esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Deductions from Proposed Model
#### 3.3.2 The Relationship between Robustness \(R\) and Watermark Information Length \(L\)  
In this experiment, the relationship between robustness \(R\) and watermark information length \(L\) is explored. For watermarking algorithms, the watermark may vary between a binary sequence with a length of less than 200 or a binary image with its length of more than 1024. Hence it is of importance to determine whether there is a strong connection between robustness and watermark information length. Based on the proposed computation model, \(N\) is set as 2, 4, 8 and \(L\) is set to 100, 200, 400, 800, and 1200 respectively. Figure 18 shows the robustness comparisons with different \(L\).  
Figure 17: The relationship between robustness and watermark repeat times: (**a**) \(L=100\); (**b**) \(L=200\); (**c**) \(L=300\).  
Figure 18: The relationship between robustness and watermark information length: (**a**) \(N=2\); (**b**) \(N=4\); (**c**) \(N=8\).  
From Figure 18, although \(L\) varies from 100 to 1200 in the three figures, only minor differences exist where the curves approaching \(R=1\) and \(R=0\). While the trends of those curves and the sections, where \(R\) begins to drop, are similar and close. Consequently, it is reasonable to infer that the watermark robustness is obviously not determined by watermark information length.  
According to the above result, two conclusions are deduced as follows: (1) Increasing \(L\) alone, evidently cannot improve the associated robustness. Considering the watermark capacity limitation, \(L\) is suggested not be an excessively large number, for example, 1000 for a pseudo-random binary sequence; (2) The robustness computation of a large \(L\) can be estimated instead of a much smaller \(L\). For example, in the above experiments, the curve of \(L=1200\) is very close to the curve of \(L=400\). By using this conclusion, the computation complexity is further reduced dramatically when \(L\) becomes large, which provide an efficient method to estimate the watermark robustness.
## 3 Encapsulation Protocol
### Tcp
#### 3.5.2 Machine Learning  
Machine learning detection methods typically use SVMs with excellent pattern classification performances, but most of them are used to detect whether tunnel traffic is present, and little research is available on tunnel traffic protocol and application behavior. To study covert tunnels and distinguish between normal TCP traffic and tunnel traffic, [PERSON] [30] used the IP header's identification and the TCP header's serial number field.  
In addition to the header fields of the IP and TCP headers, machine learning is also an objective method for analyzing the regularity and inner correlation. The regularity or correlation between continuous packets will be changed if the information in the TCP header is hidden. Using kernel density estimation, variation coefficient, fractal entropy and autocorrelation coefficient, [PERSON] [31] further transformed these features into an eigenvector matrix.
# POL-MUX System for Noncoherent Optical Networks
## 4 Proposal of a Wide-Band Passive Optical Network (WDM-PDM-PON)
### Theoretical Description  
The POL-MUX system allows for the doubling of the transmission bandwidth, as mentioned before. However, the practical application of the POL-MUX is more effective in combination with other multiplexing systems. The polarization multiplex is transparent for other multiplexing technologies, so it can be easily used with a WDM, a TDM or other multiplexing technologies. Our model was designed as a combination of a PDM and a DWDM. This hybrid combination is called WDM-PDM-PON and allows the usage of \(n\) channels in the C or L band. Due to the polarization multiplex, it is possible to use the same wavelengths and thus double the bandwidth. The total bandwidth is given by the density of the wavelength multiplex and the number of possible DWDM channels according to the DWDM grid.  
The principal polarization multiplex has been known for many years. The problem is utilizing effective demultiplexing to achieve an optimal ratio between the polarization planes and the corresponding crosstalk, which is harmed by polarization mode dispersion and polarization-dependent loss. Currently, 100 Gbps and 400 Gbps systems are demultiplexed by digital signal processing. This method has great demands for the use of state-of-the-art technologies and thus has high financial complexity. A dynamic polarization controller could be an effective way to maximize the fiber bandwidth with low-cost components.  
The proposed WDM-PDM-PON is shown in Figure 8. \(n\)-channel multiplexed signals from wavelength multiplexers WDM-MUX1 and WDM-MUX2 are multiplexed by the polarization splitter PBS1. The multiplexed signals from both multiplexers carry different data at the same wavelengths. At the end of the optical link, using polarization demultiplexer PBS2, the two wavelength-multiplexed signals are split and fed to the corresponding demultiplexers WDM-DEMUX1 and WDM-DEMUX2. The advantage of this model is that a random number of wavelength channels could be multiplexed by one polarization multiplexer and demultiplexer; thus, only one dynamic polarization controller has to be used. On the other hand, the disadvantage of the proposed model is the necessity of utilizing polarization-maintaining (PM) fibers between all SFP transceivers and WDM multiplexers to maintain the polarization state and angle. The SFP transceivers are linearly polarized.
