esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and Problem Modeling
### Communication Model and Energy Consumption Model
According to [14], the transmission rate between GPF \(n\) and LEO satellite \(s\) is defined as  
\[R_{n,m,s}(t)=W\cdot\log_{2}\left(1+\frac{\gamma_{n,m}(t)\cdot\gamma_{m}}{1+ \gamma_{n,m}(t)+\gamma_{m}}\right), \tag{7}\]  
where \(\gamma_{n,m}(t)=\frac{p_{n}(t)\cdot h_{n,m}(t)}{\sigma^{2}}\) represents the Signal-to-Noise Ratio (SNR) between GPF \(n\) and AP \(m\), \(\gamma_{m}=\frac{p_{m}\cdot h_{m}}{\sigma^{2}}\) is the SNR between AP \(m\) and LEO satellite \(s\), and \(p_{m}\) is the fixed forwarding power of AP \(m\). It is evident that when the AP has a sufficiently large forwarding power \(p_{m}\), the transmission rate \(R_{n,m,s}(t)\) of GPF \(n\) will significantly improve compared to the direct satellite transmission rate \(R_{n,m}(t)\). In conclusion, the actual data transmission rate of GPF \(n\) for immediate communication tasks in time slot \(t\) can be expressed as  
\[R_{n,\text{immediate}}(t)=\sum_{m=1}^{M}(a_{n,m}(t)\cdot R_{n,m,s}(t))+a_{n,s}( t)\cdot R_{n,s}(t) \tag{8}\]  
where the first item \(\sum_{m=1}^{M}(a_{n,m}(t)\cdot R_{n,m,s}(t))\) represents the rate at which the GPF establishes a connection with an AP and \(a_{n,s}(t)\cdot R_{n,s}(t)\) represents the rate at which the GPF communicates directly with the LEO satellite. In (1), we can see that the GPF can only establish one connection in a single time slot. The actual data transmission rate of GPF \(n\) during time slot \(t\) for non-immediate communication tasks is  
\[R_{n,\text{non-immediate}}(t)=\sum_{m=1}^{M}(a_{n,m}(t)\cdot R_{n,m}(t)) \tag{9}\]  
The actual communication energy consumption generated by GPF \(n\) during time slot \(t\) is  
\[E_{n}(t)=\left[\left(\sum_{k=1}^{M}a_{n,k}(t)+a_{n,s}(t)\right)\cdot p_{n}(t) \right]\cdot\Delta t=p_{n,k}(t)\cdot\Delta t, \tag{10}\]  
where \(p_{n,k}(t)\) represents the user transmission power when
## 5 Results
### Vibration-Induced Loosening Performance  
The changes in preloads with increasing vibration cycles for eight different bolted joints are shown in Figure 21. For the bolted joints installed with double nuts, the preloads assigned to the upper and lower nuts were both 15 kN. It can be observed from the figure that the preload in the regular bolted joint gradually decreased during the vibration process, showing poor anti-loosening performance. The preloads in the bolted joints installed with a plain washer, wedge washer, variable-diameter nut, eccentric double nut, and double-thread bolt were almost constant after 30 cycles. The preload in the bolted joint installed with a wedge-locking nut decreased gradually during the first five cycles and remained constant subsequently. This phenomenon indicates that a wedge-locking nut can provide anti-loosening ability for the bolted joint, but its anti-loosening performance is not as strong as that of other structures with stable preload under vibration. For the regular double nuts, there was a small drop in the preload following the initial vibration cycle, possibly because of the effect of stress redistribution [27]. Overall, the seven bolted joints installed with different washers or nuts all show a certain anti-loosening performance under transversal vibration.  
It is worth mentioning that [PERSON] et al. [18] used a Junker test machine to experimentally investigate the anti-loosening abilities of different bolted joints. They found that the anti-loosening performance of a wedge-locking nut was better than that of a regular bolted joint. Thus, the results of our study and the available literature prove that a wedge-locking nut is more capable of loosening resistance than a regular nut.
