esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Data Efficiency  
This section evaluates the data efficiency of our model. To explore this issue, we randomly selected 20%, 40%, 60%, 80%, and 100% of the training data for experiments. Subsequently, we input data of different sizes into the model for training, with results displayed in Figure 7. Further, Figure 7 shows that when training data exceed 20%, both the ADE and FDE indicators show a downward trend. Model performance remains stable within the range of 40% to 80% of the dataset capacity and reaches the best performance when the dataset capacity is 100%.
The SITE (Simple Tropical Ecosystem) model is a simple point-based (offline) model that uses an integration interval \((dt)\). It incorporates site-specific data on temperature, horizontal wind speed, specific humidity, and photosynthetically active radiation (PAR) [33; 34]. The SITE model was originally developed to study the response of tropical ecosystems to varying environmental conditions while adequately representing the functioning of the ecosystem. It considers the main physical, chemical, and biological processes involved in energy and carbon fluxes. It has been employed because of its biogeochemical and biophysical character that encompasses various surface processes, including infrared radiation balance above the canopy, solar radiation balance, aerodynamic processes, canopy physiology and transpiration, canopy water balance, mass and energy transfer in the atmosphere, soil heat fluxes in two layers, soil water, and carbon flux and equilibrium [23]. Studies carried out using the SITE model have yielded promising results both within its intended scope [35], and in an experimental site within a tropical semi-deciduous forest in the southern Amazon Basin [34] and the Caatinga ecosystem [21; 23]. Little is known about how the Caatinga acts regarding CO\({}_{2}\) exchanges under conditions of extreme drought and in years of heavy rainfall. Therefore, this study aims to assess the energy and CO\({}_{2}\) balances of the Caatinga Biome when exposed to extreme rainfall conditions (including both extreme drought and heavy rainfall). Additionally, it aims to evaluate the source/sink dynamics of the Caatinga under these extreme conditions while demonstrating the compensation and saturation of gross primary productivity (GPP), net ecosystem exchange (NEE), and net ecosystem productivity (NEP) as influenced by monthly rainfall.
# Producing Synthetic Dataset for Human Fall Detection in AR/VR Environments
## 4 Experiments
### CNN Training  
The CNN approaches can be applied in many areas. In fact, other CNN are unable to obtain a mask in order to segment the coordinates of impact with the object of interaction in the process of registering a digital human fall, therefore the Mask-R-CNN network [50] effectiveness was examined. We used our generated synthetic data to train the Mask R-CNN network for the prediction of pixel masks produced after a person interacts with any environment. In our case, we checked hitting the floor after a digital human's fall. We divided the dataset into training and test samples in a ratio 4:1, which were randomly selected from all amounts of simulation samples. The library Tensorflow 2.3 was used in the framework. We used SGD for gradient optimization with learning rate 0.001, momentum 0.9, weight decay 0.0001 and patch size 100. Initial weights were taken based on the ResNet-101 model weights pre-trained on the COCO dataset [51]. Segmentation masks involved three classes--floor, fall and does not fall, and the model itself was trained for 250 epochs.  
The values of the loss function, which combines classification, localization, and segmentation mask losses, equal 0.483 and 0.394, respectively, as a result of training with synthetic data. Figure 7 shows the plot of the loss function curves.  
