou Railway is K892 + 209. It is a single-line tunnel with straight wall lining. The thickness of the arch ring is 60 cm, and the thickness of the bottom pavement is 10 cm. It was built in 1957 and has a total length of 564.6 m. The shallow layer of the surface is loess-sandy clay. The tunnel body mainly passes through the conglomerate layer, and the thickness of the vault rock layer is thin. The surface of the tunnel section is irrigated land. In recent years, the vault at the entrance of the tunnel has cracked, and the leakage of water has corroded the arch lining. In the rainy season and irrigation season, the drainage ditch in the 10 + 6-15 + 6 section of the tunnel is silted up with yellow silt, which needs to be cleared every 3 months. When the rainfall is significant, the silt comes out of the cover plate of the drainage ditch, and the mud is poured from the ballast bed, causing traffic safety hazards.  
In March 2019, it was decided to excavate a drainage tunnel 3 m outside the side wall of the left side of the large refuge recess in the 13 + 5 section to carry out the drainage of the pilot hole to control the silt of the side drainage ditch and the mud pumping of the ballast bed. The small refuge recess on the right side of the 17 + 15 section was used to drain the water in the surrounding rock near the side wall of the dive chamber, and the dark groove drainage was applied on the left side of the 18 + 4-5 section.
###### Abstract
As a result, LSTMs are especially great at handling arrangements of information such as content, discourse, and common time series. In particular, the RNN structure resembles the hidden Markov model (HMM). The main difference is the way that the parameters are calculated and created. One of the LSTM advantages is the insensitivity to the gap length. RNN and HMM focus on the hidden state before sequence. In the case that we wish to make a prediction of a sequence after 1000 intervals instead of 10, the model will forget the starting point; however, LSTM has a memory to perform this task. A detailed description of the LSTM and an application regarding field data forecasting can be found in [24]. In addition, the original LSTM is very slightly not enough in explaining the relationship of the input and output of the networks. In [25], to solve this problem, the attention-based mechanism is inserted into LSTM. Moreover, in [26] local air pollution measurements close to main streets, which reside on small Internet of Things (IoT) devices in combination with artificial intelligence algorithms were used for prediction of PM\({}_{10}\) and PM\({}_{2.5}\) concentration. Another work based on the ARIMA model is in [27], whereby the trend analysis as well forecast of PM2.5 in Fuzhou, China has been undertaken. In this paper, we collect data from a set of environmental sensors, which form a wireless environmental sensor located in the broader area of the port of Igoumenitsa in Greece. The station measures the most important pollutants including the CO in the port. We analyse the CO measurements at 6-h intervals for the entire dataset. We show that there exists an abnormal rise in values. Thereafter, we transform the dataset to a moving average values dataset, in order to reduce extremely high values. The moving average is a popular method by which to transform a dataset to a smoother problem, in terms of the values used for the prediction phase. Moving average smoothing is a naive and effective technique in time series forecasting. It can be used for data preparation, feature engineering, and even directly for making predictions. Importantly, we utilise machine learning and especially LSTM to show the predicted outcome of the collected values from the port by using different batches of the procedure. The main contributions of this paper are the following. * We produce a dataset whereby the CO is measured at a fixed period of time, in order to utilise it in the LSTM and ARIMA models. * We devise an LSTM model for values prediction, and we show that the results are close to the actual values when the batch number is 7000. * We perform a comparison with the ARIMA model, and show that the LSTM accomplishes similar results to the ARIMA, with the ARIMA being better in the forecasting. The remainder of this paper is as follows: Section 2 gives the related work, Section 3 provides details about the wireless station used, Section 4 provides the data description, Section 5 gives the method used for the prediction of the CO, Section 6 presents the results of the algorithms, and Section 7 provides the conclusions of this work.
