esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Experimental Procedure  
To test the efficacy of using the DL model to facilitate the BCR calculation process, the following three steps were used (Figure 2):  
1. Establish the DL model. A backbone and input picture size that best suits the building detection task is specified to fine-tune the DL model to be used to extract building outlines from drone images.
2. Extract the building footprints. This step is to extract building footprints from the images captured in Zone B to produce the building footprint layer, the input for the next step.
3. BCR calculation and validation. The BCR can now be calculated at two levels, at the parcel scale (where BCR was measured based on buildings' area within a single land boundary) and block scale (where the boundary was determined by surrounding roads). Finally, the accuracy of the DL-based methods is evaluated.  
Figure 1: Zone A and Zone B (within the red boundaries) of the Sihwa industrial complex.
## 2 Calculation of the Surface of the Drum-Shaped Grinding Tool  
### Tooth Surface of the Internal Helical Gear  
The transverse profiles of the internal helical gear and a single tooth are shown in Figure 1.  
\(r_{b}\) is the base circle radius of the internal helical gear, \(r_{f}\) is the dedendum circle radius of the internal helical gear, \(u\) means the involute expansion angle, and \(\sigma_{0}\) means the angle between the line connecting the starting position of the involute and the origin with the \(x_{2}\) axis.  
The transverse profile equation of the internal helical gear can be described as follows:  
\[r_{2}=\begin{bmatrix}r_{b}\cos(u+\sigma_{0})+r_{b}u\sin(u+\sigma_{0})\\ \pm r_{b}\sin(u+\sigma_{0})\mp r_{b}u\cos(u+\sigma_{0})\\ 0\\ 1\end{bmatrix} \tag{1}\]  
where the upper parts of the symbols \(\pm\) and \(\mp\) in \(r_{2}\) denote the transverse profile of the left tooth surface, and the lower parts denote the transverse profile of the right tooth surface.  
The tooth surface \(R_{2}\) of the internal gear is obtained by rotating and stretching the transverse profile along the helical angle, which can be represented as:  
\[R_{2}=M_{2t}r_{2} \tag{2}\]  
\[M_{2t}=\begin{bmatrix}\cos\theta_{2}&\pm\sin\theta_{2}&0&0\\ \mp\sin\theta_{2}&\cos\theta_{2}&0&0\\ 0&0&1&-P_{2}\theta_{2}\\ 0&0&0&1\end{bmatrix}\]  
where the upper parts of the symbols \(\pm\) and \(\mp\) in \(M_{2t}\) mean the right-hand helical tooth surface, and the lower parts mean the left-hand tooth surface, \(\theta_{2}\) is the rotational angle of the transverse profile of internal helical gear, and \(P_{2}\) is the helical parameter of the internal helical gear, which can be obtained as:  
\[P_{2}=\frac{Z_{2}m_{n}}{2\sin\beta_{2}} \tag{3}\]  
Figure 1: Transverse profiles of the internal helical gear and a single tooth: (**a**) entire transverse profile; (**b**) a single tooth transverse profile.  
where \(Z_{2}\) expresses the tooth number of the internal helical gear, \(m_{n}\) represents the module of the internal helical gear, and \(\beta_{2}\) means the helical angle of the internal helical gear. The tooth surface normal vector \(\mathbf{n}_{2}\) of the internal helical gear can be expressed as:  
\[\mathbf{n}_{2}=\frac{\partial\mathbf{R}_{2}(u,\theta_{2})}{\partial u}\times\frac{ \partial\mathbf{R}_{2}(u,\theta_{2})}{\partial\theta_{2}} \tag{4}\]  
To calculate more accurately the profile curves of the drum-shaped grinding tool, the tooth surface of the internal helical gear is discretized by dividing into nine columns and five rows in the tooth length and tooth height directions, respectively, as described in Figure 2.
## 4 Experimental Results and Discussions
### Magnetic Localization Evaluation
#### 4.2.2 Localization Performance  
The performance of the prosed TCI is assessed by comparing the localization accuracy and the processing time with two other methods, involving a traditional LM algorithm (tLMA) using random initializations, and a twice-traditional LM algorithm (ttLMA) using random initializations in the first-stage process, and the localization result using initializations in the second-stage process. The localization accuracy is represented by error \(e\), which is the mean distance between the true location and the estimated location. In magnetic localization with different methods, the solving process was performed 1000 times.  
