esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Influence of 3D Printing Parameters on the Fatigue Life of FFF Prints
### Infill Density  
The infill density refers to the ratio of the solid volume to the entire volume of print. the infill density not only affects the weight, cost, shrinkage, and dimensional accuracy of an FFF print but also has a substantial influence on the mechanical strength, stiffness, and fatigue strength of plastics [38; 61; 79].  
The effect of the infill density on the fatigue behaviour of FFF prints has been investigated in a limited number of studies. As mentioned by [PERSON] et al. [76], FFF prints with infill densities less than 100% behave like an intrinsically notched material, resulting in inferior fatigue resistance. In all previous reports, it has been shown that a higher infill density results in higher stiffness, higher mechanical strength, and higher fatigue resistance. [PERSON] et al. [66] observed that the infill density positively affects the rotating bending fatigue strength of PLA prints. In other studies, ABS [64], PLA [67], and PLA-wood composite [18] specimens were 3D-printed at three different infill densities (25, 50, and 75%) and were subjected to rotating bending fatigue. The highest fatigue performance in each group was achieved at 75% infill density. A similar trend is also seen in compressive fatigue behaviour FFF prints. PLA prints with 50% and 30% porosity resisted compressive fatigue even after 3600 cycles, but 70% porous prints failed at much lower cycles [38; 61].  
Given that the overall stiffness of FFF prints is correlated with their infill density, the same effect is expected to be seen in all types of fatigue loading. However, the intensity and significance of this effect are yet to be examined. Further, in some applications, such as biomedical implants and bone scaffolds, infill densities should be kept at low values (around 10 to 40%) for better tissue growth and integration [80]. Hence, a compromise between the infill density and fatigue strength of FFF prints should be held. This issue can be the subject of future research.
## 4 Applications
### The Impact of Relative Permeability Hysteresis on Recovery
In the application results of these models, some scholars have conducted comparative validation work. The Killough model cannot accurately predict the hysteresis effect of relative permeability in gas/oil systems. The Land model used in the Killough hysteresis model is suitable for oil/water mixed-wet systems. A significant flaw of the Carlson model is that it does not assume the presence of hysteresis effects in the wetting phase, which can affect the model results. The Beattie model does not offer a trapping model. The advantage of the Spiteri model is that it is the proposed trapping model that is applicable to the diversity of wettability systems [84].
# Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks
## 4 Simulations and Results
### Data Fusion  
In this experiment, the experimental data were derived from the Tropical Atmospheric Ocean Project (TAO). Since 1984, the project has deployed about 100 sensors at different depths in 71 moorings in the tropical Pacific to collect the temperature of seawater in the region. We used these data for the lossless compression testing, and the test computer configuration was as follows: Processor: Intel(r) Core(tm) i5-6500 CPU @ 3.20 GHz (Intel, Santa Clara, CA, USA), memory: 16 GB, operating system: 64-bit operating system. The test results are shown in Table 2. Compressed by using integer wavelet transform, the spatial and temporal data can obtain an average compression rate of 5.42. Compared with other lossless compression algorithms Huffman, LZSS, LZW, and WinRAR, the integer wavelet transform has a higher compression ratio, more than 1 times higher.  
Figure 8: The power generation performance of a bistable piezoelectric cantilever oscillator: (**A**) average voltage curves for different excitation intensities; (**B**) average power curves at the different impedances.  
Figure 9: Contrast experiment for neighbor node discovery: (**A**) symmetry scene and (**B**) asymmetric scene.  
On the other hand, we took the raw data of 96 sensors from the dataset of TAO to carry out the three-level integer wavelet transform, and then analyzed the data storage of nodes. After integer wavelet transform, the one-level wavelet coefficients were (\(s_{1,1}\), \(s_{1,2}\), \(\ldots\), \(s_{1,52}\)) and (\(d_{1,1}\), \(d_{1,2}\), \(\ldots\), \(d_{1,52}\)), the two-level wavelet coefficients were (\(s_{2,1}\), \(s_{2,2}\), \(\ldots\), \(s_{2,30}\)) and (\(d_{2,1}\), \(d_{2,2}\), \(\ldots\), \(d_{2,30}\)), and the three-level wavelet coefficients were (\(s_{3,1}\), \(s_{3,2}\), \(\ldots\), \(s_{3,19}\)) and (\(d_{3,1}\), \(d_{3,2}\), \(\ldots\), \(d_{3,19}\)). Among these data, the data (\(d_{1,1}\), \(d_{1,2}\), \(\ldots\), \(d_{1,52}\)) were stored in the bottom node, the data (\(d_{2,1}\), \(d_{2,2}\), \(\ldots\), \(d_{2,30}\)) were stored in the convergence node, and the data (\(d_{3,1}\), \(d_{3,2}\), \(\ldots\), \(d_{3,19}\)) were stored in the Sink node. Compared with the traditional communication mode, the amount of data stored in the integer wavelet transform was much smaller, as shown in Table 3, which indicates that the integer wavelet transform has certain advantages in reducing the amount of communication data and transmission energy.