###### Abstract  
On 1 September 2022, a landslide in Hongya Village, Weiyuan Town, Huzhu Tu Autonomous County, Qinghai Province, caused significant casualties and economic losses. To mitigate such risks, InSAR technology is employed due to its wide coverage, all-weather operation, and cost-effectiveness in detecting landslides. In this study, focusing on the landslide in Hongya Village, SBAS-InSAR and Sentinel-1A satellite data from July 2021 to September/October 2022 were used to accurately identify the areas of active landslides and to analyze the landslide deformation trends, in combination with the geological characteristics of the landslides and rainfall data. The results showed that strong deformation was detected in the middle and back of the landslide in Hongya Village, with a maximum deformation rate of approximately -13 mm/year. The surface of the landslide consisted of mainly Upper Pleistocene wind-deposited loes, which is extremely sensitive to water. The deformation of the landslide was closely related to the rainfall, and the deformation of the landslide increased with the increase in rainfall. The research results prove that the combination of ascending and descending orbit data based on SBAS-InSAR technology is highly feasible in the field of landslide deformation monitoring and is of great practical significance for landslide disaster prevention and mitigation.  
SBAS-InSAR; Sentinel-1A; landslide identification; influencing factors; visibility; climate change adaptation; risk reduction
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Alcohol**} \\ \hline  & & **Yes** & **No** & \(p\)**-Value** \\ \hline  & Yes & 88.3\% & 87.0\% & 0.848 \\ \cline{2-5} Tobacco may cause mouth cancer & No & 11.7\% & 13.0\% & \\ \hline Alcohol may cause mouth cancer & Yes & 64.0\% & 67.4\% & 0.593 \\ \cline{2-5} Difficulty chewing/swallowing as a symptom of oral cancer & No & 36.0\% & 32.6\% & \\ \hline Difficulty chewing/swallowing as a symptom of oral cancer & Yes & 37.8\% & 41.7\% & \\ \cline{2-5} Difficulty chewing/swallowing as a symptom of oral cancer & No & 62.2\% & 58.3\% & 0.604 \\ \hline A mouth sore that does not heal as a symptom of oral cancer & Yes & 69.4\% & 59.7\% & 0.145 \\ \cline{2-5} A mouth sore that does not heal as a symptom of oral cancer & No & 30.6\% & 40.3\% & \\ \hline Abnormal mass/lump in the mouth as a symptom of oral cancer & Yes & 60.4\% & 58.3\% & \\ \cline{2-5} Abnormal mass/lump in the mouth as a symptom of oral cancer & No & 39.6\% & 41.7\% & 0.796 \\ \hline White/red patch in the mouth as a symptom of oral cancer & Yes & 37.8\% & 44.6\% & \\ \cline{2-5} White/red patch in the mouth as a symptom of oral cancer & No & 62.2\% & 55.4\% & 0.303 \\ \hline  & Yes & 33.3\% & 35.3\% & \\ \cline{2-5} The slow change in voice quality as a symptom of oral cancer & No & 66.7\% & 64.7\% & 0.790 \\ \hline  & Yes & 82.0\% & 77.7\% & \\ \cline{2-5} Quit tobacco use to prevent oral cancer & No & 18.0\% & 22.3\% & 0.433 \\ \hline  & Yes & 70.3\% & 63.3\% & \\ \cline{2-5} Quit alcohol use to prevent oral cancer & No & 29.7\% & 36.7\% & 0.282 \\ \hline  & Yes & 38.7\% & 36.7\% & \\ \cline{2-5} Avoid contact with secondhand smoke to prevent oral cancer & No & 61.3\% & 63.3\% & 0.793 \\ \hline  & Yes & 54.1\% & 48.2\% & \\ \cline{2-5} Brush and floss your teeth twice/day to prevent oral cancer & No & 45.9\% & 51.8\% & 0.375 \\ \hline  & Yes & 22.5\% & 23.0\% & \\ \cline{2-5} Telling your dentist when your dentures do not fit well to & Yes & 22.5\% & 23.0\% & \\ \cline{2-5} Education about oral cancer & No & 77.5\% & 77.0\% & \\ \hline  & Yes & 22.5\% & 23.0\% & \\ \cline{2-5} Education about human papilloma virus and oral cancer & No & 61.4\% & 63.5\% & \\ \hline  & Yes & 37.6\% & 35.6\% & \\ \cline{2-5} Education about human papilloma virus and oral cancer & No & 62.4\% & 64.4\% & 0.789 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical analysis of survey questions according to the current use of alcohol. Bold shows statistical significance (\(p<0.05\)).
