esa-sceva/satcom-chunk-collection huggingface dataset:
# Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review
## 2 The Development and Main Research Contents of Inversion Theory
### The Main Contents of Application Research of Inversion Analysis  
According to statistics from relevant departments, the number of completed and opened highway tunnels, railroad tunnels, and urban rail transit in 2020 reached 21,316, 16,798, and 245, respectively. This represents an increase of 153.2%, 125.3%, and 222.7%, respectively, compared to the number of tunnels built in 2015, as shown in Figure 2.  
Figure 2: Statistics of highway tunnels, railway tunnels, and urban rail transit in China (2015–2020).  
As a large and diverse research branch in the field of civil engineering, research on the computational theory of tunnel and underground engineering has made great progress in recent decades. Through search engines such as CNKI and \"Web of Science\" a total of 578 related papers were found using the keywords \"inversion analysis\" and \"tunnel and underground space engineering\" in the past 20 years. The research directions of inversion analysis in tunnel and underground engineering are summarized in Figure 3a. It is not difficult to see that the proportion of material characteristic parameters and initial geo-stress research is large, accounting for 59% and 28%, respectively, while comprehensive application and supporting structure load account for only 8.13% and 5.02%, respectively.  
Figure 3b presents a graph of literature published in different years. The graph demonstrates that the number of publications for inversion analysis of material property parameters constitutes a significant portion of the total number of publications per year. In contrast, the number of papers on the inversion analysis of initial ground stress has only gradually caught up in recent years. The research results in this field have a certain lag compared to the number of tunnels constructed and lack an effective mechanism to transform the results. However, it is undeniable that inversion theory has become an effective tool for solving tunnel and underground engineering problems. In the future, it will be deeply integrated with the Internet, big data, and intelligent monitoring technology to realize the diversification of inversion analysis methods.
# Design and Experimental Verification of a PZT Pump with Streamlined Flow Tubes
## 2 Structure and Working Principle  
### Structural Principle  
To simplify the principle, three sections were defined for the streamlined flow tube, as shown in Figure 1.  
When the fluid flows in the streamlined flow tube, the main reason for the backflow vortex is the singular flow trend of boundary layer flows, which leads to the separation of boundary layer flows [33]. Therefore, the flow trend should be fully considered when designing the flow tube. The flow tube designed consists of two parts: divergence and contraction parts. Here, forward flow direction is defined in Sections 1.1-3.3, and backward flow direction is in Sections 3.3-1.1, as shown in Figure 1. Due to fluid flows forward through the tube (positive \(x\) axis), the pressure gradient changes in the boundary layer in the x direction are much larger than that in normal direction (\(y\) axis). Therefore, the pressure gradient in normal direction is neglected, and the Navier-Stokes equation can be simplified as [34; 35]  
\[\left\{\begin{array}{c}u_{x}\frac{\partial u_{x}}{\partial x}+u_{y}\frac{ \partial u_{x}}{\partial y}=-\frac{1}{\rho}\frac{\partial p}{\partial x}+v \frac{\partial^{2}u_{x}}{\partial y^{2}}\\ \frac{\partial p}{\partial y}=0\\ \frac{\partial u_{x}}{\partial x}+\frac{\partial u_{y}}{\partial y}=0\end{array}\right. \tag{1}\]  
Since the flow area from Sections 1.1-2.2 gradually increases, and the pressure gradient constantly increases, then the boundary layer equation with different pressure gradients is  
\[u_{x}\frac{\partial u_{x}}{\partial x}+\frac{\partial u_{x}}{\partial x}=- \frac{1}{\rho}\frac{\partial p}{\partial x}+v\frac{\partial^{2}u_{y}}{\partial y ^{2}} \tag{2}\]  
When the cross-sectional area of the fluid gradually increases, it is bound to cause the pressure to increase while the flow rate decreases. According to Equations (1) and (2), it can be seen that the boundary layer fluid should satisfy the following conditions when flow area gradually increases  
\[\frac{\partial p}{\partial x}>0\ \frac{\partial^{2}u_{y}}{\partial y^{2}}>0\ \frac{\partial u_{x}}{\partial x}<0 \tag{3}\]  
According to Formula (3), for a single divergent flow, the flow velocity would gradually decrease, then the boundary layer thickness would increase. When the velocity is reduced to zero, there are backflow vortexes to disturb fluid delivery.  