## 4 Results and Discussions
### Impact of Operational Parameters on the Performance of the Air Compressor
#### 4.2.2 Isentropic Efficiency  
The impact of operational parameters on the isentropic efficiency of the air compressor is illustrated in Figure 8. An analysis reveals that the isentropic efficiency increases with higher values of the rotational speed, pressure ratio, and inlet temperature. In this experiment, the maximum isentropic efficiency is observed at a rotational speed of 90,000 rpm, a pressure ratio of 2.9, and an intake air temperature of 20.7 \({}^{\circ}\)C. Taking into account both the sensitivity analysis of operational parameters on isentropic efficiency and their specific impact, the primary step should be to increase the rotational speed when high isentropic efficiency is required from the air compressor.
## 3 Materials and Methods
### Influence Mechanism Analysis  
To deeply analyze the paths of the impacts of different types of policies on the policy satisfaction of farmers through policy implementation evaluation, the mediation effect model was used for analysis. The model estimation results of the impacts of policy implementation transmission are exhibited in Table 3. Among them, the dependent variables of Models (1)-(4) were the support intensity, degree of promotion, policy sustainability, and implementation effect. The independent variables were service, financial, technical, subsidy, and supervision policies. According to the results in Table 3, the regression coefficients of service, financial, technical, and supervision policies were positive and statistically significant at the 1% level in all models, indicating that these policies can affect farmers' satisfaction with GPPs through the path of the evaluation of policy support intensity, degree  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Model (1)** & **Model (2)** & **Model (3)** \\ \hline
**Policy structure** & & & \\ Service policy & 0.583 *** & & 0.407 *** \\  & (0.0762) & & (0.0797) \\ Financial policy & 0.423 *** & & 0.336 *** \\  & (0.0662) & & (0.0702) \\ Technical policy & 0.360 *** & & 0.128 * \\  & (0.0580) & & (0.0675) \\ Subsidy policy & 0.267 *** & & 0.304 *** \\  & (0.0505) & & (0.0522) \\ Supervision policy & 0.250 *** & & 0.110 ** \\  & (0.0512) & & (0.0541) \\
**Policy implementation** & & & \\ Support intensity & & 0.290 *** & 0.111 *** \\  & & (0.0648) & (0.0702) \\ Degree of promotion & & 0.183 *** & 0.105 ** \\  & & (0.0642) & (0.0678) \\ Policy sustainability & & 0.273 *** & 0.0329 * \\  & (0.0603) & (0.0664) \\ Implementation effect & & 0.821 *** & 0.636 *** \\  & (0.0638) & (0.0682) \\
**Farmer characteristics** & & & \\ Gender & & & (0.0732) \\  & & & 0.0115 *** \\ Age & & & (0.00309) \\  & & & \\ Health condition & & & (0.0426) \\  & & & 0.0304 \\ Education & & & (0.0427) \\
**Family characteristics** & & & \\ Labor force & & & (0.00425) \\ Production specialization & & & (0.0132) \\  & & & 0.0139 *** \\  & & & (0.0304) \\ Village cadres & & & (0.0673 ** \\  & & & (0.119) \\
**Production characteristics** & & & \\ Cooperative & & & \\ Cooperative & & & (0.0915) \\  & & & \\ Training & & & (0.203 ***) \\  & & & \\ Demonstration & & & \\  & & & \\ Pseudo R\({}^{2}\) & 0.3178 & 0.2959 & 0.3707 \\ \hline \hline \end{tabular} Notes: *, ***, *** Statistically significant at 10%, 5%, 1% levels, respectively.  
\end{table}
Table 2: The estimation results of the empirical model.  
of promotion, policy sustainability, and implementation effect. Although the coefficients of the subsidy policy were positive in all models, they were only statistically significant in Model (3), indicating that subsidy policy can affect farmers' satisfaction with GPPs through the path of the evaluation of the policy sustainability. These results validated Hypothesis 3, i.e., the impact of policy structure on the satisfaction of farmers with GPPs is transmitted through policy implementation evaluation.
