esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Description and Modeling
### Mathematical Model
Equation (9) represents the orthogonal placement constraint, where \(\psi x(l_{ib},w_{ib},h_{ib},ort)\) denotes the length of the edge parallel to the \(X\)-axis when the parcel is placed in a specific orientation, as follows: Figure 1:0 (9) 3. Equation (10) ensures that parcels are placed inside the compartment and do not exceed its three-dimensional dimensions, as follows: Figure 1:1 (10) 4. Equation (11) ensures that no two parcels overlap during loading, as follows: Figure 1:2 (11) 5. Equation (12) represents the LIFO principle, meaning parcels are loaded in the reverse order of customer visits, with the assumption that customer point \(o\) is served later than customer point \(i\), as follows: Figure 1:3 (12) The placement method when \(ort=1\) is shown by the parcel on the left side of Figure 2, and the placement method when \(ort=2\) is shown by the parcel on the right side of Figure 1:4 The bottom-left-back corner point and the diagonal corner point of the parcel within the compartment are illustrated in Figure 1:5  
In this paper, the two objective functions are transformed into a single objective function. First, Equation (1) is normalized by introducing an initial distance, which is half of the sum of the distances between any two points (including the distribution center and all customer points), denoted as \(d_{total}\). The total distance saving ratio is then incorporated into the objective function (1). Since the original objective function (1) aims to minimize the total distance, the introduction of the distance saving ratio transforms the objective  
Figure 1:6 Placement method and two points position diagram. into maximizing the distance saving ratio. Thus, the objective function is reformulated as follows:  
Figure 1:7  
This paper adopts the linear weighting method to simplify the multi-objective optimization problem. By introducing the weighting coefficients \(\lambda_{1}\) and \(\lambda_{2}\), the transformed single-objective function can be expressed as follows:  
Figure 1:8
## 3 Results and Discussion
### Nitric Oxide Production Ratio  
NO production ratio of MEF cells treated with water and 80% ethanol extracts of Korean WS and NS germinated for 7, 9, and 11 days is presented in Figure 3. NO production ratio of MEF cells treated with WS and NS was measured through comparing NO production with and without cytokine stimulation in this study. NO production ratio increased with the application of WS for up to 9 days of germination, but decreased at 11 days. There was no significant difference in the ratio after the application of NS germinated for various time period. Treatment with the 80% ethanol extract increased NO production ratio more than the water extracts for WS. The ratio for WS was higher than that of NS, and even higher than that of the control after 9 days of germination.  
NO is produced by the enzyme, inducible nitric oxide synthase (iNOS) during inflammation, which causes tissue damage, organ dysfunction and tumorigenesis, thereby leading to several acute and chronic diseases [36; 37]. Therefore, NO production is used as a biomarker of the inflammatory response [38], and lower NO production indicates higher anti-inflammatory activity. The results indicate higher anti-inflammatory activity in NS than in WS, and an increase in inflammatory response by WS after 9 days of germination. Previously, 50% ethanol extract of commercial wheat sprout was shown to reduce NO production in raw 264.7 cells compared to the untreated cells [12], and the application of wheat sprout extracts to raw 264.7 macrophages inhibited the production of TNF-\(\alpha\), a biomarker of inflammation, for up to 5 days at 20 \({}^{\circ}\)C [39]. [PERSON] ment et al. [40] reported a negative correlation between the level of IL-1B mRNA expression and contents of _p_-coumaric acid and _\(\alpha\)_-tocopherol in wheat flour. Although there are no reports on the compounds responsible for the anti-inflammatory activity of wheat sprout, _p_-coumaric acid and _\(\alpha\)_-tocopherol could be one of the compounds exhibiting the activity in wheat sprout.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Germination Time**} & \multicolumn{2}{c}{**Glucosidase Inhibitory Activity (\%) 1**} \\ \cline{2-3}  & **Waxy Wheat Sprout** & **Normal Wheat Sprout** \\ \hline
7 days & 84.61 \(\pm\) 6.14 ** & 90.85 \(\pm\) 0.24  \\
9 days & 90.92 \(\pm\) 0.38  \\
11 days & 94.19 \(\pm\) 0.40  \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(\alpha\)-Glucosidase inhibitory activities of 80% ethanol extracts of Korean waxy and normal wheat sprout sprout germinated for 7, 9, and 11 days.
