esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Methodology  
The system methodology includes the multicast routing for multimodal transportation, DEA for arc efficiency evaluation, HMM for predicting transportation cost and CO2 emissions, construction of multicast tree with QoS constraints by formulating the problem as an integer linear programming (ILP) problem, and Markovian decision processes-based optimized policy for multimodal transportation.
## 2 Materials and Methods
### Measurement Equipment and Data Analysis  
The heaviest successful snatch and clean and jerk were recorded as the weightlifting performance indicators. The front and back squat foundation strength exercises were selected to assess the weightlifters' maximum strength capability (i.e., 1 RM). The snatch, clean and jerk, and front and back squat were performed using standardized and calibrated barbells, weightlifting plates (Eleiko, Halmstad, Sweden), lifting platforms, and barbell supports (squats only). A 20 kg Olympic barbell was used for men, whereas a 15 kg Olympic barbell was used for women. Previous studies have demonstrated high reproducibility of the test-retest reliability of highly trained weightlifters [20].  
The vertical ground reaction force (vGRF) applied to the whole-body centre of mass during each jumping test was recorded using a wireless dual force plates system with a sample rate of 1000 Hz. The Hawkin Dynamics software (version 4.3.1) operates via an Android tablet that connects with the force plate system via Bluetooth and automatically analyses the vGRF before immediately transferring the data via Wi-Fi to the Hawkin Dynamics cloud server [21]. The force plates were calibrated and placed on flat ground and zeroed before each test/trial was recorded, following the manufacturer's details. The accuracy of the Hawkin Dynamics hardware and software has been validated in previous studies [21; 22]. The vGRF data for each trial were automatically low-pass-filtered (50 Hz cut-off frequency). Movement onset for each trial was identified when the vGRF decreased or increased below/above (i.e., CMJ and DSJ, respectively) the system weight by more than 5 standard deviations (SD) [21] (Figure 1). The system weight was calculated using the Hawkin Dynamics guidelines, where participants stood still on the force plates for one second, and the average force was recorded. All metrics were obtained from the propulsive phase (green area) to allow comparisons between the jumps (Figure 1). The averages across the three trials were used for statistical analyses. The intra-session reliability using the intraclass correlation coefficient (ICC) and % coefficient of variation (CV) and their associated 95% confidence intervals were calculated for all selected metrics and interpreted based on [PERSON] et al. [23]. All metrics had excellent reliability for the CMJ and DSJ (ICC = 0.95-1.00) with low variability (%CV = 0.98-7.23).
# Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
## 3 Dataset Requirements
### Comparison of the Existing Datasets against the above Requirements  
Comparisons between IDS datasets are shown in Table 1, where we assess the datasets in Section 2, based on the requirements that we set in Section 3.1.  
We were unable to find the information regarding the anonymity of the UMass dataset; therefore, no indicator was given. As for the IMPACT dataset, this platform has many datasets, some parts of which are anonymized, while others are not. In the CICIDS-2017 dataset, one part of the traffic has samples for encrypted traffic with benign and attack profiles.  
We have four observations from the above comparisons. First, there is a need for encrypted samples of benign and attack traffic. We found that [15] in their dataset have information on whether the traffic is anomalous or suspicious but it depends on the anomaly detectors. The payload from the packet traces was not included. This limited the capability of IDS because many attacks cannot be detected only by network flow with only 5-tuple attributes. In addition, [53] in their datasets included the traffic from benign and attack profiles from SSH. While this is beneficial, the diversity of the attack needs to be expanded to applications, such as browser attacks, or with different protocols, such as HTTPS, and we did not find that this protocol exists in their dataset. Second, we found that most of the datasets are not anonymized. The reason is probably that their testing beds are in a controlled environment or they have consent with their activity. The former is the best option with the consequences that the traffic will have more synthetic traffic while reducing the real traffic. The latter is preferred if they can preserve privacy. Furthermore, privacy can be maintained by anonymizing the traffic, but being highly anonymized may decrease the results of the analysis [8; 60; 61]. Third, we found that most of the datasets do not have ground-truth data and background traffic, which make the analysis limited only to their model. Fourth, there is a need for a methodology on how to create a new dataset. This is due to the nature of the network environment that is subject to change over time. How to create new datasets with the practical implementation is important, so researchers may make their dataset and evaluate it with their environment. This methodology can be a guideline for IDS researchers to follow for making a practical dataset.