The averaged estimated trajectory of the spiral line and the folding line are presented in Figure 9, which gives an intuitive comparison of the localization results between tLMA and aLMA. The detailed information is given in Tables 4 and 5, including the mean processing time \(T\) and the localization error \(e\) of the above three types of methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **tLMA** & **ttLMA** & **aLMA** \\ \hline \(T\) (ms) & 62.3 & 85.2 & 71.2 \\ \(e\) (mm) & 2.4 & 1.7 & 1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The mean processing time \(T\) and localization error \(e\) in spiral line tracking.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **tLMA** & **ttLMA** & **aLMA** \\ \hline \(T\) (ms) & 38.0 & 66.2 & 60.2 \\ \(e\) (mm) & 3.5 & 2.8 & 2.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The mean processing time \(T\) and localization error \(e\) in folding line tracking.  
Figure 9: Comparison of the trajectories estimated by a traditional Levenberg–Marquardt algorithm (tLMA) and a twice-traditional Levenberg–Marquardt algorithm (aLMA) of the proposed TCI system.  
In the spiral line tracking, the mean localization error \(e\) for tLMA, ttLMA, and aLMA is 2.4 mm, 1.7 mm, and 1.1 mm, respectively, implying that an improvement of about 54.2% can be brought by the aLMA proposed in this paper with a sacrifice of time about 14.3% increase. Although the improvement brought by ttLMA is 29.2%, 22.4% more time will be required. The improvement in localization accuracy between aLMA and ttLMA is 35.3%, verifying the effect brought by adaptive initialization in aLMA.  
In the folding line tracking, the mean localization error \(e\) for all three kinds of methods is larger than that in the spiral line tracking. The main reason for this is that the distance between the folding line trajectory and the sensor array is further than that of the spiral trajectory. The improvement of the localization accuracy in the folding line tracking brought by aLMA is 37.1% with the sacrifice of time increasing by about 58.4%, compared to a 20.0% improvement in localization accuracy with the sacrifice of time increasing by about 74.2% using ttLMA. It can be seen from the localization results in Figure 9 and Tables 4 and 5 that the proposed TCI can realize magnetic localization with a mean accuracy of 1.65 mm within 65.7 ms.
## 2 Models of Emotion in Pictures
### The Dimensional Emotion Model  
The dimensional model of emotion is often referred to as the circumplex model of emotion, the Russell model of emotion, or the PAD (Pleasure-Arousal-Dominance) model [27; 28; 29]. It is the most commonly used emotion model for annotating multimedia stimuli. The dimensional theory of emotion assumes that a small number of dimensions can well characterize affective meaning. Therefore, dimensions are selected for their ability to statistically represent subjective emotional ratings with the smallest possible number of dimensions [23]. [PERSON] estimated the approximate central coordinates of certain discrete emotions in the dimensional model space [27; 28; 29]. He hypothesized that these locations are not fixed but change over the life course and differ from person to person or between homogeneous groups of people based on their character traits.  
The dimensional model is built around three mutually orthogonal emotion dimensions: valence (\(Val\)), arousal (\(Ar\)), and dominance (\(Dom\)). Positivity and negativity of a stimulus are specified by valence, while arousal describes the intensity or energy level, and dominance represents the controlling and dominant nature of the emotion. Frequently, only the first two are used because dominance is the least informative measure of the elicited affect [23]. The three dimensions are described with continuous variables \(val\), \(ar\), \(dom\), where \(val\in[1,9]\in Val\), \(ar\in[1,9]\in Ar\), and \(dom\in[1,9]\in Dom\), respectively. In some databases, emotion values are scaled and represented in percentages, or authors use a smaller Likert scale, e.g., in the range \([1,7]\). Thus, in the dimensional emotion model, a single emotionally annotated picture can be projected onto the two-dimensional emotion space \(\Omega_{Emo}=Val\times Ar\), as exemplified in Figure 1, with each data point representing one picture from the Open Affective Standardized Image Set (OASIS) database containing 900 picture stimuli [25].  
