esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Ground Motion in Ireland
Rock-dominated coasts are present mainly in the southwestern, western, and northern regions of Ireland, while the eastern and south-eastern regions are made of unconsolidated Quaternary sediments [40]. The coasts, particularly those of western regions, are exposed to the full effects of eastwards-moving cyclonic weather systems and swell wave energy from the north Atlantic (e.g., [41]). The Irish coastline, and prominent cliffs in particular, nonetheless represents an important tourist attraction. For example, the Cliffs of Moher area (Co. Clare), is one of the main natural attractions in Ireland with almost 1.5 Million visitors per year [42]. The detection and monitoring of such geological hazards with traditional ground-based methods, such as levelling, is usually expensive and time consuming. Moreover, instabilities often occur in remote places not easily accessible, leading to information gaps. Traditional remote sensing techniques used to try to fill such gaps have involved analysis of high-resolution optical images from satellite-based or airborne sensors. More recently, these have been complemented by analysis of digital terrain models derived from airborne light detection and ranging (LiDAR) [43]. Such techniques are limited in areas of temperate oceanic climate such as Ireland, however, by cloud coverage that is frequently extensive at low altitude and by attendant precipitation. In addition, these approaches have tended to be very expensive which can be a limiting factor for low budget projects and/or for large-scale studies. Satellite-based radar imaging offers a potential solution to such limitations, as radar can penetrate cloud cover and can operate during day and night under all-weather conditions. In particular, multi-temporal InSAR techniques can potentially measure mm-scale ground motions and, thus, help identify and study geological hazards, particularly in relation to infrastructure and buildings [44]. Rates of some ground motion events in Ireland, such as landslides or sinkhole collapses, could in principle exceed the ones detectable by InSAR due to local image decorrelation. Nonetheless, precursors to such very rapid motions, as well as slower ground motions linked to other causes, may still occur at detectable rates. One other great advantage of multi-temporal InSAR techniques is the ability to look back in time thanks to archive data starting from more than two decades ago that provide important data sets for understanding the process involved in ground displacements.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 2 State of the Art
### Current State of Continuous Authentication  
The current mobile device market is dominated by point of entry authentication systems. The leading operating systems, Google Android and Apple iOS, have a combined market share of almost 99% [18]. Our research, however, found that there is currently no possibility to enable any form of real continuous authentication on either system, where the verification happens continuously in the background. Both major systems, however, implement some forms of periodically recurring authentication in certain situations, e.g., after an extended period of usage or after a particular form of authentication failed (most prominently, when multiple biometric scan attempts fail, the user is forced to enter the passcode).  
After the Adaptive Security Architecture introduced in 2014 [19], Gartner proposed a new security approach for the digital age, called CARTA (Continuous Adaptive Risk and Trust Assessment) [20] in 2017. They state that the current one-time authentication processes for access protection are fundamentally flawed [20]. The system advises that those contextual access decisions should be made while continuously monitoring all users. This concludes, therefore, that users who are currently logged in also cannot be trusted and have to be evaluated ([[https://www.ssh.com/academy/iam/carta](https://www.ssh.com/academy/iam/carta)]([https://www.ssh.com/academy/iam/carta](https://www.ssh.com/academy/iam/carta)), accessed on 4 December 2021). The company observes the necessity of machine learning, to be capable of performing continuous user authentication with data gathered from a user's behavior. They also claim that traditional passwords will be replaced by AI-enabled systems by 2022, for 90% of all digital authentications [21]. To make authentication continuous, it should use data that the user is producing anyway, like behavioral input [22]. Furthermore, there will always be a trade-off between usability and security [23].
# State of the Art of Telecommunication Systems in Isolated and Constrained Areas
## 4 Assessment of Solutions Comparisons  
As presented in Section 3, each of the solutions mentioned above has advantages and limitations. We compared in Table 2 the different solutions according to the five criteria discussed above. The comparison uses a normative assessment from A (as the best solution) to E (as the worst solution).  
The table shows the dominance of the flying solution and, more particularly, of the balloons. However, such a solution's limiting factor can be the cost with major architectures like the Stratobus. However, a solution with smaller tethered balloons (connected to the ground) that does not require a ground control room to pilot them and large teams for their design and development would be better adapted to the isolated and constrained environment.