# Voltage Distribution on Transformer Windings Subjected to Lightning Strike Using State-Space Method
## 3 Materials and Methodology  
### Lightning Source and Transmission Line Parameters  
In this study, the Heidler lightning model shown in Figure 1, a single-phase distributed-parameter transmission line model shown in Figure 2, and the high-voltage side of a transformer with a disc-based winding structure were used. The parameters of the lightning model and the lightning path impedance are given in Table 1.  
The parameters of the distributed transmission line are presented in Table 2, which are obtained from [21]. The length of the single-phase distributed-parameter transmission line is \(d\), the series resistance of the line is \(r\), the series inductance of the line is \(l\), the capacitance of the line is \(c\), and the series inductance of the source side is \(L\).
# A Space-Borne Ka-Band Tile-Type Phased Array Transmitter Module Based on HTCC Technology
## 3 Tile-Type Transmitter Module Based on HTCC Technology
### Transmitter Module Scheme Design
A number of grounded shielding through-holes are densely distributed around the cavity, and a small metal cover is bonded above the cavity to form an independent cavity structure, which effectively eliminates the spatial crosstalk of electromagnetic signals and the potential instability of the circuit caused by excessive gain in a single cavity. The frame made of  
Figure 4: Schematic of the 3-D heterogeneous integrated multifunctional chip. Figure 3: Schematic of the proposed transmitter module. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Components**} & \multicolumn{1}{c}{**Gain of Components**} & \multicolumn{1}{c}{**Saturation Output Power**} & \multicolumn{1}{c}{**Signal Power**} \\  & **(dB)** & **(dBm)** & **(dBm)** \\ \hline Amplifier 1 & 16 & 11.0 & 11.0 \\
1:4 divider & \(-\)7.2 & 25.0 & 3.8 \\
4-bit attenuator & \(-\)4.0 & 25.0 & \(-\)2.2 \\
6-bit phase Shifter & \(-\)7.0 & 25.0 & \(-\)9.2 \\ Amplifier 2 & 11.0 & 5.6 & 1.8 \\ Power amplifier & 21.5 & 22.0 & 22.0 \\ Connector Loss & \(-\)0.8 & 30.0 & 21.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Link budget analysis of transmitter module. Kovar material is sintered with the substrate, and the cover plate above is sealed and welded in parallel to achieve an airtight seal of all the bare chip cavities. The circuits on the front and back sides are connected through vertical transition inside the substrate. The complex low-frequency wiring and radio frequency interconnections are integrated into the multi-layer HTCC substrate. The interface of the signal and DC power is in the form of a BGA structure.
## 3 Results and Discussion
### Evolution of Mechanical Properties
#### 3.3.2 Tensile Strength  
The evolution of the tensile strength of the inner shaft lining concrete on Day 1, Day 3, and Day 7 after pouring is depicted in Figure 9. As shown in the graph, the tensile strength of the shaft lining concrete significantly increases with age, and the increase rate gradually decreases with an increase in age. The tensile strength of the shaft lining concrete at the same age increased gradually with its thickness, but the increase rate showed no apparent relation to thickness. The above phenomenon is the same as the reason why the compressive strength increases with the lining thickness. Because the thick lining ensures higher internal moisture content and higher temperature at the same curing time, the good curing environment increases the growth rate of concrete strength. The tensile strength of the shaft lining concrete with three different thicknesses ranged between 2.86 and 3.96 MPa within the first 7 d. Finally, the evolution equation for tensile strength with age within the first 7 d after pouring was fitted based on the experimental data (Table 5).  
Figure 8: Evolution of compressive strength with age on Day 1, Day 3, and Day 7 after pouring.  
Figure 9: Evolution of tensile strength with age Day 1, Day 3, and Day 7 after pouring.
