esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design Considerations and Implementation Challenges  
### Selection and Sizing of Damping Devices  
The process of selecting and sizing damping devices is a fundamental component in the integration of supplementary damping systems within the framework of structural seismic design. [PERSON] and [PERSON] [132] have demonstrated that engineers are tasked with evaluating a range of factors, including the dynamic properties of the structure, anticipated seismic forces, and the desired performance outcomes. [PERSON] et al. [133] emphasize that the decision regarding the type of damper--whether viscous, viscoelastic, or friction-based--hinges on its characteristics and compatibility with the structural requirements. Their research highlights key considerations in this selection process, including force capacity, velocity dependence, and sensitivity to temperature variations. Moreover, studies by [PERSON] et al. [134] have shown that the quantity, dimensions, and strategic placement of dampers within the structure are pivotal in enhancing their efficacy in mitigating seismic responses. This body of research collectively underscores the complexity and criticality of the damper selection and sizing process in achieving optimal seismic performance of structures. Achieving the appropriate sizing of damping devices necessitates a careful equilibrium between enhancing performance and maintaining cost efficiency. [PERSON] and [PERSON] [135] emphasize that the sizing process must consider practical limitations, includingthe spatial constraints for installation, architectural considerations, and ease of maintenance. Their research underscores the importance of these practical aspects in the design process.
# Principle and Implementation of Stokes Vector Polarization Imaging Technology
## 3 Implementation of Stokes Vector Polarization Imaging Technology
### Simultaneous Polarization Imaging Mode
#### 3.2.2 Split-Aperture Polarization Imaging  
The split-aperture polarization imaging system is to divide the whole system into multiple split-aperture systems by the method of aperture segmentation. Each split-aperture system adopts different polarization elements, so that each split-aperture system can obtain intensity images of different polarization states. At the aperture of the system, four imaging lens arrays were placed off-axis to form four channels. In each channel, the polarization analyzer was placed to obtain the intensity map of polarization component, and the Stokes vector map obtained by computer processing was used. Four polarization channels share a preobjective lens, and the images of the four polarization channels can be imaged on a CCD through reasonable arrangement. By using an objective lens and an optical imaging system, image the different polarization states of the scene radiation on the same detector through the polarized optical elements, which not only realizes the simultaneous polarization imaging, but also ensures that the field of view of the four polarization channels is co-axial. The advantages of the split aperture simultaneous polarization imaging system are that there are no excessive splitter elements, the optical system is stable, simple, compact and convenient for data processing, and the disadvantage is the loss of spatial resolution [14]. As shown in Figure 8, the schematic diagram of simultaneous polarization imaging with split aperture and the polarization imaging effect are quoted [29].
# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 4 Routing Algorithm
### Link Utility
\(D_{i,j}\) is the range of \(\Delta d_{ij,i1-t0}\), which has been discussed in four scenarios. When the transmission distance is the same, we usually choose the node with good link stability to take the next hop. In fact, the link stability is different and the transmission distance is also different. In order to reduce the routing hops and consider the communication quality, we define the link utility as follows:  
\[d_{ij,t_{1}}=d_{ij,t_{0}}+\Delta d_{ij,t_{1}-t_{0}} \tag{6}\]4  
where \(d_{ij,i0}\) is the distance between \(i,j\) at \(t_{0},R\) is the maximum transmission distance of the node, and \(r(l)\) is the link reliability of \(i,j\).
# A Comprehensive Experimental Emulation For OTFS Waveform RF-Impairments
## 3 Channel Effects Furthermore, RF-Impairments
### Carrier Frequency Offset
#### 3.1.2 CFO Due to Frequency Mismatching between the TX and RX Oscillators  
Now, different frequency offsets have been implemented in the N5172B-vector signal generator (VSG) in order to replicate the carrier frequency mismatching that occurs between  
Figure 5: Emulation of the Doppler shift (\(f_{\mathrm{D}}\approx 370\) Hz) effect on the 5 GHz tone using reverberation chamber. (**a**) Instantaneous spectrum stirrer-off; (**b**) instantaneous spectrum stirrer-on; (**c**) cumulative PSD when the stirrer-off; (**d**) cumulative PSD when the stirrer-on; (**e**) spectrogram when stirrer-off; (**f**) spectrogram when stirrer-on.  
