esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of Correlation and Sensitivity  
### Theoretical Foundation  
According to the \"Dynamic Performance Evaluation and Test Method for Particular Class Vehicles and Tracked Machine\" (GB/T17426-1998) published by the State Bureau of Quality and Technical Supervision in 6 July 1998 [25], the vertical and lateral stability of particular class vehicles and tracked machines can be evaluated by the maximum vibration acceleration and stability index.  
The stability index is calculated as follows:  
\[W=0.896\sqrt[10]{\frac{A^{3}}{f}F(f)}, \tag{5}\]  
where \(W\) is the stability index; \(A\) is the vibration acceleration (m/s\({}^{2}\)); \(f\) is the vibration frequency (Hz); and \(F(f)\) is the frequency correcting coefficient.  
Correlation analysis measures the extent to which different suspension parameters correlate with vertical and lateral accelerations. Sensitivity analysis studies analyze the sensitivity of changes in the vertical and lateral accelerations to changes in the primary suspension parameters, identifying parameters with a significant impact on the system or model [20], and determining the input parameters for the subsequent multi-objective optimization [26].
## 5 Results from BVLOS Flight Tests  
Figure 17a shows the take-off of a RAVEN Giant BigStik UA. The ADS-B box is mounted on the upper nose of the aircraft. As the flight logs show in Figure 17b, BVLOS flights are conducted in round-trips between the take-off point on the runway and Fox Island in Placentia Bay, a distance of approximately 7 km each way. The cruise altitude achieved is approximately 400 m above MSL. During BVLOS flights, the UA can only be seen during takeoff and landing at the runway area. Once the UA climbs to above 200 m, the PIC and other UAS ground operators have to rely on the autopilot C2 link and the FPV video link to monitor the attitude (roll, pitch, and yaw), position (latitude, longitude, and altitude), and status of the UA at the GCS, and control the UA to complete the flight missions for the RAVEN BVLOS research on the development of the proposed small UAS DAA system.
# Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence
## 4 Experiments  
Through numerical and physical experiments, we verify whether the proposed method operates as expected and discuss effective distance functions for evaluating the comparison between forward and measured projections. To demonstrate the advantages of iterative methods over transformation methods, we set conditions with fewer projection directions relative to the number of pixels and with a relatively high level of noise.
# CFD-Simulink Modeling of the Inflatable Solar Dryer for Drying Paddy Rice
## 2 Materials and Methods
### Inflatable Solar Dryer (ISD)
#### 2.1.2 Field Experiments  
The experiments were performed at the International Rice Research Institute (IRRI) experimental station in Los Banos, Laguna, Republic of the Philippines (14\({}^{\circ}\)11\({}^{\prime}\) N, 121\({}^{\circ}\)15\({}^{\prime}\) E, 21 m a.s.l.). Mixed rice varieties were used for the three batches of experiments during the rainy season between October and November 2013. The dryer was assembled on the ground. The two plastic films were unzipped to load the paddy rice. Then, the zipper of the dryer was closed, and the blowers were turned on to provide pressure to inflate the unit. The special roller provided for mixing the grains was pulled underneath the PVC film every hour between 8:00 and 17:00. After mixing, the moisture content was monitored. Ten grams of paddy rice was collected at three positions, as shown in Figure 2a. The samples were stored in plastic bags and transported to the IRRI laboratories. The moisture content was determined by the gravimetric method at 103 \(\pm\) 2 \({}^{\circ}\)C for 24 h [25]. The experiments were terminated when the rice reached a target moisture content of 14% wet basis (MCwb).