# An Efficient Routing Scheme Based on Node Attributes for Opportunistic Networks in Oceans
## 4 Performance Evaluation  
We run the simulation with the opportunistic network environment (ONE) simulator [22] to evaluate the performance of proposed routing scheme (RSNA). In simulation scenarios, we assume that all nodes are not selfish or malicious to exchange messages with each other. The random movement model of nodes and intelligent link prediction are based on reference [21]. The simulation parameters are shown in Table 1.  
We compare the performance of the RSNA with some classic routing schemes, including Prophet, Epidemic and SprayAndWait. The following metrics are used for performance analysis:  
(1) Delivery ratio: the ratio of the number of packets delivered to the destination to the number of packets sent by the source.  
(2) Average latency: the average time taken to transmit the number of packets from source to destination node.  
(3) Overhead ratio: the ratio of the total number packets generated by source nodes to the total number packets forwarded by all nodes.
# An Improved Empirical Wavelet Transform and Its Applications in Rolling Bearing Fault Diagnosis
## 2 Exposition of Traditional EWT Method
### Drawbacks of the EWT Method  
The key step in the process of an empirical wavelet transform is dividing the appropriate boundaries in the spectrum. When a large number of parameters are set up to achieve adaptive partitioning, it is difficult in most cases to obtain the appropriate boundary: (1) it is difficult to separate them when the spectra of different components are closer, which will lead to modal aliasing; and (2) when the signal-to-noise ratio (SNR) is low, the noise components cannot be eliminated and more invalid components will be obtained.  
In Figure 1, the areas labeled A show that the two cosine signals with a relatively close frequency are not separated, and the area labeled B shows that the noise is divided into many ineffective components.  
The adaptive method needs to set parameters: InitBounds. The initial boundary is difficult to determine, and the number of edges directly affects the results of segmentation. There are a number of types of split spectrum in scale-space, but they lead to too many invalid components in the frequency domain, as shown in B. Therefore, it is necessary to explore a fast and reasonable method to divide the boundaries from the spectrum to improve the empirical wavelet transform.  
Figure 1: The spectrum segmentation of the empirical wavelet transform (EWT) method: (**a**) adaptive; (**b**) scale–space empirical law type.  
Figure 2: A flowchart of the fast empirical wavelet transform (FEWT) method.
## 3 Results
### Optimization  
For performing desirability optimization, input parameters can be optimized. The optimization process searches for a combination of factor values that simultaneously satisfy the criteria (wishes and priorities) placed on each of the responses and factors. Optimization can be displayed numerically and graphically. Numerical optimization uses the models to search the factor space for the best trade-offs to achieve multiple goals. For numerical optimization, the possible goals can be set to maximize, minimize, target, within range, none (for responses only), and set to an exact value (factors only). The goals are combined into an overall desirability function. Graphical optimization uses the models to show the volume where acceptable response outcomes can be found.  
In this research, the optimization criteria (for an optimal solution) were maximum tensile strength and target values for dimension (length \(\times\) thickness \(\times\) width of 150 \(\times\) 4 \(\times\) 10 mm) with input factors orientations and hatch distance within the chosen limits of the experiment. The combination of the selected values for optimization of products produced with selected parameters gives results presented in Table 20 for the upper part  
Figure 11: Dependence of hatch distance and orientation on the width in the lower part of the working chamber.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Width \(b\), mm** \\ \hline Standard deviation & 0.0219 \\ Mean & 9.98 \\ Coefficient of determination (\(R\)-squared (\(R^{2}\))) & 0.9228 \\ Adjusted \(R^{2}\) & 0.8871 \\ Predicted \(R^{2}\) & 0.7984 \\ Adeq Precision & 22.772 \\ \hline \hline \end{tabular}
\end{table}
Table 19: Fit statistics for the width at the lower level of the working chamber.  
of the working chamber and in Table 21 for the lower part of the working chamber. The upper and lower limits of the dimensions in the optimization were chosen in accordance with the permitted tolerance specified in the ISO 527 standard [18] for the production of test specimens for testing tensile properties.  
For the upper level of the working chamber, two solutions were found with a desirability of \(d=0.843\) (Table 20).  
The desirability curve for the upper part of the working chamber within the optimization constraints is shown in Figure 12.  