## 2 The Underlying IoT Communication Architecture  
In this section, we present the IoT communication architecture, which our proposed BSNCare+ system is modeled on. Figure 1 illustrates the applied scenarios, such as the IoT-based ward, the IoT-based rehabilitation environment and the IoT-based healthcare place, as well as the detailed communication procedures of the underlying network architecture. In these applied scenarios, fixed sensors and intelligent wearable devices are deployed in the field and on the patient, respectively, to support the nurse/doctor in activities of patient care, rehabilitation and even healthcare. There are three indispensable components in the proposed IoT communication architecture: a BSN-Care server, mobile gateway, and edge devices (i.e., body sensors or wearable bio-devices). These edge devices are responsible for collecting environment parameters and bio-data from the patient. The nurse/doctor is then able to retrieve this data via his/her intelligent handheld device as a mobile gateway, which is responsible for retrieving data from the edge devices. With the specific bio-data and environment parameters, the nurse can recognize and satisfy the patient's need in a faster and more efficient way. For example, with the patient's bio-data, i.e., ECG, EEG, EMG and BP, the nurse can provide more accurate and timely treatment services and reduce medical delay accordingly. In the proposed IoT communication architecture, all the edge devices and the mobile gateway need to perform registrations at the BSN-Care server. After registration, security credentials are shared and stored among the edge device, the mobile gateway and the BSN-Care server. When the BSNCare+ system is in operation, all interactions between any two operating entities must be based on a robust identification and authentication mechanism for establishing a secure communication channel. The security credentials are exploited to achieve the goal of entity authentication, and, in addition, data confidentiality and data integrity can thus be guaranteed via the secure communication.
# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
## 5 Result and Analysis
### Impact of Number of Attributes on Average Aggregation  
The aggregation denotes the total data packets that are gathered at CHs before transmitting it to the Fog server. For this metric, the simulation is performed 5 times and the average is calculated. In case number of attribute is 1, the performance of most of the techniques is well. But when the attributes start increasing, the complexity increases, and the aggregation factor starts dropping. When attributes increase, the DICA and DICA_EXTENSION efficacy starts decreasing. The reason behind is that it starts generating different data packets that might be 16 or 32 bits and continuously transmitting these data packets to CH, which increases the load on CH and the aggregation factor starts decreasing. While EHDA and FDAS have achieved high aggregation levels because EHDA compresses its data size before transmission FDAS has selected a suitable parent node for each generated data. When the parent node is selected intelligently, it needs to process lower data packets further and it improves the aggregation. The proposed scheme has higher aggregation as it checks for duplicated normal values at the sensor level and replaces it with Boolean digit 0 which reduces load on CH and aggregation reaches up to its higher level.  
The scheme maintains the effectiveness of compression by focusing on preserving crucial information while reducing data size. EE-FDAS minimizes the probability of losing important information by reducing redundant data by changing it with Boolean digits. The rationale behind this approach is that when data is compacted into smaller sizes, the likelihood of data loss during transmission decreases, especially in environments with low bandwidth. By replacing redundant information with Boolean digits, EE-FDAS ensures that  
\begin{table}
\begin{tabular}{l c} \hline
**Parameters** & **Values** \\ \hline Simulation Duration & 2500 s \\ No. of arranged nodes & 300 \\ Organization of nodes & Random \\ Transmission range & 30 m \\ Broadcasting power consumption & 0.5819 \\ Receiving energy utilization & 0.049 \\ Size of network & 3000 m \(\times\) 3000 m \\ \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters with respective values.  
the compressed data remains informative while reducing the risk of loss, contributing to the scheme's overall efficacy in optimizing data size without neglecting critical details. The X-axis represents the number of attributes and the Y-axis shows the average aggregation factor. In Figure 3a, when the number of attributes is 4, the aggregation factor remains 0.3, 0.39, 0.81, 0.85 and 0.87 in DICA, DICA_EXTENSION, FDAS, EHDA and EE-FDAS, respectively.
