esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes
## 1 Introduction
To counter this, the scheme employs a blockchain consensus to neutralize the impact of misleading information interference, ultimately achieving higher evaluation accuracy and sensitivity. Throughout the design of the above approaches, it is imperative to consider the balance between block generation speed and block propagation delay among users. Delays in updating user reputation values can lead to prolonged difficulties in identifying malicious users, consequently elevating the vulnerability of other users to potential attacks. Given the above-mentioned deficiencies and problems, this paper proposes a trustworthy evaluation management model for drone clusters based on hierarchical blockchain. The main contributions of this paper are listed below. 1. To address the issues of a single trust evaluation factor and inadequate trust computation in the assessment of trustworthiness for UAV nodes, this paper proposes a UAV node dynamic trust evaluation method, which utilizes multiple trust factors. The model incorporates a dynamic trust decay factor to effectively leverage the historical trustworthiness of UAV nodes. It employs an information entropy-based optimization method for calculating the trustworthiness weight and computes the reputation value for each node through a reputation fusion algorithm. 2. To address the need for real-time trust evaluation of newly joined nodes during the dynamic composition of drone formations, we propose a trust management mechanism for UAVs based on a hierarchical blockchain. According to the characteristics of a cross-domain combination of UAVs, the layered blockchain is used to manage and record its trust value to improve the efficiency of the blockchain's transaction validation process. Additionally, considering that regular UAV nodes may have limited computational power, we leverage the high computational power of cluster head nodes to offload the consensus validation tasks of UAV nodes, reducing the consensus latency and enhancing the overall efficiency of the blockchain system. 3. We build a simulated drone cluster scene through the NS3 and MATLAB simulation platform, install the reputation value evaluation algorithm on the UAV node, and compare it with other existing schemes to verify the effectiveness of the evaluation algorithm in this paper. By comparing the blockchain delays of different resource allocation schemes, the efficiency of the blockchain system scheme in this paper is verified. The rest of the paper is organized as follows. Section 2 introduces the hierarchical blockchain-based trust measurement model for drone cluster nodes proposed in this paper. Section 3 verifies the effectiveness of the algorithm through experiments. Section 4 gives the discussion and future work and Section 5 concludes this paper.
## 3 Multi-GNSS PPP/INS Mathematical Model  
The researchers recorded that some smartphones such as Mi 8 received above four satellites with L5/E5 band for only about 13 h over a 24 h collection [10], and there are only a few Galileo satellites observed by smartphones such as Huawei Mate 30 that can be logged with dual-frequency observations [8]. It is difficult to conduct dual-frequency PPP with an ionosphere-free combined model [12; 34] especially in the GNSS-degraded environment. Therefore, the uncombined PPP model for dual-frequency GNSS observations of smartphones was used in this experiment, and the flow chart of the GNSS PPP/INS integrated navigation scheme is shown in Figure 10.  
Figure 10: The flow chart of GNSS PPP/INS integrated navigation on smartphones.  
Figure 9: Comparison of cumulative distribution function (CDF) of three-axis gyroscope and accelerometer differences from Mate 30, ADIS16405, and Mate 10. (a) Three-axis gyroscope; (b) three-axis accelerometer. Mate 30-ADIS16405 denotes the CDF of gyroscope and accelerometer differences between Mate 30 and ADIS16405, and Mate 30–Mate 10 denotes the CDF of gyroscope and accelerometer differences between Mate 30 and Mate 10.  
The PPP/INS integrated navigation scheme for smartphones mainly consists of uncombined PPP model and INS model. The initial absolute position of the smartphone was provided by GNSS PPP solutions, and the initial alignment of PPP/INS was conducted in dynamic mode. Meanwhile, the stochastic model is composed of GNSS step function variances depending on C/N\({}_{0}\), and the RKF is applied to parameter estimation. The specific mathematical models are as follows.
## 3 Non-Thermal Technologies in Food Processing
### Ultrasounds
#### 3.1.2 Application in Meat and Fish Products  
The use of ultrasounds in meat from pork, beef, chicken, and rabbits induces tenderness enhancement, improvements in water dynamics of tissue, increase in the water-holdingcapacity and color enhancement, along with acceleration of mass transfer and increase of shelf life [91, 92, 93, 94]. Furthermore, US technology assists industrial processes and affects positively some process parameters as observed for meat bringing, where the diffusion coefficient (D) increases exponentially with the ultrasonic intensity, and the rate of mass transfer is accelerated. When the lower ultrasound intensity is applied, the value of D for NaCl is higher than that of D for water, whereas the higher ultrasound intensity led to the reverse result [95].  