# Non-Centrifugal Sugar (NCS) and Health: A Review on Functional Components and Health Benefits
## 3 Chemical Composition  
NCS is a natural sweetener composed of sugar, water, and other substances, such as amino acids, minerals, vitamins, polyphenolic compounds, volatile compounds, and other trace substances. As such, it is rich in macro- and micronutrients. The mean chemical composition of various forms of NCS is shown in Table 1. Sucrose is an essential component, in the range of 76.55 and 89.48%, followed by monosaccharides, glucose and fructose and water (1.5-15.8%) [8,19]. Discrepancies in the processing conditions cause a comparatively broad range of moisture, protein, and fat content below 2% [19]. NCS is composed of an abundance of both primary and trace minerals in the range of 1648.11-2971.90 mg/100 g [8]. Calcium, magnesium, phosphorus, chloride, and potassium are the most presented minerals in NCS products, as shown in Table 2[20]. NCS is a formidable source of iron and contains more iron and copper content than refined sugar [14]. Iron is the component responsible for the anti-anaemia effect of NCS. The cause-and-effect relationship between iron consumption and haemoglobin and red blood cells increase is welcomed by the European Food Safety Agency. As such, Europe permits such claims [24]. Selenium has also been detected in minimally refined brown sugar (MRBS) [23]. Since selenium is an insulin mimic [25], NCS might be a recommended option for patients with diabetes. NCS contains up to 21 amino acids, where the predominant components are asparagine, aspartic acid, phenylalanine, and alanine [8]. Additionally, NCS also contains health-beneficial \(\gamma\)-aminobutyric acid, known as GABA, at levels of 2.32-7.54 mg/100 g [8]. GABA amino acids are involved in the regulation of synaptic transmission, promoting neuronal development and relaxation, and preventing sleeplessness and depression [26]. NCS contains a trace amount of B vitamins such as thiamine (B1), riboflavin (B2), niacin (B3), pantothelic (B5), Pyridoxine (B6), folic acid (B9) and vitamin C, E and A, as shown in Table 3[19]. No less than two rare sugars with functional capacity have been determined in NCS: d-Psicoso and cyclodext or Cycloisomaltooligosaccharides [27,28]. Policosanols (PCs) were also found in NCS products [15]. PCs are a mixture of primary long-chain alcohols, which possess cholesterol and lipid-reducing properties [21,29].  
\begin{table}
\begin{tabular}{c c c} \hline
**Nutrients** & **Unit** & **Quantity** \\ \hline Moisture & g & 1.5–15.8 \\ Carbohydrate & g & 83.90–97.2 \\ Total sugar & g & 87.5–95.4 \\ Sucrose & g & 76.55–89.48 \\ Reducing sugar & g & 3.69–10.5 \\ Fiber & g & 0.00 \\ Protein & g & 0.37–1.7 \\ Fat & g & 0.00–0.10 \\ Ash & g & 0.3–3.6 \\ Amino Acids & mg & 205.22–805.28 \\ Total Minerals & mg & 1648.11–2971.90 \\ \end{tabular}
\end{table}
Table 1: General Chemical Composition of NCS [8,19].
# Field Study on Earth Pressure of Finite Soil Considering Soil Displacement
## 2 Project Overview  
Jiuhua Avenue Tunnel (Phase II) and Ziwei Road Tunnel Project are located in the Peninsula block in the West District of Quzhou City, China. Several large-scale projects are under construction at the same time in the Peninsula block. The three underground garage foundation pits of the Cultural Pioneer Park project (Areas A, B, and C) are adjacent to the Jiuhua Avenue Tunnel (Phase II), as shown in Figure 1.  
The traditional \"deep first and shallow\" foundation pit excavation construction sequence is more conducive to construction safety and structural quality, but at present, the foundation pit of the surrounding projects will be ahead of the Jiuhua Phase II of this project. During construction, the excavation step of adjacent foundation pits \"shallow first and then deep\" has higher requirements on the control of deformation. Additionally, the minimum clearance between the Area B and the Jiuhua Avenue Tunnel (Phase II) is only 4.7 m.  
Therefore, in this project, the deformation of the envelope structure is tested by burying optical fibers in the test piles. The three test piles are JZ905, JY843, and JY919, respectively. The pile lengths are 19.1 m, 19.8 m, and 19.1 m, and the pile diameters are 0.8 m, 0.8 m, and 1 m, respectively. For JZ905: the original ground elevation is 67.2 m, the crown beam elevation is 64.6 m, the first steel support is 60.3 m, the second steel support is 57.1 m, and the base plate is 52.6 m. For JY843: the original ground elevation is 66.7 m, the crown beam elevation is 65.3 m, the first steel support is 60.3 m, the second steel support is 57.6 m, and the base plate is 52.8 m. For JY919: the original ground elevation is 67.2 m, the crown beam elevation is 64.6 m, the first steel support is 60.3 m, the second steel support is 57.1 m, and the base plate is 52.6 m.  
