esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Turbulence Parameters of Typhoon Morakot along the Southeast Coast of China
## 2 Introduction of Measurement Procedure and Method
### Observation Site and Measurement Procedure
#### 2.1.2 Introduction of Monitoring System  
Wenzhou is located on the southeastern coast of mainland China, and a large number of typhoon formations in the northwest Pacific make landfall or affect Wenzhou yearly. Data show [33] that the number of typhoons making landfall in China increases yearly, with an average of 6.6 per year. The field measurement site is located on roof of the architectural engineering building at Wenzhou University. The height of the surrounding buildings is approximately 8 m. The location is bordered on the east and south by hills, and on the west and north by broad plains. The area is classified as a city suburb, and its roughness category is Class III according to the Chinese standard [34]. To avoid wind interference, two WJ-3 anemometers were installed on the 9 m high straight poles on the roof of the architectural engineering building. The horizontal distance and the height above the ground of two straight poles are 17 m and 30 m, respectively. The wind speed data were collected concurrently by the two anemometers, with a sample frequency of 20 Hz. Figure 2 depicts the anemometer configuration [28]:
## 8 Result Analysis
### Computation Cost  
This section evaluates the computational overhead of the proposed cross-domain handover authentication protocol and compares it with other protocols. The experiments were conducted in an environment with a quad-core Intel(R) Core(TM) i7-8750H 2.20 GHz CPU, 16 GB RAM (Intel, Santa Clara, CA, USA), and the operating system Ubuntu-22.04.4-desktop-amd64. Various cryptographic primitives were implemented using the MIRACL cryptographic library. In addition, the matrix iteration algorithm was used to write the corresponding C language code for testing the computation time of Chebyshev polynomials. Since the execution time of the XOR operation is negligible, it was ignored in the evaluation. The data is generated from the experiments implemented by MIRACL cryptographic library and Hyperledger Fabric frameworks. Table 2 and Figure 4 present the execution times of various cryptographic primitives.  
In the cross-domain handover authentication protocol proposed in this paper, the device side performs three hash operations and two Chebyshev chaotic map computations, with an execution time of \(3T_{h}+2T_{c}=0.468\) ms. On the edge gateway side, four hash operations and two Chebyshev polynomial computations are carried out, resulting in an execution time of \(4T_{h}+2T_{c}=0.456\) ms. Therefore, the total computational overhead is \(0.924\) ms.  
According to the computational overhead comparison in Table 3, bilinear pairing operations incur significantly higher costs. As shown in Figure 5, the scheme in Reference [44]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Computational** & **Definition** & **Execution Time (ms)** \\
**Operation** & & \\ \hline \(T_{mul}\) & Elliptic Curve Point Multiplication & 0.545 \\ \(T_{add}\) & Elliptic Curve Addition & 0.004 \\ \(T_{c}\) & Chebyshev Polynomial & 0.216 \\ \(T_{bp}\) & Bilinear Pairing & 1.236 \\ \(T_{h}\) & Hash Operation & 0.004 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Execution time of related operations.  
Figure 4: Execution time of related operations.  
exhibits the highest computational overhead, while the proposed protocol incurs the lowest. This efficiency stems from the use of the semigroup property of Chebyshev polynomials to establish session keys, thereby avoiding computationally intensive operations such as bilinear pairing and elliptic curve point multiplication.  
The proposed protocol thus achieves low computational overhead while ensuring security, making it a lightweight and efficient solution.
## 3 Proposed Protocol
### Requirements from Sensor Node Platforms  
No special requirements are imposed by NEURON in order to be applied to existing and next generation sensor motes. In NEURON, nodes may be identified according to their 6 LoWPAN address while low power 802.15.4 radio may be used for communication among them. According to  
Figure 5: Route Request Mechanism.  
6 LowPAN, IEEE 802.15.4 devices may use either IEEE 64 bit extended addresses or 16 bit addresses that are unique within a Personal Area Network (PAN).  