Tenderization and cooling process improvement for poultry meat can be reached by treating the meat with US [96]. US is used to raise meat tenderness, as shown by [PERSON] et al. Thanks to specific operating conditions, the water-holding capacity and tenderness of beef during curing are enhanced. The former is due to the modest oxidation of myosin that starts polymerization, which helps increase water retention; the latter is confirmed by the enhancement myofibrillar fragmentation index values and proteolysis of desmin and troponin-T [97]. More recently, after 7 and 14 days of storage at 4 \({}^{\circ}\)C, the tenderization capacity of US has been experimented on beef _m. Longissimus_ dorsi muscle samples with 60 min of sonication. The beef showed a reduction in red color and increased pH, luminosity, fascicle size, and a greater interfibrillary space that results in softer meat [98]. [PERSON] et al. have analyzed the features of intramuscular heat-insoluble collagen as well as the textural properties and meat quality of beef semitendinosus muscle after US treatments of 10, 20, 30, 40, 50, and 60 min. The meat color does not change except for a decrease in yellowness, muscle fiber diameter, and filtering residues, which have been reduced by the treatment, but the content of heat-insoluble collagen was not. In general, it has been seen that US treatment deteriorates the stability of collagen and thus meat textural properties [99].  
Tenderization through US has been exploited also for squid meat. This causes the muscle fiber to break and proteins degradation, leading to a softer meat [100].
# A New Method for Fatigue Evaluation of Titanium Alloy Welded Structures
## 2 The Principle of Notch Equivalent Stress and the Proposal of a New Method for Life Prediction
### Proposition of Augmented-Reverse Notch Equivalent Stress Method
#### 2.3.2 The Proposed Method of the Notch Equivalent Stress Correction Factor  
1. Perform fatigue test on \(n\) groups of welded joints, and obtain \(n\) groups of test data as \((S_{1},N_{1})\),..., \((S_{i},N_{i})\),..., \((S_{n},N_{n})\);
2. According to the methods described in Sections 2.1 and 2.2, calculate the notch equivalent stress at each stress amplitude \(S_{i}\) at the weld position of the welded structure: \((\sigma_{y1}\),..., \(\sigma_{yi}\),..., \(\sigma_{yn})\);
3. Calculate the notch equivalent stress correction factor \(f_{\text{as}}\), the formula is as follows: \[N=\frac{C}{\sigma_{\text{as}}^{m}}=\frac{C}{(f_{\text{as}}\cdot\sigma_{y})^{m}}\] (9) where \(\sigma_{\text{as}}\) is the corrected notch equivalent stress, \(\sigma_{y}\) is the notch equivalent stress, and \(f_{\text{as}}\) is a correction factor introduced to take into account the influence of material properties and welding performance on fatigue strength, \(m\) and \(C\) are material parameters.  
Substitute the \(n\) fatigue lives in step (1) and the \(n\) notch equivalent stresses in step (2) into Equation (9) to obtain the \(n\) notch equivalent stress correction factors: \(f_{\text{as}1}\),..., \(f_{\text{as}i}\),..., \(f_{\text{as}n}\);
4. Take the mean value of the obtained \(n\) notch equivalent stress correction factors, as shown in Equation (10): \[\overline{f_{\text{as}}}=\frac{\sum\limits_{i=1}^{i=n}f_{\text{as}i}}{n}\] (10)  
The calculation formula of the correction notch equivalent stress \(\sigma_{\text{as}}\) can be obtained as follows:  
\[\sigma_{\text{as}}=\frac{\overline{f_{\text{as}}}\cdot\sigma_{\text{n}}\cdot \left(\text{as}+x_{\text{as}}\right)}{\left[\left[2^{\frac{1}{2\overline{p}}} \cdot a^{\left(\frac{1}{p}-1\right)}\cdot x_{\text{as}}\right]^{q}+x_{\text{as }}\frac{q}{p}\right]^{\frac{p}{q}}} \tag{11}\]  
After the correction notch equivalent stress \(\sigma_{\text{as}}\) is calculated, the fatigue life predicted by the augmented-reverse notch equivalent stress method can be obtained by substituting into Equation (8). The ideological roadmap of this research is shown in Figure 4.  