Figure 6: BER performance comparison between OTFS waveform with \(M=32\), \(N=32\) and OFDM waveform with \(N=1024\) affected by the Doppler shift using on the central frequency is 5 GHz reverberation chamber.  
the TX oscillator and the RX oscillator. owing to the fact that the frequency offset of the N5172B-VSG might be adjusted.  
The effect of the Doppler shifts caused by normalized frequency offsets may be seen in the BER performance of the OFDM and OTFS, which is presented in Figure 7. As can be seen in the figure, the BER performance of both the OTFS and OFDM waveforms decays when there is an increase in the value of the mismatching frequency offset. In addition to this, it was demonstrated that OTFS has superior performance in comparison to OFDM, considering the reasons that have been discussed previously.
## 3 Experimental Methods and Data Acquisition
Specific temperature measurement points are shown in Figure 4.  
The welding materials and dimensions used in the test was a 2A14-T6 aluminum alloy plate, which was the same as the simulation, and a ME-K6D175 pressure sensor installed under the workpiece. The mechanical sensor can synchronously output force and torque in the XYZ direction with high acquisition frequency and accuracy. The temperature measurement and force measurement diagrams are shown in Figure 5. The acetone reagent was used to remove the oil stain from the surface of the workpiece, and then the workpiece was clamped along the length of the workpiece on the pressure sensor and welded. The welding parameters were the same as the simulation parameters. The axial force and temperature data were synchronously collected during welding. After the welding was finished, the cross section of the joint was taken perpendicular to the welding direction. After rough grinding, fine grinding, and polishing, the sample was corroded with a mixed acid solution (3 mL HNO\({}_{3}\) + 6 mL HCL + 6 mL HF + 150 mL distilled water), and the internal shape of the joint and the microstructure of each area were analyzed with an optical microscope. Figure 4: Location distribution of temperature measuring points. Figure 5: Simplified diagram of FSW temperature and force measurement. On the cross section of the polished specimen, Vickers hardness tests were carried out on the upper, middle, and lower layers of the joint by a microhardness tester (Beijing TIME High Technology Ltd, Beijing, China). The test position was 1, 3, and 5 mm from the upper surface of the weld. The joint was cut into standard specimens by a wire electric discharge machine, the normal temperature tensile test was carried out on a mechanical property tester, the average value of the three specimens was used as the evaluation result of the joint performance, and then the tensile fracture characteristics of the joint were analyzed.
## 4 Data, Analysis, and Observations
### Data Collection and Pre-Processing
To overcome this issue, we combined the data across months while creating trees to represent the activity corresponding to the posts. The data were processed iteratively using three hierarchies (please see Figure 7): (1) Raw data: These are the data downloaded from Reddit for a number of months using the crawler before any processing. These were the rawest forms of the data and were further processed into nodes and buckets. A node corresponds to a post or comment and contains the username of the poster but not the content of the post. It is linked to its parent and child nodes. (2) Ocean of Buckets is an intermediate representation of the data that consists of buckets obtained from the raw data by processing the subreddits. The processing took place as follows: first, the raw data were parsed to identify the subreddits. A bucket was created for each new subreddit encountered while parsing the raw data. For each new post or comment in the subreddit, a node was created and added to the bucket for the corresponding subreddit. Note that nodes are organized by days in a bucket. Thus, for a post \(P_{i}\) made on day \(i\), we had a node \(n_{i}\) and this was added to the bucket for the corresponding subreddit in the entry for day \(i\). Note that as mentioned before, in our case a bucket had entries for 60 days only. We must also point out that comments on post \(P_{i}\) appeared in entries for days \(i,i+1,\ldots,i+5\). It should be clear that at this level of abstraction, we had all the posts across subreddits and comments on each post. However, we did not have the complete _tree_ of each post and its corresponding comments. (3) Post Tree: This was the final level of abstraction where we parsed each bucket and created the trees corresponding to each post in a subreddit. This was performed for all posts across all subreddits. Constructing the trees was a time-consuming process and hence we used two major multiprocessing structures. The first assigned one subreddit per thread and reconstructed the data at the end. The second structure divided the threads by days within a bucket (subreddit). We used a mix of these two approaches for creating the post trees as both methods had their own drawbacks and were not perfect on their own. An example of the data processing pipeline as described above is shown in Figure 8. Examples of posts and replies are shown in Figures 9 and 10. An example of a subreddit is shown in Figure 11.  