# Effect of Microwave-Assisted Processing of Acorn Flour on Muffin Quality
## 2 Materials and Methods
### Wheat-Acorn Muffins Preparation  
The ratio of acorn flour to wheat flour in the muffin formulation was 50:50 (\(w\)/\(w\)), based on the dry matter content of each flour type. The proportion of acorn flour used in muffin preparation was determined based on preliminary trials, in which the acorn flour content ranged from 10% to 70%. Based on the evaluation of its impact on the baking quality, a 50% substitution was found to provide a satisfactory enhancement of antioxidant properties without significantly compromising the physical or sensory attributes of the final product.  
The amount of water added was established experimentally, taking into account the moisture content of the acorn flour. It was assumed that optimal muffin volume and texture could be achieved by adding 1.2 g of water per 1 g of dry matter in the acorn flour. The proportions of the remaining ingredients were kept constant across all formulations: sugar (50 g), whole hen eggs (30 g), rapeseed oil (40 mL), baking powder (4 g), vanilla sugar (4 g), and salt (0.5 g).  
All muffin variants were prepared using the same standardized procedure. First, the dry ingredients were mixed. The eggs were whipped with sugar using a stand mixer equipped with a whisk attachment (JAZ S.R.O., Nove Mesto nad Vahom, Slovakia) for 5 min until a light and fluffy consistency was achieved. Rapeseed oil was then gradually added in a thin stream while mixing continuously. Subsequently, water and the dry ingredient mixture were incorporated, and the dough was mixed until homogeneous. The mixture was portioned into silicone muffin models at 30 g per portion. Baking was conducted at 180 \({}^{\circ}\)C for 25 min in a modular Quail Mini Piccolo 4 bakery and confectionery oven (Wachtel, Hilden, Germany). In total, sixteen experimental muffin variants were prepared using acorn flour subjected to microwave treatment under various conditions, along with one control sample containing untreated acorn flour.  
For sample identification, we used the following abbreviations and codes: AF--acorn flour; 0--control samples; W--wet microwave treatment; 925, 1295, 1850--microwave power (in watts); 0.5...3.0--microwave processing time (in minutes).
# Cognitive Radio Network Technology for IoT-Enabled Devices +
## 3 Cognitive Radio Network Based on IoT
### IoT Applications and CR  
As shown in Figure 5, the IoT will be implemented in every facet of society, from private residences to public infrastructures, from the academy to the hospital, from the factory to the local supermarket. The internet of things presents unprecedented possibilities; its full influence and potential will be seen when more and more things become online in the future. Applications in the medical field, the military, cognitive radio-vehicular ad hoc networks, emergency networks, smart grids, and smart metering are all possible uses for this technology. All of these are examples of probable applications for CR technology inside the Internet of Things; however, there are currently only a small number of studies in the literature that address these issues. The authors of [27; 28; 29; 30; 31; 32; 33] provide an overview of IoT applications and demonstrate how CR can be used to solve some of the problems inherent to these programs.
## 5 Simulation Results
### The Correctness Verification of AFC-ANF When the Rotor Runs at a Constant Speed  
The control effect of rotor vibration control is verified in the case of positive polarity control and negative polarity control, as shown in Figures 9 and 10.  
Figure 8: Displacement of center of mass. (**a**) PID control. (**b**) AFC.  
Figure 9: Rotor vibration with or without negative polarity control. (**a**) \(x\) direction of AMBs-A. (**b**) \(y\) direction of AMBs-A. (**c**) \(x\) direction of AMB-B. (**d**) \(y\) direction of AMB-B. (**e**) A 3D plot of AMB-A. (**f**) A 3D plot of AMB-B.  
In Figure 9a-f, it is evident that when the AMBs high-speed motor operates below the critical speed, the negative polarity control can reduce the rotor vibration from 0.51 \(\times\) 10\({}^{-6}\) m to 0.27 \(\times\) 10\({}^{-6}\) m at the AMB-A end, and the rotor vibration also reduces from 0.52 \(\times\) 10\({}^{-6}\) m to 0.27 \(\times\) 10\({}^{-6}\) m at the AMB-B end. Similarly, Figure 10a-f show that the vibration control effect of the AMBs rigid rotor system operates above the critical speed under the positive polarity control. Thus, the rotor vibration at the AMB-A end is reduced from 1.96 \(\times\) 10\({}^{-6}\) m to 0.66 \(\times\) 10\({}^{-6}\) m, reducing by 66.3%, and the rotor vibration at the AMB-B end is reduced from 1.97 \(\times\) 10\({}^{-6}\) m to 0.62 \(\times\) 10\({}^{-6}\) m, reducing by 68.5%.  