Since the optical fiber of the test pile JZ905 was damaged during the test, only the deformation of JY843 (No. 1 pile) and JY919 (No. 2 pile) and the corresponding earth pressure were tested and analyzed (Figure 2). The buried depths of the earth pressure cells at each measuring point were 3 m, 6 m, 9 m, 12 m, and 15 m; they are mainly located in the silty clay layer and pebble layer, and their static earth pressure coefficients are 0.5, 0.5, 0.3, 0.3 and 0.3, respectively. The soil parameters are shown in Table 1.  
Figure 1: Project site.
## 3 Results and Discussion
### Microscopical Analysis by Scanning Electron Microscopy (SEM)  
Figures 3 and 4 present transverse and frontal SEM images of HS1 and HS2, respectively. These images facilitate the observation of the shape and size of cell types within a hemp shiv particle, including parenchyma cells, dotted vessels, spiral tracheids, and round microorganisms that appear to be either bacteria or spores aggregated within the parenchyma cells (cluster of microorganisms encircle in red in Figure 3). Some artifacts (encircle in red in the Figure 4), which are unintended features likely caused by the sample preparation process, have also been observed on the surface of the hemp shiv (Figure 4).  
The SEM images of HS1 (Figure 3) reveal a higher concentration of microorganisms compared to HS2 (Figure 4). A variety of vessel sizes, ranging from 15 to 60 \(\upmu\)m, can be observed. Although the hemp shiv samples were not retted, it is plausible that these microorganisms originated from the soil. Previous studies have highlighted fungi as a significant component of the microflora, particularly in retted hemp, while other research has suggested the coexistence of two microbial communities' bacteria such as _Pseudomonas_ and _Escherichia coli_, and fungi like _Cryptococcus_ and _Cladosporium_[49]. Furthermore, optical microscopy identified colonies of _Penicillium_ and _Aspergillus_ types within the samples, which are known to contribute to the enzymatic hydrolysis of pectins through the action of pectinases (including polygalacturonases, pectin lysates, and pectin esterases). The clusters observed in the SEM images may consist of these fungi and bacteria, reinforcing the potential for bacterial-fungal interactions (BFIs) as discussed earlier [49; 50].  
Additionally, the cell wall images reveal the porosity of the hemp shiv samples. Once dried, the plant material's pores trap air, contributing to the lightness of the material and resulting in low thermal conductivity expressed in Watts per meter Kelvin, making it an effective insulator. These physical characteristics can be further explored through density measurements.  
Figure 3: SEM images of HS1 at \(\times\)500 and \(\times\)1000 magnification in longitudinal (**a**–**c**) and transverse (**d**,**e**) sections. The imaging parameters are presented below each image.  
Figure 4: SEM images of HS2 at \(\times\)500 and \(\times\)1000 magnification in longitudinal (**a**-**c**) and transverse (**d**,**e**) sections. The imaging parameters are presented below each image.
## 3 Results
### Heavy Metal Removal
#### 3.2.3 Zinc Removal Rate  
Figure 6 shows the zinc removal efficiencies of kaolin and kaolin/nanomaterial samples over 90 days. The initial zinc value in the leachate was measured as 3.07 mg/L. On the 30 th day, the zinc removal rate of all kaolin samples was below 25%. On the 60 th day, the zinc removal efficiencies of kaolin samples increased, and the zinc removal rate varied between 35.83% and 50.16%. The lowest zinc concentration and the highest zinc removal rate were 1.53 mg/L and 50.16%, respectively, for kaolin + 5% Oltu clay at day 60. At the end of the 90 th day, the zinc removal efficiency of the samples decreased. When the ratio of nanomaterials in kaolin increased from 1% to 5%, the zinc removal rates also increased.  
Figure 5: Manganese removal rate of kaolin and kaolin/nanomaterial samples.  
In terms of zinc removal rate, Oltu clay, aluminum oxide and iron oxide added to kaolin showed better performance, respectively. The small size and high surface area of Oltu clay provided the best zinc removal performance among all of the nanomaterials.  
The addition of 1% Oltu clay to kaolin showed higher performance in terms of zinc removal rate than kaolin with 5% iron oxide and 5% aluminum oxide. For example, even over a long operating time (90 days), kaolin with 1% Oltu clay offered 6% and 2% higher zinc removal performance than kaolin with 5% iron oxide and 5% aluminum oxide. These results show that the use of a lower amount of Oltu clay is more beneficial than the use of higher amounts of iron oxide or aluminum oxide in terms of increasing the zinc removal rate.