Figure 6: Components of the reddit crawler using Pushshift API. Figure 8: Example of processing data obtained from a subreddit based on days and posts. Figure 7: Schema for processing data obtained from Reddit. Each bucket corresponds to a subreddit and contains entries for each day of the month. For each day, there are multiple post trees, with each post tree corresponding to a post made on that day in the given subreddit.
## 5 Impact on Boundary Layer Transition on a Wind Turbine Blade  
In the preceding sections, we have described that small-scale turbulence below a Taylor's length-scale of less than 50 mm are likely to show non-Gaussian behavior. We now briefly describe how this may be related to turbulent-laminar transitions on wind turbine blades. It has to be noted that more detailed investigations have been performed in the upper atmosphere for small aircraft [8; 9]. Corresponding outdoor experiments on wind turbines were reported in reference [10; 28]. It can be concluded that despite a high integral turbulence intensity of more that 10%, the energy content suitable for the receptivity of TS-waves is even smaller than in a wind-tunnel environment. More evidence for this **low-frequency cut-off** for aerodynamic important turbulence can be given by a simple argument to estimate an **upper** frequency \(f^{\star}\) for a boundary layer (BL) responding to an oscillating outer flow. [PERSON] [29], pp 191 ff, showed that this outer flow (with \(\omega\)) is damped out by viscosity according to \(U=u_{0}\cdot e^{-ky}\cdot cos(\omega t-ky)\) with \(k=\sqrt{\omega/2\
u}\), \(v\) being the kinematic viscosity and U being the main flow in the x-direction, with y being perpendicular to that. Now, if we introduce a _Stokes boundary layer thickness_ by \(\delta_{S}=2\pi/k\), we get \(f^{\star}=4\pi\
u/\delta^{2}\approx 200\ Hz\). Here, we have set \(\delta_{S}\) to an approximate value of 1 mm, a typical value for laminar boundary layers on airfoils at Reynolds numbers of several million. Therefore, this high-frequency, non-Gaussian regime may play an important role in triggering the TS-type of (blade) boundary layer instability on the pathway towards fully developed turbulence.
# Light-Weighting of Planetary Gearbox Based on Multi-Strategy Optimization Sparrow Search Algorithm
## 3 Methodology
### Multi-Strategy Optimized Sparrow Search Algorithm
#### 3.2.1 Improved Circle Mapping Optimization Strategy  
The SSA employs a randomly initialized population, which can lead to significant clustering of sparrow individuals and uneven distribution. Chaotic mapping addresses this issue by enhancing population diversity. Common chaotic mappings include Logistic mapping, Cubic mapping, and Circle mapping. The MSOSSA utilizes the Circle map, which is known for its good order and existence. However, the Circle map also has the drawback of uneven chaotic value distribution. To address this issue, we have improved the Circle map to enhance its randomness and mitigate the uneven distribution problem inherent in the original Circle mapping.  
The original formula of the Circle map is given in Equation (34).  
\[x_{n+1}=mod\bigg{(}x_{n}+0.2-\frac{0.5}{2\pi}\text{sin}(2\pi\times x_{n}),1 \bigg{)} \tag{34}\]  
The initial population distribution diagram is shown in Figure 3. The distribution diagrams of sequences generated in 3000 iterations in different intervals are shown in Figure 4.  
Figure 3: Circle map initial population distribution.  
The improved Circle mapping formula is given by Equation (35).  
\[x_{n+1}=mod\bigg{(}2x_{n}+0.2-\frac{0.7}{2\pi}\text{cos}\left(2\pi\times x_{n}+0. 5\pi\right)+random(0,1),1\bigg{)} \tag{35}\]  
The initial population distribution diagram is shown in Figure 5. The distribution diagrams of sequences generated in 3000 iterations in different intervals are shown in Figure 6.  
