esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Tracking Technique Based on BPSK-CSK Signals
## 3 Results
### Tracking Error Comparison and Analysis  
The tracking accuracy of BPSK-CSK signals is quantitatively analysed, and the code-tracking accuracy and carrier-tracking accuracy of the three tracking methods are simulated under different carrier-to-noise ratios. The results are shown in Figures 9 and 10. From the point of view of code-tracking error, the tracking accuracies of the three methods are comparable when the carrier-to-noise ratio is higher than 44 dB-Hz. As the carrier-to-noise ratio decreases, the code-tracking error of this paper's method is between Method 1 and Method 3. As far as the carrier-tracking error is concerned, the tracking accuracies of the three methods are comparable when the carrier-to-noise ratio is higher than 40 dB-Hz. As the carrier-to-noise ratio decreases, the carrier-tracking error of the methods in this paper is smaller than 44 dB-Hz. As the carrier-to-noise ratio decreases, the  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**CNR/(dB-Hz)**} & **Method 3 Higher than** & **Method 2 Higher than** \\  & **Method 1/\%** & **Method 1/\%** \\ \hline
36 & 27.72 & 16.38 \\
37 & 28.67 & 15  
paper is between Method 1 and Method 3. Therefore, the tracking method in this paper outperforms the CSK-tracking algorithm (Method 3) but is lower than the BPSK-tracking algorithm (Method 1), with a higher information transmission rate and lower tracking error, especially under low SNR conditions.
## 3 The Proposed Method
### Adaptive Estimation of the Modes Number  
Because the object signal is measured from a single channel acoustic sensor in this study, the dimension can be increased by DEWT. The specific steps are as follows:  
Suppose you have the observation signal \(x(t)\) of the sensor;  
\(x(t)\) is decomposed adaptively by DEWT to obtain the intrinsic mode, represented by matrix \(x_{i}=[c_{11},\cdots,c_{1n}]\), where \(c\) represents the intrinsic mode;  
Combine \(x(t)\) and \(x_{i}\) into a virtual multi-channel sensor observation signal \(x_{oi}\);  
Calculate the covariance matrix \(R_{i}\) of \(x_{oi}\);  
Singular value decomposition is carried out on \(R_{i}\), by which n singular values are obtained. They are then arranged in descending order;  
The difference of adjacent singular values is obtained, and the number of sources is estimated according to the adjacent singular value difference method.  
A schematic diagram of the method proposed in this section is illustrated in Figure 3, in which \(\Lambda_{1,2}\) to \(\Lambda_{6,7}\) represent the differences between two adjacent singular values. When the difference between two adjacent singular values is as close to 0 as possible, the number of modes is the point abscissa value minus 1. For example, in this diagram, the value of \(\Lambda_{4,5}\) is close to 0, and the subsequent values are also close to 0, which means that the number of modes is 3.
## 5 Case Study for Mitigating Security Attacks in Smart Healthcare System
### Blockchain Layer  
The data or file which is forwarded from the intelligence layer is still at a high risk of being exploited by the attackers; usually, they can perform data modification attacks in order to jeopardize healthcare operations. For example, an attacker can launch an MiTM attack to change the emergency alert of the patient data into non-emergency, thereby placing the patient's life at risk. Thus, there is an urgent need for technology, such as a blockchain that can securely store non-malicious data or files in a decentralized and immutable ledger [108]. Here, the ledger is distributed among all the blockchain members (all the entities of hospitals), so if any change is made to the stored data by attackers, this will be known to all the blockchain members. This builds robust transparency in smart healthcare systems. Initially, the healthcare data (non-malicious data) is validated using a  
Figure 5: Parametric test to observe the normal distribution of the dataset.  
smart contract, which has predetermined conditions; upon meeting those conditions, the data are validated [109]. Then, the data are received by the interplanetary file system (IPFS), which hash the raw data to improve the response time of the blockchain network. Finally, the hashed data are stored inside the public blockchain, where it is safeguarded from data integrity attacks.
