esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Practical Mobility Management in Modern Scenarios
### Dealing with Mobility via VPN Applications
In [35], the WireGuard VPN is considered in the context of 5G network slicing, for the success of its security performance. The importance of [35] consists of the practical demonstration of the Wireguard strength, performed by the implementation of a real, secure network, tunneling protocol in a real 5G network, providing virtualized network functions. The market penetration of OpenVPN and Wireguard is demonstrated by their integration into some new static and mobile devices, such as UAVs and RaspberryPi (37 Hills Rd, Cambridge CB2 1 NF, UK) (from versions two onwards), as well as in the most advanced IoT sensor systems, even if it is always possible to use firmware such as OpenWRT [38, 39] as an MQTT [40, 41, 42] broker on the endpoints to convey traffic safely. Figure 4 illustrates a typical use case of a VPN (which can be integrated by WireGuard) over a connection between an LTE-connected (or 5G-connected) Unmanned Aerial Vehicle (UAVs) and the related Ground Control Station (GCS), which could be also connected via LTE or 5G. Both UAV and GCS have their IP addresses on the basis of their service provider (we assume that the UAV is not directly connected to the GCS, because their IP addresses are not visible to each other). The right management can be obtained by a VPN (Figure 4): we assume that the server has a static address IP1 address, while the UAV has obtained its IP4 address from the LTE Wireless Network, as well as the GCS with IP5. The OpenVPN server runs on the IP1 device, while the OpenVPN clients run on the IP4 (UAV) and IP5 (GCS) devices, so they can receive VPN addresses as illustrated, i.e., IP2 for the UAV and IP3 for the GCS. The IP2 and IP3 addresses belong to the same VPN network, so the UAV can directly communicate with the GCS. Figure 4: An example of VPN management for a Long Term Evolution (LTE) Unmanned Aerial Vehicle (UAV) and a Ground Control Station (GCS) device. Additionally, in [43], a secure interconnection of UAVs with GCSs via VPN is presented (Figure 5). The authors demonstrated the feasibility of using a 4G connection to link the drone directly to a data server (green and short dotted line), where the \"interesting\" data (audio, video, telemetry, etc.) can be stored for further processing. The second VPN connection (red long dotted line) is realized between drone and GCS, in order to perform First Person View (FPV) operations at Line of Sight (LOS) or Beyond Visual LOS (BVLOS) conditions. The proposed VPN-based idea is able to provide a long-range and high-capacity connection (they based the experiments on video and audio streaming [44]), by creating the VPN tunnel to the public Internet and towards the GCS. The proposed architecture underlines the need to overcoming security issues in UAVs scenarios [45].
# Effects of Shading on the Growth and Carbon Storage of _Enhalus acoroides_
## 4 Discussion
### Response of Seagrass Chlorophyll Content to Shading  
Photosynthetic pigments play an irreplaceable role in photosynthesis in plants [26]. Studies have shown that the regulation of the photosynthetic structure in response to low light usually includes an increase in the chlorophyll content and a decrease in the chlorophyll a/b ratio [27; 28]. The increase in chlorophyll content can significantly enhance the absorption, transfer and conversion of energy from weak light in the optical system to improve the utilization efficiency of light energy [29]. Different results were obtained in our study. When the duration of the shading treatment was 3 months, the chlorophyll content of _E. acoroides_ was not significantly different from that of the control, indicating that this seagrass was tolerant to short-term light stress; as the duration of shading increased, the chlorophyll content decreased sharply. This shows that a long-term light intensity that is too low is detrimental to chlorophyll synthesis, which is similar to observations made in previous studies [30]. After shading, the chlorophyll a/b values of _E. acoroides_ were lower than those in the full-light treatment. Light reduction usually decreases the proportion of red light absorption in seagrass, and chlorophyll b is more capable of absorbing blue light than chlorophyll a. The reduction in chlorophyll a/b values in low-light environments indicates that the seagrass has an enhanced ability to utilize blue light, thus improving light capture in low-light environments [31].