The loss function was a combination of many resulting values for the basic loss parameters of the training model that generated a sum. There is the cross-entropy of an anchor classifier loss, bounding box difference (smooth \(L_{1}\) norm) between target and recognized object and the difference (smooth \(L_{1}\) norm) between target and predicted object mask. The difference or \(L_{1}\) norm was calculated using the following expression:  
\[L_{1,smooth}=\left\{\begin{array}{cc}0.5(y-x)^{2},&if|y-x|<1\\ |y-x|-0.5,&otherwise\end{array}\right.\]  
where \(x\) is a training sample and \(y\) is a predicted result. Additionally, for cross-entropy, the function was used in the form:\[L_{cross-entropy}=-\sum_{i=1}^{n}t_{i}\log(p_{i})\]  
where \(t_{i}\) is the truth label and \(p_{i}\) is the Softmax probability for the \(i\)-th class.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 2 Jellyfish Attack Overview
### Jellyfish Delay Variance Attacks  
In a delay variance attack, the malicious nodes are selfishly delaying packets. As shown in Figure 3, the impacted Jellyfish node maintains the FIFO queue's rules while forwarding data packets, but it introduces some random delay before transmitting them to their destination without scrambling the packet order. In this scenario, the sender will not receive an ACK within the time frame given, which makes the source node assume that packets have been lost and will begin retransmitting them [26]. Because there is no other way to detect lost packets due to significant network congestion, managing network traffic becomes extremely challenging [24].
## 3 Solution Approach
### Stage of Initial Solution Generation and Pre-Optimization
#### 3.1.2 Initial Solution Generation Based on the Scanning Method  
From the perspective of mathematical model solving, the process of obtaining the initial solution by the scanning method is subject to all the constraints of the mathematical model of the problem in this study. Therefore, these solutions can be identified as feasible solutions. Next, this paper will explain how to obtain this feasible initial solution from the perspective of algorithm design.  
First, the distribution center is set as the pole \(o\), and the customer node \(p\) is randomly selected. The connection between the node \(p\) and distribution center \(o\) is taken as the polar axis, and all other customer nodes are converted into polar coordinates \((r,t)\). Second, customer nodes are assigned to the MCV one by one according to the order of the angles \(t\) in the polar coordinates from small to large. At this point, whether the current customer node is assigned to the current MCV needs to be limited by two constraints. The first is the compatibility constraint between different types of products and compartments. The second is the maximum capacity limit of different compartments of the MCV. If the load capacity of at least one compartment exceeds its maximum capacity constraint after allocation, the customer node allocation of the current MCV is terminated. At the same time, a new MCV is sent to reopen a path. The current customer node is taken as the initial node of the reopened path, and the remaining customer nodes are allocated one by one. This process is repeated to complete the assignment of all client nodes and generate the initial solution. Note that the number of nodes in a path and the number of paths in a solution do not affect the operation of the program, such as the path formed by a single client node and the solution formed by a single path.
## 4 Relationship between \(K_{ic}\) and \(\sigma_{t}\)
### Theoretical Analysis
Therefore, Equation (16) can be written as [21]  
\[\frac{K_{IC}}{\sigma_{t}}=\sqrt{2\pi r_{IC}} \tag{17}\]  
However, Equation (17) only shows a positive correlation between mode-I fracture toughness (\(K_{IC}\)) and tensile strength (\(\sigma_{t}\)), and cannot be used to accurately estimate the critical crack propagation radius (\(r_{IC}\)). Instead, it is necessary to determine it empirically, as Equation (17) indicates that it should be possible to determine the ratio of \(K_{IC}\) to \(\sigma_{t}\) for the same rock subject to the same conditions and using the same test method and sample type. That is, the critical crack extension radius (\(r_{IC}\)) should be a constant. Specifically, the value of \(r_{IC}\) is related to the type of rock and the conditions (e.g., temperature, humidity). The effect of temperature on the crack propagation radius is shown in Table 4.  