## 5 Conclusions  
A Roots pump with a new rotor profile for hydrogen recirculation in the PEM fuel cell system was developed and integrated into the PEM fuel cell system successfully. Extensive testing was carried out to evaluate the performance of the Roots pump. The performance maps composed of all the measured data of the Roots pump are very helpful to the design and operation of the fuel cell system. The detailed conclusions are as follows:  
1. The volume flow rate varies almost linearly with the rotation speed. The requirements of various flow rates for different fuel cell systems can be satisfied by controlling the rotation speed of the Roots pump.
2. The effect of the pressure difference on the flow rate and volumetric efficiency of the Roots pump is significant, and the effect of the suction pressure is limited.
3. Leakage is the key factor which has a major influence on the volumetric and isentropic efficiency of the Roots pump, while the impact of flow resistance is insignificant.
4. The performance of the Roots pump is highly sensitive to the property of the working gas. The measured values of volume flow rate and volumetric efficiency are quite different when the same Roots pump operates with air, helium, and hydrogen.
5. The performance of the Roots pump integrated into the fuel cell system is better than that measured with pure hydrogen on the pump test rig. This implies that the effects of the water and nitrogen that existed inevitably in the recirculating hydrogen on the performance of the Roots pump are worth studying further.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed materials and analysis tools; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (Grant No. 2018 YFB0105303) and National Natural Science Foundation of China (Grant No. 51976154).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{**Parameters**} & \multicolumn{6}{c}{**Value**} \\ \hline Load of the fuel cell & & 100\% & & & 25\% & \\ Power of the fuel cell (kW) & 60 & 80 & 110 & 15 & 20 & 27.5 \\ Pressure difference (kPa) & 25 & 29.5 & 32 & 10 & 9.6 & 13 \\ Power consumption of the Roots pump (W) & 535 & 695 & 850 & 180 & 235 & 280 \\ Rotation speed of the Roots pump (rpm) & 5400 & 7000 & 8000 & 3000 & 4600 & 5500 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Operating parameters of the Roots pump integrated into fuel cell systems.
## 3 Results, Analysis, and Discussions
### Experimental Dam-Break Test of the IBS Frame
#### 3.2.1. IBS Frame Test 1 for 1 m Reservoir Water Level  
Figure 14 shows the first test of the frame, which was conducted using 1 m reservoir water level. The horizontal hydrodynamic force was subjected to the structure for investigating the behavior of the IBS frame structure.  
The test lasted up to 13 s from the beginning to the end. In the first 3 s, the hydraulic jump rebounded height reached the height of the slab. During this test, all eight PMs detected the forces that impacted the front surface of the frame. When the test reached 12-13 s, the bore flow around the structure dropped drastically and dissipated. Among the nine PMs, PM5 measured the highest force value as 13.75 kN. The distance between PM5 and the floor deck was 20 cm. Due to the closeness of PM5 to the floor deck, there was a possibility for detecting a high impact force because of the sudden impact of hydrodynamic force at the area. PM9 was used to measure the vibration of the bolt connected to the slab, column, and the footing at any little deflection. PM9 did not detect any value during this test due to the low impact of hydrodynamic force on the structure; Figure 15 shows the graph. The height of the water on the structure after the hydraulic impact reached a 40 cm height, and the total water volume that impacted the structure was around 3.15 m\({}^{3}\).  
Figure 14. Dam-break test for 1 m reservoir water level: (**a**) schematic diagram of the dam-break test: (**b**) during test 1 of 2D frame.