## 2 Materials and Methods
### Application of Noise Reduction Algorithms
#### 2.1.2 Relative Performance Evaluation of the Optimized BM3D Algorithm  
To compare the performance efficiency of the BM3D algorithm, conventional noise reduction algorithms referred to as Gaussian, median, and Wiener filters were applied [23; 24; 25]. These conventional noise reduction algorithms were developed in the very early stages of image processing history, and various modified approaches with adaptive techniques have been applied in the medical imaging field. However, the noise reduction performance of conventional algorithms is proved with various studies. Therefore, we employed the above-mentioned filters for comparison with the optimized BM3D algorithm aiming to assess the performance of the optimized BM3D algorithms. The following are the equations for each conventional noise reduction algorithm. The kernel size was set to be the same as  
Figure 1: The flowchart illustrating the principle of the noise reduction process of the block matching and 3D filtering algorithm.  
the BM3D algorithm. Entire noise reduction algorithms were modeled and applied using the MATLAB program (MathWorks, Boston, MA, USA).  
\[\text{Gau}(x)=\frac{1}{\sqrt{2\pi}s}e^{-\frac{x^{2}}{2\sigma^{2}}}, \tag{6}\]  
where \(x\) denotes the distance from the center pixel of the kernel and \(s\) is the standard deviation of the noise in the kernel.  
\[\text{Med}(n)=MED[Y(n-k),\ Y(n-k+1),\ \cdots,\ X(n),\ X(n+k)], \tag{7}\]  
where when the length of the median filter is satisfied as \(n=2k+1\), \(X(n)\), and \(Y(n)\) denote \(n-\text{th}\) elements of the input and output images, respectively.  
\[\text{Wie}(m,\ n)=\mu+\frac{v^{2}-\sigma^{2}}{v^{2}}(a(m,\ n)-\mu), \tag{8}\]  
where \(\mu\) denotes the mean value of the Wiener filter with the kernel size of \(m\times n\), \(v\) means the variance of the input image, and \(\sigma\) is the setting of noise variance.
## 3 Analytical Model
### Clock Feedthrough Effect  
In addition to the charge redistribution effect caused by parasitic capacitances in the discharge path, the two parasitic capacitors \(C_{gs}\) and \(C_{gd}\) of the discharge switch induce a clock feedthrough effect. This impact alters the voltages at nodes \(V_{c}\) and \(V_{n1}\) during both  
Figure 5: (**a**) TCG block with two lump capacitors simplifying the parasitic capacitances of resistors and MOSFET switches. (**b**) Charge flow of the TCG block.  
the low-to-high and high-to-low transitions of the discharge pulse. While \(C_{gs}\) and \(C_{gld}\) are voltage-dependent, the time-based sensing method inherently averages this nonlinearity during operation, and the analytical model uses these average values to maintain compactness while preserving accuracy.  
Upon the rising edge of the discharge pulse arrival, the voltages at nodes \(V_{c}\) and \(V_{n1}\) increase due to capacitive coupling. This increase in voltage slows down the discharge process on \(C_{store}\), as depicted in Figure 6. Conversely, during the falling edge, the voltages at nodes \(V_{c}\) and \(V_{n1}\) decrease, resulting in an accelerated discharge process.  
It should be emphasized that while the low-to-high and high-to-low transitions sum a zero effect on \(V_{c}\) node, this is not the case for the \(V_{n1}\) node. When the falling edge of the discharge pulse arrives and \(S_{discharge}\) opens, the discharging path of \(C_{store}\) is disconnected. Hence, the decrease in voltage on the \(V_{n1}\) node does not affect \(C_{store}\) and merely accelerates the reset process. To incorporate this clock feedthrough effect into the analytical model, a non-zero initial condition is used for the \(V_{n1}\) node when solving the equations. This approach captures the impact of the clock feedthrough effect while preserving the simplicity of the analytical model.
