esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress in Phase Calibration for Liquid Crystal Spatial Light Modulators
## 3 Interferometric Phase Calibration Method
### Self-Referenced Method
By using discrete Fourier transform [21], the fringes' displacements could be derived from arguments of two peaks in the Fourier domain, which vary with the fringes' positions or grayscales of interference on the captured image. This method could be utilized for spatial nonuniformity measurement as well. Before the measurement, a rectangular array of a 128-pixel-diameter pupil mask is uploaded sequentially on SLM for matching the SLM and camera coordinates. During the spatial nonuniformity measurement, the SLM is uploaded with the pattern of a 128-pixel width-narrowed grating unit on a uniform grayscale background as shown in Figure 4b. The grating unit is composed of eight 16-pixel-period columns, and the first diffraction orders diffracted by the narrowed grating will cause interference with the beams coming from the uniform area. By changing the grayscale on the background uniform zone and analyzing the fringes' displacements on the captured image, the shifted phase on the \(128\times 128\)-pixel blocks of uniform background zones could be acquired. The spatial nonuniformity measurement could be carried out by moving the grating unit and testing the shifted phase in the different regions of SLM, and the grayscale-phase relation for sub-regions around the SLM panel (\(128\times 128\) pixels for instance) could be built. To improve the spatial resolution, a smaller grating unit could be built and the spatial resolution of several pixels could be reached. For improving the performance of SLM with calibration, the grayscale image for uploading could be pre-modified by matrix operation for each sub-region [11]. The self-referenced phase calibration method could be simple and easy to apply, which makes it a suggested method in the manual of the SLM products. However, the reference beam comes from a certain active area of the SLM, which means that the measured phase irregular or distortion may come from the reference zones instead of the measured zones. In addition, it could lead to a misunderstanding on the phase measurement of the measured beam. For minimizing the phase distortion from the self-reference beam, a joint analysis of the interferograms for the same region on SLM could be carried out. Considering that the reference beam can be held still, so can the phase distortion induced by the reference beam, the compensate could be carried out.
## 3 Model Interpretation for Signal Classifier
### Perturbed Sample Generation for Time Series Data
#### 3.2.1 Separating Representative Component of Signal Data Using STFT and Super-Pixel  
In practice, observed signals in medical centers are non-stationary, e.g., recorded EEG for diagnosis seizure. In other words, biomedical signals change frequency over time. Moreover, because physicians consider both periodic patterns and frequency bands simultaneously to diagnose a patient [27; 28; 29], we exploit STFT to transform the input signal.  
STFT is considered to capture the change of frequency content over time, whereas the conventional discrete time Fourier transform (DTFT) averages frequency contents over time [36]. STFT given the input signal is defined as follows:  
\[X[k,m]=\sum_{n=m}^{m+(N-1)}x[n]w[n-m]e^{-j\omega(n-m)} \tag{3}\]  
where \(x[n]\) is the observed data point at time \(n\), \(w[\cdot]\) is the window function given the window size and \(\omega\) represents the angular frequency as well as \(N\) is the number of samples. Therefore, transformed \(X\) is represented as a 2-dimensional matrix that has the axes of the frequency and the time segment. STFT describes the magnitudes of the frequencies for the \(m\)-th segment. For example, we transform the input \(x\) using STFT and visualize it as the spectrogram at the left diagram in Figure 2. As we can see in the second-row, the high magnitude of the low frequency is represented well around the 19 th time segment.  
Since our main purpose in this subsection is to divide the input signal into representative components, our next step is to segment the spectrogram. In this work, we utilize Felzenszwalb [34], which is proposed to segment an image into sub-regions. The intuition of Felzenszwalb is that pixels in an image and the differences between pixel values are considered as nodes \(v_{i}\in V\) and weights \(e_{ij}\in E\) for nodes \(v_{i}\) and \(v_{j}\) in an undirected graph \(G=(V,E)\). \(v_{i}\) is already in a region \(S\), but neighbor node \(v_{j}\) is not yet. It compares the weights \(e_{ij}\) to the maximum weight \(Max(w)\) in the region \(S\). After that, it decides whether those pixels are in a same region or not using threshold \(\tau\). The threshold is added to the maximum weight \(Max(w)\). We apply it for the spectgrogram as shown in Figure 2. Consequently, we separate clusters, which are divided by associated frequency and time pixels as we depicted in the third-row of the left diagram.