# Defining Blood Plasma and Serum Metabolome by GC-MS
## 5 Workflow of GC-MS Analysis of Blood Metabolome
### Gas Chromatography
#### 5.2.5 Multidimensional Chromatography  
In some cases, even high-resolution gas chromatography may be insufficient for the reliable separation of complex biological mixtures.  
With two- or even three-dimensional [130] chromatographic systems, that run the sample through two orthogonal columns in series, advanced resolution can be achieved. The second column is usually much shorter than the first, is filled with a different stationary phase, and operates at a higher temperature. If two metabolites leave the first column simultaneously, then the second column can provide separation due to a different stationary phase and temperature conditions [123; 131; 132]. For example, 100 metabolites in blood were identified using this method, divided in the first column by volatility and in the second by polarity [133].  
In another study, which compared serum analysis by one-dimensional and two-dimensional gas chromatography in combination with mass spectrometry, there was a threefold increase in both the total number of peaks (490 \(\pm\) 26 and 1571 \(\pm\) 174, respectively) and the number of identified metabolites (348 \(\pm\) 16 and 1099 \(\pm\) 118, respectively) in the case of GC\(\times\)GC-MS [134].  
A modulator is located between the columns in a 2D chromatographic system, regulating the temperature or pressure [135]. The modulator continuously accumulates small fractions of the substance from the first column and sends the already concentrated portions to the second column with short pulses for additional separation.  
Focusing gives an additional advantage: high and narrow chromatographic peaks (about 100 ms at baseline [65]) increase the resolution and sensitivity of the analysis [135; 136; 137].
## 2 Materials and Methods
### Feature Extraction and Classification Algorithms
#### 2.5.2 Canonical Correlation Analysis  
CCA is an unsupervised machine learning algorithm used to explore the relationship between two sets of variables, \(X=(X_{1},X_{2},X_{3},\ldots,X_{n})\) and \(Y=(Y_{1},Y_{2},Y_{3},\ldots,Y_{n})\). The fundamental idea of CCA is to find two sets of non-zero vectors, \(\alpha=(\alpha_{1},\alpha_{1},\alpha_{1},\alpha_{1},\ldots\alpha_{n})\) and \(\beta=(\beta_{1},\beta_{1},\beta_{1},\beta_{1},\ldots\beta_{n})\), which are used to linearly combine the variables in each set into new variables, \(U\) and \(V\). Specifically, the linear combinations are defined as \(U=\alpha_{1}X_{1}+\alpha_{2}X_{2}+\cdots+\alpha_{n}X_{n}\) and \(V=\beta_{1}Y_{1}+\beta_{2}Y_{2}+\cdots+\beta_{n}Y_{n}\). By applying the coefficient vectors \(\alpha\) and \(\beta\), the analysis shifts from examining the relationship between the original variable sets, \(X\) and \(Y\), to investigating the correlation between the derived variables, \(U\) and \(V\).  
In the process of using CCA to identify SSMVEP signals, a set of sine and cosine functions must be constructed as reference signals, as defined by the following equation:  
\[Y=\begin{pmatrix}\sin(2\pi f_{i}t)\\ \cos(2\pi f_{i}t)\\ \vdots\\ \sin(2\pi Hf_{i}t)\end{pmatrix},t=\begin{bmatrix}\frac{1}{F_{s}},\frac{2}{F_{s }},\ldots,\frac{N}{F_{s}}\end{bmatrix} \tag{20}\]  
In this equation, \(H\) denotes the number of harmonics, \(N\) represents the number of sampling points, and \(F_{s}\) is the sampling frequency.  
The recorded evoked EEG signal is denoted as \(X=(X_{1},X_{2},X_{3},\ldots,X_{C})\), where \(C\) is the number of electrode channels. By solving Equation (21), a set of canonical correlation coefficients between the EEG signal \(X\) and the reference signal \(Y\) can be obtained, represented as \(\rho_{f_{i}}=(\rho_{1},\rho_{2},\rho_{3},\ldots,\rho_{C})\).  
\[\rho_{(XY)}=\frac{E(\alpha^{T}XY^{T}\beta)}{\sqrt{E(\alpha^{T}XX^{T}\alpha)E( \beta^{T}YY^{T}\beta)}} \tag{21}\]  
After computing each set of data, four correlation coefficient vectors, \(\rho_{f_{i}}\), are obtained, each corresponding to a specific flickering frequency, \(f_{i}\), as shown in Equation (22). The frequency associated with the maximum correlation coefficient \(\rho_{\rm x}\) in the vector \(\rho_{f_{i}}\) is selected as the final identified target frequency, denoted as \(f_{target}\).  
\[f_{target}=\underset{f_{i}}{\rm argmax}\,\rho_{\rm x} \tag{22}\]  
Since the CCA algorithm employs the correlation coefficient corresponding to each stimulus frequency as the basis for classification, its accuracy can to some extent reflect the quality of the evoked signals induced by the paradigm.