Table 4 shows that the \(r_{IC}\) value varies from 0.00021 m to 0.00299 m for FS marble, 0.00177 m to 0.00382 m for FS gabbro, 0.00131 m to 0.00310 m for SP granite, 0.00012 m to 0.00183 m for BS granite, and 0.00234 m to 0.00306 m for SC sandstone. Thus, the \(r_{IC}\) values of different rock types are different. In the temperature dimension, the maximum values of the \(r_{IC}\) of FS marble, FS gabbro, SP granite, BS granite, and SC sandstone were 1323.81%, 115.82%, 136.64%, 1425.00%, and 30.77% above the minimum values, respectively. That is, for the same rock, the \(r_{IC}\) values differ greatly for  
samples exposed to different temperatures: temperature exposure has a significant effect on the crack propagation radius \(r_{IC}\) at the time of rock fracture. However, the test values for \(K_{IC}\) and \(\sigma_{t}\) can be influenced by numerous factors (e.g., the loading rate, test method, and sample type), so it may not be possible to acquire an exact value for the critical crack propagation radius \(r_{IC}\) through evaluation of \(K_{IC}\) and \(\sigma_{t}\). The \(K_{IC}\) of the rock has a linear proportional relationship with \(\sigma_{t}\), and the proportionality coefficient is related to the crack propagation radius (\(r_{IC}\)). Experimental data can be used to statistically determine the average \(r_{IC}\) value of various rocks exposed to different temperatures.
## 4 Higher-Order Cubature Kalman Filter Design
### Road Adhesion Coefficient Estimation Algorithm  
The road adhesion coefficient estimation algorithm estimates the road adhesion coefficient in real time, and the state estimation model is expressed in accordance with Equation (19).  
If we take the adhesion coefficient of four wheels as the state vector of the road adhesion coefficient estimator \(x_{p}(t)=[\mu_{\theta},\mu_{\theta},\mu_{rl},\mu_{rl},\mu_{rr}]^{\mathrm{T}}\); the longitudinal acceleration, lateral acceleration and yaw rate of the vehicle, measured via sensors, are used for the measurement vector of the road adhesion coefficient estimator \(z_{p}(t)=[a_{x},a_{y},\overset{\bullet}{\omega}_{r}]^{\mathrm{T}}\); the front wheel angle and the normalized longitudinal and lateral forces of each wheel are used as the input vector of the road adhesion coefficient estimator: \(u_{c}(t)=[\delta_{ij},\,Fx_{ij}\overset{0}{,}Fy_{ij}\overset{0}{]}^{\mathrm{T}}\).  
In a short period of time, the road adhesion coefficient can be regarded as constant. The state equation and measurement equation for the road adhesion coefficient estimator are as follows:  
The state equation for the road adhesion coefficient estimator is established:  
\[\mathbf{x}_{p}(t)=\left[\begin{array}{ccc}1&0&0&0\\ 0&1&0&0\\ 0&0&1&0\\ 0&0&0&1\end{array}\right]\left[\begin{array}{c}\mu_{fl}\\ \mu_{rl}\\ \mu_{rr}\end{array}\right]+\mathbf{w}(t) \tag{23}\]  
\[\mathbf{z}_{p}(t)=\left[\begin{array}{ccc}h_{11}&h_{12}&h_{13}&h_{14}\\ h_{21}&h_{22}&h_{23}&h_{24}\\ h_{31}&h_{32}&h_{33}&h_{34}\end{array}\right]\left[\begin{array}{c}\mu_{fl }\\ \mu_{fr}\\ \mu_{rl}\\ \mu_{rr}\end{array}\right]-\left[\begin{array}{c}\frac{1}{m}\\ 0\\ 0\end{array}\right]\mathbf{F_{w}(t)+v(t)} \tag{24}\]  
In the formula,  
\[h_{11}=\frac{F_{xfl}\overset{0}{\sim}\delta_{fl}-F_{xfl}\overset{ \bullet}{\sim}\delta_{fl}}{m};\,\,h_{13}=\frac{F_{xfl}\overset{0}{\sim}}{m};\] \[h_{12}=\frac{F_{xfl}\overset{\bullet}{\sim}\delta_{fr}-F_{xfl} \overset{\bullet}{\sim}\delta_{fr}}{m};\,\,h_{14}=\frac{F_{xfl}\overset{0}{ \sim}}{m};\] \[h_{21}=\frac{F_{xfl}\overset{\bullet}{\sim}\delta_{fl}+F_{yfl} \overset{\bullet}{\sim}\delta_{fl}}{m};\,\,h_{23}=\frac{F_{yfl}\overset{0}{ \sim}}{m};\] \[h_{22}=\frac{F_{xfl}\overset{\bullet}{\sim}\delta_{fr}+F_{yfl} \overset{\bullet}{\sim}\delta_{fr}}{m};\,\,h_{24}=\frac{F_{yfl}\overset{0}{ \sim}}{m}; \tag{25}\] \[h_{31}=\frac{2a(F_{xfl}\overset{0}{\sim}\delta_{fl}+F_{yfl} \overset{\bullet}{\sim}\delta_{fr})+2a(F_{xfl}\overset{0}{\sim}\delta_{fl}+F_ {yfl}\overset{0}{\sim}\delta_{fl})}{2I_{z}};\] \[h_{32}=\frac{2a(F_{xfl}\overset{0}{\sim}\delta_{fr}+F_{yfl} \overset{\bullet}{\sim}\delta_{fr})+B_{f}(F_{xfl}\overset{0}{\sim}\delta_{fr} -F_{yfl}\overset{\bullet}{\sim}\delta_{fr})}{2I_{z}};\] \[h_{33}=\frac{-B_{F_{xfl}}\overset{0}{\sim}2 bF_{yfl}\overset{ \bullet}{\sim}}{2I_{z}};\,\,h_{34}=\frac{B_{x}F_{xfl}\overset{0}{\sim}-2 bF_{yfl }\overset{0}{\sim}}{2I_{z}}.\]