# The Impact of Spoofing Attacks in Connected Autonomous Vehicles under Traffic Congestion Conditions
## 3 System Model
### Spoofing Attack
The description of this attack is depicted in Figures 3 and 4. As already mentioned, during platoon movement, vehicles exchange WSMs containing various information, such as their speed, acceleration, and position. According to these WSM values, we extract a number of metrics to examine the consequences of the spoofing attack. These refer to the total distance traveled, the PDR percentage of WSMs for each vehicle, as well as the delay time that vehicles stay in the queue due to spoofing attack. Figure 3: Vehicles create a queue of vehicles with almost zero speed due to spoofing attack while the Node 0 (Spoofer) continues its route normally. Figure 2: (**a**) The platoon of vehicles under normal conditions; (**b**) the platoon of vehicles under spoofing attack.
## 3 Proposed Vehicle Detection and Tracking Scheme
### Vehicle Identification and Vehicle Tracking  
In this process, YOLOv5 is applied to identify the objects from the input videos in every frame and calculate the bounding box. YOLOv5 first identifies the object and then it gives the label to the bounding box of every vehicle. The results of YOLOv5 in terms of the bounding box of every vehicle are given to Deep SORT. In the process of employing deep learning for vehicle detection and classification, the algorithm systematically processes vision data within individual frames, primarily focusing on generating information regarding the types of vehicles present, such as cars, trucks, and buses. For vehicle-type identification, the data obtained from YOLOv5 is directly utilized, supplemented by additional independent training using target site data to enhance the accuracy of vehicle-type classification. Vehicle location information is derived from the details of each vertex and the center point of the bounding box. These data are then transformed into longitude and latitude coordinates, utilizing the vehicle's bottom center point as a reference. The vehicle's location information, extracted from each image frame, is extended to form a continuous frame, facilitating the extraction of vehicle trajectory information for location correction purposes.  
In video images captured by a camera, the algorithm employs the similarity between the feature information of the objects in successive image frames to track their changing locations. The tracking process involves comparing the location and size of the vehicle in the current frame with those detected in the previous frame. Consequently, the vehicle with the largest intersection over union (IOU) is identified as the same vehicle from the preceding frame, enabling the continuous tracking of its movement. Additionally, if no intersection over union is detected within a specified frame duration (0.2 s), indicating no overlap in location and size with the previous frame, the object is recognized as a new vehicle, and a unique tracking ID is assigned.
## 3 Results and Discussion  
### Comparison of the Ability of OCT and bOCT Images to Observe Internal Biological Activity  
After the proper sterilization process, the lentil seeds were exposed to 200 nm and 2000 nm alumina at a 100 mg/L concentration for 24 h to activate internal biological activities. Thereafter, OCT images were acquired, and bOCT images were calculated and examined to observe the internal biological activities and compare them with the conventional OCT images. To observe the clear changes in biological activities, the longest exposure time of 24 h was selected for the comparison. Figure 2a-c corresponds to the bOCT images, and Figure 2d-f corresponds to the conventional OCT images. In the biospeckle pseudo color images, red regions indicate higher temporal fluctuations or higher activities, whereas blue regions indicate lower temporal variations or lower activities. Compared to the bOCT image selected as a control, both of those taken under AlNPs exposure display increments of red color regions, and these increments were more significant for the smallest particle size of 200 nm AlNPs. This kind of change in internal biological activities depending upon the exposure time, concentration, and particle size could only be visualized by bOCT images. However, in OCT structural images, the internal laminar structure of the seed can be visualized where bright regions represent a strong reflectivity of tissue, while dark regions represent a weak reflectivity of tissue. The seed coat and epidermis can be seen at the top of the structural image with strong reflectivity. However, any changes in internal biological activities cannot be monitored in OCT images like they can in bOCT images.  