The distance between two emotions expressed in the dimensional space is Euclidian. Therefore, the similarity between two dimensional emotions \(a^{dim}\) and \(b^{dim}\) with each emotion vector containing components of valence, arousal, and dominance may be expressed as:  
\[\begin{split}& sim\left(a^{dim},b^{dim}\right)=1-dist\left(a^{dim},b^{ dim}\right)\\ & dist\left(a^{dim},b^{dim}\right)=\sqrt{\left(a^{dim}_{val}-b^{ dim}_{val}\right)^{2}+\left(a^{dim}_{ar}-b^{dim}_{ar}\right)^{2}+\left(a^{dim}_{ dom}-b^{dim}_{dom}\right)^{2}}\end{split} \tag{1}\]
## 3 Results and Discussions
### Impact of Gaussian-Shaped Mountain Width on LIVs
For the case of SL2, Figure 10d,f exhibit identical results due to the symmetry of the terrain, it can be found that as mountain width increases to 500 m, it leads to an attenuation in LIVs at the ends of the line, whereas the induced  
Figure 9: Comparison among the lightning-induced voltages for Configuration A over Gaussian-shaped mountain, considering different mountain widths ranging from 200 m to 700 m. LIVs of line close end, center, and remote end when lightning strikes at (**a–c**) SL1, (**d–f**) SL2, (**g–i**) SL3, respectively. voltage at center of the line increases when the mountain width reaches 700 m. For SL3, the calculated LIVs show that a significant decrease in the induced voltage at the center and ends of the line can be observed from Figure 10g-i when the mountain width reaches 700 m, this may be attributed to the attenuation effect introduced by the mountainous terrain [53]. Moreover, in the case of SL3 as shown in Figure 8g-i, taller mountains result in a delayed arrival time of the induced voltage peak. In contrast, variations in mountain width result in negligible differences in the peak arrival time. This can be explained by the propagation of the lightning EM waves, where the propagation path is primarily governed by the height of the mountain. As [PERSON] et al. [54] discovered, the arrival time of EM waves is mainly dependent on the mountain height and is insensitive to the change in mountain width. \begin{table}
\begin{tabular}{l l l l l} \hline
**Strike Location** & **Width** & **Close End (kV)** & **Center (kV)** & **Remote End (kV)** \\ \hline SL1 & 200 & 13.54 & 8.18 & 7.61 \\  & 300 & 13.31 & 8.34 & 7.73 \\  & 500 & 12.61 & 8.39 & 7.58 \\  & 700 & 10.78 & 2.27 & 8.18 \\ \hline \end{tabular}
\end{table} 
Table 6: Maximum LIVs of different mountain widths in Configuration B.  
Figure 10: Comparison among the lightning-induced voltages for Configuration B over Gaussian-shaped mountain, considering different mountain widths ranging from 200 m to 700 m. LIVs of line close end, center, and remote end when lightning strikes at **(a–c)** SL1, **(d–f)** SL2, **(g–i)** SL3, respectively.
# Dynamic Response Analysis of Mega-Sub Isolated Structures under Multiaxial Earthquakes
## 3 Analysis of the Influence of Horizontal Parameters of the Mega-Sub Isolated Structure
### Analysis of the Influence of the Horizontal Frequency of the Seismic Isolation Layer  
The mega frame isolated structure has set isolation bearings between the subframe and the main frame. On the one hand, the earthquake energy is dissipated by the damping effect of the isolation bearings to achieve the purpose of energy consumption. On the other hand, the isolation bearings prolong the natural vibration period of the subframe and form a multi-frequency-modulated mass vibration reduction system. The isolation subframe plays the role of frequency modulation, thus changing the natural vibration period of the whole structure.  
In this section, the change in frequency ratio of the main and sub-frame is achieved by changing the stiffness of the seismic isolation layer of the sub-frame. The self-oscillation circle frequency of the main and subframe is achieved by the following equation, which defines the frequency ratio \(f=\omega_{\rm s}/\omega_{\rm q}\), (\(\omega_{\rm s}\) is the self-oscillation circle frequency of the vibration isolation sub-frame and \(\omega_{\rm q}\) is the first order self-oscillation frequency of the main frame). Since the stiffness of the vibration isolation layer cannot be taken too high to ensure the damping effect of the subframe, the frequency ratio \(f\) is set in the range (0.1 to 1.2).  
\[\omega_{\rm s}=\sqrt{(k_{\rm s}/m_{\rm s})} \tag{1}\]  
\[\left|K_{\rm q}-\omega_{\rm q}^{2}M_{\rm q}\right|=0 \tag{2}\]  
where \(\omega_{\rm s}\) is the self-oscillation frequency of the isolated subframe, \(k_{\rm s}\) and \(m_{\rm s}\) are the stiffness of the isolation layer and the mass of the sub-structure, respectively, \(\omega_{\rm q}\) is the self-oscillation frequency of the main frame (the first order self-oscillation frequency is taken in this example), \(K_{\rm q}\) and \(M_{\rm q}\) are the stiffness matrix and mass matrix of the main frame, respectively.