## 4 Validation Through Sea Trials
### Processing and Analysis of the First Group of Sea Trial Data
This indicates that relying on the maximum correlation peak to select the CFO compensation position can lead to inaccuracies, whereas using the correlation cost factor allows for the selection of CFO compensation values that align with the correct frequency shift trend. Figure 11b,d shows the despreading results for symbols 32 and 33 under different CFO compensations for FDE-MSS, which are completely erroneous. From Figure 11, it is evident that there are significant differences in the range of CFO compensations between the two reception methods, which can be attributed to the fact that FDE-MSS did not utilize the cost factor for Doppler estimation, resulting in inaccuracies in the estimation direction, leading to a gradual accumulation of errors and subsequent decoding issues. Figure 9: The accuracy rates of each frame obtained by different methods: (**a,b**) correspond to the first sea trial, (**c,d**) correspond to the second sea trial, and (**e,f**) correspond to the third sea trial.
## 5 Experimental Results and Discussion  
This paper selects the Hitotsubashi and Shinjuku area in Tokyo to be the experimental middle and deep urban canyons, respectively. The constructed 3D building models are shown in Figure 6. The single point GPS positioning results, such as weighted least square (WLS), are poor in the two areas. The tests in this paper are performed at two sides of a street and a road intersection.  
\begin{table}
\begin{tabular}{|c c c c c c c c|} \hline
**DOF** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline
**Value** & 22.93 & 27.64 & 30.67 & 33.37 & 35.89 & 38.25 & 40.53 \\ \hline \end{tabular}
\end{table}
Table 2: Values of chi-square threshold (probabilities of false alarm is \(10^{-4}\)) for given degree of freedom (DOF).  
The cut-off angle is 20\({}^{\circ}\) in this paper. The data were collected on 5 November 2014, 14 December 2014 and 26 January 2015. The durations of both data are about 210 s. This paper uses a commercial grade receiver, u-blox EVK-M8 GNSS model. The u-blox receiver is set to output pseudorange measurements and positioning result every second. GPS, GLObal NAvigation Satellite System (GLONASS) and QZSS measurements are used in this paper. The quasi ground truth is generated using a topographical method. The video cameras are set in the 18 th and 9 th floors of a building near the Hitotsubashi and Shinjuku area, respectively, to record the travelled path. The video data output by the cameras are used in combination with one high-resolution aerial photo we bought to get the ground truth data. The aerial photo is 25 cm/pixel and therefore the error distance for each estimate can be calculated. This paper evaluated the lateral positioning error. There are two conventional positioning methods that used for the purpose of comparison. The first one is WLS, and the weighting matrix follows the manual of RTKLIB, which is an open source program [40]. The second one is the WLS using only the LOS visible satellites. The LOS satellites are determined by the ray-tracing results based on the ground truth trajectory. The performance metrics used are the mean and standard deviation of the lateral error and the availability. Availability means the percentage of solutions in a fix period. For example, if a method outputs 80 epochs in a 100 s period, the availability of the method is 80%. Note that the positioning solution will be excluded if its error is larger than 100 m. In addition, the satellite will be excluded if its C/N\({}_{0}\) is less than 25 dB-Hz.
## 2 Theoretical Analysis
### Principle of Double-Exposure Method
By comparing the intensity formulas of the single-exposure method (Equation (8)) and the double-exposure method (Equation (25)), it is evident that the latter provides significantly enhanced fringe visibility and reduced noise [18; 19]. **Fringe extrema:**  
\[\left\{ \begin{matrix}\Delta\phi(x,y)=2n\pi&n=0,\pm 1,\pm 2,\dots&I(x,y)=I_{ \text{max}}(x,y)=2I_{0}&\text{Bright stripes}\\ \Delta\phi(x,y)=(2n+1)\pi&n=0,\pm 1,\pm 2,\dots&I(x,y)=I_{\text{min}}(x,y)=0& \text{Dark stripes}\end{matrix} \right. \tag{28}\]  
**Fringe contrast:**  
\[V=\frac{I_{\text{max}}(x,y)-I_{\text{min}}(x,y)}{I_{\text{max}}(x,y)+I_{\text{ min}}(x,y)}=1 \tag{29}\]  
In double-exposure holography, interference patterns are perfectly modeled by a cosine function. These patterns depend only on phase changes from deformation, ensuring ideal fringe contrast (near 1) and minimal noise.