# Multi-Device to Multi-Device (MD2 MD) Content-Centric Networking Based on Multi-RAT Device
## 2 Related Works and Problems  
There are several related works that address D2D communication, CCN, and Bluetooth Beacon. In this section, D2D communication, CCN, and Bluetooth Beacon are explained. Furthermore, the problems related to a MD2 MD content-centric network environment are discussed.
# Assessing Voided Reinforced Concrete by Numerical Modelling of Impact-Generated Rayleigh Waves
## 3 Experimental Validation
### Wave Velocity  
The theoretical velocities of the P-wave \(V_{P}\) and R-wave components \(V_{R}\) were calculated using Equations (7) and (8) ([PERSON], 1962) [28].  
\[V_{P}=\sqrt{\frac{\mathbf{E}(1-\mathbf{v})}{\mathbf{\rho}(1+\mathbf{v})(1-2\mathbf{v})}} \tag{7}\]  
\[V_{R}=\frac{0.87+1.12\mathbf{v}}{1+\mathbf{v}}\sqrt{\frac{\mathbf{E}}{\rho}\frac{1}{2(1+ \mathbf{v})}} \tag{8}\]  
The properties of the material used for the numerical model were substituted into the equations to compute the velocities. Table 3 indicates the differences between the theoretical, experimental and numerical values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Theoretical** & **Concrete** & **Reinforcing Steel** \\ \hline \(V_{P}\) (m/s) & 3667 & 5856 \\ \(V_{R}\)(m/s) & 2047 & 2904 \\ \hline \hline
**Experimental** & & \\ \hline \(V_{P}\)(m/s) & 4286 & 6099 \\ \hline \(V_{R}\)(m/s) & 2308 & 3220 \\ \hline
**Numerical** & & \\ \hline \(V_{P}\)(m/s) & 3940 & 5707 \\ \hline \(V_{R}\)(m/s) & 2000 & 2778 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons of P- and R-wave velocities.  
Figure 7: Superposed waveforms (blue: experimental waveform; yellow: simulated waveform).  
The values obtained from the numerical model are closer to the theoretical predictions than to the experimental results, except for the P-wave velocity obtained from the plain concrete model. The differences in numerical results are within 10% when compared with the theoretical predictions and within 15% when compared with the experimental results. The main contributors to the discrepancies are considered to include limitations and variabilities associated with measuring the physical and mechanical properties of the materials in the laboratory. These influence the predictions of [PERSON]'s modulus, density and Poisson's ratio of the materials modelled for the numerical simulations. In addition, there are potential uncertainties in the methods by which wave velocities were obtained experimentally. For example, these may include the existence of background noise that affects the signal-to-noise ratio and thus the accuracy of detected waveformsdamping and distortion of waves. Furthermore, the boundary reflection absorption layer applied in the numerical model may have also imposed unidentified computational uncertainties in simulating the propagation of waves.
# Analytic Derivation of Ascent Trajectories and Performance of Launch Vehicles
## Appendix A  
Let us recall the definitions of stage and subrocket structural mass ratios of an N-stage launcher:  
\[\epsilon_{k}=\frac{m_{s_{k}}}{m_{k}}\ \,\ \ U_{k}=\frac{M_{k-m_{p_{k}}}}{M_{k}}\ \ k=1,\ldots N\]  
where \(m_{k}=m_{s_{k}}+m_{p_{k}}\) is the mass of each stage (structure plus propellant mass) and \(M_{k}=\Sigma_{j=k}^{N}m_{k}+m_{pay}\) is the mass of each subrocket.  