For the lower level of the working chamber, two solutions were found with a desirability of \(d=0.877\) (Table 21).  
The desirability curve for the lower part of the working chamber within the optimization constraints is shown in Figure 13.
# A Comprehensive Evaluation Algorithm of Multi-Point Relay Based on Link-State Awareness for UANETs
## 3 Proposed Algorithm
### Evaluation Parameters
#### 3.1.1 Awareness of Mobility
The definition of \(\delta d_{ij}\) is as follows:  
\[\delta d_{ij}=\frac{1}{n-1}(\sum_{t-T}^{t}d_{ij}^{2}-n\overline{d_{ij}^{2}}) \tag{4}\]  
In the above equation, \(d_{ij}\) and \(\overline{d_{ij}}\), respectively, on behalf of the distance and average distance between node \(i\) and node \(j\) during time period \([t-T,t]\), and \(n\) is the number of distance measurements at the same time period. To characterize the impact of distance variation on link communication quality, we introduce a distance step function \(\varphi(\overline{d})\). This function signifies that as nodes draw closer, the communication quality of the link improves, with distance variation exerting a more pronounced impact on link communication quality. \[\varphi(\overline{d})=\left\{ \begin{array}{l}1.5,d_{min}\leq\overline{d}\leq \frac{1}{2}R\\ 1.2,\frac{1}{2}R<\overline{d}\leq\frac{3}{2}R\\ 1,\frac{5}{4}R<\overline{d}\leq R\end{array} \right. \tag{5}\]  
After introducing the step function \(\varphi(\overline{d})\), the definition of link stability between node \(i\) and node \(j\) called \(SDL_{ij}(t)\) is as follows:  
\[SDL_{ij}(t)=\frac{\varphi(\overline{d})}{\delta d_{ij}} \tag{6}\]  
3. Average Neighbor Set Change Rate  
The rapid movement of drone nodes can result in alterations in network topology, frequent shifts in neighboring nodes, and an increased probability of link disruptions, thereby diminishing network performance. For this reason, we propose the concept of _average neighbor set change rate_ (ANSCR) by monitoring the changes in the neighbor set of nodes over a period of time, which can measure the topological changes of its surrounding neighbors. It refers to the change rate of the neighbor set per unit time, expressed as follows:  
\[ANSCR_{i}=\frac{INC_{i}+DEC_{i}}{N_{i}(t_{2}-t_{1})} \tag{7}\]  
Among the above, \(INC_{i}\) represents the number of newly added nodes in the neighbor set of node \(i\) during time period \([t_{1},t_{2}]\), while \(DEC_{i}\) represents the number of decreased nodes at the similar period of time. \(N_{i}\) represents the number of nodes in the neighbor set of node \(i\) at time \(t_{2}\). All of these can be obtained by monitoring the neighbor table content of node \(i\). To reflect time correlation and prevent drastic changes caused by sudden changes in the values of \(INC_{i}\) and \(DEC_{i}\), we use an exponential moving average strategy for \(ANSCR_{i}\). The final expression is as follows, where \(\xi\) is the smoothing factor:  
\[ANSCR_{i}(t)=\xi\cdot ANSCR_{i}(t)+(1-\xi)\cdot ANSCR_{i}(t-1) \tag{8}\]  
The above expression means that the smaller the value of the ANSCR, the smaller the change in neighbors around the node, and the lower the degree of topology change.
## 2 Methodology
### Even Spatial Distribution of Matched Tie Points  
The uneven spatial distribution of the matched tie points may lead to local geometric distortions of the rectified images. In addition, redundant tie points covering some local regions may increase the number of computations and therefore reduce the efficiency of image processing. To solve this problem, a greedy algorithm [40] was used in this work to remove matched tie points with high density.  
Given that a number of \(N_{\text{tp}}\) tie point pairs were obtained after the elimination of the outliers, the following steps were performed to achieve the even distribution of the tie points.  
1. The perspective transform model between the input image and the reference image was estimated using all the matched tie points. The \(RMSE\) value of each tie point pair was calculated, and all the tie point pairs were then sorted in ascending order according to the \(RMSE\) values.