# Modeling and Analyzing Preemption-Based Service Prioritization in 5G Networks Slicing Framework
## 2 System Model
### General Case of S NSIs  
Consider the general case of \(S\) NSIs, i.e., \(\mathcal{S}\subset\mathbb{N}\setminus\{0\}\), instantiated at a common 5G BS. Similarly to previous Subsection, introduce Priority levels to clarify the Pre-emption scheme. Let \(s\) represent the priority level of all servicing requests at the \(s^{\text{th}}\) NSI, e.g., the highest priority is assigned to all servicing requests at the 1 st NSI, and the lowest priority is assigned to all servicing requests at the \(S^{\text{th}}\) NSI. Thus, introduce the Pre-emption vector-function  
\[\mathbf{u}^{(s,\mathbf{n})}=\left(u_{s}^{(s,\mathbf{n})}\right)_{\hat{s}=1, \ldots,S}=\left(u_{1}^{(s,\mathbf{n})},\ldots,u_{S}^{(s,\mathbf{n})}\right), \quad s\in\mathcal{S}, \tag{6}\]  
whose entries \(u_{s}^{(s,\mathbf{n})}\), \(\hat{s}\in\mathcal{S}\), represent the number of violators servicing at the \(s^{\text{th}}\) NSI that must be discarded when an arriving request is bound to the via pre-emption admission path at the \(s^{\text{th}}\) NSI.  
**Theorem 1**.: _Given the initial condition \(\mathbf{u}^{(s,\mathbf{n})}=\mathbf{0}\), describe the entry_  
\[u_{s}^{(s,\mathbf{n})}=\min\Biggl{\{}R\{(\mathbf{n}-\mathbf{N}^{\mathcal{S}}) \cdot\mathbf{e}_{\hat{s}}\},R\Biggl{\{}\left[\frac{\left(\mathbf{n}+\mathbf{ e}_{s}-\mathbf{u}^{(s,\mathbf{n})}\right)\cdot\mathbf{b}-C}{\mathbf{e}_{\hat{s} }\cdot\mathbf{b}}\right]\bigg{\}}\Biggr{\}},\quad s=S,\ldots,1,\] (7a) _where \[R\{x\}=xH(x)\] represents the Ramp function ([[https://mathworld.volofram.com/RampFunction.html](https://mathworld.volofram.com/RampFunction.html)]([https://mathworld.volofram.com/RampFunction.html](https://mathworld.volofram.com/RampFunction.html)), accessed 1 September 2022), and \[H(x)\] --the Heaviside function:_ \[H(x)=\begin{cases}1,&x>0,\\ 0,&\text{otherwise}.\end{cases} \tag{7b}\]  
Note that when \(\hat{s}=s\) the entry \(u_{\hat{s}}^{(s,\mathbf{n})}\) is zero, i.e., an arriving request cannot follow the via pre-emption admission path at the expense of one servicing request at the same NSI.  
Given the Pre-emption vector-function (Theorem 1), formalize in Figure 1 the RAC scheme for accessing the \(s^{\text{th}}\) NSI, i.e., \(s\in\mathcal{S}\), at the 5G BS.  
Figure 1: Flowchart formalizing the RAC scheme for accessing the \(s^{\text{th}}\) NSI, i.e., \(s\in\mathcal{S}\), at the 5G BS.  
Evaluate and analyze the efficiency of the proposed Pre-emption-based Prioritization (PP) scheme stated by Theorem 1. To this end, construct a mathematical model using the Queueing Theory apparatus, and propose formulas to calculate key performance indicators (KPIs) in the following section.