NEURON, as stated earlier, necessitates the storage of routing information in the routing cache of each node. This information regards the routes that are stored in each node during the cluster formulation and maintenance process. Considering that the maximum depth (distance in hops) from a CH is approximately five, the size of each routing entry in NEURON varies from 10 to 15 bytes (when nodes are indentified by their 16 bit addresses) or from 20 to 45 bytes (when nodes are indentified by their IEEE 64 bit extended addresses). Thus, in case of a routing cache with 1,000 units, the maximum memory that could be necessary is 45 kB (in case that all the routes are more than four hops) while in a realistic scenario 15 kB are sufficient, as also shown in Section 5.2.  
Requirements for energy depend on the type of the sensor more. However, the behaviour of NEURON regarding energy efficiency is presented in detail in Section 5. Finally, NEURON does not impose special processing requirements or constraints for the operating system used, since NEURON mechanisms perform simple functionalities that may be implemented in each operating system.
## 4 System Architecture
### RF-Data Converters  
The system was developed just for L1/E1 frequency signals centered at 1.57542 GHz. The XCZU49 DR SoC contains 16 14-bit ADCs and DACs, capable of sampling at a maximum frequency of 2.5 GHz and 9.85 GHz, respectively. This means that the sampled signal sits in the second Nyquist zone in relation to the maximum sampling frequency, causing aliasing. Nevertheless, this characteristic can be used to directly sample the signal without IF demodulation. This allows the signal to be folded to the first Nyquist zone while keeping the information. It is to be noted that the aliased signal is flipped left to right when folding. However, this can be compensated by the internal digital mixer present in the data path.  
The sampling sequence is described in Figures 4 and 5, where the L1/E1 signal (centered at 1.57542 GHz) is undersampled at 2.4 GHz, shifting to 0.82458 GHz, as represented in Figure 3. Then, the resulting signal is demodulated to baseband resorting to a \(-0.82458\) GHz signal, as shown in Figure 4.  
Some additional considerations were taken for the configuration of the data converter. The desire for the maximum sampling frequency possible to improve performance and consequently reduce delay in the FPGA fabric while keeping in mind the complexity, resource usage of the beamforming IP core, has resulted in the following:  
* Sampling at 2.4 GHz.
* Convert the signal to baseband IQ and flip it using the complex mixer at a frequency of \(-824.58\) MHz.
* Decimate by 8, resulting in a rate of 300 MHz.  
After beamforming, the digital to analog conversion is performed by the DAC. In order to negate signal delay in the FPGA fabric, a similar approach was taken to that of the ADCs, not requiring any additional logic, contrary to if different rates or word lengths were used.  
Figure 4: Second Nyquist zone sampling of the L1/E1 band.  
The data path preceding the DAC, interpolates the beamformed samples by 8 and mixes the baseband complex signal with the original carrier frequency of 1.57542 GHz. This is then converted to analog at a frequency of 2.4 GHz.
## 2 Principle of SM Using a Novel Active Transmitter Detection Scheme with SSD
### Digital Implementation of SM with SSD  
To implement SM with SSD, the original binary signal bits are divided into many subsets, followed by two consecutive subsets grouped exclusively for implementing SSD, as exemplified by the two underbaced subsets \"100\" and \"010\" in Figure 1a. The bits in each subset are also divided to be used for SM (called spatial bits for short, exemplified by blue numbers) and signal modulation (exemplified by purple numbers). Here, the SM approach, as shown in the gray inset, is employed with spatial bit = 0 and 1 for activating Tx\({}_{1}\) and Tx\({}_{2}\), respectively, where spatial bits differ in a signal group to enable the best SSD diversity performance [23]. As for the signal modulation, the two-subset-based signal group is used to provide SSD via a two-step signal constellation transformation. Thus, as a prerequisite, each pair of signal constellation points, such as \"\(-1-j\)\" and \"\(1-j\)\", must be created via the quadrature amplitude modulation (QAM). The first step of constellation transformation is through the constellation rotation by multiplying \"\(e^{i\varphi}\)\" in the constellation pair. As two spatial bits differ in a signal group, diversity interleaving with a 100% interleaving ratio is then performed as the second step by exchanging the imaginary part of one signal with the real part of the other signal. After the constellation transformation, CAP modulation is utilized to create real-valued signals for optical modulation. To enhance the robustness of spatial bit detection, here, the CAP signals are further pre-distorted without knowing much CSI. The pre-distortion technique will be described in detail in Section 2.3.  
