esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Multicast Repair Fast Reroute Mechanism for Smart Sensors IoT and Network Infrastructure
## 4 M-REP FRR Mechanism
### Description of the Original M-REP Mechanism
Consequently, the alternative path created by the M-REP mechanism is not the shortest possible path (Figure 3). However, other IPFRR mechanisms do not generally provide the shortest alternative paths [30; 62]. Figure 2: Principle of the M-REP mechanism  
The destination D router may be a provider edge (PE) router or a router that is directly connected to the destination network of the original protected unicast data flow. When multicast M-REP packets arrive at the destination D router, these encapsulated protected flow packets must be restored (decapsulated) to their original format and then routed according to the unicast routing table. The decapsulation process of the M-REP multicast flow is performed by the destination router D. This router should be directly connected to the destination network of the original unicast packets. To restore the flow back to its original unicast form correctly, destination router D must have the original unicast flow information (source and destination addresses). The M-REP mechanism currently uses a tunneling technique, which means encapsulation of IPv4 unicast communication in new multicast packets (Figure 4). This technique is one of many possible solutions of how we can preserve the original source and destination address of the packet. Another option is to use extension headers (for example in IPv6). RPF in PIM-DM is a mechanism to ensure that packets are received on a correct interface and to prevent the creation of the micro-loops. For the needs of the M-REP mechanism, the original RPF was modified following the rule of First-Arrival. The research question we faced was whether this modification could cause micro-loops. The verification was performed using the mathematical proof by a contradiction [56], which confirmed that the modification does not cause routing loops. However, the M-REP mechanism operates in topologies which must meet two conditions, the network topology must use point-to-point links only and the target of original protected flow must be directly connected to router D.  
The M-REP mechanism will result in exactly one path created between routers S and D. Switching from the alternate M-REP path back to the original unicast path after network convergence is controlled by a dedicated timer. This timer is set to a value greater than the unicast routing protocol convergence time. Figure 4: The M-REP encapsulation of the unicast communication. Figure 3: The first arrival rule. It is important to point out that the legacy multicast tree creation procedure used in the PIM-DM protocol remains unmodified. A router with an empty list of output interfaces (OIL) for a specific flow (S, G) logs out of the multicast distribution tree by sending a Prune message from its RPF interface. The Prune message will also be sent out on non-RPF interfaces that received packets form (S, G) pair.
## 4 Experimental Results with a Scaled Tunnel Model  
To verify the MMW propagation model suggested in this study, lab experiments were carried out using a scaled model. We built a sub-scale tunnel made of wood covered with Formica to simulate the tunnel (Figure 10) and set the transmitter frequency to a higher frequency 94 GHz (shorter wavelength). The transmitter and receiver were connected to a pyramidal horn antenna with a gain of 24 dBi each (see Figure 11), and their radiation pattern was calculated using the formulation given in [24; 25]. The detected power at the receiver was measured for different distances up to 6 m along the tunnel, starting from 0.6 m. The steps between the measurements were set to 1-2 cm. The experiments were carried out and repeated for vertical and for horizontal polarizations. Figure 12 compares the experimental results and the simulation, and a significant correspondence is notable between both graphs. We applied the criterion developed in Equation (15) for the maximum number of reflected rays. The results are illustrated in Figure 13, comparing simulations with the real antenna pattern (solid line), the resulting number of reflected rays (dashed line), and experimental results (dotted line).  
Figure 8: (**a**) Number of maximum rays arriving at the detector side for each distance inside Lincoln Tunnel, using a horn antenna, calculated from Equation (15). (**b**) Link budget along the first 140 m of the tunnel. (**c**) \(\Delta\tau\) along the tunnel  
Figure 9: Normalized RMSE at 28 GHz inside the first 140m of Lincoln Tunnel, using a directive horn antenna.  
In order to compare between the simulation results and experimental measurements, we employed the Pearson correlation coefficient, \(\rho(A,B)\), defined by [26]:  
\[\rho(A,B)=\frac{1}{L-1}\sum_{i=1}^{L}\left(\frac{A_{i}-\mu_{A}}{\sigma_{A}}\right) \left(\frac{B_{i}-\mu_{B}}{\sigma_{B}}\right) \tag{16}\]  
Here, \(A\) represents a vector of the experimental measurements, where \(\mu_{A}\) is its mean and \(\sigma_{A}\) is its standard deviation. The vector \(B\) represents the simulation results with mean \(\mu_{B}\) and standard deviation \(\sigma_{B}\). When \(\rho=0\), there is no correlation between A and B, while \(\rho=1\) stands for perfect correlation.  