OCT measures the variation in optical reflectivity, which we see as structures. The OCT images consist of not only laminar organization, but also fine granular structures called speckles, which were produced by the random interference between the scattered lights produced due to finer internal microstructures, such as mitochondria, Golgi bodies, and possibly chloroplasts within the seed. Cell growth, cell division, and cytoplasmic flow may cause these structures to move, and these movements can alter the biospeckles. A clear increment of biospeckle contrast can be seen for both AlNPs exposures compared to that of the control. However, the effect was more significant for the smaller particle size. Thus, AlNPs have a possible size-dependent positive effect. This clear change of contrast, which cannot be observed from OCT structural images, can only be observed in bOCT biospeckle images.  
Figure 2: bOCT contrast images (top row) and OCT structural images (bottom row) after 24 h exposure of the control (**a**,**d**), 100 mg/L alumina (2000 nm) (**b**,**e**), 100 mg/L alumina (200 nm) (**c**,**f**). A clear increment in biospeckle contrast could be seen for both alumina exposures. However, the contrast was higher for the 200 nm alumina exposure compared to the other treatment. This clear change of contrast, which cannot be observed in OCT structural images, can only be observed in bOCT biospeckle images.
## 2 Autonomous Underwater Vehicle (AUV) Architecture  
### Autonomous Underwater Vehicle (AUV) Configuration  
Bigeye Barracuda is a torpedo-like AUV with visual functionality, manufactured in our laboratory. It consists of three basic parts: the fore, parallel middle, and aft parts. The fore body is equipped with an image-processing module, ring-shaped light emitter, and depth sensor. The control room, in the parallel middle part, performs attitude sensing, communication, power management, motion control, and real-time integration management. The function of the aft part is mainly used to manage AUV dynamics. It uses four sets of steerable machines that can be independently operated to control for roll, pitch, and yaw. The four-blade propeller is powered by a direct current (DC) brushless motor. The configuration of Bigeye Barracuda was designed in accordance with that of the Remote Environmental Monitoring Units developed by Massachusetts Institute of Technology (MIT) [21]. The internal units of Bigeye Barracuda are illustrated in Figure 1, and its basic specifications are listed in Table 1. The control system uses the embedded Windows operating system as the core controller and is supported by navigation, communication, sensing, batteries, and motion units. A block diagram of the embedded system is illustrated in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Torpedo** \\ \hline
**Diameter** & 17 cm \\
**Total length** & 180 cm \\
**Weight (in the air)** & 35 kg \\
**Max. operating depth (ideal)** & 200 m \\
**Power source** & Lithium battery \\
**Max. velocity (ideal)** & 5 Knot \\
**Endurance (ideal)** & 12 h at 2.5 knot \\
**Propeller type** & Four-blade propeller \\ \hline
**Rudder control** & Four independent servo control interfaces \\
**Attitude sensor** & High precision INS \\
**Communication** & 2.4 GHz wireless \\
**Image module** & Dual-lens camera and wind-angle camera \\
**Processing** & Inter ATOM SoC E3845 \\
**HDD** & 64 GB SSD HD \\
**SD card** & 32 GB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bigeye Barracuda specifications.  
Figure 1: Schematic of Bigeye Barracuda.
## 4 Discussion  
To ensure that the simulated results accurately represent the experimental material, we employed a molecular dynamics (MD) simulation approach tailored to the specific glass compositions in this study. The simulations were performed using the DL_POLY 2.20 software package and adhered to a rigorous melt-quench protocol to replicate the glass structures. The input parameters included atomic charges and Buckingham potential along with atomic compositions and theoretical densities that were derived directly from experimental measurements of the 6P55 glass series and its boron-substituted derivatives. The glass densities were simulated using a cubic box containing -10,000 atoms under periodic boundary conditions, and the melt-quench process was designed to closely replicate the experimental conditions with high fidelity. The theoretical densities calculated during the simulation matched the experimentally measured densities, confirming the reliability of the models.  