# Development and Preliminary Evaluation of a Lower Body Exosuit to Support Ankle Dorsiflexion
## 2 Exosuit Functionality and Design
### Exosuit Design
#### 2.2.4 Control  
An Arduino Uno is used for signal processing of the sensory input signals and for control of the actuation output. The force sensors under the foot and the IMU at the thigh are used as input signals for situation detection, whereas the force sensor detects gait events and the IMU differentiates between stair and level walking. The Bang-band control can switch between two states: extension and retraction of the pneumatic actuator. The underlying control scheme is illustrated in Figure 4.  
A fixed threshold for the force sensors is selected to detect ground contact. Furthermore, 50 ms of continuous force is necessary to avoid false contact detection from spontaneous force peaks during walking. This time period was pre-determined by sensitivity analysis with parameter variations. The delay doesn't prevent a timely actuation because of the localization of the FSR forefoot sensor and the underlying time-period starting from the lifted 1 st captt oossis metataralsais (no ground contact on FSR) till the toe-off during the pre-swing phase. The detected ground contact events are associated with either heel strike (loading response), push-off (terminal stance) or toe-off (swing phase). At toe-off, no force is detected neither at heel nor at forefoot, and the thigh angle is controlled to detect current gait mode. As a preliminary experiment has shown, the sagittal thigh angle at toe-off is in a more positive orientation (approx. 30 degree) when walking downstairs compared to level walking (approx. 0 degree). By using the midpoint of 15 degrees as a fixed threshold at toe-off, it is possible to differentiate between level walking and stair descent condition with the feedback of the IMU. Therefore, if the thigh angle is above the threshold at toe-off, the subject is walking downstairs. During stair descent there is not any support of dorsiflexion necessary due to the plantarflexion movement at swing phase, hence there will be no actuation. If the thigh angle is below the threshold, the gait mode switches to level walking. During level walking, there is no actuation at heel strike. At push-off phase, the ankle plantarflexion is supported and during the following swing phase, the dorsiflexion actuation is on.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
###### Abstract  
This paper presents a method for speech steganography using two levels of security: The first one related to the scrambling process, the second one related to the hiding process. The scrambling block uses a technique based on the ability of adaptation of speech signals to super-Gaussian signals. The security of this block relies on the value of the seed for generating the super-Gaussian signal. Once the speech signal has been scrambled, this is hidden in a non-sensitive speech signal. The hiding process is adaptive and controlled by the value of bits to hold (\(BH\)). Several tests were performed in order to quantify the influence of \(BH\) in the quality of the stego signal and the recovered message. When \(BH\) is equal to six, symmetry was found between the modified bits and unchanged bits, and therefore hiding capacity is 50%. In that case, the quality of the stego signal is 99.2% and of the recovered signal is 97.4%. On the other hand, it is concluded that without knowledge of the seed an intruder cannot reverse the scrambling process because all values of the seed are likely. With the above results, it can be affirmed that the proposed algorithm symmetrically considers both the quality of the signal (stego and recovered) as well as the hiding capacity, with a very large value of the key space.  
Keywords: steganography; scrambling; hiding; covert communication +
Footnote †: journal:
## 2 Materials and Methods
### Hardware Design  
Hardware design was one of the most important parts of our research. The hardware was implemented with Aluminum Designer v.19 (Altium, San Diego, CA, USA) and was divided into two parts: schematic design and PCB layout. In this hardware design, we focused on a high-speed design for signal communication between the FPGA chip and the STHV800 chip, as well as the analog signal received from the transducer to be entered into the digitizer device. Besides that, the PCB layout is presented in detail for each module, including the designs of the pulser/receiver module, trigger module, and amplifier module.
## 3 Time-Frequency Point Selection  
The advantages of polarization DOA estimation based on T-F cross terms can only be reflected if reasonable T-F points are selected for SPTFDs matrix establishment.
## 2 Materials and Methods
### Field Experiments
#### Channel Modeling  
Range tests were performed at six orientations between the UPD and router to determine the variation of the signal strength with the UPD position. In the first three tests, the user faced the router and set the inclination of the UPD held in his hands to \(-30^{\circ}\), \(0^{\circ}\), and \(+30^{\circ}\). In the fourth test, the user turned his back towards the router, and in the last two tests, either his left or right side faced the router. The data were sampled at a frequency of 0.5 Hz for 60 s at distances from the router of 5, 10, 20, and 40 m. During the processing of the results, the test during which the user turned his back toward the router were omitted because the signal strength during these tests varied substantially, and the signal was 20% weaker than in the other positions. However, in RHEA, the user supervises an entire mission, which means facing at least one autonomous vehicle and frequently changing his direction of focus.