# Low-Temperature Direct Bonding of SiC to Si via Plasma Activation
###### Abstract  
We investigated the low-temperature direct bonding of SiC/Si via O\({}_{2}\) plasma activation. After optimization, a high bonding efficiency of over 90% was obtained. Surface activation was achieved via reactive ion etching (RIE) O\({}_{2}\) plasma for 30 s without significant bombardment damage. A smooth and void-free interface was observed by transmission electron microscopy (TEM), while a significant amorphous oxide layer was also detected. By increasing the annealing temperature from 150 to 300 \({}^{\circ}\)C, the amorphous layer decreased drastically from 48 nm to 11 nm. Based on systematic experiments and analysis, the mechanism of SiC/Si low-temperature plasma-activated bonding was discussed.  
direct bonding; plasma activation; silicon carbide; bonding interface
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### INS Components
#### 3.1.3 Bridge Navigational Watch Alarm System (BNWAS)  
The BNWAS monitors the awareness of the bridge team to prevent marine accidents. The BNWAS has three operational modes, namely \"Automatic\", \"Manual ON\", and \"Manual OFF\". In Manual ON mode, it is continuously enabled. In Manual OFF mode, it is continuously disabled. In Automatic mode it is activated automatically whenever the heading or track control system of the vessel is in operation. The BNWAS should be reset within an interval set by the OOW. Otherwise, visual and audible alarms sound in the bridge, the back-up officer's or/ and master's cabin, and the locations of other crew members who can take corrective action. The alarm can be reset through reset buttons, or automatically by motion detectors on the bridge. The BNWAS should also provide an Emergency Call function by means of a push-button [54].
# TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
## 2 Preliminary
### Long Short-Term Memory  
An LSTM network is a special form of RNN, which is a type of gated RNN. LSTM was proposed by [PERSON] and [PERSON] in 1997. In the earliest LSTM model, the ingenious concept of self-circulation was introduced to generate a path for the long-term continuous flow of gradients [PERSON] and [PERSON] (1997). One of the most important extensions was to make the weight of self-looping context-sensitive rather than fixed. This overcomes, to some extent, the most direct gradient disappearance or explosion problem caused by a traditional RNN due to an excessive number of layers in the time dimension. By using the hidden layer as a memory unit, the LSTM network can handle correlations between time series, both for short and long periods of time. Among them, the gated structure controls the weight of the self-loop, and the accumulated time scale can be dynamically changed. Even for the LSTM of fixed parameters, the time scale can be changed due to the input sequence.  
The LSTM model actually uses an LSTM cell structure to replace the cellular structure of the RNN hidden layer, giving it the ability to remember for long periods of time. After continuous improvement, the cell structure of the most widely used LSTM model is shown in Figure 2. Among them, the input  
Figure 1: Grid data of ocean temperature.  
gate controls whether its value can be added to the state (\"memory cell\"). The state unit can be linearly self-looping, and the forgetting gate controls its weight. The output of the cell is controlled by an output gate that can be controlled to turn off. All gating units can perform a sigmoid nonlinear transformation, and the input unit can have any compression nonlinearity. The entire calculation of the LSTM network can be defined by a series of equations as follows:  
\[i_{t} =\sigma(W^{i}H+b^{i}) \tag{1}\] \[f_{t} =\sigma(W^{f}H+b^{f})\] (2) \[o_{t} =\sigma(W^{o}H+b^{o})\] (3) \[c_{t} =\tanh(W^{c}H+b^{c})\] (4) \[m_{t} =f_{t}\bigodot m_{t-1}+i_{t}\bigodot c_{t}\] (5) \[h_{t} =\tanh(o_{t}\bigodot m_{t}) \tag{6}\]  
Among the equations, \(i_{t}\), \(f_{t}\), \(o_{t}\), \(c_{t}\) denote enter threshold values, forgetting gate values, output gate values, and new states of memory cells. \(\sigma\) is a sigmoid function, and \(W^{i}\), \(W^{f}\), \(W^{o}\), and \(W^{c}\) are weight matrices. \(b^{i}\), \(b^{f}\), \(b^{o}\), and \(b^{c}\) are the corresponding offset terms. \(H\) is the concatenation of the new input \(x_{t}\) and the previous hidden vector \(h_{t-1}\). \(m_{t}\) is the final state of the memory cell, and \(h_{t}\) is the final output of the memory unit.