On the contrary, when flow area gradually decreases, the boundary layer fluid should satisfy the follow expression as  
\[\frac{\partial p}{\partial x}<0\ \frac{\partial^{2}u_{y}}{\partial y^{2}}<0\ \frac{\partial u_{x}}{\partial x}>0 \tag{4}\]  
Figure 1: The section view of the streamlined flow tube.  
According to Formula (4), for a single convergent flow the internal pressure decreases, then the flow velocity increases and the boundary thickness decreases. Therefore, the flow tube structure designed should be able to change the fluid flow trend, whose principle should obey both Equations (3) and (4), for the improvement of flow stability.
###### Abstract  
Sensor networks have become more popular in recent years, now featuring plenty of options and capabilities. Notwithstanding this, remote locations present many difficulties for their study and monitoring. High-frequency (HF) communications are presented as an alternative to satellite communications, being a low-cost and easy-to-deploy solution. Near vertical incidence skywave (NVIS) technology provides a coverage of approximately 250 km (depending on the frequency being used and the ionospheric conditions) without a line of sight using the ionosphere as a communication channel. This paper centers on the study of the ionosphere and its characteristic waves as two independent channels in order to improve any NVIS link, increasing its robustness or decreasing the size of the node antennas through the appliance of specific techniques. We studied the channel sounding of both the ordinary and extraordinary waves and their respective channels, analyzing parameters such as the delay spread and the channel's availability for each wave. The frequency instability of the hardware used was also measured. Furthermore, the correlation coefficient of the impulse response between both signals was studied. Finally, we applied polarization diversity and two different combining techniques. These measurements were performed on a single frequency link, tuned to 5.4 MHz. An improvement on the mean bit energy-to-noise power spectral density (E\({}_{b}\)/N\({}_{0}\)) was received and the bit error rate (BER) was achieved. The results obtained showed that the extraordinary mode had a higher availability throughout the day (15% more availability), but a delayed spread (approximately 0.3 ms mean value), similar to those of the ordinary wave. Furthermore, an improvement of up to 4 dB was achieved with the usage of polarization diversity, thus reducing transmission errors.  
HF; NVIS; SIMO; diversity combining; sounding; communication channel; ionosphere; STANAG; MIL-STD-188; polarization diversity; remote sensing +
Footnote †: journal: Sensors
## Case Study
Figure 8: Flow diagram showing research components and space analysis process. Figure 9: 3D grid conversion process. The matching experiments were conducted using a total of three types of node values for two types of equipment and three different space types. Figure 11 was classified by node size (grid box size), which was set to 0.5, 1.0, and 2.0 m.  
Figure 10: Software processing sequence ((**a**) processing point cloud data, (**b**) finding valid space per box, (**c**) finding valid space per equipment unit). The equipment and space transformed into 3D grids were matched through the above process, and the results were calculated. The results are shown in Figure 12. The numbers on the graph represent the number of points in which the correlated spaces (indicated as Space 1, Space 2, and Space 3) and equipment (indicated as Tool 1 and Tool 2) in the algorithm matched. The resulting experiment shows that the number of valid spaces that can be used for allocating equipment varies depending on the node size (i.e., size of the grid box). We have searched the valid installation spaces for equipment as follows: in case of space 1, tool 1 (equipment 1), 62 each (node size 0.5 m), 15 each (node size 1 m), 3 each (node size 2 m); in case of space 1 and tool 2 (equipment 2), 54 each (node size 0.5 m), 14 each (node size 1 m), 3 each (node size 2 m); in case of space 2 and tool 1 (equipment 1), 34 each (node size 0.5 m), 8 each (node size 1 m), and 2 each (node size 2 m); in case of space 2 and tool 2 (equipment 2), 28 each (node size 0.5 m), 7 each (node size 1 m), and 1 each (node size 2 m). In case of space 3, tool 1 (equipment 1), 11 each (node size 0.5 m), 2 each (node size 1 m), and 0 each (node size 2 m); in case of space 3, tool 2 (equipment 2), 5 each (node size 0.5 m), 1 each (node size 1 m), and 0 each (node size 2 m) were searched. In case of space 3, the valid spaces were found at node sizes (grid box sizes) of 0.5 m and 1 m, but no valid space was found at 2 m or more. If a detailed aspect of space analysis needs to be considered, the smaller the grid box size, the more the available spaces. However, because of the increase in space analysis processing time, it is necessary to consider the size of node only when intending to identify whether an equipment can be installed or not.