After optical signal detection, digital signal bits are obtained. The spatial bits that indicate the active transmitter are firstly recovered via a novel power-based statistical method, which will also be further introduced in Section 2.3. Since the CAP signal levels are significantly affected by the distinct channel gains from different transmitters, such  
Figure 1: The architecture of the overall indoor OWC system.  
differences in channel gain can be compensated separately to recover the whole CAP signals in terms of each channel, i.e., \(\mathrm{Tx_{1}}\)'s channel gain is compensated to that of \(\mathrm{Tx_{2}}\) to recover the whole CAP signals in terms of signal levels from \(\mathrm{Tx_{2}}\), and vice versa. After two sets of the whole CAP signals are recovered in terms of signal levels from \(\mathrm{Tx_{1}}\) and \(\mathrm{Tx_{2}}\), respectively, the signals from different transmitters can be picked up, respectively, and combined to perform the inverse transformation of the signal constellation, including diversity deinterleaving, K-means decision and QAM demapping. Here, K-means clustering is used for the signal decision to combat the nonlinearity introduced by pre-distortion and channel-gain-compensated CAP signal recovery. The recovered spatial bits and signal modulation bits are then combined to finalize the signal recovery.
## 1 Introduction
Examples of such materials are metallic foams or metallic honeycomb-like structures. Such cellular structured materials when they collapse under axial loading allow the structure to compress without significant transversal deformation. The influence of cell microstructure on the behavior is due to the fact that the deformation mechanism of the material at cell level is governed by bending and stretching of the cell wall followed by buckling at a post-yield stage [25]. This along with the plateau stress behavior (near constant deformation resistance) makes metallic cellular structures attractive for crashworthiness applications [24; 25; 26; 27; 28; 29; 30; 31], as depicted in Figure 3.  
Metallic foam cellular structures have long been used for energy absorption. However, due to inconsistent mechanical properties that arise from the traditional methods of manufacturing, they cannot be used reliably [25; 32]. This is because the stabilization mechanism of metallic foams is still not fully understood, and is mainly due to the mechanisms of metal melting and thermal decomposition of the blowing agent [32; 33; 34; 35]. The approach taken here is to design novel cellular structures inspired by nature and at the same time can be produced from additive manufacturing to maintain consistency in material properties [36; 37; 38], unlike the traditional means of producing metal foams as discussed previously. Nature offers many excellent examples of structures with low density, high strength, and high energy absorption capacities. One of these is the honeycomb [39; 40]. The cellular structure geometry for this study is inspired by the honeycomb structure. Furthermore, with additive manufacturing becoming widely available, fabrication of complex cellular structures is no longer a challenging feat [36; 37; 38]. The design of the inspiration is shown in Figure 4. Following the trend, in this paper, we propose novel cellular lightweight structures design for the electric vehicle battery housing for crashworthiness. These cellular structures are designed and modeled using the finite element software. The focus of this  
Figure 3: Typical stress-strain characteristics for cellular structures such as foams and honeycombs. Figure 2: Illustration of a multilayered housing for battery casing. paper will be on metallic cellular structures for weight reduction purposes, in particular aluminum based cellular structures for their lightweight.
## 5 Conclusions  
This paper proposes a multivariate attention neural network (MANN)-based orbit uncertainty propagation (OUP) and orbit determination (OD) method for Earth-Jupiter transfer. The proposed method relies on a carefully designed MANN for orbit deviation propagation, which is further employed to predict uncertainties for Earth-Jupiter transfer OD. Training results show that the MANN outperforms conventional deep neural networks (DNNs) regarding accuracy and generalization. The minimal loss of the MANN is lower than \(10^{-7}\), which is at least two orders of magnitude smaller than that of a conventional DNN. Using the proposed MANN-based method, the orbit estimation errors quickly converge. Orbit estimation results reveal that the MANN-based method has accuracy close to that of an unscented Kalman filter (UKF) while saving more than 90% of the computational time of UKF. Moreover, the Mahalanobis distance results show that the proposed method has worse uncertainty estimation performance than the UKF method.  