## 4 Result and Analysis
### Groundwater Permeability Efficiency  
The study area's associated surface water infiltration capacity is reflected in the groundwater infiltration efficiency. A strong infiltration capability in the region is indicated by a higher groundwater infiltration efficiency, which is correlated with the local lithology, soil, and land use. Different land uses and surface water yields throughout the year result in varying levels of infiltration effectiveness. The permeability capacity in the research area under various inter-annual fluctuations is reflected in the permeability efficiency in this work, which is calculated as the ratio of groundwater reserves to surface water yield. The Mudanjiang Forestry Bureau provided statistics showing that 62.31% of the study area is forest land and 35% is cultivated land, with forest land having a vast number of trees, shrubs, and herbaceous plants. A portion of the rainfall will be retained by the vegetation's branches and leaves, which will also lessen the kinetic energy of raindrops, slowing their direct impact on the ground, and decreasing the likelihood that surface soil particles will get distributed and compacted. Additionally, the long-term buildup and decomposition of dead branches and fallen leaves in forest land will improve the porosity, raise soil organic matter, improve the aggregate structure, smooth out the infiltration channel, and frequently boost the infiltration rate. Frequent tilling of farmed land improved the infiltration capacity, loosened the surface soil, and quickly increased the porosity, allowing the infiltration rate to reach 15% to 30%. Over the course of the study, the percentage of cultivated and forest land surpassed 90% annually, and there were no discernible fluctuations in the groundwater infiltration efficiency, which stayed at roughly 0.32 to 0.4, see Figure 6. The impact of land use on the permeability efficiency is negligible in this study area. The lithology in the study area has a significant impact on the permeability of this region, as shown in Figure 7, where the infiltration efficiency was maintained at roughly 0.35 during the remaining years and at a relatively high level in 2011 and 2014 due to the lack of annual precipitation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & **Rainfall** & **Water Yield** & **Wyield** & \multirow{2}{*}{**Year**} & **Rainfall** & **Water Yield** & **Wyield** \\  & **(mm)** & **(m3)** & **(mm)** & & **(mm)** & **(m3)** & **(mm)** \\ \hline
2010 & 706.24 & 10,186,153,254 & 224.41 & 2011 & 436.93 & 1,770,416,894 & 39.00 \\
2012 & 619.77 & 8,029,001,463 & 176.89 & 2013 & 743.19 & 9,466,336,772 & 208.55 \\
2014 & 552.66 & 736,486,105.8 & 16.23 & 2015 & 631.64 & 4,363,001,546 & 96.12 \\
2016 & 778.31 & 10,672,422,689 & 235.13 & 2017 & 613.22 & 4,314,574,301 & 95.06 \\
2018 & 750.38 & 10,986,873,785 & 242.05 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Annual rainfall and water yield from 2010 to 2018.
## 3 Multivariate Statistical Techniques and Data Treatment  
### Multivariate Statistical Techniques  
Multivariate statistical techniques (MVST) are a valuable tool to estimate efficiently the spatio-temporal variability in a watershed and the influences of human intervention on the characteristics of physicochemical parameters at monitoring stations [22]. In addition, MVST like cluster analysis (CA), discriminant analysis (DA), and PCA/factor analysis can be implemented to interpret complex databases to offer better visualization of water quality in the studied watershed [23]. The statistical techniques PCA, CA, and DA are vital to determine the primary relationships among the physicochemical parameters measured in experimental data standardized to the Z-scale to avoid inaccurate grouping because of the huge variability in the data dimensionality [5; 24; 25; 26].  
Principal component analysis (PCA), cluster analysis (CA) and discriminant Analysis (DA) were carried out to examine the seasonal variations, identify possible pollution sources, and analyze and interpret surface water quality data to draw meaningful information in China [2; 7; 27; 28; 29; 30], South Asia--Bangladesh [31], the Middle East--Iran [3], India [23; 32; 33], South African [34], Ethiopia [22; 35], South Asia Malaysia [36], the Middle East--Lebanon [6; 37], Spain [38], and Serbia [39].  