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 4 Adversarial Attack Generation Methods for IoT Networks
### Exploratory Attack Methods
#### 4.1.2 Basic Iteration Method  
Proposed by [PERSON] et al. in 2017 [50], the Basic Iteration Method (BIM) represents a basic extension of the FGSM, where instead of making a single large step, it adopts an iterative approach by applying FGSM multiple times to an input with small step-size perturbations in the direction that maximizes the model's loss. The goal is to generate an AE that appears similar to the original input but can mislead the model's predictions.  
The basic idea behind the method is to start with an initial estimation of the solution and then iteratively improve the estimation by applying the Gradient Descent (GD) to the current guess. The resulting adversarial sample is then clipped to limit the maximumperformance for each pixel. The formula can be summarized by the following Expression (2).  
\[X_{0}^{adv}=X,\qquad X_{N+1}^{adv}=Clip_{X+\varepsilon}\left\{X_{N}^{adv}+\alpha. Sign\left(\
abla x\ J\left(X_{N}^{adv},Y\right)\right)\right\} \tag{2}\]  
where \(J\) denotes the loss function, \(X\) is the original input data (i.e., image), \(Y\) is the original input class label, \(N\) denotes the iteration count and \(\alpha\) is the constant that controls the magnitude of the disturbance. The \(Clip\left\{\right\}\) function guarantees that the crafted AE remains within the space of both the \(\varepsilon\) ball (i.e., \([x-\varepsilon,x+\varepsilon]\)) and the input space.  
The BIM algorithm involves starting with clean data (i.e., image) as the initial input. The gradient of the loss function is computed relative to the input, and a small perturbation is added along the gradient direction, scaled by a defined step size. The perturbed input is then clipped to ensure it stays within a valid range. These steps are iterated until a desired condition is met or for a set number of iterations.  
Although this method is simple to generate AEs, it might demand an extensive series of iterations to find the most effective and optimal AEs, and this may be computationally expensive and may not converge for all functions or initial assumptions.
## 9 Conclusions  
The working environment of FOGs-based MWD is hostile: the vibration is very strong and the temperature is very high. The gyroscope is heavily influenced by factors such as temperature, vibration, aging, and sudden breakdowns. A slight variation of the gyroscope stability can turn into a measurement error. Hence, it is important to monitor the behavior of gyroscopes through the use of DAVAR.  
The DAVAR is a representative of the time-varying stability of the gyroscope. The fast DAVAR is a fast algorithm for the classical DAVAR. However, both the fast DAVAR and the classical DAVAR could not analyze the discontinuous gyroscope data. What is worse, in many applications, the gyroscope often gives discontinuous data, for example in a FOGs-based MWD system.  
In this paper, utilizing the recursive characteristic of the fast DAVAR, we make a further advance on the fast algorithm to extend the fast DAVAR to discontinuous gyroscope data. This not only dramatically reduces the computation time, but could also allow us to analyze the discontinuous gyroscope data. Both the simulation results and the experimental results show that the improved fast DAVAR could not only save more than 90% of the computational time, but also deal successfully with discontinuous data.  
**Acknowledgments:** The authors would like to thank [PERSON] for his helpful discussions and comments. This research was supported by the National Science Foundation of China (Grant No. 60207002).  
**Author Contributions:** All the authors made contributions to this work. The idea was originally from [PERSON] and [PERSON]; [PERSON] proposed the scheme, developed the algorithms, completed the simulation, and wrote the manuscript; [PERSON] developed the primary algorithms; [PERSON] and [PERSON] processed the simulation and critically reviewed the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 14: The coefficients of each noise term.
# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 3 Proposed Methodology: DCBA Fault Diagnosis
### Integrated Diagnosis Model
#### 3.2.4 Attention Layer  
In traditional neural networks, the influence of different inputs on the output result is the same. However, when solving real-world problems, different inputs often have varying levels of impact on the output. To address this issue, we introduce the additive attention mechanism. Through the additive attention mechanism, we can compute the attention weights of each input element, allowing the model to selectively focus on different parts of the input. In this way, the neural network can dynamically adjust the importance of each input element based on its relevance to the current context, obtaining the attention weight for each input element, thus improving the model's expressive power and performance. The specific computation is illustrated in Figure 7.  