## 5 Conclusions  
In this study, an adaptive trajectory tracking control scheme called NTTC is proposed for non-strict trajectory tracking of a fully actuated USV. The controller is designed using a regression matrix, RC scheme, and a switching gain controller term, such that the USV converges into the desired moving region trajectory and tracks the desired position within the region boundary with desired orientation. The closed-loop stability of the system is proved by the Lyapunov stability theorem. The simulation results demonstrate the effectiveness of the proposed control scheme and shows that under the proposed NTTC method, the USV can track straight-line region trajectory and curve region trajectory well. The simulation results also show that the proposed NTTC method performs better than the traditional RC method. The method allows the trajectory of the USV to converge into the desired moving region rather than the boundary of the moving region, and reduces the system's chattering at the same time. This condition is good for the actuating mechanisms of the USV and is energy efficient. However, the proposed method is only used for a fully actuated USV in this study. Further work should consider using underactuated USVs.  
**Acknowledgments:** The authors wish to thank the State Key Laboratory of Ocean Engineering, Laboratory of Intelligent Equipment and System at Sea of Shanghai Jiao Tong University, for their support of this study.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived the main concept and contributed the analysis. [PERSON] wrote the paper, and [PERSON] performed a spell check. All authors contributed in writing the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**RMS**} \\ \cline{2-4}  & \(\mathbf{\omega}_{u}/\mathbf{N}\) & \(\mathbf{\omega}_{v}\ /\ N\) & \(\mathbf{\omega}_{r}\ /\ (\mathbf{N}m)\) \\ \hline RC Method & 6.41 & 3.71 & 1.26 \\ NTTC Method & 4.65 & 3.45 & 1.14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Control input comparisons of RC and NTTC method.  
Figure 18: Control inputs of the USV under the NTTC.
# Numerical Simulation and Analysis of Fish-Like Robots Swarm
## 3 Results and Discussion
### The Phalanx Swarm  
The experiments find that when the spacing between fishes was \(0.5L\), the net force of the fish body was not zero. The results are shown in Figure 12. Through [PERSON]'s experiment [47] and [PERSON]'s simulation [29], we learn that in the high-efficiency phalanx fish swarm at the spacing between \(0.15L\) and \(0.65L\), fishes swim by \"out-phase\". However, in this work, all fish swim by \"in-phase\". Therefore, the drag of the phalanx swarm at close spacing (\(0.5L\)) was larger than thrust. Thus, in this work, the phalanx swarm does not discuss the case where the spacing between fishes is \(0.5L\).  
We calculate the non-dimensional thrust, lateral power loss coefficients and the Froude efficiency of each fish in other spacing cases. The results are shown in Figure 13. In the phalanx swarm, the thrust coefficient of the three fishes had the same trend as the spacing changes, and the values of fishes on both sides were close. The no.2 fish had the smallest lateral power loss. However, there was no fish which had the best Froude efficiency.  
From the phalanx swarm, we can get that the pressure generated by the swinging of fish body will affect the adjacent fish. When the spacing between fishes was \(0.75L\), the contours of the pressure field is shown in Figure 14a. We find that the pressure fields of adjacent fishes overlap and the Froude efficiency was the lowest. Figure 14b shows the contours of pressure field when the spacing was \(1.25L\). The pressure fields of each fish are independent, and do not affect each other and the Froude efficiency is better. In a word, if the spacing between fishes was too close, the fish swam unsteadily and reduced the swimming efficiency.  