## 4 Discussion
Although markerless systems have demonstrated good agreement with marker-based systems in the sagittal plane, reduced accuracy has been shown in the frontal and transverse planes, particularly for joint rotations involving small angular displacements [49; 50]. Therefore, while these kinematic trends may hold clinical relevance, especially regarding injury risk or the development of osteoarthritis, readers should interpret these results within the context of the technical limitations of markerless systems and the exploratory nature of our analysis. Indeed, a previous study using a markerless approach [11] also found no significant differences between the HT and PT groups during drop jumps. In that study, participants dropped from an elevated platform and, upon contacting the ground, were required to control their landing and perform a small jump. In this study, the kinematics of landing were also analyzed, but participants had greater control over the movement performed. The consistent results across both studies further reinforce the likely absence of differences between HT and PT 12 months post-surgery. Our findings, while preliminary, underscore the importance of considering not only structural integrity but also the potential implications for neuromuscular performance during functional tasks. Clinically, rehabilitation protocols for PT recipients may benefit from an increased emphasis on frontal plane control and glyometric retraining, whereas HT recipients may require targeted strategies to address deficits in knee extension strength. Although the current sample size limits definitive conclusions, the identified trends provide a rationale for individualized rehabilitation approaches and highlight the need for longitudinal studies with larger cohorts to validate these biomechanical distinctions. The post hoc power analysis conducted revealed low power across most comparisons (ranging from 0.06 to 0.38); moderate effect sizes were observed for key parameters, such as maximum valgus (\(g\) = 0.60) and flexion at maximum valgus (\(g\) = 0.65). These results suggest that, although our study may have been underpowered to achieve statistical significance with the current sample size (\(n\) = 28), meaningful biomechanical differences between graft types may still exist. Notably, the moderate effect sizes in valgus-related measures align with the observed trends, supporting the clinical relevance of subtle alterations in knee stability following ACLR. Consequently, these findings emphasize the necessity for future research involving larger sample sizes to further investigate these biomechanical distinctions.
## 4 Aggregate Analysis
### Commuting Time  
Table 4 shows the weighted average commuting time by mode in 1994 and 2011. For all HBW trips, the weighted average travel time increased by 8.40%, from 22.8 min in 1994 to 24.8 min in 2011.  
The driving commuting time increased slightly, from 22.0 min to 22.6 min. The average bike commuting time decreased from 26.6 min in 1994 to 25.8 min in 2011. The extensive construction of bike infrastructure during this period may have contributed to the changes in the commuting patterns, although further research is needed to establish a direct link. There was a 7.43% increase in the average walking travel time. Compared with the 0.11% decrease in the average travel time of the bus commuters, the decrease in the average rail commuting time was much larger at 10.18%. The large decrease in rail trip travel time was likely due to the expansion of the light rail system.  
For the CBD rail trips, the average commuting time decreased by 18.89% between 1994 and 2011, dropping from 57.7 min to 46.8 min, while there was a 1.05% decrease in the weighted average commuting time for the non-CBD rail trips.  
In our analysis, we conducted the Shapiro-Wilk test to assess normality and [PERSON]'s test to evaluate the homogeneity of variances, finding that both prerequisites for using a \(t\)-test were satisfied. The Shapiro-Wilk test indicated that the data for both groups followed a normal distribution, while [PERSON]'s test confirmed that the variances were statistically similar. Consequently, \(t\)-tests were conducted to examine the differences in weighted average commuting time between 1994 and 2011. Table 4 shows the results of these \(t\)-tests. Bonferroni correction was applied for multiple comparisons (weighted \(t\)-test, \(p<0.0083\)). When considering the commuting trips by all modes, the travel time difference was significant (weighted \(t\)-test, \(p<0.05\)) for CBD and non-CBD trips. For the commuting time by individual mode, the travel time difference was only significant for all passenger trips.
# The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey
This report warns that smart devices are involved in harvesting huge amount of personal information and are exposed to a variety of potential security threats, such as unauthorized access and misuse of personal information. Data originating from ubiquitous devices are usually stored in cloud infrastructures, which not only attract intruders but also stakeholders, such as service providers and cloud operators interested to use these data for their own benefits (e.g., advertising) [16; 17]. Fog computing can help to address some security concerns related to IoT-generated data. For instance, fog computing facilitates the on-site data storage and analysis of time-sensitive heterogeneous data by reducing the amount of confidential data stored and transmitted to the cloud. However, fog computing has its own privacy and security challenges due to low latency, proximity to IoT devices, decentralized architecture and transient support [8; 18]. This research was conducted to understand the challenges to secure future digital infrastructure while it is still evolving. The key contributions of our study are as follows:  
1. An in-depth analysis of the fog computing security challenges, big data privacy, and trust concerns was performed in relation to fog-based IoT along with their existing solutions and respective limitations. It enacts the securing of big data with a novel functionality-based fog architecture taxonomy to categorize threats and security challenges in fog-enabled IoT systems accompanied by fog-enabled IoT applications security requirements. This provides a comprehensive comparison of state-of-the-art contributions in the field according to their security service. 3. Study of complementary interrelationship between blockchain and fog computing exploring blockchain-based solutions to cater privacy and security problems in fog paradigm along with the review of security requirements analysis of the fog-enabled IoT application with the combined blockchain. This paper is structured as follows: Section 2 defines IoT and explores several IoT applications. Section 3 consists of fog computing generic and functionality-based architectures. Section 4 presents Fog-enabled IoT applications security requirements followed by fog computing security challenges in Section 5. In Section 6, we highlight interrelationships between blockchain and fog computing along with the blockchain-based security solutions in fog-enabled IoT systems. Finally, the conclusion is drawn in Section 7.