Establishment of a New Three-Parameter Power Function S-N Curve for Titanium Alloy Welded Structures
###### Abstract  
The paper presents the development of the power, propulsion, and thermal systems for a 3U CubeSat orbiting Earth at a radius of 600 km measuring the radiation imbalance using the RAVAN (Radiometer Assessment using Vertically Aligned NanoTubes) payload developed by NASA (National Aeronautics and Space Administration). The propulsion system was selected as a Mars-Space PPTCUP -Pulsed Plasma Thruster for CubeSat Propulsion, micro-pulsed plasma thruster with satisfactory capability to provide enough impulse to overcome the generated force due to drag to maintain an altitude of 600 km and bring the CubeSat down to a graveyard orbit of 513 km. Thermal analysis for hot case found that the integration of a black high-emissivity paint and MLI was required to prevent excessive heating within the structure. Furthermore, the power system analysis successfully defined electrical consumption scenarios for the CubeSat's 600 km orbit. The analysis concluded that a singular 7 W solar panel mounted on a sun-facing side of the CubeSat using a sun sensor could satisfactorily power the electrical system throughout the hot phase and charge the craft's battery enough to ensure constant electrical operation during the cold phase, even with the additional integration of an active thermal heater. However, when the inevitable end-of-life degradation of the solar cell was factored into the analysis, an approximate power deficit of 2 kJ was found. This was supplemented by additional solar cell integrated into the antenna housing face.  
CubeSats; thermal design; PMAD; power system design; propulsion system design; albedo
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 3 Performance Evaluation of Infrared and Visible Image Fusion
### Qualitative Evaluation (Objective Evaluation Method)
If the average gradient is larger, the fusion image effect is better and the information is richer. * Spatial frequency (SF) Spatial frequency [81] reflects the detailed texture and clarity of the image through the image gradient. The spatial frequency SF can be divided into the spatial row frequency RF and the spatial column frequency CF. SF is defined as: \[SF=\sqrt{RF^{2}+CF^{2}}\] (23) In Equation (23), the RF and CF distributions are defined as: \[RF=\sqrt{\frac{1}{M*N}\sum_{i=1}^{M}\sum_{j=1}^{N}\left[P(i,j)-P(i,j-1)\right] ^{2}}\] (24) \[CF=\sqrt{\frac{1}{M*N}\sum_{i=2}^{M}\sum_{j=1}^{N}\left[P(i,j)-P(i-1,j)\right] ^{2}}\] (25) In Equations (24) and (25), the size of the fused image is \(M*N\). According to the above formula, it can be concluded that if you want to calculate SF, you should first calculate the horizontal frequency RF and the vertical frequency CF. The larger the SF value, the richer the edge texture information of the image. * Correlation coefficient (CC) \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Index** & **Desired Value** & **Evaluation Effect** & **Reference** \\ \hline SF & Higher value & The richer the edge texture information, the better the fusion quality & [81] \\ CC & Higher value & The better the quality of fusion & [82] \\ COSIN & Higher value & The more similar the pixels in the image & [83] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._The correlation coefficient [82] is an index to measure the degree of linear correlation between the original image and the fused image. CC is defined as:  
\[CC=\frac{(r_{l,F}+r_{V,F})}{2} \tag{26}\]  
Among them,  
\[r_{X,F}=\frac{\sum\limits_{i+1}^{H}\sum\limits_{j=1}^{W}\left(X(i,j)-\overline{ X}\right)(F(i,j)-\overline{F})}{\sqrt{\sum\limits_{i+1}^{H}\sum\limits_{j=1}^{W} \left(X(i,j)-\overline{X}\right)^{2}}(\sum\limits_{i+1}^{H}\sum\limits_{j=1}^{ W}\left(F(i,j)-\overline{F}\right)^{2})} \tag{27}\]  
In Equation (27), \(X\) represents an infrared image (IR) or visible image (VIS). \(\overline{X}\) and \(\overline{F}\) represent the average pixel values of the source image and the fused image \(F\), and \(H\) and \(W\) represent the length and width of the test image. The larger the CC, the better the fusion performance. * Similarity (COSIN) The function of similarity [83] is to convert the corresponding image into a vector and then calculate the cosine similarity of the vector to date. COSIN is defined as: \[COSIN=\cos(\theta)=\frac{A*B}{\|A\|\|B\|}=\frac{\sum\limits_{i=1}^{n}A_{i}*B_{ i}}{\sqrt{\sum\limits_{i=1}^{n}\left(A_{i}\right)^{2}}*\sqrt{\sum\limits_{i=1}^{n} \left(B_{i}\right)^{2}}}\] (28) This method requires a lot of calculation, but the result is more accurate. The larger the value of COSIN, the more similar the pixels in the image are.