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 3 Cooperative Data Fusion in JSC Network for Vehicular Scenarios
### Multipath Effects on Sensing  
The presence of multipath effects may significantly affect sensing, as it produces target smearing, i.e., even a point-like target is sensed as a superposition of several components with different delays, Doppler shifts, and AoAs values, which might blur the target. This aspect is particularly critical in vehicular scenarios, which are rich in multipath effects. To account for multiple paths, one can rely on deterministic channel models, e.g., ray tracing, which can accurately mimic the sensing channel but pose challenges in generating large-scale scenarios due to high computational requirements. Conversely, statistical channel models are much simpler but typically suffer a loss of spatial information, which is valuable for sensing.  
Organizations like Third Generation Partnership Project (3 GPP) strive to establish reference wireless channel models, aiding in assessing communication technologies [5]. Unfortunately, despite the considerable benefits and broad acceptance of such models, they do not fully capture the characteristics of the sensing channel, especially for monostatic setups [28]. In fact, while sensing in a bistatic configuration resembles a communication setup, in monostatic sensing, the transmitter and receiver are colocated, and this arrangement is not contemplated in current communication channel models.  
It is important to note that the ETSI Industry Specification Group (ISG) on Integrated Sensing and Communications (ISAC), established in 2023, is dedicated to developing advanced channel and target models for various 6G use cases, aiming to address the limitations of existing communication-based channel models.
## 3 Results and Discussion
### Characterization
#### 3.2.3 Electrical Conductivity Test  
Figure 10 illustrates the conductivity curve of the PAC-PDMDAAC hybrid flocculant and the composite flocculant as a function of concentration. The figure indicates that, within the experimental concentration range, the conductivity of the hybrid flocculant solution exhibited a strong linear correlation with the concentration. In contrast, the conductivity of the composite flocculant solution experienced a gradual decline following an abrupt increase during dilution, deviating from linearity. This behavior could be attributed to the fact that the composite flocculant was merely a physical mixture of two distinct flocculants without any chemical bonding interactions. In this solution, N\({}^{+}\)-Cl\({}^{-}\) ions in PDMDAAC become ionized. As dilution occurs, aluminum particles in polyaluminum chloride undergo multiple stages of hydrolysis, reaching maximum hydrolysis at a specific point in time, leading to a gradual decrease in electrical conductivity with decreasing concentration. Conversely, due to the covalent bond interactions that stabilize the modified PAC particles on PDMDAAC, no sudden increase was observed during dilution for the hybrid flocculant, thereby demonstrating consistent linear behavior.
# Data Processing and Analysis of Eight-Beam Wind Profile Coherent Wind Measurement Lidar
## 2 Materials and Methods
### Range Gate Division of the Sampling Echo Signal  
In this study, the pulse width was \(\tau\) = 200 ns, the sampling frequency was \(f_{s}\) = 1 GHz, and the number of sampling points at each radial distance was \(N\) = 400. To simplify the analysis, a section of effective data containing the peak value in the power spectrum was intercepted for analysis. The actual effective number of data points was \(N_{p}\) = 112, and the corresponding range resolution was as follows:  
\[\Delta R=\frac{c\tau}{2}=30\ (\mathrm{m}), \tag{1}\]  
The height of the blind area is related to the width of the mirror, i.e., the pulse width. In this study, the pulse width was \(\tau\) = 200 ns; so, the blind area was \(B\) = 30 m. The single distance door diameter length \(L\) can be calculated using the following formula:  
\[L=\frac{c}{2}\cdot\frac{N}{f_{s}}=60\ (\mathrm{m}), \tag{2}\]  
Therefore, the length of the nth distance from the door radial center is \(L_{\mathrm{nt}}\) = \(B+L\times(n-0.5)\), and the nth vertical center height of the distance gate is related to the wedge deflection angle \(\theta\). In this study, the wedge deflection angle was \(\theta\) = 30\({}^{\circ}\); so, the vertical center height of the nth distance gate was Ln times cosine of theta. For example, the center of the first distance gate was 60 m in the radial direction (\(L_{r1}\)) and 51.96 m in the vertical direction (\(L_{v1}\)).  