# VENuS-Derived NDVI and REIP at Different View Azimuth Angles
###### Abstract
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bands** & **Central Wavelength (nm)** & **Bandwidth (nm)** & **Main Application** \\ \hline B1 & 423.9 & 40 & Atmospheric Correction, Water \\ B2 & 446.9 & 40 & Aerosols, Clouds \\ B3 & 491.9 & 40 & Atmospheric Correction, Water \\ B4 & 555.0 & 40 & Land \\ B5 & 619.7 & 40 & Vegetation Indices \\ B6 & 619.7 & 40 & DEM, Image Quality \\ B7 & 666.2 & 30 & Red Edge \\ B8 & 702.0 & 24 & Red Edge \\ B9 & 741.1 & 16 & Red Edge \\ B10 & 782.2 & 16 & Red Edge \\ B11 & 861.1 & 40 & Vegetation Indices \\ B12 & 908.7 & 20 & Water Vapor \\ \hline \hline \end{tabular}
\end{table} 
Table 1: VEN\(\upmu\)S satellite band information. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**W08**} & \multicolumn{3}{c}{**S01**} \\  & **Apr. 18** & **Jun. 27** & **Sep. 11** & **Apr. 18** & **Jun. 27** & **Sep. 11** \\ \hline View zenith angle (deg) & 35.29 & 35.54 & 35.29 & 30.10 & 30.31 & 30.13 \\ View azimuth angle (deg) & 25.88 & 27.16 & 25.77 & 179.15 & 177.41 & 179.26 \\ Solar zenith angle (deg) & 26.21 & 18.17 & 31.14 & 25.85 & 17.63 & 30.86 \\ Solar azimuth angle (deg) & 138.39 & 112.50 & 146.93 & 139.78 & 113.76 & 148.21 \\ Acquisition time (UTC) & 08:31:27 & 08:31:43 & 8:32:58 & 08:33:23 & 08:33:40 & 08:34:54 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: BRDF components and acquisition time for tiles W08 (west strip) and S01 (south strip) on 18 April, 27 June, and 11 September 2018.
# Deep Learning for Predicting Traffic in V2X Networks
## 4 LSTM Deep Learning
\[\sigma(x)=1/(1+e^{-x}) \tag{1}\]  
Firstly, LSTM determines any signals to forget from the cell state via a sigmoidal layer known as the forget gate. It takes \(h\) and \(x\) as inputs, and its outputs have values from 0 to 1, where \"1\" means \"totally keep\" and \"0\" means \"totally delete\". \[f_{t}=\sigma\Big{(}W_{f}(h_{t-1},x_{t})+b_{f}\Big{)} \tag{2}\]  
Next, we must find out what new signals are stored in the cell state. This process can be divided into two parts. First, the input gate layer of the sigmoidal layer determines which values need to be updated. Then, the candidate cell state is created by the _tanh_ layer  
Figure 2: Structure of LSTM Model. using a vector of the most recent candidate values, \(C\). In the following step, these two values are combined to update the state. \[i_{t}=\sigma(W_{i}(h_{t-1}\,x_{t})+b_{i}) \tag{3}\]  
\[\widetilde{C}_{t}=tanh(W_{c}(h_{t-1},x_{t})+b_{c}) \tag{4}\]  
Input \(x\) at time \(t\) and a hidden state \(h\) at time \(t-1\) to determine the new information that will be passed to the cell state. \(tanh\) is the activation function in this case. The value of the new information will range from \(-1\) to \(1\) due to the \(tanh\) function. \[\tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{5}\]  
The state of the past cell to be updated is multiplied by \(f\), losing the information we forgot. Then \(i\)\({}^{*}C\) is added. These are the current candidate values, estimated according to how we want to update each state variable. \[C_{t}=f_{t}\times C_{t-1}+i_{t}*\widetilde{C}_{t} \tag{6}\]  
In the end, we must specify everything we want to obtain as an output. Most importantly, apply the sigmoid layer and determine which parts of the cell state should be taken out (the output). Then, apply the cell state over \(tanh\) (to put all values in the interval [-1, 1]) and multiply it by the sigmoid gate output to produce a hidden state. \[o_{t}=(W_{o}(h_{t-1},x_{t})+b_{o}) \tag{7}\]  
Figure 2.0  
Table 1 shows the list of LSTM variables.