Figure 14: MC results comparisons between MANN-UKF and UKF (second case).  
\begin{table}
\begin{tabular}{l c c} \hline
**Parameters** & & **Values** \\ \hline \multirow{3}{*}{Initial position vector (km)} & x & 9.45604828 \(\times\) 10\({}^{7}\) \\  & y & 1.43195513 \(\times\) 10\({}^{8}\) \\  & z & 4.76187910 \(\times\) 10\({}^{7}\) \\ \hline \multirow{3}{*}{Initial velocity vector (km/s)} & x & \(-\)2.05939633 \(\times\) 10\({}^{1}\) \\  & y & 2.64682048 \(\times\) 10\({}^{1}\) \\  & z & 9.49888317 \(\times\) 10\({}^{9}\) \\ \hline Initial epoch & & 9 November 2025 03:39:46 \\ \hline Ending epoch & & 15 June 2028 06:27:40 \\ \hline \end{tabular}
\end{table}
Table 5: Parameters for the simulations (second case).  
In the future, more complex dynamics should be considered to test the robustness of the proposed method. In addition, combining the MANN with other estimation methods, such as a cubature Kalman filter or Gaussian sum filter, is suggested.  
Methodology, Z.Z.; Writing--review & editing, Y.S.; Supervision, H.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 62394353).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting
## 3 Methodology
### Predictive Modeling for Road Network Traffic  
Predicting traffic presents a traditional hurdle in time-series forecasting, requiring the estimation of likely traffic data attributes (e.g., road capacity, vehicle movement) for the next H time intervals based on preceding M traffic observations. Mathematically, this involves determining.  
\[\hat{v}_{t+1},\ldots,v_{t+H}\ =argmaxlogP(v_{t+1}\,\ldots\ldots v_{t+H}\ |v_{t-M+1},\ldots \ldots,\ v_{t}) \tag{1}\]  
where \(v_{t}\in R^{n}\) represents an observation vector of \(n\) road segment at \(t\), with each element documenting historical data for a signal road segment. Our research uses a graph framework to characterize the traffic network, emphasizing structure traffic time series analysis. Each observation \(v_{t}\) can be conceptualized as a graph signal defined on an undirected or directed graph G, with weight \(w_{ij}\), as illustrated in Figure 1, where, at time \(t\), the graph \(G_{t}=(V_{t},E,W)\) comprises a finite set of vertices \(V_{t}\) corresponding to observation from n monitoring stations in a traffic network, E denotes a set of edges representing interconnection between stations, and \(W\in R^{n\times n}\) signifies the weighted adjacency matrix \(G_{t}\).
# Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems
## 4 Underwater Acoustic OTFS System Design  
This part introduces the design of the proposed OTFS system, as shown in Figure 1. It includes the design of the UWA OTFS frame (blue dashed line) and the UWA OTFS receiver (red and green dashed lines).
## 4 WT/PV ES Optimization Decision
### An Algorithm Based on a PSO Algorithm and Power-Flow Calculation
#### 4.5.2 Method II: Multi-Objective Economic-Limited Mode  
According to budget of the investment, the Pareto optimality of WT/PV capacity is solved, and the ES capacity is calculated as a fixed percentage of the total WT/PV capacity. A multi-objective PSO algorithm is proposed, which is based on a niche multi-objective PSO algorithm [24] and power-flow calculation. The specific capacity planning process is as follows.  
Step 1  
Initialize the first stage particle swarm. Initialize the WT/PV capacity and location and set the size and iterations of the external archive.  
Step 2  
Initialize the noninferior solution and the global optimum decision. Calculate the power flow and each objective value according to the initial value to obtain the first noninferior solution, then randomly select the individuals in the external archive as the global optimal decision, according to the roulette method proportional to the fitness.  
Step 3  
Update the first stage particle swarm. Update the WT/PV capacity and location within the constraint range and calculate the power flow and objective function.  