## 2 Models and Methods
### Governing Equations
#### 2.1.2 VOF Model  
The VOF model assumes that all immiscible fluid phases present in a control volume share their velocity, pressure, and temperature fields. Therefore, the same set of basic governing equations describing momentum, mass, and energy transport in a single-phase flow is solved. It is necessary to know the volume fraction \(\alpha_{i}\) of phase i (liquid or gas), and the conservation equation that describes the transport of \(\alpha_{i}\) is:  
\[\frac{\partial\left(\alpha_{i}\rho_{\text{i}}\right)}{\partial t}+\
abla\cdot \left(\alpha_{i}\rho_{\text{i}}\mathbf{v}\right)=0 \tag{18}\]  
where \(\rho_{i}\) is the density, \(t\) is time and \(\mathbf{v}\) is the velocity. The momentum equation is given as:  
\[\frac{\partial\left(\rho\mathbf{v}\right)}{\partial t}+\
abla\cdot\left(\rho \mathbf{v}\otimes\mathbf{v}\right)=-\
abla p+\
abla\cdot\left[\mu\left( \
abla\mathbf{v}+\
abla\mathbf{v}^{\text{T}}\right)\right]+\rho\mathbf{g}+ \mathbf{F} \tag{19}\]  
where \(p\) is pressure, \(g\) is gravity and \(\mathbf{F}\) is the resultant of other body forces. The physical properties are calculated as functions of the physical properties of its constituent phases and their volume fractions:  
\[\rho=\sum_{i}\rho_{i}\alpha_{i} \tag{20}\]  
\[\mu=\sum_{i}\mu_{i}\alpha_{i} \tag{21}\]  
The surface tension force is a tensile force tangential to the interface separating the two fluids. It works to keep the fluid molecules at the free surface in contact with the rest of the fluid and shows remarkable effects on liquid behaviors in TBRs. The surface tension force\(f_{o}\) calculation method named continuum surface force (CSF) model was proposed by [PERSON] et al. as the following [27]:  
\[f_{o}=\sigma\kappa\mathbf{n} \tag{22}\]  
\(\sigma\) is the surface tension coefficient and the vector normal to the interface \(\mathbf{n}\) is calculated using the smooth field of the phase volume fraction \(\alpha_{i}\):  
\[\mathbf{n}=\
abla\alpha_{i} \tag{23}\]  
The curvature of the interface \(\kappa\) can therefore be expressed in terms of the divergence of the unit normal vector as follows:  
\[\kappa=-\
abla\cdot\frac{\
abla\alpha_{i}}{\left|\
abla\alpha_{i}\right|} \tag{24}\]  
Wall adhesion was considered by adjusting normal vector with contact angle:  
\[\mathbf{n}=\mathbf{n_{wall}}\cos\theta+\mathbf{n_{t}}\sin\theta \tag{25}\]  
where \(\theta\) is the contact angle, \(\mathbf{n_{wall}}\) is the unit normal vector of solid wall, and \(\mathbf{n_{t}}\) is the unit vector inside the wall normal to the solid-liquid-gas contact line.
## 2 Data Description and Preprocessing  
The raw intermediate frequency (IF) was captured from the up-and down-looking antennae by the dual-isolated GNSS radio frequency application specific integrated circuit (RF-ASIC) components, from which the antennae point to the ocean. The bandwidth of the captured IF signal for BeiDou G4 is approximately 4 MHz, centered at 3.996 MHz. Two-bit data were collected from both the direct and reflected signals. In order to illustrate the proposed technique for wind speed estimation, the observation data collected from 3 to 8 and 12 to 14 August 2013 at the Yangjiang ground station. Figure 2 shows the station site which located on the Eastern coast of China at 21\({}^{\circ}\)33\({}^{\prime}\)58.50\({}^{\prime\prime}\) N and 111\({}^{\circ}\)51\({}^{\prime}\)38.93\({}^{\prime\prime}\) E at a height of 120 m above the mean sea level. The station received the signal from the transmitter position of the BeiDou G4 satellite (0\({}^{\circ}\)0\({}^{\prime}\)0\({}^{\prime\prime}\) N, 160\({}^{\circ}\)0\({}^{\prime}\)0\({}^{\prime\prime}\) E) at a height of 35,801 km, and the specular point was approximately 21\({}^{\circ}\)32\({}^{\prime}\)11.4\({}^{\prime\prime}\) N, 111\({}^{\circ}\)57\({}^{\prime}\)28.08\({}^{\prime\prime}\) E. The reflected signal captured from the receiver station consisted of phase \(I\) and \(Q\) components, which are presented in the Experimental Results section.