The correspondence between the experimental results and those estimated by the link budget simulation was examined using Equation (16). The Pearson correlation coefficient was calculated while assuming radiation patterns demonstrated in Figure 11b and found to be \(\rho=0.9054\). Evidently, the model predicted the expected indoor link budget quite accurately.  
Figure 11: (a) Horn antenna. (b) Radiation pattern in 3D view.  
Figure 10: Downscaled tunnel experiment setup in the laboratory. (a) Cross section of the tunnel. (b) Down scale tunnel.
# Design, Implementation, and Analysis of a Block Cipher Based on a Secure Chaotic Generator
## 3 Experiments Results and Security Analysis
### Correlation Analysis
#### 3.3.4 Robustness against Noise and Occlusion  
Encrypted images transmitted may be affected by channel noise and data loss. In this paragraph, we study the resistance capacity of the proposed cryptosystem against noise and occlusion. We utilized salt and pepper noise and an occlusion attack [25].  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Size**} & \multicolumn{3}{c}{**Plaintext Sensitivity**} \\ \cline{3-5}  & & **NPCR (\%)** & **UACI (\%)** & **HD (\%)** \\ \hline Airplane & 512 \(\times\) 512 \(\times\) 3 & 99.6102 & 33.4659 & 49.9972 \\ Black & 256 \(\times\) 256 \(\times\) 1 & 99.6067 & 33.4840 & 50.0143 \\ Bridge & 512 \(\times\) 512 \(\times\) 1 & 99.6114 & 33.4960 & 50.0060 \\ Cameraman & 256 \(\times\) 256 \(\times\) 1 & 99.6068 & 33.4616 & 49.9907 \\ Flowers & 256 \(\times\) 256 \(\times\) 3 & 99.6016 & 33.4563 & 49.9954 \\ Goldhill & 512 \(\times\) 512 \(\times\) 3 & 99.6081 & 33.4460 & 50.0003 \\ Kiel & 512 \(\times\) 512 \(\times\) 1 & 99.6075 & 33.4658 & 49.9974 \\ Lena & 512 \(\times\) 512 \(\times\) 3 & 99.6092 & 33.4669 & 50.0028 \\ Sailboat & 512 \(\times\) 512 \(\times\) 3 & 99.6058 & 33.4837 & 50.0062 \\ White & 256 \(\times\) 256 \(\times\) 1 & 99.6008 & 33.5139 & 49.9981 \\ \hline \hline \end{tabular}
\end{table}
Table 11: NPCR, UACI, and HD of the plaintext sensitivity test.  
Figure 18: Plaintext sensitivity test evaluated by the Hamming distance.  
We added salt and pepper noise with different intensities (0.01, 0.02, 0.05, and 0.1) to the encrypted Cameraman image (see Figure 10c), as shown in Figure 19a-d, respectively, and then we decrypted them. The recovered images are shown in Figure 19e-h, respectively. It can be seen that under limited noise, the decrypted images were always identified. Thus, our proposed decryption system had good robustness against noise attacks.  
Another kind of attack is occlusion. To assess the robustness of the cryptosystem against such an attack, first, we occluded the Cameraman encrypted image (see Figure 10c) with different data loss sizes and positions: 1/16, 1/8, 1/4, and 1/2, as shown in Figure 20a-d, respectively, then, we decrypted and shown them in Figure 20e-h. We can observe that all the recovered images were recognized but their qualities decreased with the increase of the occlusion size. Therefore, the proposed cryptosystem had a high level of robustness against occlusion attacks.  
Figure 19: Robustness against salt and pepper noise.  
Figure 20: Robustness against occlusion attack.