Figure 12: The thrust and drag at different spacing of the phalanx swarm.  
Figure 13: Results of the phalanx swarm: (**a**) non-dimensional thrust coefficients \(C_{t}\), (**b**) non-dimensional lateral power loss coefficients \(C_{p}\), (**c**) the Froude efficiency \(\eta\).
# Establishment and Numerical Analysis of Rolling Force Model Based on Dynamic Roll Gap
## 3 Consider the Dynamic Roll Gap Cold Rolling Process Model  
### Analysis of Cold Rolling Working Interface  
In the cold rolling process, the geometric model of the working interface is depicted in Figure 1. Multiple physical quantities of the working interface change dynamically, and each physical quantity is dynamically correlated with one another, making it a highly complex metal forming process [18; 19; 20]. When studying the variation characteristics of the rolling force in the cold rolling process, it is necessary to consider the influence coefficient of stress, the influence coefficient of tension, the friction coefficient, and the roll flattening radius.  
\(d_{i}\) represents the inlet thickness of the deformation area of the rolled piece, (unit: mm); \(d_{o}\) represents the outlet thickness of the deformation zone of the rolled piece, (unit: mm); \(\tau_{i}\) represents the tension before rolling parts, (unit: MPa); \(\tau_{o}\) represents the tension after rolling, (unit: MPa); \(r\) represents roll radius, (unit: mm); \(r^{\prime}\) represents the roll flattening radius, (unit: mm); \(u\) represents the linear speed of roll surface, (unit: m/s).  
According to the metal rolling theory, the relative reduction rate \(\varepsilon\) is determined by the following equation:  
\[\varepsilon=\frac{d_{i}-d_{o}}{d_{i}} \tag{1}\]  
The following equation determines the stress state coefficient \(Q_{p}\) model:  
\[Q_{P}=1.79\mu\varepsilon\sqrt{1-\varepsilon}\sqrt{\frac{r^{\prime}}{d_{o}}}+1.08-1.02\varepsilon \tag{2}\]  
where \(\mu\) represents the friction coefficient between rolls. The friction coefficient is a non-constant term, due to the fact that the actual working interface is in a state of unsteady lubrication friction, which can be approximated by the Roberts formula [PERSON] (1963), and can be determined via the following equation:  
\[\mu=\sqrt{\frac{\Delta h}{r}}[0.5+(f_{1}-0.5)e^{-f_{2}\mu}] \tag{3}\]  
where \(\mu\) is the unsteady friction coefficient; \(\Delta h\) is the amount of decision pressing; \(f_{1}\) and \(f_{2}\) represent the coefficient of friction characteristics. They can be named according to the actual situation for a reasonable calculation value. The following equation can determine the tension influence coefficient \(k_{T}\) model:  
\[K_{T}=1-\frac{\alpha\tau_{i}+(1-\alpha)\tau_{o}}{\overline{K}} \tag{4}\]  
where \(\alpha\) represents the weighting coefficient of front strip tension, specified as \(\alpha=0.6\); \(\overline{K}\) represents the average deformation resistance, (unit: MPa).  
Figure 1: Geometric model of the working interface in the rolling process.
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 5 Proposed Secure and Efficient Authentication Protocol for GLOMONET
### Registration Process  
A new \(MU\) should register with \(HA\) to receive the roaming services. Figure 3 presents the user registration process of our protocol.  
1. A \(MU\) selects \(ID_{MU}\), \(PW_{MU}\) and imprints biometric \(BIO_{i}\). After that, \(MU\) computes \(\langle R_{i},P_{i}\rangle\)=\(Gen(BIO_{i})\), \(RPW_{i}=h(PW_{MU}||R_{i})\) and sends \(\{ID_{MU},RPW_{i}\}\) to the \(HA\) over a secure communication.
2. After obtaining messages \(\{ID_{MU},RPW_{i}\}\), the \(HA\) computes \(RID_{i}=h(ID_{MU}||RPW_{i})\), \(X_{i}=h(RID_{i}||K_{S}||R_{S})\), \(A_{i}=X_{i}\oplus h(RID_{i}||RPW_{i})\) and \(B_{i}=h(RPW_{i}||X_{i})\). After that,  
Figure 3: The user registration process of the proposed protocol.  