Negative polarity control and positive polarity control are required when the rotor system is operated in the sub-stiff body critical speed region and super-stiff body critical speed region, respectively. Therefore, polarity-switching control is required when the rotor system is operated at full speed. The results also show that the rotor vibration displacement is reduced to a larger extent after the inclusion of the ANF, which effectively suppresses the unbalanced vibration of the rotor and proves the effectiveness of AFC-ANF for online unbalanced compensation.
# A Gesture-Based Teleoperation System for Compliant Robot Motion
## 2 Gesture-Based Human-Robot Interface
### Force Feedback Loop  
During the working process of the robot, such as assembly, it is unavoidable that one part contacts other parts. Sensing, controlling and learning contact force are an essential parts of the compliant teleoperation platform. Firstly, the contact force is feedback to the operator to avoid damaging the robot and the parts; secondly, the contact force usually contains skill knowledge of assembly process, which is a key to the knowledge transferring between human and robots.  
According to the characters of the gesture-based teleoperation, a vibration/tactile based contact force feedback system is proposed to provide the operator with telepresence experience. As shown in Figure 4, the contact force is detected by the force sensor located between the robot tool and wrist. The force signals are directly fed in the force controller, which is a component connecting two loops--the teleoperation loop and the control loop, where passive and active force control is realized. The forces are sent to the vibration tactile generator, which will then drive three micro vibrators located on the hands. The vibration frequency is proportional to the amplitude of contact forces. The operator can \"feel\" the force direction and values by the vibration patterns. Then, the user can adjust their demonstration to ensure a proper skill is conducted. The inner control loop is a fail-safe mechanism, which guarantees the safety of the robot and parts when the operator does not regulate the force well. A threshold force is set. Once the contact force exceeds this threshold, the robot motion is limited; i.e., the robot can only be commanded to move in the reverse direction.  
Figure 3: The gesture-based assembly skill teleoperation platform and the corresponding coordinate frames.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 4 Recent Advancement on UAV Tracking Antenna
### UAV Tracking Techniques
#### 4.3.4 UAV Tracking Based on Monopulse  
In the absence of a GPS module, the most reliable approach to estimate the angle/direction of a received beam is via the monopulse technique. Monopulse is an advanced signal processing technique widely deployed in radar technology to resolve the angular position of targets with the aid of antenna arrays and beam-forming weights [112; 113]. Monopulse relies on the extracting the sum and differences of the resulting overlapping beam patterns to obtain the angular position of the target. Hence, monopulse is a multi-beam system technology. In this fashion, the monopulse is able provide the five most fundamental UAV tracking parameters of: position vector, velocity vector, yaw angle, roll angle, and pitch angle. Unlike the previously listed tracking methods, UAV can be tracked using the beam formed by the directional antenna on the ground control station, as shown in Figure 12[113]. Antennas create beam and, as such, the signal received from the UAV is stronger when the UAV is at the center of the beam. However, the signal becomes weaker when the UAV yaws or elevates, thereby moving away from the center of the beam of the ground station antenna that has been pointing at it. Thus, the movement of the UAV is detected but its direction is not known. This limitation can be addressed by the use of multiple GCS, as shown in Figure 12, in which all the GCS report their estimated UAV position to the main GCS which combines the angular estimates and decides the UAV site [114]. Motivated by the positive prospect of a monopulse UAV tracking technique, several works have been implemented in this regard. Reference [115] proposed the use of phase calibration method for single-channel monopulse tracking (SCMT) systems based on error voltage's convergence trajectory data facilitated by curve fitting techniques. By exploiting commercial off-the-shelf (COTS) equipment, a three-channel monopulse tracking has been designed [116]. Reference [117] presented a mathematical closed-form solution for identifying two targets based on a four-channel monopulse including both phase and amplitude monopulse measurement. There have been suggested strategies to localize multiple unresolved extended targets [118]. Apart from the major UAV antenna tracking schemes discussed so far, there are other strategies that has been proposed and highlighted in Table 2.