XLSTAT 2016 (Addingoft, New York, USA), Microsoft Excel 2016, and \"Statistical Package for the Social Sciences Software, IBM SPSS 25 for Windows\" were employed to perform statistical analysis integrally.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Analytical Method and Instrument** \\ \hline pH, EC, TDS, and Temperature & Portable multi-parameter analyzer (Zoto, Germany) \\ Turbidity & Nephelemetric ([PERSON], model 2100A) \\ DO & Modified Winkler \\ BOD & Manometric, BOD sensor \\ CCD & Closed Reflux, colorimetric \\ SRP and TP & Spectrophotometrically by molybdenum ([PERSON], model DR 3900) \\ TN & Spectrophotometrically by TNT Persulfate digestion ([PERSON], model DR 3900) \\ NO\({}_{2}\)\({}^{-}\) and TAN & Spectrophotometrically by salicate ([PERSON], model DR 3900) \\ (NH\({}_{3}\)\(-\)N + NH\({}_{4}\)\(-\)N) & Spectrophotometrically by salicate ([PERSON], model DR 3900) \\ NO\({}_{3}\)\({}^{-}\) & Photometric measurements, Wagtech Photometer 7100 at 520 nm wavelength \\ SS & Filtration by standard glass fiber filter \\ Mg\({}^{2+}\), Na\({}^{+}\), Ca\({}^{2+}\), and K\({}^{+}\) & Atomic Absorption Spectrophotometer, AAS, model NOVAA400 \\ \hline \hline \end{tabular} Total ammonium nitrogen (TAN), electrical conductivity (EC), total dissolved solids (TDS), dissolved oxygen (DO), biochemical oxygen demand (BOD\({}_{5}\)), chemical oxygen demand (COD), soluble reactive phosphorous (SRP), total phosphorous (TP), nitrate (NO\({}_{3}\)\({}^{-}\)), nitrite (NO\({}_{2}\)\({}^{-}\)), magnesium ion (Mg\({}^{+2}\)), sodium ion (Na\({}^{+}\)), potassium ion (K\({}^{+}\)), calcium ion (Ca\({}^{+2}\)), and suspended solids (SS).  
\end{table}
Table 2: Analytical methods and instruments used for analysis.
## 2 Related Works
### Triangular Rewiring Method for the RRT Algorithm  
This section shows the principle and pseudocode of the Triangular Rewiring Method for the RRT algorithm.  
The triangular rewiring method is used to rewire the component based on the triangular inequality concept [8]. The triangular inequality-based RRT algorithm is a rewiring of the RRT method that is based on the concept of triangular inequality between nodes in path planning; thus, it is closer to the optimum than the RRT.  
The triangular rewiring method not only can find a better initial solution but also can converge to a better solution rapidly.  
The pseudocode for the triangular rewiring method is shown in Algorithm 2. This iterative process continues until no \(q_{ancestor}\) exists (when no parent node exists for the previous \(q_{ancestor}\), i.e., when \(q_{ancestor}\) is \(q_{start}\)) or an obstacle exists between \(q_{child}\) and \(q_{ancestor}\). The method for triangular rewiring is as follows: the node with the position \(q_{parent}\) and the edge connecting the \(q_{child}\) and \(q_{ancestor}\) nodes are deleted. After the edge is deleted, the \(q_{ancestor}\) node is updated with the \(q_{parent}\) node, and the parent node of the \(q_{ancestor}\) is updated with the \(q_{ancestor}\) node. The existing \(q_{parent}\) node is then deleted. Then, the Trapped method is used to check if it collides with an obstacle between the \(q_{child}\) node and the updated \(q_{parent}\) node. Then, in tree T, the last created \(q_{parent}\) is inserted as the parent node of \(q_{child}\).