## 5 Conclusions  
In this study, a type of elbow intake structure was proposed, and a mathematical model was established to compare and analyze the experimental data with the simulated data and the simulated data of different intake structures. The temperature profiles, Richardson number, hot water output rate, and MIX number, which characterize the performance characteristics of thermal stratification, were used for comparative analysis. The conclusions are as follows:  
The mathematical model proposed in this study showed a high degree of agreement between experimental data and simulation results, with a deviation of less than 10%. This confirms the accuracy and reliability of the model.  
In the simulation results of the elbow type and water distributor type inlets, the maximum value of the Richardson number of the water distributor-type inlets was 16.9, the maximum value of the elbow type was 16.3, the minimum value of MIX number of the water distributor-type inlets was 0.1976, and the minimum value of MIX number of the elbow type was 0.2048. The deviation of the Richardson number of the two structures and the MIX number is not large, which can be seen as the elbow type and the water distributor type of the water into the tank will not destroy the original stratification structure of the tank; thus, the tank has better thermal stratification performance characteristics.  
The hot water output rate of the elbow-type inlet was 84.9%, and the hot water output rate of the water distributor-type inlet was 76.39%. Compared with the water distributor-type inlet, the elbow-type inlet achieved an 8.51% increase in hot water output rate. This translates into providing more hot water to users in practical applications, resulting in the improved operational efficiency of the system.  
The design of the elbow-type inlet minimized disturbances within the tank, enabling the preservation of the original thermal stratification structure of the tank, thereby enhancing its thermal stratification performance.  
In particular, the elbow-type inlet structure minimizes the disturbances within the tank, allows for natural stratification, and enhances the hot water output rate, thereby  
Figure 17: Comparison of simulated MIX numbers for different inlet structures.  
effectively optimizing the stratification performance of the tank. This innovative design holds promise for practical applications and enhances overall system efficiency.  
Conceptualization, Y.X. and F.L.; methodology, Y.X.; software, X.Z.; validation, Y.X., F.L. and Z.Z.; formal analysis, X.Z.; investigation, Y.X.; resources, F.L.; data curation, X.Z.; writing--original draft preparation, Y.X.; writing--review and editing, X.Z.; visualization, Y.X.; supervision, F.L.; project administration, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This study received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy.  
The authors declare no conflicts of interest.
## 1 Introduction
It operates with non-integer oversampling and less than two samples per symbol. The timing estimate is obtained from the frequency domain coefficients. It is designed for single carrier modulation formats, and benefits especially from a coherent system of the already existing Fourier transformation blocks. The modified Godard algorithm shows reduced jitter when compared to the Godard algorithm, especially for low roll-off factors. A further optimization permits the elimination of all multiplications for the actual implementation. Our hardware implementation shows a significant reduction in resource requirements and proves to be practical for real-time applications. The simulations are supported by an experiment using 32 GBd 16 QAM signals. The remainder of the paper is organized as follows. Section 2 proposes an architecture of a coherent optical receiver with frequency domain processing. Here, the timing synchronization is realized in the frequency domain. Section 3 derives the modified Godard algorithm, and shows how all multiplications can be removed. Section 4 shows the jitter of the Gardner algorithms and gives detail to the resulting bit error ratio (BER) for a certain timing jitter. Section 5 presents the real-time implementation of the multiplier-free modified Godard algorithm and its resource occupation. The capability of the modified Godard algorithm is substantiated with experimental results in Section 6.