# A Gas-Kinetic BGK Scheme for Natural Convection in a Rotating Annulus
## 3 Numerical Results and Discussions
### Flow Pattern  
It has been shown from the above numerical results that a steady-state flow field can be obtained for \(Ra\leq 10^{6}\) and the convective flow becomes unstable for \(Ra\geq 10^{6.1}\). Figures 5 and 6 show the streamlines (based on relative velocities) and temperature contours, respectively. For \(Ra\leq 10^{6}\), they do not change with time, while for \(Ra\geq 10^{6.1}\), they are obtained at a certain time, i.e., at \(t=2\) for \(Ra=10^{7}\), at \(t=0.05\) for \(Ra=10^{8}\) and at \(t=0.02\) for \(Ra=10^{9}\).  
Figure 4: Comparison of computed variations of (time-averaged) \(Nu\) with \(Ra\).  
As shown in Figure 5, several cyclonic and anti-cyclonic pairs of vortices (convective roll pairs) are observed in the rotating cavity, which is similar to the Rayleigh-Benard convection in a stationary, horizontal enclosure. The number of these vortex pairs not only varies with \(Ra\), but also varies with \(Ra\).  
Figure 5: Computed instantaneous streamlines for different \(Ra\).  
Figure 6: Computed instantaneous temperature contours for different \(Ra\).  
time at higher \(Ra\). This irregular number is changing within the range of 4 to 6, which is same as the observation in [6]. With the increase of \(Ra\), the convective rolls become more distinguished from each other and the symmetry of the flow structure is broken. Also for \(Ra\geq 10^{7}\), extra secondary vortices are found among the primary rolls, occurring as a result of flow instability.  
As for the temperature contours shown in Figure 6, similar thermal roll pairs start to appear at \(Ra=10^{5}\) and gradually break down at \(Ra=10^{8}\). The number of these pairs is equal to that of convective roll pairs for each \(Ra\). When \(Ra\) is higher than \(10^{8}\), the thermal mixing is obvious, leading to chaos thermal structures. It is argued that the flow vacillation should be responsible for the lack of coherence in the temperature contours.
# Machine-Learning-Based Optimization for Multiple-IRS-Aided Communication System
## 3 Proposed Algorithm for Static UE Scenario
### The Proposed New GRNN-Based Algorithm
#### 3.1.1 Data Preprocessing  
To solve \((P1)\) based on the new GRNN model, the direct and indirect path channel matrices between all nodes in our presented system, i.e., \(\{\mathbf{G}_{l}\}_{l=1}^{L},\{\mathbf{H}_{rl}\}_{l=1}^{L}\), and \(\mathbf{H}_{d},\forall l,k\), logically can be used as the model inputs, where \(\mathbf{H}_{rl}=[\mathbf{h}_{rl1},\mathbf{h}_{rl2},\dots,\mathbf{h}_{rlK}]\in\mathbb{C}^{N \times K}\), and \(\mathbf{H}_{d}=[\mathbf{h}_{d1},\mathbf{h}_{d2},\dots,\mathbf{h}_{dK}]\in\mathbb{C}^{M\times K}\). However, these may induce many features to be inputted and processed through the model layers as they are proportional to the number of reflecting elements, which may affect the model's performance. Hence, a more appropriate input is to use only one matrix for the indirect path channel between the BS and UEs through each IRS. In other words, the products \(\mathbf{G}_{l}{}^{H}\mathbf{H}_{rl}\) are adopted as model inputs, rather than \(\mathbf{G}_{l}\) and \(\mathbf{H}_{rl}\) for each \(l\)-th connected IRS. As shown in Figure 2, \(\{\mathbf{G}_{Rl}\}_{l=1}^{L}\) and \(\mathbf{H}_{d}\) represent the model total inputs, where \(\mathbf{G}_{Rl}=\mathbf{G}_{l}{}^{H}\mathbf{H}_{rl}\). Additionally, both the real and imaginary values of the model inputs are treated as two separate features. By applying this preprocessing step to the model, the input dimension can be reduced, as well as exploiting the internal structural information in indirect paths, which improves the model's efficiency. It can be calculated that the final dimension of the input vector is \(2(LMK+MK)\). To ensure reliability and stability, all data are normalized using Min-Max normalization, calculated as  
\[a=(a_{\max}-a_{\min})[\frac{b-b_{\min}}{b_{\max}-b_{\min}}]+a_{\min}, \tag{11}\]  
where \(a\) and \(b\) denote the normalized and corresponding non-normalized values, respectively, in our model, while \(a_{min}\) and \(a_{max}\) are constants to determine the range of the normalized values; we set the \(a_{max}=1\) and \(a_{min}=0\), which fits well with the collected data set distribution following various tests and tries.