```
0:\(q_{child}\leftarrow\) Position \(\{q_{new}/q_{new}/q_{new}\}\) \(q_{parent}\leftarrow\) Position \(q_{max}\) \(T\leftarrow\) Tree \(\{T_{merged}/T_{a}/T_{b}\}\) \(C\leftarrow\) Position Set \(C\) Output:\(\{T_{merged}/T_{a}/T_{b}\}\leftarrow\) Result of \(T\) Begin triangular Rewiring Procedure
1\(q_{ancestor}\leftarrow\) Position of Parent Node of \(q_{parent}\) in \(T\)
2 IfNot isTrapped(\(q_{ancestor}\), \(q_{child}\), \(C\))then
3\(T\leftarrow\) Delete Node<\(q_{parent}\)>, Edge<\(q_{parent}\), \(q_{child}\)> and Edge<\(q_{parent}\), \(q_{ancestor}\)> from \(T\)
4\(q_{parent}\leftarrow\)\(q_{ancestor}\)
5\(q_{ancestor}\leftarrow\) Position of Parent Node of \(q_{ancestor}\) in \(T\)
6 While Not\(q_{ancestor}\) = Null do
7 IfNot isTrapped(\(q_{ancestor}\), \(q_{child}\), \(C\)) then
8\(T\leftarrow\) Delete Node<\(q_{parent}\)> and Edge<\(q_{parent}\), \(q_{ancestor}\)> from \(T\)
9\(q_{parent}\leftarrow\)\(q_{ancestor}\)
10\(q_{ancestor}\leftarrow\) Position of Parent Node of \(q_{ancestor}\) in \(T\)
11 Else
12 Break
13\(T\leftarrow\) Insert Edge<\(q_{parent}\), \(q_{child}\)> to \(T\)
14 Else
15\(T\leftarrow\) Insert Node<\(q_{child}\)> and Edge<\(q_{child}\), \(q_{parent}\)> to \(T\) End triangular Rewiring Procedure
```
**Algorithm 2** Pseudocode of Triangular Rewiring Method for RRT Algorithm
## 3 Results
### Application of Complex Continuous Wavelet Transform for the Field Low Strain Integrity Testing  
The proposed method to carry out the low strain integrity testing (the application of a set of hammers that produce pulses of various frequency content) and analyze the recorded  
Figure 6: CCWT analysis of the low strain test numerical simulation results for a pile with a splice of 1 mm sand gap (Model 3) with input force pulses of 0.5, 0.8, 1.2, and 2 ms duration. 1 st row—the simulated signals, 2 nd row—the distributions of energy of the wavelet transform coefficients, 3 rd row—the distributions of phase angles of the wavelet coefficients. Lines 1, 2, and 3 delineate the phase shift moments.  
Figure 7: CCWT analysis of the low strain test numerical simulation results for a pile with a splice of 1 mm air gap (Model 4) with input force pulses of 0.5, 0.8, 1.2, and 2 ms duration. 1 st row—the simulated signals, 2 nd row—the distributions of energy of the wavelet transform coefficients, 3 rd row—the distributions of phase angles of the wavelet coefficients. Lines 1, 2, and 3 delineate the phase shift moments.  
signals (the joint analysis of the energy and phase images of CCWT) was applied for the field data collected at a construction site in Moscow.  
The existing building was demolished, and the question arose about the possibility of using the exposed driven pile foundation for future construction. In the absence of executive documentation, it was necessary to determine the length and integrity of the piles.  
The low strain integrity test was carried out from the top of the pile cap 1 m high. To generate the acoustic waves, a set of three hammers was used: hammer 1 of 100 g with a metal tip, hammer 2 of 200 g with a metal tip, and hammer 3 of 500 g with a rubber tip (a rubber mallet).  
The low strain test signals obtained for one of the tested piles are presented in Figure 8, first row. The recorded data are complicated by a large number of features that do not allow us to safely outline the responses from the pile toe and possible existing splices when analyzing the data in the time domain.  
To increase the data decoding reliability, the CCWT with a complex Morlet wavelet was applied to process the obtained field data (Figure 8, second and third rows). The distributions of energy of the wavelet transform coefficients show a complex pattern of anomalies. When analyzing the phase distributions, for each registered signal, one can note the characteristic phase shifts that distribute from higher to lower frequencies at 4 ms (Line 2) and ~6.5 ms (Line 3). These phase changes are interpreted as responses from a pile splice at a depth of 8 m and a pile toe at a depth of 13 m, respectively (with the wave speed assumed to be equal to 4000 m/s). Considering the 1 m height of the pile beam, we can conclude that the length of a top pile section is 7 m, and the total pile length is 12 m.