2. The sorted tie point pairs were numbered according to the \(RMSE\) values. The first item of the sorted tie point pairs, which is numbered as 1 (i.e., \(i=1\)), was selected as the first node.
3. The distances between tie point \(i\) and the other tie points \(j\) (\(j=i+1\),..., \(N_{\text{tp}}\)) were calculated using the Euclidean distance. If the distance of a tie point \(j\) was less than a threshold \((T_{\text{L}})\), this tie point was considered to be too close to the tie point \(i\). Then, this tie point was removed, and let \(i=i+1\).  
Step 4: Step 3 was repeated until \(i=N_{\text{tp}}\).  
To test the employed approach for the even distribution of tie points, an experiment was performed using an \(800\times 800\) RS image (experimental results are shown in Figure 7). As shown in Figure 7a, 181 matched tie points (dots in red) with a model error of 0.93 pixels were obtained after removing outliers using the combination of RANSAC and the homography matrix. After removing high-density tie point pairs using a threshold (\(T_{L}\)) of 30 pixels, 108 tie points with a model error of 0.75 pixels remained, as shown in Figure 7b. The remaining tie points were more evenly distributed and offered higher accuracy than the initial tie points.
## 2 RS Systems
### Passive
#### 2.1.2 TIR Radiometers  
TIR radiometers measure the radiations emitted from the Earth's surface in the TIR bands. TIR sensors usually operate at 3-5 \(\upmu\)m and 8-14 \(\upmu\)m regions of the electromagnetic spectrum. In these regions, the atmosphere is transparent, and the amount of solar-reflected radiation is less than that emitted from the Earth. Thus, these sensors primarily detect the thermal properties of objects on Earth [25]. There is a general consensus that any object with a temperature above absolute zero emits radiation. The amount of radiation that each object emits toward the TIR sensors depends on its temperature and a physical characteristic known as emissivity. Thermal images can be captured during both daytime and nighttime because TIR sensors do not require sunlight. However, for many applications, nighttime or predawn images are preferable because the impact of solar heating is at its minimum rate [15]. TIR systems have been widely used to estimate sea surface and cloud-top temperatures [26]. The measurement of Sea Surface Temperature (SST) is indispensable for various purposes, including investigating western boundary currents, studying global change, and estimating the source of heat at sea [27]. Additionally, thermal images facilitate the mapping of the current's flow pattern and Gulf Stream eddies. More importantly, they allow us to detect OOS because there is a temperature difference between OOS and sea surface [28]. Although thermal images have various applications, they suffer from clouds in the atmosphere, which hinder frequent Ocean Surface Current (OSC) and temperature observations [26].
## 2 Numerical Analysis
### CFD Model Validation  
The CFD models used in this study have been validated by comparing the numerical simulation results to the experimental results reported in the engine's shop test data. The comparisons between the simulation and experimental results for both diesel and DF modes are shown in Figure 6. The variables used for the CFD models' validation are in-cylinder peak pressure (Figure 6a), indicated mean efficiency pressure (IMEP) (Figure 6b), and NO emission (Figure 6c). It is obvious that the numerical simulation results and experimental results are in good agreement. The maximum deviation between the simulation and experimental results was only 7% in the case of NO emission comparison in the diesel mode. Meanwhile, the deviations between the remaining cases were less than 1%. These deviations are within the 10% limitation, which is widely accepted in CFD analysis [43, 44]. The validated CFD models were then utilized for numerical simulations when adjusting the SA and IP of the fuel nozzle in this study.  
The model validation demonstrated that the right models were selected among the CFD models for the simulation of the engine. Therefore, these models can in principle be applied to simulate the same phenomena occurring in similar types of engines (i.e., gas-diesel DF engines using the direct-injection method for fuel introduction). In fact, the accuracy of a CFD simulation result depends not only on the models chosen but also on the quality of the mesh. Therefore, when applying these validated models in this study to simulate similar phenomena in similar types of engines, performing a mesh independence analysis of the CFD results is necessary. This was to ensure the suitability of the chosen CFD models for another geometry. To perform a mesh independence analysis, it is recommended to refer to the procedure suggested by [PERSON] [45].