## 3 Physicochemical Properties of Honey
### Moisture and Water Content  
Water is one of the principal constituents found in honey, the proportion of which is determined by several factors including geographical and floral origin of the collected nectar, pedoclimatic conditions, season of harvesting, and maturation degree. The most influential of these being the manipulation by beekeepers during harvest, extraction, processing, as well as storage conditions [53]. An important study [103] reported the moisture content for mint and thyme honey samples of Tunisia, which ranged between the value of 19.8% (mint) and 18.16% for thyme honey. These results are similar to the values reported for Romanian samples [86] which revealed a moisture content between 17.36% (thyme honey) and 17.77% (mint honey). All honey samples had moisture contents in the limits established by the European legislation (maximum 20%) [104]. On the contrary, some studies have found honey samples that did not fulfil the limits imposed by the European Union, as seen in the case of Italian citrus honey, where the water content was established at 20.56% [105]. When honey samples do not meet the criteria of maximum 20% moisture, they can become susceptible to deterioration, as the yeast and different bacteria found originally in honey can ferment [74]. Water activity (aw) depends on carbohydrate composition (glucose/fructose content), but also on environmental conditions [100], as it is used to predict moisture exchange with the environment, thus the low water activity values present in honey, ranging from 0.562 to 0.62 [106]. Even if at the European level no limits were established for the water activity standards, it is well-known that, depending on the quantity of water present in honey's composition, the growth/limitation of different microbial organisms that may appear throughout the fermentation process can be influenced [107]. As a consequence, the so-called osmotic pressure that determines the loss of water inside the bacteria is produced, which causes the cells to dehydrate and thus being unable to grow and proliferate [108]. Colour, crystallisation, flavour, viscosity, density [53] or solubility and conservation [32] can also be affected by water content. As honey has a tendency to absorb airborne humidity, beekeepers should pay special attention during honey processing in order to avoid extra moisture content and thus bacterial activity.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 4 Recent Advancement on UAV Tracking Antenna
### UAV Tracking Techniques
#### 4.3.2 UAV Tracking Based on TDOA  
UAV tracking using time difference of arrival (TDOA) involves an estimation of transmitter location using time difference of arrival measurements formed by the correlation of signals received by different nodes at the receiving end [102]. TDOA methods compute the time difference of arrival of a wave at multiple measurement points and calculate the source point direction based on timing and wave comparisons. The basic approach to determining TDOA is to calculate the cross-correlation of the signals arriving at the receiver. The estimate of the TDOA is the delay which helps us maximize the cross-correlation function. Knowing information, such as the location of each receiver and the time of arrival on each, an estimate of the source of the signal can be made. TDOA have generally been applied in different areas due to the following advantages: (i) low cost; (ii) low complexity; and (iii) small in size. However, the performance of TDOA is a function of the signal bandwidth and the performance degrades as the signal bandwidth decreases [103]. In addition, TDOA also require a high-quality time synchronization which is relative to the inverse bandwidth of the signal. Reference [104] focused their research on the performance analysis for TDOA localization using UAVs with flight disturbances, and reference [105] deployed the use of particle filtering for three-dimensional TDOA-based positioning using four anchor nodes driven by statistical models to determine the UAV localization.