Figure 4: Circle map initial population histogram.  
Figure 5: Improved Circle map initial population distribution.  
According to Figure 4, the original Circle map produces values concentrated between 0.2 and 0.6. This phenomenon will lead to a lack of population diversity, which may bring poor global search results. The improved Circle map produces a more uniform distribution of individuals. This can enable the algorithm to explore the search space more fully in the early stage and avoid the search blind area caused by the uneven distribution of the initial population.
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 5 Results and Testing
### Extracting Hidden Images from Video Result  
The hidden data (image) was extracted from within the video frames. Table 11 shows the extraction results.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Original Video** & **Frame of Video** & **Encryption Image** & **Stego-Video** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Steganography Result.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Proposed** & **Ref. [52]** & **Ref. [53]** \\ \hline Entropy & 7.9996 & 7.9992 & 7.9995 \\ \hline NPCR (\%) & 99.998 & 99.61 & 99.997 \\ \hline UACI (\%) & 33.49 & 33.45 & 33.47 \\ \hline PSNR (dB) & 42.67 & - & 15.78 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparative Evaluation of the Proposed Method and Recent State-of-the-Art Techniques Based on Entropy, NPCR, UACI, PSNR, and Correlation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Video** & **Duration** & **FPS** & **Number of Frames** \\ \hline Video 1 & 4.34 min & 25.11 & 6881 \\ \hline Video 2 & 12 s & 30.67 & 368 \\ \hline Video 3 & 1.10 min & 30.16 & 2111 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Video Segmentation.
## 4 Doppler Frequency Estimation in the Presence of Noise
### Least-Square Method  
Another approach to exploit the CAF points \(\widetilde{\overline{S}}_{A},\widetilde{\overline{S}}_{B},\widetilde{ \overline{S}}_{C}\) as defined in Equation (39) is to set up a least squares (LS) problem as follows.  
Since we know that the main lobe of the CAF is a sinc function, it is possible to use the LS method in which the fitting curve is the sinc function Equation (21). The LS method requires that the sum of the squared residuals  
\[Q_{LS}(\bar{A},f_{d})=\left(\widetilde{\overline{S}}_{A}-\overline{S}_{A} \right)^{2}+\left(\widetilde{\overline{S}}_{B}-\overline{S}_{B}\right)^{2}+ \left(\widetilde{\overline{S}}_{C}-\overline{S}_{C}\right)^{2} \tag{42}\]  
is minimized with respect to the unknown parameters, where \(\bar{A}=A/(2R(\Delta\tau))\). This leads to the equations  
\[\begin{array}{c}\frac{\partial Q_{LS}(\bar{A},f_{d})}{\partial A}=0\\ \frac{\partial Q_{LS}(\bar{A},f_{d})}{\partial f_{d}}=0\end{array} \tag{43}\]Since we are interested in the performance comparison with R\({}_{n}\)-3 or C\({}_{n}\)-3, we will use a numerical method to solve Equation (43) for \(f_{d}\).
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 2 Wi-Fi Basics
### PHY in Wi-Fi
#### 2.1.2 Multiple-Input Multiple-Output (MIMO) systems  
MIMO was introduced in 802.11n to overcome the multipath effects that arose with the standards 802.11a/b/g in complex radio environments (because of, e.g., corridors, walls).  
Figure 3: Wi-Fi 5 and Wi-Fi 6 comparison in terms of subcarrier spacing and OFDM symbol duration. Payload symbol duration is reciprocal to subcarrier spacing.  
With MIMO, the signal is transmitted and received simultaneously through multiple antennas, increasing the number of data streams that can be transmitted in parallel.  
MIMO offers three different methods for data transmission within a channel:  
**Spatial Multiplexing:** Each antenna can transmit independent data signals to the receiver. These data signals are called spatial streams [16]. MIMO increases the throughput linearly with the number of antennas. Figure 3(a) shows this transmission method.  
**Spatial Diversity:** This method turns the multipath effect into an advantage by transmitting the same data over multiple antennas. Since every antenna on the receiver side might receive data copies from other streams too, it provides redundancy. A Digital Signal Processing (DSP) module recombines the received spatial streams to recover the whole data chunk (see Figure 3(a)).  