## 3 Day-Ahead Distributionally Robust Optimization Model
### Data-Driven Distributionally Robust Framework  
Leveraging the distinct characteristics of the various devices, the discrete decision variable representing the state of energy storage is defined as the first-stage variable \(x\). Other continuous decision variables, such as the output levels of different devices, are designated as the second-stage variables \(y_{s}\). Based on this formulation, the following objective function for uncertainty programming can be established:  
\[\min_{x\in X}\max_{p_{s}\in\Omega^{p}}\sum_{s=1}^{N_{k}}p_{s}\min_{y_{s}\in Y} f(y_{s}) \tag{26}\]  
where \(p_{s}\) is the probability of the \(s\)-th scenario, \(N_{s}\) is the total number of scenarios, and \(\Omega^{p}\) is the set of values that \(p_{s}\) satisfies. The outer layer (\(\min_{x\in X}\)) makes conservative, first-stage decisions to hedge against the worst-case scenario. The middle layer (\(\max_{p_{s}\in\Omega^{p}}\)) identifies the most adverse probability distribution from within an ambiguity set \(\Omega^{p}\). The inner layer (\(\min_{y_{s}\in Y}\)) determines the optimal, second-stage dispatch of devices to minimize costs for any given scenario.  
Acknowledging the potential disparity between the initial distribution derived from historical data and the true underlying distribution, the incorporation of \(\infty\)-norm and \(1\)-norm constraints is deemed necessary. These constraints are crucial for ensuring that the estimated probability distribution \(p_{s}\) converges to the true probability distribution, especially when a substantial amount of historical data is available. The set \(\Omega^{p}\) is defined as follows:\[\Omega^{p}=\left\{\{p_{s}\right\}\begin{aligned} &\left|p_{s}\geq 0,s=1,\ldots,N_{s} \right.\\ &\left|\sum_{s=1}^{N_{s}}p_{s}=1\right.\\ &\left|\left|p-p_{0}\right|\right|_{1}\leq\theta_{1}\\ &\left|\left|p-p_{0}\right|\right|_{\infty}\leq\theta_{\infty} \end{aligned}\right\} \tag{27}\]  
where, \(\theta_{1}\) and \(\theta_{\infty}\) represent the probability deviation limits corresponding to the 1-norm and \(\infty\)-norm constraints, respectively. \(p\) is the vector composed of the element \(p_{s}\).  
The probability of the scenarios must satisfy the following confidence constraints:  
\[\begin{cases}\Pr\{\left\|p-p_{0}\right\|_{1}\leq\theta_{1}\}\geq 1-2N_{s} \text{exp}(-2M\theta_{1}/N_{s})\\ \Pr\{\left\|\left|p-p_{0}\right|\right|_{\infty}\leq\theta_{\infty}\}\geq 1 -2N_{s}\text{exp}(-2M\theta_{\infty})\end{cases} \tag{28}\]  
Equating the right-hand side of Equation (28) to \(\alpha_{1}\) and \(\alpha_{\infty}\), respectively, yields Equation (29).  
\[\begin{cases}\theta_{1}=\frac{N_{s}}{2M}\ln\frac{2N_{s}}{1-\alpha_{1}}\\ \theta_{\infty}=\frac{1}{2M}\ln\frac{2N_{s}}{1-\alpha_{\infty}}\end{cases} \tag{29}\]
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 4 Execution Environment Analysis
### Proxy Analysis  
Proxy patterns make smart contracts upgradeable for possible feature updates and against unknown vulnerabilities. With proxy patterns, the logic contract is deployed at another address, and the proxy contract forwards the call to the actual implementation contract. Consequently, the transaction-reverting statements are in the implementation contract, not the proxy contract. ErrorExplainer therefore resolves the implementation contract address before fetching the source code.  
Depending on the proxy pattern, the address of the implementation contract is stored at a well-known storage slot [17,19]. ErrorExplainer supports the following popular patterns:  
* [noitemsep]
* **EIP-1967 Pattern:** EIP-1967 [20] is a standard for upgradeable proxy contracts, where the address of the storage slot is obtained from the hash value of 'eip1967.proxy.implementation' minus one.
* **Transparent Proxy Pattern:** This pattern is used by OpenZeppelin [21] and is the default pattern for TransparentUpgradeableProxy.