## 2 Frame Model and Calculation Method
### Boundary Conditions
In order to simulate the state change in the frame under braking conditions, a gravitational acceleration of 1g and a deceleration acceleration of 0.45 g were applied to the frame in this paper. When setting the boundary conditions, the three directions of translational degrees of freedom UX, UY, and UZ of the two front-end wheels were constrained, and the rotational degrees of freedom of the ROTX, ROTY, and ROTZ directions were released. The vertical degrees of freedom UY and longitudinal degrees of freedom UZ of the two wheels at the rear end were constrained, and the other degrees of freedom were released. Table 4 shows the setting of boundary conditions for each working condition. The front and rear axles, front and rear crossbeams, and left and right longitudinal beams of the frame were all hollow structures. In this paper, monitoring paths (1: start point, 2: end point) were added at the midline of the upper surface of these six main components. The schematic diagram of the monitoring paths is shown in Figure 4. \begin{table}
\begin{tabular}{c c c} \hline
**Contact Type** & **Normal Separation** & **Tangential Separation** \\ \hline Bonded & No & No \\ No separation & No & Yes \\ Frictionless & Yes & No \\ Rough & Yes & Yes \\ Friction & Yes & Yes \\ \hline \end{tabular}
\end{table} 
Table 3: Differences in contact types.
# Wireless Remote Control for Underwater Vehicles
## 2 Underwater Communication Technologies
### Optical Communications
The company declares that the modem is affected by direct sunlight noise but is generally robust to low sources of ambient light noise, as it can be used in the presence of ROV lights without compromising the communication link. The same happens for the CoSa optical modem [5], able to reach 2 Mbps at up to 20 m, with a transmitting beam aperture angle of 45\({}^{\circ}\) and a receiver field of view of 90\({}^{\circ}\). Another low-cost modem that is quite robust to sunlight noise is the optical modem developed by IST [84] that can reach 200 kbps at a maximum distance of up to 10 m and, different from the other modems presented so far, uses green instead of blue LEDs, as it is specifically tailored for shallow-water operations. This modem uses optical lenses to reduce the beam aperture angle to 12\({}^{\circ}\) and an optical filter to reduce sunlight noise. The most representative underwater optical modems are summarized in Table 2.
# Phase Risk Analysis of Overhead Lines Under Complex Icing Conditions
## 4 Analysis of Discharge Risk Coefficients of Overhead Conductors Under Different Icing Conditions
### Impact of Ice Cover Thickness on Discharge Risk  
Assuming a transmission line with five consecutive sections, where only the middle section's conductor is de-iced, the model parameters are as follows: a de-icing span of 400 m, divided into 400 grids, no height variation, an initial conductor tension of 29,740 N, and initial ground line tension of 21,000 N. Using 100% de-icing, simulations are performed with ice cover thicknesses of 10, 15, 20, 25, and 30 mm. The maximum sag of the conductor-ground line and the maximum height of the conductor's ice bounce are calculated, with the minimum spacing between the conductor and ground line detailed in Table 2.  