The proposed model is related to the state at time step \(t\), using an additive model to compute the relevance function. The specific calculation process is as follows:  
\[s(p_{i},q)={W_{0}}^{T}tanh\big{(}W_{p}p_{i}+W_{q}q\big{)}, \tag{10}\]  
and  
\[a_{i}=softmax(s(p_{i},q)). \tag{11}\]  
Here, the query vector q represents the state value at time step t, \(p_{i}\) represents the output of the hidden layer, \(a_{i}\) denotes the computed weight of \(p_{i}\), while \(W_{v},~{}W_{p}~{},~{}\text{and}~{}W_{q}\) are the matrices associated with \(s\).
# A Rapid Verification System for Automatic Emergency Braking Control Algorithm of Passenger Car
## 4 Verification of Passenger Car AEB Control Algorithm
### Test Results of AEB Control Algorithm
#### 4.3.3 CCRb Condition  
The parameters of the CCRb condition are set as follows: the relative distance is 30 m, the initial speed of VUT and the initial speed of VT are both 80 km/h, and the VT operates at an acceleration of \(-3\) m/s\({}^{2}\). The HIL testing and SIL testing results in the CCRb condition are shown in Figure 12.  
Take the HIL testing curve in Figure 12 as an example, the relative speed reaches 24 km/h at 2.3 s, and the VUT starts braking. Then, the TTC value rapidly decreases to below 2.25 s, and the VUT directly enters the L II AEB braking status. As the VT continues to decelerate, the collision risk continues to increase, and the VUT enters the L III AEB braking status at 3.4 s. At the end of the braking at 7.4 s, the speed of VUT and the speed of VT both decrease to 0. During the braking process, the minimum relative distance is 5.11 m, and there is no collision between the VUT and the VT.
# A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm
## 4 Analysis of Proprietary Internet Video Traffic Datasets
### Feature Correlation  
Correlation analysis between different features in proprietary Internet video traffic datasets is essential. Based on different DFI tool labels, we merged the datasets into two categories as shown in Figure 3, and selected all numerical features for Pearson's Correlation Coefficient computation. The number of numerical features selected under the CICFlowmeter and nDPI tools were 80 and 68, respectively. Given a set of vectors \(\mathcal{V}=\{V_{1},V_{2},\dots,V_{m}\}\), where each \(V_{i}\in\mathbb{R}^{n}\), \(m\) represents the total number of features. Each pair of vectors \((V_{i},V_{j})\) with \(i\
eq j\) is chosen to compute the Pearson's Correlation Coefficient \(\rho_{V_{i},V_{j}}\). The formula for the Pearson's Correlation Coefficient between two vectors \(X=V_{i}\) and \(Y=V_{j}\) is defined in Equation (7).  
\[\rho_{V_{i},V_{j}}=\frac{\sum_{k=1}^{n}(V_{i_{k}}-\bar{V_{i}})(V_{j_{k}}-\bar{ V_{j}})}{\sqrt{\sum_{k=1}^{n}(V_{i_{k}}-\bar{V_{i}})^{2}}\sqrt{\sum_{k=1}^{n}(V_{ j_{k}}-\bar{V_{j}})^{2}}} \tag{7}\]  
where  
* \(V_{i_{k}}\) and \(V_{i_{k}}\) are the \(k\)-th elements of vectors \(V_{i}\) and \(V_{j}\);
* \(\bar{V_{i}}\) and \(\bar{V_{j}}\) are the means of vectors \(V_{i}\) and \(V_{j}\);
* \(n\) is the number of elements in each vector.  
Based on the computational results presented in Figure 7, we observe that some features have a value of 0 in all samples. These concentrate on specific TCP flags, unidirectional flow in a certain direction, and some flow-related temporal features, which must be filtered using feature selection in the training model. The [PERSON]'s Correlation Coefficient for most features is around 0, indicating a low correlation between them. In contrast to Figure 7, Figure 8 shows fewer features with a value of 0. However, the nDPI tool employs numerous statistical methods to generate features, which leads to strong correlations among them, primarily focusing on features related to packet interarrival time and packet size. This increases noise and complexity in the training model, necessitating additional feature selection and dimensionality reduction techniques to mitigate the impact of high correlation.  