# Voting-Based Contour-Aware Framework for Medical Image Segmentation
## 2 Method  
The proposed approach is described in this section. We presented a contour-aware network and employed a voting strategy during training inspired by [29; 31; 41]. Figure 1 depicts the pipeline of our two-stage framework. First, we built a contour-aware network by inserting contour enhancement modules into the encoder-decoder backbone CNN and supervising it with an auxiliary loss to learn shape information, as shown in Figure 1a. Secondly, we trained three contour-aware CNNs at the same time iteratively, and each target network could learn high-confidence samples in each mini-batch as chosen by a voting committee made up of its peer two networks, as seen in Figure 1c.
## 1 Introduction
ANN optimization is limited in practice by a finite training sample and is accomplished through a stochastic training process which gives ANN the ability to avoid being trapped at local minima. On the contrary, this stochastic process makes ANN optimization empirical and subject to strong influence from statistical variations [20]. To overcome these issues with ANN, a hybrid approach with the fuzzy system has introduced. Fuzzy systems are quite good at handling uncertainties and can interpret the relationship between input and output by producing rules. Therefore, to increase the capability of Fuzzy and ANN, hybridization of ANN and fuzzy is usually implemented. [PERSON] et al. [21] used ANFIS for wheat grain classification with 99.46% of classification accuracy. [PERSON] et al. [22] coupled a fuzzy inference system (FIS) with image processing technique for a decision-support system for qualitative grading of milled rice. The results are reported with 89.8% agreement between the grading results obtained from the FIS system and those determined by the experts. In the context of rice grading, some head grains are easily misclassified with broken grain due to the resemblance in single feature (e.g., eccentricity) extracted from digital images and are not deterministically separable. In such cases, fuzzy approach [23] is more convenient for discrimination of head and broken rice grains [24]. [PERSON] et al. [25] investigated the rice milling degree using colour features (RGB) with an adaptive network-based fuzzy inference model. It was reported an error of 3.55-5.62% in milling degree using this process. However, the morphological features can improve the efficiency in terms of classification. In this context, a grading system based on fuzzy rules can simulate the behaviour of an expert in the evaluation and classification of physical properties of rice grains for grading. In this present work, the predictive capacity of ANFIS is assessed for quality testing and identification of basmati rice based on morphological features. This has been motivated by the fact that well-documented knowledge regarding rice kernels are usually available [26; 27]. This knowledge has been incorporated in forming the rules of the fuzzy inference system used to determine head and broken rice kernels. Moreover, the proposed ANFIS based classification method provides a rationale behind the knowledge of morphological features and their underlying dependencies with rice grains. Subsequently, the milling efficiency is estimated with broken grain and head grain ratio for test images. Furthermore, the proposed classification method is compared with standard data-driven machine learning techniques viz., support vector machine (SVM) and k-nearest neighbour (KNN) classifier. The rest of the manuscript is organized as follows: Section 2 briefly describes the materials and methods. Section 3 explain in detail the results and finally the work is succinctly summarized and concluded in Section 4.