\(HA\) stores \(\{R_{S}\}\) in a secure database. Finally, the \(HA\) sends \(\{A_{i},B_{i}\}\) to the \(MU\) via a secure communication.  
**Step 3:**: Upon reception of \(\{A_{i},B_{i}\}\), the \(MU\) stores \(\{A_{i},B_{i},P_{i}\}\) in the mobile device.
## 4 ITS Threats Analysis and Classification
### Classification of ITS Attacks and Cryptographic Countermeasures
#### 4.4.3 Attacks on Integrity/Data Trust and Countermeasures
The main countermeasure consists of verifying the signatures of the received messages and detecting and blacklisting the misbehaving nodes;
* Data tampering attacks: This attack can be realized by a legitimate node, can destroy the network and causes dangerous consequences, such as accidents [51], by fabricating and broadcasting false messages. Its mechanism consists of hiding the true safety messages to legitimate users and tries to generate and inject fake security alert messages in the network. The main countermeasure is to sign and verify the transmitted messages. A non-repudiation mechanism is also required to detect the attacker identity, which should be added to the CRLs [51];
* Man-in-the-middle attacks: The man-in-the-middle attack can be achieved in several contexts. As its name indicates, the attacker is inserted between the transmitter and the receiver. In the case of ITS, the attacker can be an OBU or RSU, which is inserted between two vehicles that communicate. The attacker controls the communication between the two victims [17], while they believe that they are in direct communication with each other. In the literature, the man-in-the-middle attack is used to violate the authentication, integrity and non-repudiation mechanisms. A typical cryptographic countermeasure consists of using digital certification to properly authenticate the legitimate users [78].
## 3 Results  
### Malodor and VSC levels  
The influence of several predisinfection solutions on malodor scores and VSC-production is shown in Figures 1 and 2. Both chlorhexidine (CHX) and herbal extract (HE) formulations effectively reduced malodor and VSC levels below detection threshold (O) scores \(<\) 2 and VSC \(<\) 75 ppb), with only borderline statistically significant difference between them (\(p\) = 0.037 and 0.045, respectively).  
Figure 1: The mean results (\(\pm\)SD) of the odor judge scores given using an organoleptic scale of 0–5, for the various tested solutions: Saline, essential oils (EO), herbal extracts (HE), and chlorhexidine (CHX) formulations as indicated.
## 4 Results and Discussion  
### Experiment with Random Test Cases  
A train-test split of 75/25 was used in the first experiment. The model that achieved high accuracy was then tested with subject wise fully unbiased test cases which are elaborated in the subsequent sections. However, a large amount of variability was present in individual smartphone PPG signal instances, even when acquired from the same subject. Among these trials, there were different quality PPG signals, as shown in Figure 8. The preprocessing steps were held constant throughout the trials--which involved denoising through Gaussian filter and baseline corrections using ALS. The first derivative was computed upon the preprocessed signal. After that, only the positive and negative peak values were extracted as features. Figure 8 shows two representative PPG signals, their transformation after preprocessing, and the corresponding extracted features. It can be observed that first derivative features were extracted from both the signals without any issue. The proposed signal processing model offers reliability in handling PPG signals of various signal qualities, and may therefore reduce the burden during data acquisition, as an acceptable PPG signal may be acquired in spite of baseline drift and high-frequency noise.  
In this study, each recorded signal contains 60 s of data. However, 5 s was initially removed from the beginning of the segment and the end of the segment. This was carried out due to the natural prevalence of large motion artifacts in the initialization and the conclusion of a recording. Several PCR, PLS, SVR and RFR models were developed with 50 s of raw PPG signal in every instance with a variety of different features. These include models based on a combination of the preprocessed PPG signal via ALS and Gaussian filter, first derivative and second derivative features extracted from the preprocessed PPG signal. The following sections outline the observed results from the various regression techniques used to predict blood glucose level and draw comparisons between the methods shown.