Figure 6: Traffic flow bidirectional byte accumulation histogram density and cumulative distribution.  
Figure 8: Feature correlation matrix in video quality category datasets created with nDPI.  
Figure 7: Feature correlation matrix in video quality category datasets created with ClCFlowmeter.
# Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
## 1 Introduction
### Significance of Unscented Kalman Filter  
The basic algorithm for solving tracking problems in nonlinear systems is the EKF, which effectively provides good results for first-order nonlinearity but is not robust [9]. The stability of the estimation process for low-order nonlinearity is enhanced by the modified [21] and extended Kalman gain filter [21]. However, it performs poorly for more complex, higher-order nonlinearity [13]. The UKF algorithm uses a set of sigma points to approximate the nonlinear relationship between the state of the system, control input, and noise by updating the state estimate and covariance at each time step based on a measurement of the system, and the weights used in the algorithm are calculated based on the mean and covariance of the predicted state distribution [21,22]. By doing so, the UKF can accurately capture any nonlinear function's posterior mean and variance up to the second order. This feature makes the UKF particularly well-suited for solving higher-order nonlinear problems where traditional linear approaches may fail.  
The standard deviation depends on the material used in sensors and other factors such as the design of the sensor, the operating conditions, and the measurement method. Therefore, it affects variability or uncertainty within measurements [23]. The accuracy of the standard deviation is crucial in UKF to ensure reliable and robust performance target tracking tasks to update the system's state in real-time, mainly when dealing with complex nonlinear systems. Otherwise, the target states can never be accurately estimated [7].
## 5 Proposed Multi-EC Enabled Air-Ground VN Scenarios
### Cost Efficient Federated Learning Platform for Vehicular Applications  
#### 5.1.1 Scenario Description  
For the case of VN, each connected vehicle is equipped with multiple sensor devices that can generate tons of data in real-time. These data, through proper analysis, can be utilized for providing a better QoS to the end-users. Several ML tools are available for analyzing data and inferring important information from them. Furthermore, there are multiple ways for performing the ML model training over dynamic wireless environments, like the VNA centralized ML model training approach is not much suitable technique to perform over VNs, mainly because of high communication and computation overheads. If a centralized training approach is adapted over VNs, VUs need to transmit their complete data towards the centralized server which performs training operations. Such an approach adds significant cost in terms of data transmission and training latency at the server. Thus, other distributed and collaborative training approaches like FL can be a better approach for ML model training over VUs data.  
The importance of FL-based approaches for solving VUs problems is highlighted in several recent works [19, 20, 21]. Here we aim at proposing an efficient FL-based platform for generic VN applications such as optimal pathfinding, multimedia streaming, collision avoidance, etc. The model training cost of the FL process should be analyzed for implementing the optimal FL-based platform to solve the vehicular problem.
# An Analysis of Bubble Migration in Horizontal Thermo-Capillarity Using the VOF Modeling
## 4 Results and Discussion
### Bubble Sizes from \(R=0.0045\)-0.01 m
#### 4.3.1 Effect of Temperature Gradients  
Figure 7a-f show the movement of the centroid position of the bubble in the \(x\)-direction with a radius from \(R=0.0045\)-0.01 m under different temperature gradients, 0 K, 20 K, 40 K, 60 K, 80 K, and 100 K, respectively. It is found that the bubble stays at the center under the uniform temperature, as shown in Figure 7a. Once the temperature difference \(\Delta T\) between the two walls increases, the vapor bubble starts to migrate slowly towards the hotter side, as shown in Figure 7b-d. Compared with the first group (\(R=0.01\) m to 0.05 m), it takes more time for smaller bubbles to reach the left wall. At a higher temperature gradient, \(\Delta T=80\)-100 K, it is much easier for the bubble to arrive at the left wall in a shorter time, as shown in Figure 7e-f.  
Figure 7: Schematic representation of the movement of the bubbles with different bubble radii in the \(x\)-direction under different lateral temperature gradients (**a**) \(\Delta T=0\) K, (**b**) \(\Delta T=20\) K, (**c**) \(\Delta T=40\) K, (**d**) \(\Delta T=60\) K, (**e**) \(\Delta T=80\) K and (**f**) \(\Delta T=100\) K.