## 1 Introduction
[17] used numerical simulations to study the oil and gas flow between the bearing inner race and the cage. After leaving the oil inlet hole, the oil forms a wetting area on the inner surface of the bearing cage, spreading and falling off to both sides. [PERSON] et al. [18] studied the flow field and temperature distribution of jet lubrication ball bearings by numerical simulation and experiment. The temperature distribution is affected by the oil volume fraction, and the higher temperature appears at the position where the oil volume fraction is lower. [PERSON] et al. [19] used the CFD method to analyze the oil-gas two-phase flow in ball bearings. Compared with the single nozzle structure, the temperature and pressure of the double nozzle bearing cavity are relatively low, and the airflow mode of the double nozzle is more complicated. [PERSON] et al. [20] used the CLSVOF multiphase flow method to track the oil/air two-phase flow inside the under-race lubrication roller bearing and obtained the influence of various factors. The results show that there is an optimal oil inlet velocity to maintain more oil accumulation in the bearing cavity. [PERSON] et al. [21] studied the oil-air two-phase flow inside the under-race lubrication ball bearing through CFD simulation and found that the diffusion degree of oil on the outer ring, cage, ball, and inner ring decreased in turn. Optimizing the oil inlet hole according to the average oil volume fraction helps to achieve accurate lubrication of under-race lubrication ball bearings. In summary, in view of the complicated internal geometry of the bearing and its operating environment, research on fluid behavior inside under-race lubricating ball bearings is scarce and early-stage, and the understanding of oil-air two-phase flow inside the bearing cavity is very limited. The study of oil phase distribution in the bearing is helpful to study the heat transfer in the bearing. The accurate prediction model for oil volume fraction is beneficial to the optimization of the oil supplying flow rate for the bearing and bearing drag and churning loss estimation. As it is quite difficult to accurately obtain oil distribution and motion behavior in high-speed ball bearings through theoretical or experimental methods, the numerical simulation method is used in this article. Oil distribution characteristics along circumferential and radial directions inside the ball bearing with under-race lubrication are analyzed, as well as the influence of the bearing's running and lubricating conditions, such as bearing rotating speed, oil flow rate, oil viscosity, oil density on the oil distribution. Finally, a suitable method for estimating the average oil volume percent in the bearing is established.
## 2 Introduction of Fast Delay Estimation Based on BCC
### Fast Estimators of Time-Delay  
The estimation of the integral period is accurate in the general case; without any loss of generality, let \(I_{0}=0\). To quickly solve \(\arg\max_{\tau}R_{x_{1}x_{2}}(\tau)\), suppose that we obtain three sets of data \((-\theta,\hat{R}_{x_{1}x_{2}}(-\theta))\), \((0,\hat{R}_{x_{1}x_{2}}(0))\) and \((\theta,\hat{R}_{x_{1}x_{2}}(\theta))\), where \(\theta\leq 1\). The curve of the correlation function can be approximated by a convex parabola, which can be defined as  
\[\hat{R}_{x_{1}x_{2}}(\delta)=a\delta^{2}+b\delta+c. \tag{4}\]  
where the unknown variables \(\{a,b,c\}\) can be solved using the three sets of data obtained as follows:  
\[\left[\begin{array}{ccc}\theta^{2}&-\theta&1\\ 0&0&1\\ \theta^{2}&\theta&1\end{array}\right]\left[\begin{array}{c}a\\ b\\ c\end{array}\right]=\left[\begin{array}{c}\hat{R}_{x_{1}x_{2}}(-\theta)\\ \hat{R}_{x_{1}x_{2}}(0)\\ \hat{R}_{x_{1}x_{2}}(\theta)\end{array}\right] \tag{5}\]  
Solving Equation (5) yields:  
\[\left\{\begin{array}{ccc}2\theta^{2}a&=&\hat{R}_{x_{1}x_{2}}(-\theta)+\hat {R}_{x_{1}x_{2}}(+\theta)-2\hat{R}_{x_{1}x_{2}}(0)\\ 2\theta b&=&\hat{R}_{x_{1}x_{2}}(\theta)-\hat{R}_{x_{1}x_{2}}(-\theta)\\ c&=&\hat{R}_{x_{1}x_{2}}(0).\end{array}\right. \tag{6}\]Solving for the extreme value by the derivation of Equation (4) yields an estimate \(\hat{\tau}_{0}\), as follows:  
\[\hat{\tau}_{0} =-\frac{b}{2a}\] \[=-\frac{\theta}{2}\times\frac{\hat{R}_{x_{1}x_{2}}(\theta)-\hat{R} _{x_{1}x_{2}}(-\theta)}{\hat{R}_{x_{1}x_{2}}(-\theta)+\hat{R}_{x_{1}x_{2}}( \theta)-2\hat{R}_{x_{1}x_{2}}(0)} \tag{7}\]  
Then, \(\hat{\tau}_{0}\approx\tau_{0}\). However, a convex parabola is used to fit the function \(R_{x_{1}x_{2}}(\tau)\), which can lead to the'misfit' phenomenon, as shown in Figure 2.  
The result reveals that the estimation performance deteriorates as the bandwidth \(B\) increases.