* **Legacy ZeppelinOS Pattern:** It is the pattern by ZeppelinOS [22] and is used in legacy contracts.  
Table 3 lists the storage slot address for each pattern.  
To identify the proxy pattern, ErrorExplainer reads the value at the address of the storage slots in Table 3 via a method, eth_getStorageAt, in the standard JSON-RPC interface of Ethereum. If the slot contains a non-zero address, then the address indicates the implementation contract, and ErrorExplainer updates the TO address to the implementation address. Because forwarding calls are automatically handled by the proxy contract, ErrorExplainer focuses on finding the implementation contract address.
# Fundus Image Registration Technique Based on Local Feature of Retinal Vessels
## 4 Experimental Setup
### Evaluation Metrics
#### 4.2.2 Registration Performance  
(a) Success rate  
Success rate measures the ability of a feature-based RIR technique to register image pairs and meet the specified requirement of target registration error (TRE). TRE is the mean distance in pixel between the ground truth annotations in a fixed image to the transformed ground truth annotations from the moving image. A perfect registration for an image pair is represented by TRE values equal to 0.  
However, achieving a perfect registration can be challenging in a real-world application. Thus, the registration for an image pair is considered successful if the obtained TRE is below one pixel for super-resolution applications and five pixels for image mosaicking and longitudinal study applications [54]. The success rate can be computed as given below:  
\[\begin{array}{c}\text{\it{}}\text{\it{}}\text{\it{}}\text{\it{}}\text{ \it{}}\text{\it{}}\text{\it{}}\text{\it{}}\text{\it{}}\text{\it{}}\text{\it{} \it{}}\text{\it{}}\text{\it{}}\text{\it{}}\text{\it{}}\text  
The one-way ANOVA with [PERSON]'s post hoc was performed to compare the success rate between the feature-based RIR techniques.  
(b) Factors influencing the success rate  
Factors of overlapping area and rotation were investigated for their influence on the success rate using [PERSON]'s rank-order correlation. It should be noted that for this evaluation, the successful registration was set below five pixels for all image pairs despite its registration application. As the details of the overlapping area and rotation are not initially provided by the FIRE dataset, this information is measured as follows.  
The overlapping area in percentage is obtained from the overlap area between the fixed image and transformed moving image. The moving image is transformed to the orientation of the fixed image using affine transformation inferred from the corresponding ground truth annotations. The rotation for an image pair is measured from the average angle between corresponding ground truth annotations without considering the effect of translation, as in [14]. Thus, results were in the larger angle of rotation. The range of overlapping area and rotation in the FIRE dataset is provided in Table 3.
## 4 Target Performance
### Detection Performance of Markers Against Motion Blur  
Motion blur is caused by the angular velocity applied to a camera during the exposure time when the lens is released. The strength of motion blur is related to the angular velocity applied to the camera, the viewing angle, and the exposure time. It can be modeled using these three variables. Its strength R is expressed in Equation (10).  
\[\mathrm{R}=\frac{\omega\times E}{\theta} \tag{10}\]  
Using Equation (10), we found that the intensity of motion blur R becomes stronger as the angular velocity applied to the camera increases. The angular velocity applied when the wireless relay system using a small fixed-wing UAV directs its antenna to the ground station should be calculated, and the marker detection performance against motion blur should be determined based on this value.  
MATLAB (Ver. 9.4) and Simulink (Ver. 9.1) simulations were performed to determine the angular velocity. Geometrical parameters and the speed of the UAV are indicated in Figure 8.  
The simulation results of the error angular velocity between the direction of the antenna and the direction of the ground station are shown in Figure 9. This error angular velocity is the same as the angular velocity applied to the camera in detecting the RCM.  
The simulation results in Figure 9 show that the average absolute value of the elevation angular velocity is 13.44 [deg./s] and the average absolute value of the azimuth angular  
Figure 8: Usage of the UAV in the wireless relay system.  
velocity is 15.40 [deg./s]. Finally, the angular velocity \(\omega\) applied to the camera is calculated from Equation (11) as  
\[\omega=\sqrt{13.44^{2}+15.40^{2}}=20.44\ [\text{deg./s}] \tag{11}\]  
Based on the previous information, the required marker detection performance for motion blur using Equation (11) is 0.0120 [-].
# Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN
## 3 DEC (Deterministic Energy-Efficient Clustering) Protocol  
### DEC Characteristics  
The deterministic energy-efficient clustering (DEC) protocol is a clustering protocol designed for wireless sensor networks (WSNs) to improve energy efficiency and prolong the network's lifetime. It focuses on organising nodes into clusters and efficiently managing the communication and data transmission within these clusters. DEC aims to reduce energy consumption, minimise data transmission overhead, and extend the operational lifespan of battery-powered sensor nodes [19; 20]. The DEC protocol divides the network into several rounds, and every round contains two stages: the set-up and steady-state stages, as shown in Figure 2. The DEC protocol has three main operations in the set-up stage: start to select cluster heads, choose cluster heads randomly, and establish. Data transmission will be completed in the steady-state stage, and the information will be transferred from cluster heads to a base station [18; 21].  
The set-up stage in the DEC protocol is different from other protocols. This stage is divided into three main parts, as mentioned earlier. When the number of rounds increases in DEC protocol, some problems appear that affect the network's performance. For example, the nodes become dead earlier than in LEACH, SEP and TEEN. Therefore, balancing the stability and number of rounds in the network is challenging [1; 17].  
The cluster heads and node sensors will be selected in DEC protocol depending on the remaining energy level, which can be calculated depending on the threshold value from Equation (1) [29]. The selection is made by comparing the remaining energy of each node with neighbour nodes [16; 30].  
\[T(n_{x})=\left\{\begin{array}[]  
The transmitted energy \(E_{Tx}\) of the size of a packet _L-bit_ message over the distance of transmitted data (_d_) can be calculated from the following equation [14]:  
\[E_{Tx}=\left\{\begin{array}{ll}L\ E_{elec}+t\ E_{fs}d^{2}&if\ d<d_{0}\\ L\ E_{elec}+t\ E_{mp}d^{4}&if\ d>d_{0}\end{array}\right. \tag{2}\]  
where: \(E_{Tx}\)--is the energy consumption in the sending process, \(E_{elec}\)--is the energy dissipation per bit in the electronics circuit, _L_--is the size of the packet (number of bits), \(E_{fs}\), \(E_{mp}\)--are the energy dissipation for free space (_fs_) and multipath loss (_mp_), features of \(d\), _d_--distance of transmission between two communicating endpoints, \(d_{0}\)--is the distance threshold, which can be calculated from Equation (3):  
\[d_{0=}\sqrt{\frac{E_{fs}}{E_{mp}}} \tag{3}\]  
\(E_{Rx}\)--is the energy consumption in the receiving process, which can be calculated from Equation (4):  
\[E_{Rx}=E_{elec}\ L \tag{4}\]
## 2 Background Work
### Video & Audio Synchronization  
Video and audio synchronization is the attempt to gather information about an event from various devices or time segments and organize this information in the correct order so that the produced outcome will reflect the truth about the event [26]. The need for audio and video synchronization has peaked, due to the widespread use of mobile devices with embedded cameras, resulting in multiple recordings of the same event, from multiple attendees, without the need for specialized equipment. This results in low-resolution videos and audio, blurry, and most often noisy [26; 27]. Having much data about an event from different sources makes extracting information hard; the extraction task is even more challenging when that data is also of different quality or specifications. Therefore, the synchronization of video and audio has many applications in the modern world, from free-view video to the detection of human rights violations [26].  
Techniques  
Most standard techniques to synchronize two or more videos are based on tracking visual features [28; 29]. Such methods require the same visual features to be visible in both videos, which is not always applicable as videos can be captured from a different perspective or location. Another technique is to mark the beginning of videos using clapping boards or by using jam sync, which synchronizes the camera clocks [30], but these techniques are not applicable \"in the wild\", as both are professional techniques requiring not only expensive hardware but also a stable set up of cameras in a controllable environment.  
Due to the visual features tracking limitations, audio synchronization is used for outdoor videos, and audio fingerprinting has proven a promising technique for synchronization. This, in turn, imposes new challenges, as microphones capturing the scene might be far apart, or the sound might cut on one of them. Furthermore, sound can lose quality from compression or noise, thus leading to deviations in pairwise matches. RANSAC or similar methods are regularly used to improve and form a more robust result.  
According to the literature, bottom-up approaches are the most used [29; 31]. This approach starts by matching single pairs and continues by merging them gradually until it reaches a global alignment. This perspective implements audio fingerprinting to match single pairs into larger clusters through reversed-indexing correlation evaluation. As in [32], many similar techniques apply clustering to the matched scores. However, bottom-up approaches have proven the frailty of outliers due to poor matches, which should be removed from the start. Otherwise, false data can be gathered throughout the process resulting in erroneous outcomes.