Step 4  
Update the external archives and the global optimum decision. Update the external archive with the noninferior solution of the current particle. If the number of individuals in the archive reaches the maximum, replace the individuals with the minimum fitness, according to the roulette method in Step 2, and update the global optimal decision. Increase the number of iterations by one.  
Step 5  
Check the number of iterations. If the maximum number of iterations is reached, output the Pareto optimal solution set; otherwise, return to Step 3 to continue the iteration.  
Step 6  
Determine ES location and capacity. According to the results of Step 5, partition the distribution network cluster in the second stage, select the ES for the third stage, and obtain the output capacity optimization planning results.  
After the above planning, it is necessary to evaluate the planned WT/PV ES using the stability-evaluation indicator and the power-, capacity-, and energy-penetration indicators of renewable energy [25].
## 3 Experimental Results and Discussion
### Stage 2--Production
#### 3.1.3 Surface Coatings--RJS Ageing  
Steel RJS specimens were coated through oxidation and nickel-plating. The results of the shear tests of coated specimens can be seen in Figure 9.  
As expected, the corrosive chemical baths required for the surface coating of steel led to a decrease of the shear strength of the bonded connection. Oxidation is the process that most affects the adhesive, resulting in a 22% loss of shear strength. Overall, the final mean values were above the threshold established. Nonetheless, the reduction in strength could be detrimental in the future of the tool considering the other stages of the tool's life.
# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
## 4 Algorithm Design
### Parameter Settings  
The location privacy protection requirement is presented by privacy protection level \(V\), which indicates the success rate of location privacy protection. That is, \(V=1-p=1-\frac{1}{k}\), and \(V\)\(\in\)[0,1).  
Privacy parameter \(k\) is determined by privacy protection level \(V\) set by the vehicle user. That is,  
\[k=\left\lceil\frac{1}{1-V}\right\rceil, \tag{5}\]  
where \(\left\lceil\cdot\right\rceil\) denotes the upper integer operation.
# Challenges and Potential in Increasing Lutein Content in Microalgae
## 3 Limitation of Lutein Sequestration in Algal Oil Droplets
### Lipid Droplet Access Limitation  
Three types of lipid droplets were found in microalgal strains: eyespot, \(\beta\)C-plastogloule and cytoplasmic lipid droplets (CLD) [90; 91; 92]. The biogenesis of lutein, eyespot, and \(\beta\)C-plastogloule occurs in the chloroplasts of algal cells. In theory, eyespot and \(\beta\)C-plastogloule could be the sink for lutein sequestration. However, eyespot and \(\beta\)C-plastogloule biogenesis are missing in most lutein-producing strains, and massive \(\beta\)-carotene accumulates only in the lipid vessels of a few microalgal species [67; 92].  
Most microalgae strains consist of CLD in the cytoplasm, which only accumulates an amount of neutral lipids [91]. However, the carotenoid-containing CLD has only been found in _Haematococcus_ spp. and _C. zofingiensis_[68; 69; 83; 93]. A \(\beta\)-carotene transport mechanism was suggested in _Haematococcus_ spp., which channeled the \(\beta\)-carotene from the chloroplast to the CLD. \(\beta\)-carotene was then converted into astaxanthin and astaxanthin esters, which were sequestrated in the CLD of _Haematococcus_ spp. [68]. For _C. zofingiensis_, the MVA pathway was triggered with the biogenesis of CLD, which synthesized astaxanthin and keto-lutein in the cytoplasm. Esterified astaxanthin and keto-lutein are concentrated in the CLD of _C. zofingiensis_[83]. Therefore, without biogenesis of lipid droplets in the chloroplast and the transport mechanism that channels lutein into the cytoplasm, it is difficult for lutein to access lipid droplets.  
The accessibility of lutein to the lipid droplet must be \"granted\" in microalgal strains. Currently, the mechanism of the biogenesis of \(\beta\)C-plastoglobules in _Dunaliella_ spp. is still unclear, as is the mechanism of the \(\beta\)-carotene trafficking through chloroplast membrane in _Haematococcus_ spp. and _C. zofingiensis_. Exploring the two mechanisms depends on further proteomic research and a deeper understanding of the regulation of carotenoid accumulation in these microalgal strains.