As the ice cover thickness increases, the conductor's sag also rises. When the thickness increases from 10 to 30 mm, the conductor's maximum jump height grows by 259%, and the minimum conductor-ground distance decreases from 9.101 to 2 m, reaching the safety distance and impacting the transmission line's operation. A risk is defined when \(W\geq 1\). The phase-to-ground discharge risk factor decreases from the midpoint toward the insulator. The closest grid point where \(W\geq 1\) is defined to determine the risk section. With 400 grid per span, the corresponding intervals were plotted as in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ice Thickness/mm** & \begin{tabular}{c} **Maximum Sag of the** \\ **Conductor Wire/m** \\ \end{tabular} & \begin{tabular}{c} **The Maximum Sag of the** \\ **Wire/m** \\ \end{tabular} & \
\begin{tabular}{c} **The Maximum Jump** \\ **Height of the Wire/m** \\ \end{tabular} &
\begin{tabular}{c} **Minimum Spacing** \\ **Between Ground and** \\ **Conductor Wires/m** \\ \end{tabular} \\ \hline No icing & 10.221 & 13.878 & / & 15.150 \\
10 & 11.503 & 14.634 & 7.252 & 9.101 \\
15 & 12.299 & 15.091 & 10.634 & 5.532 \\
20 & 13.143 & 15.584 & 13.671 & 2.670 \\
25 & 14.011 & 16.105 & 16.381 & 2.000 \\
30 & 14.888 & 16.646 & 18.815 & 2.000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of different icing thickness on the safety distance of the ground conductor.  
Figure 6: Schematic diagram of 30 mm icing thickness and de-icing bounce of the ground conductor.  
Increased ice thickness on the line leads to greater conversion of gravitational potential energy into elastic potential energy during de-icing, resulting in higher bounce heights. As shown in Figure 7, for ice thicknesses of 10, 15, and 20 mm, the danger zone is minimal, nearly zero. For 25 mm ice cover, the risk zone spans 140-260 m, about 30% of the total line length. With 30 mm ice cover, the risk zone expands to 49% of the line length, significantly increasing the risk of conductor-ground flashover. Increlement weather, such as rain and snow, exacerbates this risk, potentially disrupting transmission line operations. Therefore, additional protective measures should be considered for affected sections.
## 2 Modelling
### Feature of the Second Eigenmode  
In Equation (15), the coupling factor \(n\) determines the influence of \(k_{\text{eff}}\) on the frequency modulation (FM) mode sensitivity. If \(n>1\), the influence is magnified, otherwise, it is reduced. In the first eigenmode as simulated above, \(n=26.67\), implying that the resonant frequency is quite sensitive to the value of \(k_{\text{eff}}\); but in the second eigenmode (Table 5), \(n<1\), and the sensitivity thereto is very low, and the simulation results show that the resonance frequency will not change as \(k_{\text{eff}}\) increases.  
The second eigenfrequency of the [PERSON] probe is not sensitive to \(k_{\text{eff}}\), which means that the eigenfrequency is stable when the tip makes contact with the sample. This is a drawback when studying the relationship between the second eigenfrequency and the contact stiffness. However, the constant resonant frequency means that the amplitude is related to energy dissipation directly.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(k_{\text{eff}}\) (N/m) & ANSYS/Hz & \(n\) & Model/Hz & Deviation/Hz \\ \hline
0 & 52,888 & 13.38 & 52,888 & 0 \\
0.001 & 52,890 & 12.92 & 52,891 & 1 \\
0.005 & 52,896 & 11.18 & 52,898 & 2 \\
0.01 & 52,901 & 9.49 & 52,902 & 1 \\
0.02 & 52,909 & 7.15 & 52,904 & \(-5\) \\ \hline \end{tabular}
\end{table}
Table 4: The results of ANSYS simulation with different values of \(k_{\text{eff}}\).  
Figure 13: The measurement of factors using laser Doppler vibrometer.  
The amplitude change in amplitude modulation (AM) mode can be divided into two parts as shown in Figure 14. If the resonant frequency is constant, \(\Delta A_{\rm{k}}=0\), \(\Delta A=\Delta A_{\rm{b}}\), the amplitude change is caused only by the energy dissipation.