## 3 System Model
### V2V Channel Model for Straight Road Scenario
#### 3.2.2 Modeling of NLOS Components  
If we consider a type of scatterer at the roadside and only account for single reflections, \(D_{TS}^{NLOS}-D_{SR}^{NLOS}\) forms a complete NLOS path \(D_{TR}^{NLOS}\), and the channel fading \(h_{TR}^{NLOS}\) caused by the NLOS path can be expressed as follows:  
\[h_{TR}^{NLOS}(t)=\sqrt{\frac{1}{R+1}}\mu\cdot e^{i(\theta-2\pi f_{c}D_{TR}^{ NLOS}/c)}\cdot e^{2\pi t(f_{T}^{NLOS}+f_{R}^{NLOS})}, \tag{5}\]  
\[f_{R}^{NLOS}=f_{c}v_{R_{x}}\cos(\alpha_{R_{1}}-\beta_{R})/c, \tag{6}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Parameter** & **Definition** \\ \hline hr1 & The distance between \(T_{X}\) and S. \\ \hline hr2 & The distance between \(R_{X}\) and \(T_{X}\). \\ \hline D & The horizontal straight-line distance between \(T_{X}\) and \(R_{X}\). \\ \hline \(D_{0}\) & The straight-line distance between \(T_{X}\) and \(R_{X}\). \\ \hline \(V_{T_{x}}/V_{R_{x}}\) & The speed of the signal transmitting/receiving vehicle. \\ \hline \(\beta_{T}/\beta_{R}\) & The direction of travel of the signal-transmitting/receiving vehicle. \\ \hline \(\alpha_{T_{0}}/\alpha_{R_{0}}\) & The angle of the direct component for transmission/reception. \\ \hline \(\alpha_{T_{1}}/\alpha_{R_{1}}\) & The signal’s angle of departure/angle of arrival. \\ \hline \(D_{TR}^{LOS}\) & The line-of-sight propagation path between \(T_{X}\) and \(R_{X}\). \\ \hline \(D_{TS}^{NLOS}\) & The propagation path between \(T_{X}\) and S. \\ \hline \(D_{SR}^{NLOS}\) & The propagation path between S and \(R_{X}\) forms a single-reflection non-line-of-sight propagation path with \(D_{TS}^{NLOS}\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Channel model parameters.  
\[f_{T}^{NLOS}=f_{c}v_{T_{x}}\cos(\alpha_{T_{1}}-\beta_{T})/c, \tag{7}\]  
where \(\mu\) is the reflection coefficient of the scatterer \(S\)'s surface. In various vehicular network scenarios, roadside scatterers have different material compositions and quantities, leading to different reflection coefficients. \(\theta\) is the random phase generated by the electromagnetic wave on the scatterer, following a uniform distribution within [0, 2\(\pi\)].
# Study of Gravelly Soil Core Material Using a Large-Scale Triaxial Wetting Test
## 5 Conclusions  
Triaxial wetting tests were performed to investigate the wetting deformation mechanism of gravelly soil core material. Based on the test results, the characteristics of wetting deformation are discussed, and a new wetting deformation model is proposed. The main conclusions can be drawn as follows.  
(1) The strain path during wetting differs from the strain path during the loading phase. The shear contraction characteristic of the volumetric strain is more pronounced during wetting compared to loading. At high stress levels, the evolution of the wetting strain direction lags behind that of the wetting deformation, and a significant volumetric contraction is observed in the later stages of wetting.  
(2) The mechanism of the wetting deformation of the gravelly soil core materials was analyzed. Material weakening is thought to be the source of wetting deformation. The coupling effect of particulate rearrangement and wetted weakening returns the gravelly soil core material to its pre-wetting bearing capacity state while wetting deformation takes place.  
(3) The relationship between the wetting strain ratio and stress level follows an exponential function. Based on this, a wetting calculation model suitable for gravelly soil core material has been derived, which can effectively predict the wetting deformation quantity and behavior.  
A thorough grasp of the wetting deformation characteristics of the dam body materials would be beneficial for gaining a more precise understanding of the operational state of the dam as well as provide some reference values for future studies. In this study, only the gravel core wall materials were investigated to reveal the wetting deformation mechanism and characteristics. The wetting deformation of other core wall materials or dam body materials cannot be ignored, which is the aspect of this area of study that needs supplementary research in the future.  