Additionally, the molecular dynamics analysis provided structural insights, including \(Q^{n}\) distribution, oxygen speciation (bridging and non-bridging oxygens), and the coordination environment, which were validated against experimental findings. While properties  
Figure 9: Ring size distribution of glass series composition from computational MD simulation.  
\begin{table}
\begin{tabular}{c c} \hline
**Glass** & **Computational CTE (\(U^{\circ}\)C)** \\ \hline
6P55-0B & 13.41 \(\times\) 10\({}^{-6}\) \\
6P55-5B & 13.30 \(\times\) 10\({}^{-6}\) \\
6P55-10B & 14.34 \(\times\) 10\({}^{-6}\) \\
6P55-15B & 13.22 \(\times\) 10\({}^{-6}\) \\ \hline \end{tabular}
\end{table}
Table 5: Calculated CTE for studied glass series based on computational MD simulation run on each system from 300 \({}^{\circ}\)C to 650 \({}^{\circ}\)C.  
such as the coefficient of thermal expansion (CTE) were derived as secondary data through calculations, the simulated structural data (e.g., pair distribution functions and bond angle distributions) aligned well with experimental observations. The modified Buckingham potential, developed specifically for borosilicate glasses, ensured the accurate depiction of composition-dependent interactions. This alignment between simulated and experimental structural parameters underscores the robustness of the computational approach and its ability to provide meaningful insights into the properties of these materials.
## 2 Descriptions of Conventional Histogram-Based Reversible Data Hiding
### Comparisons with Related Schemes  
Several histogram-based reversible data hiding schemes to improve the performance of the conventional scheme in [8] have been proposed recently. We choose two closely relating schemes with the histogram-based scheme in [10] and [11] to make comparisons.  
In [10], authors modified the scheme in [8] to increase the embedding capacity. The authors proposed to use the location map to record the maximum and minimum points in the histogram of original image, and reserve some spaces in the location map to mark the coordinates of the max and zero points of the histogram. The method in [10] has the advantages of increased embedding capacity and guaranteed output image quality of at least 48.13 dB. However, the induction of location map would reduce the capacity to some extent. In short, results in [10] outperform those in [8].  
In [11], its authors took the human visual system (HVS) into consideration. Authors employed a causal window, 3 \(\times\) 3 or 5 \(\times\) 5 respectively, to calculate the predicted pixel value for data embedding based on HVS. By modifying the difference between the predicted pixel value and the actual pixel value, secret bits can be embedded. Resulting difference values should lie within the range of just noticeable distortion (JND). The method in [11] has the advantages of a much more amount of embedding capacity for hiding the secret bits into unnoticeable regions. However, the calculation of HVS adds more burden to implementation, and severe degradation in output image quality can be observed objectively.  
With the comparisons above, in this paper, we propose the histogram-based reversible data hiding with quadtree decomposition in order to look for the better performances and practical applications.  
Unlike the use HVS in [11], we employ quadtree decomposition to hide the secret bits into appropriate regions after decomposition, and output image quality can be guaranteed objectively. After data hiding, secret bits would be imperceptible subjectively. Most important of all, reversibility of the data hiding algorithm must be retained for the application of authenticity preservation.
# Free Vibration Analysis of Functionally Graded Sandwich Plates with a Homogeneous Core
## 1 Introduction
[21] semi-analytically investigated the free vibration of FG sandwich plates through the scaled boundary element method, in which a layerwise approach based on 3D elasticity theory was adopted. [PERSON] [22; 23] investigated the static bending, free vibration and buckling of a bi-directional FG sandwich plate through a hybrid, quasi-3D theory and the combination of a higher-order, shear deformation theory with FEM. [PERSON] and [PERSON] [24] established the FE model, based on a new hyperbolic SDT, to investigate the static bending, free vibration and buckling of FG sandwich plates with porosity. [PERSON] et al. [25] presented a novel \(C^{0}\) higher-order layerwise FE model for the static and free vibration analysis of FG sandwich plates in which the number of variables does not increase when increasing the number of layers. [PERSON] et al. [26] studied the size-dependent, free vibration response of FG nanoplates using a layerwise theory, and [PERSON] and [PERSON] [27] investigated the role of spatial variation of the nonlocal parameter on the free vibration of FG sandwich nanoplates. However, the free-vibration behavior of the FG sandwich plate is still in need of parametric investigation with respect to the major parameters such as the aspect ratio. Moreover, the NEM has been mostly applied to non-sandwich plate-like structures, such that its application to FG sandwich plates has been rarely reported. Therefore, the parametric free vibration investigation of the FG sandwich plate through the NEM has significance. In this context, this paper intends to introduce a reliable and effective numerical method for investigating the free vibration characteristics of FG sandwich plates with homogeneous cores. The numerical method was based upon hierarchical models showing the spectral modeling error and implemented by the 2-D natural element method (NEM) [28; 29]. The hierarchical models were derived from 3-D elasticity and the NEM is a type of meshfree method characterized by smooth Laplace interpolation functions [30]. The proposed method was verified by comparing it with the reference and 3-D elasticity. Using the developed method, the free vibration characteristics of FG sandwich plates with homogeneous cores were parametrically investigated with respect to the material type of core, the volume fraction index, and the relative thickness and position of core.
# Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
## 3 Dataset and Traffic Usage Features
### Traffic Patterns of Weekdays and Weekends
weekends, the pattern is different as the proportion of IPv6 network traffic volume ranges from an average of 2.4% from 04:00 to 06:00 CST to 4.8% from 18:00 to 20:00 CST. The network traffic pattern on weekdays is not much different from that on weekends, but the usage volume is smaller on weekends. Therefore, the IPv6 users from these two universities are more active during weekdays, which is the opposite of what happens in commercial networks [11]. In addition, we find that the percentage of IPv6 network traffic volume in DHU decreases slightly over the weekends, while the percentage of IPv6 network traffic volume in ECNU increases slightly over the weekends.
# A Binocular Vision-Based 3D Sampling Moire Method for Complex Shape Measurement
## 3 Digital Simulation Experiment and Discussions
### Effect of the Fringe Pattern  
In actual experiments, besides image noise, non-linear and oversaturated interferences often exist in the captured fringe images. Common factors affecting fringe images include non-linearity and oversaturation [17]. Figure 8 shows the influence of various factors on the phase calculation of the sampling moire and the traditional four step phase shifting method (PS) [17]. In the simulation, the fringe period was 10 pixels, and the introduced displacement field was \(u\). We performed a phase analysis on the ideal sinusoidal fringe, square wave fringe, oversaturated fringe, and gamma-distorted fringe images. Specifically, the duty ratio of the square wave fringe was 50%. The over-saturated fringe and gamma distortion fringe were generated according to Equation (11) (\(S=150\)) and Equation (12) (\(\gamma=5\)), respectively. In Figure 8, the intensity distribution of the fringe is drawn as a red curve.  
\[I_{0}(x)=255\cdot\cos(2\pi x/p_{S})+S\]  
\[I_{S}(x)=\left\{\begin{array}{cc}I_{0}(x)&I_{0}(x)<255\\ 255&I_{0}(x)\geq 255\end{array}\right.\]  
\[I_{\gamma}(x)=255\cdot\left[\cos(2\pi x/p_{S})\right]^{\gamma}\]  
Figure 7: The RMS displacement errors when the original fringe pitch changes from 6 to 14 pixels and the sampling pitch changes from 4 to 17 pixels.  
For optical 3D measurement, phase shifting (PS) is one of the most common phase analysis methods and is applicable to the phase measurement of fringe images or interference images [4,30]. For an ideal noise-free fringe image with a sinusoidal intensity distribution (Figure 8A), the phase error caused by phase shifting is one order of magnitude smaller than sampling moire. However, in actual experiments, the fringe, as a deformable carrier, is often similar to a square wave [31]. In this case, the phase error caused by phase shifting increases sharply (the RMS of the phase error increases by nearly 100 times), and correspondingly, the RMS of sampling moire only increases from 0.0268 to 0.1721. In the experiment, an overlong time of exposure and overlarge aperture set caused image oversaturation, leading to the loss of detailed fringe information [17] (Figure 8C). In this case, the RMS of the sampling moire error was 0.0335, close to the ideal result of 0.0268. Finally, the commercial projector is often gamma-corrected to adapt to the human eye. This process often leads to non-linear gamma distortion in the captured fringe image [17] (Figure 8D). It should be noted that sampling moire is barely affected by gamma distortion, while the error of RMS (0.0298) is very close to the ideal result.