\[L_{r1}=B+L\cdot(n-0.5)=30+60\times(1-0.5)=60\ (\mathrm{m}), \tag{3}\]  
\[L_{v1}=L_{1}\cdot\cos\theta=60\times\cos 30^{\circ}\approx 51.96\ (\mathrm{m}), \tag{4}\]  
The data collected by the eight-beam wind profile coherent wind measurement Lidar were divided into a number of continuous distance doors according to the time sequence. The eight-beam wind measurement Lidar has eight directions, namely, 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\), 180\({}^{\circ}\), 225\({}^{\circ}\), 270\({}^{\circ}\), 315\({}^{\circ}\), and 360\({}^{\circ}\), a total of 65,536 power spectrum data points in each direction, and a total of 128 effective power spectrum data. Each range gate has 112 data points,
# RAPT: A Robust Attack Path Tracing Algorithm to Mitigate SYN-Flood DDoS Cyberattacks
## 5 Conclusions and Future Work  
Since the spread of DoS/DDoS attacks in the early 2000s, they were and are still causing nuisance and financial losses to famous businesses. The Internet community both in academia and industry proposed a plethora of solutions. Heretofore, efficient and viable solutions are still on the research focus. In this work, the RAPT algorithm was proposed to efficiently solve the SYN-Flood DDoS attacks. The algorithm is deployed on edge and core routers enabling them to initiate the trace packet that accompanies the attack. The edge routers collect statistical information about the features and patterns of the traffic, which are used to predict if an attack is potentially taking place. The main characteristics of the RAPT algorithm are being lightweight and robust, having efficient detection and tracing time, and backward compatibility with IPv4, and it does not introduce extra traffic in the Internet. Extensive simulation runs were used to evaluate the efficiency of the RAPT algorithm. The obtained results prove the aforementioned characteristics; moreover, it outperforms the relevant TAPPM scheme. In the proposed RAPT algorithm, there are still some issues that need to be addressed in future work, such as the authentication of tracing packets where an attacker may inject forged tracing packets that might accuse legitimate hosts. Additionally, the solution requires modifications to the routers' functionality of Autonomous Systems (AS). Inconsistency with IPsec [44] that authenticates the IP header must be considered. All of these mentioned issues can be motivating for future work that builds on the RAPT algorithm.  
Methodology, Z.A.; software, N.M.; formal analysis, W.H.F.A.; investigation, N.M., A.S. and S.A.; data curation, S.A.; writing--original draft preparation, Z.A.; writing--review and editing, Z.A.; visualization, A.S.; supervision, Z.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.  
Figure 12: The relationship between the distance of the attack and its detection time.
The feasibility of using microalgae to commercially produce fucoxanthin, a carotenoid, is questionable [34]. However, culturing microalgae at favorable conditions has great potential to increase the viability of fucoxanthin sources, and most of the microalgae that are currently used to study the content of fucoxanthin are marine microalgae due to their abundance of compounds and ease of cultivation [6]. Many scientists have conducted studies aimed at optimizing the culture conditions of microalgae with a view to obtaining higher production of fucoxanthin [6; 35; 36; 37]. Within the realm of microalgae cultivation, the accumulation of fucoxanthin is primarily governed by two critical factors: illumination and the availability of nutrients [17; 28]. Diatoms of microalgae possess fucoxanthin chlorophyll proteins (FCPs) that act as light-harvesting systems [38]. Two major FCP complexes can be isolated from _Cyclotella meneghiniana_ as a central diatom [38; 39]. It has been shown that the FCP complexes are sensitive to changes in environmental conditions and that the enhanced formation of FCP proteins has the potential to increase fucoxanthin biosynthesis [40; 41]. Therefore, different environmental conditions may affect the production of fucoxanthin by _Cyclotella meneghiniana_. Hence, in this study, _Cyclotella meneghiniana_ was isolated from Rongjiang River and cultured under different light and nutrient conditions to increase the possible sources of fucoxanthin for commercial production, thereby enhancing the practicality and sustainability of commercially producing fucoxanthin for its practical and enduring production.