Methodology, G.L. and Z.M.; validation, Y.Q., K.F. and Z.M.; writing--original draft preparation, Y.Q. and Z.M.; writing--review and editing Y.Q. and K.F. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Science and Technology Major Project of Tibetan Autonomous Region of China (No. XZ202201 ZD0003G) and the Department of Water Resources of Jiangxi Province Science and technology project (No. 202124 ZDKT06).  
Not applicable.  
The data presented in this study are available in article.  
The authors declare no conflict of interest.
## 3 The Clustering Protocol
### CHs Election  
This section is mainly divided into two parts: (1) CHs election in general clusters; and (2) CHs election in large clusters.
# Sparse Data-Extended Fusion Method for Sea Surface Temperature Prediction on the East China Sea
## 3 Implementation of the Proposed Approach
### Sparse Data Expansion Fusion Method  
In order to obtain more accurate background field data so that the large-scale remote sensing data in each area can be fused and corrected with more accurate observation data, the fusion method in this section is to fuse the large-scale remote sensing data through accurate sparse data. The principle in the process of fusion is that the stronger the correlation, the stronger the degree of fusion of remote sensing data by measured data, and vice versa.  
Because there are fewer precise data and more imprecise data, the fusion method is used to fuse precise data into remote sensing data. This is to achieve the purpose of calibrating fuzzy data and increasing its data volume.  
The measured data is the temperature data measured at a specific location in the East China Sea, which is point data, whereas the remote sensing data is the temperature data inversion of the entire East China Sea, which is grid data. The measured data is located in some grid points of the remote sensing data. Using the measured temperature data in these grid points to assimilate the remote sensing data in the same area will make the assimilated data not only contain multi-source data, but also more accurate.  
In the previous section, data clustering was completed, and n clustering sets were obtained. Set B with remote sensing data was fused and calibrated by set A with observation data.  
Because set A and set B are both time series data, assuming that the central point data of a cluster set is \(X^{i}\) and the data to be fused is \(X^{j}\), the fusion calculation formula is as follows:  
\[X^{j}=r_{i}^{j}X^{i}+(1-r_{i}^{j})X^{j}. \tag{7}\]  
Therefore, the assimilated data can be obtained, Figure 3 is a schematic diagram of the assimilation method of Equation (7).
# Application of Machine Learning Techniques to
## 2 Study Area and Data
### Aphrodite Data  
To delineate the climate zones in the study area, a dense network of data stations was required. There has been an increasing trend of using easily available gridded precipitation datasets for hydrologic and climatic assessments [38; 39; 40]. Previous studies have identified the performance of the APHRODITE dataset (version V1101) as the best-gridded product over the high mountainous regions of Asia [41].  
Figure 1: Topography of the study area with the grid points/stations and observed gauging station used in the study.  
Figure 2: The distribution of average total seasonal precipitation based on APHRODITE (1970–2004) for (a) warm-wet (b) cold-dry (c) cold-wet and (d) warm-dry seasons.  
To justify the use of the gridded dataset of APHRODITE, we performed the comparative analysis of the APHRODITE dataset and European Reanalysis gridded dataset (ERA5) [42] and Global Metrological forcing dataset for land surface Modeling (GMFD) [43] with the observed dataset and monthly temporal scale, at 11 gauging stations at different altitudes, as shown in Figure 1. ERA5 is also popular for its accuracy. The results of the comparative analysis have been shown in Table 1. The Pearson correlation coefficient and Kolmogorov Simirnov (KS) test (the method is discussed in Section 3.3.2) were used to compare the monthly precipitation datasets. The results tend to show agreement with the APHRODITE dataset with higher correlation coefficients at all the stations. For the KS test, \(p\)-values greater than 0.05 have been shaded, depicting the null hypothesis of similar probability distribution is not rejected. The results of comparative analysis suggests that the APHRODITE datasets have higher correlation with the observed data that is, more than 0.8 in nearly all the observed gauging station as shown in Table 1, so the APHRODITE dataset [40] of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) spatial resolution for the period of 1970-2005 has been used at a daily temporal scale, for the delineation of climate zones. Nevertheless, the regionalization framework is equally applicable to the other datasets having comparatively higher resolution and accuracy.  
Figure 2 presents the total seasonal average precipitation sampled from APHRODITE. The daily gridded precipitation long term data from 1951 and onwards, is provided by the APHRODITE product (V1101). It provides the data at the continental scale, including a dense rain gauge data network of Asia, comprising the Middle East, South and Southeast Asia with valid stations of 5000 to 12,000 [40].