## 3 Measurement Set-Up and Prototype
### Scenarios
#### 3.3.1 Railway Laboratory Tests  
The tests use a mock-up of a real object controller and an IXL simulator. The results show the correlation between the application layer (signalling response time and data rate) and network parameters (bit rate, latency and jitter) according to the signalling system requirements defined in Table 1.
## 1 Introduction
It is necessary to identify how these factors affect the data to ensure the retrieval algorithm extracts accurate data by correcting for the mechanisms that reduce data accuracy. Studies have identified the various ways in which land cover [26; 27], landform [28], human activity [29], meteorological conditions [30], and geographic factors [31] reduce the accuracy of satellite-based LST products, and researchers have created corresponding algorithms to increase the accuracy of detected LST data [31]. Identification and modeling of the environmental factors that affect the accuracy of FY-4A/LST data products at a regional scale and analysis of the influencing mechanisms are conducive to the improvement of the algorithms and thus increase product utility. This study was intended to complete a comprehensive evaluation of the FY-4A/AGRI LST product for Hunan Province, China, and proceeded as follows. We conducted an accuracy test by direct comparison of FY-4A data with in situ measured data. We then conducted TCA with FY-4A data and two reanalysis products and determined the influence of several environmental factors. We used the Himawari-8 Advanced Himawari Imager LST product for comparison; this product is extensively used and has been well-validated in remote sensing LST assessments. The sensing instrumentation is functionally similar to FY-4A/AGRI, which makes the Himawari-8 product good for comparison [32; 33] that will provide information used to improve and promote the use of FY-4A LST products. In situ measurement and grid products were included in our assessment, and a variety of methods were used in our comprehensive evaluation. The use of a large quantity of data suggests that the research conclusions are universally generalizable. The results of this study provide a baseline reference for assessing the detection capability of FY-4A and the utility of FY-4A LST products. This study also provides a basic analysis of how environmental factors, such as day and night, seasons, elevation, topography, land cover, landform, soil moisture, and vegetation, affect the accuracy of retrieved LST data. The work of this study supports the adoption and improvement of Fengyun 4 satellite LST products.
## 2 Removal of Optical Thick Clouds  
In this study the removal of optical thick clouds from multi-spectral images is conducted. In this case the information beneath the cloud is not accessible by multi-spectral image sensors. Therefore the areas obscured by cloud cover can be regarded as No Data and must be reconstructed by additional data sources. Existing methods for the removal of optically thick cloud can be categorized into substitution and interpolation techniques.
# Stress Corrosion Cracking Analysis of a Hot Blast Stove Shell with an Internal Combustion Chamber
## 2 Numerical Modelling
### Theoretical Corrosion Rate  
In the theoretical model, followed by the modelling used in the work by [PERSON] and [PERSON] [29], the analysis takes into account both the transfer of mass between water and acid vapour and the transfer of heat from the flue gas to the metallic shell. A fixed region that includes the flue gas and the metallic shell is the control volume to derive the governing equations for the condensation rate. The metallic shell was assumed to have a smooth wall. Within this control volume, the transfer of heat and mass for the condensation of water, nitric, and sulfuric acids is taken into account. The numerical analysis procedure implemented to predict the acid corrosion rate was subdivided into three different parts:  
1. In the first part, based on the assumed mixed concentration (300 ppm) of NOx and SOx, the NO2 and SO3, which would cause the formation of HNO3 and H2 SO4, were estimated as a function of the flue gas temperature employed by the function by [PERSON] et al. [30] and [PERSON] et al. [31]. The initial concentration of sulfur trioxide, nitrogen dioxide, and water in the flue gas for the reference blast temperature is described in Table 2.
2. The temperature difference between the shell and the flue gas was calculated based on the simulation results presented in the authors' previously published paper [28]. The reaction mechanisms for sulfuric and nitric acids that are considered in the CFD analysis are listed in Table 3.
3. The acid distribution was calculated using the controlled condensation method to determine the SCC propagation rate.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Species** & **Initial Concentration** \\ \hline SO3 & 780 mg/m3 \\ NO2 & 564 mg/m3 \\ H2O & 6\% wet \\ \hline \hline \end{tabular}
\end{table}
Table 2: The initial concentration of sulfur trioxide, nitrogen dioxide, and water in the flue gas for the reference blast temperature.