**Beamforming:** This technique modifies dynamically the radiation pattern of the group of antennas. It is similar to directing the signal in a specific direction to strengthen the signal rather than spreading the energy in all directions. Narrower beams bring stronger signals and reduced interference (see Figure 3(b)).  
In 802.11n, an access point with multiple antennas communicates with only a single station at a time. It may transmit multiple data streams in parallel to the same station but multiple stations must be served sequentially. This feature is also called Single-User Multiple-Input Multiple-Output (SU MIMO).  
IEEE 802.11 ac (Wi-Fi 5) introduces downlink MU MIMO to serve multiple stations in parallel. Wi-Fi 5 devices support up to eight spatial streams in parallel for downlink transmission. That is, the access point can transmit data to up to four different stations (with two spatial streams per station). MU MIMO leverages a transmit beamforming (TxBF) technique to concentrate the signal in four different directions, i.e., one direction per station.
# SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things
## 5 Numerical Results
### Packet Drop Ratio  
This section presents the analysis of the packet drop ratio between SASC and other existing solutions. In Figure 4, the experiment results illustrated that proposed SASC gives better results in terms of packet drop ratio by an average of 37% in the comparison of the existing solution under a varying number of nodes. Similarly, Figure 5 has also proven improved performance of SASC concerning packet drop ratio by an average of 46% in the comparison of other solutions. This is due to the fact that the existing solutions lack the capabilities to detect the congestion quantity on data links because of a large number of nodes and route request packets. In addtion, the existing solutions consume unnecessary energy in data protection and integrity, which reduce the lifetime of routing paths and lead to increased packet drop ratio. On the other hand, SASC decreases the ratio of packet drop due to its lightweight security and data integrity mechanisms under the presence of malicious nodes.  
Figure 4: Packet drop ratio in a varying number of nodes.  
Figure 3: Network lifetime in varying CBR data traffic.
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 5 Improved Scheme
### Registration  
**LE Registration**: In this procedure, an LE registers itself with the AS via the manufacturer or a secure channel. The secure key set \(\left\{PSK_{i},i=1,...,n\right\}\) and the public parameters \(\left\{G,p,P\right\}\) are sent to the LE by the AS. Only the secure key set and the public parameters are required to be stored in the secure hardware of the LE. No other user information needs to be stored. Similarly, the lifetime of each \(PSK_{i}\) is set to be short for robust security. When the lifetime of each trusted vehicle's key expires, this vehicle is required to perform the key update procedure with an LE.  
**Normal Vehicle Registration**: All vehicles except LEs need to perform this procedure when they are delivered to market. This registration procedure is performed only once by each vehicle. The steps of the normal vehicle registration procedure are described in Figure 3.  
* \(U_{i}\to AS\): A user \(U_{i}\) chooses his password \(PW_{i}\) and sends its public identity \(ID_{i}\) and \(PW_{i}\) to the AS via the manufacturer or a secure channel.
* The AS chooses a random number \(y_{i}\) with which to evaluate the following parameters for \(U_{i}\) after it receives \(ID_{i}\) and \(PW_{i}\): \(A_{i}=h\left(ID_{i}||x\right)\), \(B_{i}=h\left(PW_{i}\right)\oplus A_{i}\),\(C_{i}=h\left(PSK||y_{i}\right)\oplus A_{i}\), and \(D_{i}=h(ID_{i}||PW_{i}||A_{i})\).
* \(AS\to U_{i}\): The parameters (i.e., \(B_{i}\),\(C_{i}\),\(D_{i}\),\(y_{i}\),\(h\left(\right)\),\(G\),\(p\),\(P\)) are stored in the OBU's secure hardware by the AS via a secure channel.
* \(U_{i}\) chooses a number \(x_{i}\) as his private key and computes \(P_{pub_{i}}=x_{i}P\) as his public key, and then computes \(Z_{i}=x_{i}\oplus h(PW_{i})\) and stores (\(P_{pub_{i}}\), \(Z_{i}\)) in its OBU secure hardware.