## 3 Compressive Sensing Framework
### Reconstruction Methods
#### 3.1.2. A* Orthogonal Matching Pursuit  
The A*OMP is an algorithm based on the _Orthogonal Matching Pursuit_ (OMP) algorithm [35], that uses atoms from a dictionary to expand \(\mathbf{x}\). At each iteration, the expansion uses the dictionary atom having the largest inner-product with the residue. After each iteration, the orthogonal projection of the residue onto the selected atoms is computed. At iteration \(l\), the set of atoms \(\mathbf{A}_{l}\) selected from \(\mathbf{\Phi}\) for representing \(\mathbf{y}\) incorporates the dictionary atom that best matches \(\mathbf{r}^{l-1}\), doing  
\[\mathbf{s} =\arg\min_{\mathbf{V}_{n}\in\mathbf{\Phi}}\left\langle\mathbf{r} ^{l-1},\mathbf{V}_{n}\right\rangle, \tag{5}\] \[\mathbf{A}_{l} =\mathbf{A}_{l}\cup\mathbf{s},\] (6) \[\mathbf{c} =\arg\min_{\mathbf{c}\in\mathbb{R}^{l}}\|\mathbf{y}-\mathbf{A}_{l }\tilde{\mathbf{c}}\|_{2},\] (7) \[\mathbf{r}^{l} =\mathbf{y}-\mathbf{A}_{l}\mathbf{c}, \tag{8}\]  
in which \(\mathbf{V}_{n}\in\mathbb{R}^{N}\) and \(n=1,2,\ldots,N\) are dictionary atoms (columns of the dictionary \(\mathbf{\Phi}\)), \(\mathbf{c}\) is the vector of the coefficients and \(\mathbf{r}^{l}\) is the residue after the \(l\)-th interaction. At the end, \(\mathbf{A}_{l}\) contains the support of \(\mathbf{x}\) (the original signal to be coded), and \(\mathbf{c}\) contains their nonzero entries (that define the sparsity of the signal).  
The A*OMP considers the best-first search tree [36], in which multiple paths can be evaluated during the search to improve reconstruction accuracy. The search is called A* and looks for the sparsest solution [31]. The _A*Search_ gives an advantage to the A*OMP against the OMP algorithm, smaller average run-times [31]. Authors in [6] considered MP-based algorithms to reconstruct environmental signals, such as temperature, humidity, and illumination.
# Internet of Things in Marine Environment Monitoring: A Review
## 1 Introduction
Other issues: Devices and sensor nodes should be highly reliable because of the difficult deployment and maintenance; the need for buoy and mooring devices; sensor coverage needs to be carefully calculated because of large areas [17]; equipment should be designed against possible acts of vandalism. We conducted a comprehensive review of WSN applications in marine environment monitoring in 2014 [18]. When we planned and started this updated review, we wanted to expand the scope from WSN to IoT and to cover the protection aspect in addition to the monitoring because of the characteristics of IoT not only on sensing but also on actuation. However, we did not find enough references related to protection. In this paper, we do not consider the protection aspect as a focus but keep the related discussions. We envision the protection from two perspectives: (1) Results from the advanced data analytics based on the data collected by the monitoring system can be fed back to the marine environment management agencies/control centers for quick decision making and real time manual interventions in order to protect the marine environment from some disasters (e.g., oil spills and bad weather damages); (2) some autonomous vessels or other devices (actuators) can also automatically and quickly react to disasters and other events in order to protect the marine environment. Similarly, we wanted to cover the Big Data analytics and its applications to marine environment monitoring and protection, but we did not find sufficient references. Therefore, we did not make the Big Data a focus of this paper. However, we strongly believe that Big Data analytics will play a more and more important role in this area in the future. The remainder of the paper is organized as follows: Section 2 provides an overview of the fundamentals of IoT-based marine environment monitoring systems; Section 3 presents a summary of some related projects and systems under five application areas; Section 4 reviews a few new technologies applied in the marine environment monitoring systems; Section 5 addresses some research challenges and opportunities in this area; Section 6 provides some concluding remarks.
# Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD Method
## 4 Experimentation and Evaluation
### Denoising of SO\({}_{2}\) UV Spectral Signal Simulation
Consequently, it becomes challenging to evaluate the denoising performance of these two methods under such circumstances. The SVDS method exhibits an overall average denoising effect and has limited capacity to process spectral data with specific SNR.
## 5 Experimental Results and Discussion
### Middle Urban Canyon
As a result, the pseudorange difference between the simulated and raw pseudorange is large. This large difference results in the particle on the left side of the street in Figure 8a to become invalid. With the aid of the proposed RAIM, GPS 11 is excluded from the particles of Figure 8b. This exclusion facilitates the proposed 3D map method to estimate the correct side of the street in this case. With regard to point 264,038, the positioning results before and after applying RAIM are similar, as shown in the right side of Figure 7. Figure 10 shows the weighting of all the particles in this point. The GLONASS satellites 5 and 20 are both single reflected NLOS, as indicated by the pseudorange measurements, which are similar to their simulated pseudorange, as shown in Figure 11b. In this case, both the GLONASS NLOS satellites' measurements are safe to use. Thus, the position estimated by the 3D map method standalone is close to the ground truth. Figure 11: **(a)** Skyplot of point 264,308, and the dark grey color indicates the view blocked by surrounding buildings; **(b)** Ray-tracing result of point 264,308 based on the ground truth position. Figure 10: Weighting of all the particles of point 264,308 before **(a)** and after RAIM **(b)**. After applying the RAIM, GPS 17 is excluded from the upper particles in Figure 10b. GPS PRN 17 is a relatively low elevation satellite, which usually contains stronger multipath effect. Therefore, these particles become valid. The estimated position of the proposed 3D map method with the RAIM is slightly closer to the ground truth compared to that of the 3D map method standalone. Hence, the proposed RAIM algorithm can, not only exclude the double reflected NLOS, but also strong multipath signals.
# Analytical Prediction of Balling, Lack-of-Fusion and Keyholing Thresholds in Powder Bed Fusion
## 3 Experimental Validation and Discussion
The sensitivity of the threshold models for the lack-of-fusion, balling and keyholing defects to the laser power and scanning speed were also carried out. The detailed results are shown in Figures 8 and 9. It can be observed that the value of the balling threshold formula and lack-of-fusion formula have a negative relationship with the laser power, while the keyhole formula has a positive relationship with the laser power. As for sensitivity to scanning speed, the balling formula and keyhole formula have negative relationships with scanning speed, while lack-of-fusion formula has a positive relationship with scanning speed. The process window of the Ti6 Al4V in laser powder-bed fusion additive manufacturing was also predicted by the presented analytical models. The laser power changes from 20 W to 200 W, with 20 W as an increment. The scanning speed changes from 200 mm/s to 1400 mm/s, with 200 mm/s as an increment. Table 7 shows the detailed prediction for defects under various process conditions. This table can be used to optimize the suitable process conditions for the fabrications of products without any defects. Figure 8: Sensitivity of thresholds to laser power. Figure 7: Validation of keyholing threshold prediction. (**a**) Ti6 Al4V. (**b**) Inconel 718. The presented analytical models in this study have been validated by experimental measurements of different materials under various process conditions. In addition, the computational time for the presented models on a personal computer is less than five minutes, which shows their high computational efficiency. In the future, the temperature-dependent material properties and sensitivity to laser power absorption can be considered in the calculation process so as to improve the prediction accuracy of these analytical models. The proposed analytical modeling method can be a promising tool for future study of manufacturing conditions for large and complex products.