\[m_{s_{k}}=\frac{M_{k}(1-U_{k})}{1-\epsilon_{k}}\,\ \ m_{p_{k}}=M_{k}(1-U_{k}) \tag{1}\]  
\[M_{k}=M_{k-1}\frac{(U_{k-1}-\epsilon_{k-1})}{1-\epsilon_{k-1}}\,k=2,N-1\ \,\ \ M_{N}=m_{pay} \tag{2}\]  
Let the values \(M_{1}\) and \(m_{pay}\) be given, then, by  
\[\frac{m_{pay}}{M_{1}}=\Pi_{i=2}^{N}\frac{M_{i}}{M_{i-1}} \tag{3}\]  
The Tziolkowski velocity is equal to  
\[\Delta V_{Tz}=-g\sum_{i=1}^{N}\,\,Isp_{i}\,\log(U_{i})\]  
The problem is now: select the values \(U_{k}^{*}\) to minimize  
\[J=g\Sigma_{i=1}^{N}\,Isp_{i}\,\log(U_{i})\]  
under the constraint (3), which is equal to (see (2)):  
\[\Phi=\frac{m_{pay}}{M_{1}}-\Pi_{i=2}^{N}\frac{U_{i}-\epsilon_{i}}{1-\epsilon_ {i}}=0 \tag{4}\]  
To solve the constrained minimum problem, the Hamilton function is introduced with Lagrangian multiplier \(\lambda\):  
\[H=J+\lambda\,\Phi\]  
and the solution for the \(N+1\) unknowns \(U_{i},\lambda\) derives from the \(N+1\) equations  
\[\begin{split}&\frac{\partial H}{\partial U_{i}}=0\\ &\cdot\\ &\frac{\partial H}{\partial\lambda}=0\end{split} \tag{5}\]These equations correspond to  
\[\frac{gIsp_{i}}{U_{i}}-\frac{\lambda}{1-\epsilon_{i}}\Pi_{k=1,k\
eq i}^{N}\frac{U_ {k}-\epsilon_{k}}{1-\epsilon_{k}}=0 \tag{10}\]  
and to Equation (11). From (10), the Lagrangian multiplier \(\lambda\) is derived:  
\[\lambda=\frac{gIsp_{i}(U_{i}-\epsilon_{i})}{U_{i}}\Pi_{k=1}^{N}\frac{1-\epsilon _{k}}{U_{k}-\epsilon_{k}} \tag{11}\]  
Equation (11) holds true for any \(i=1,N\), and this corresponds to  
\[\frac{gIsp_{1}(U_{1}-\epsilon_{1})}{U_{1}}=\cdots\cdots=\frac{gIsp_{N}(U_{N}- \epsilon_{N})}{U_{N}}\]  
that is  
\[\frac{gIsp_{i}(U_{i}-\epsilon_{i})}{U_{i}}=\frac{gIsp_{1}(U_{1}-\epsilon_{1})} {U_{1}}\ \,i=2,N \tag{12}\]  
Equation (12) corresponds to  
\[U_{i}=\frac{Isp_{i}\epsilon_{i}}{U_{1}(Isp_{i}-Isp_{1})+Isp_{1}\epsilon_{1}}U _{1}\ \,i=2,N \tag{13}\]  
Replacing the Formulas (13) into (11), one has the following algebraic equation of order N in the unique unknown \(U_{1}\):  
\[\frac{m_{pay}}{M_{1}}\Pi_{i=1}^{N}[U_{1}(Isp_{i}-Isp_{1})+Isp_{1}\epsilon_{1}] -\Pi_{i_{1}}^{N}\frac{\epsilon_{i}}{1-\epsilon_{i}}(Isp_{1}(U_{1}-\epsilon_{1} ))^{N}=0 \tag{14}\]  
The solution \(U_{1}^{*}\) of (14) is used in (13) to obtain all the subrocket structural mass ratios \(\left\{U_{i}^{*}\right\}_{i=1,N}\), maximizing the Tziolkowski variation of velocity \(\Delta V_{Tz}\).
## 4 Classification of Lung CT Images  
### Dataset and Preparing  
In this study, CT scan images of Data Science Bowl and Kaggle [18] are used. This dataset consists of 2101 high-risk patients with CT scans of chest cavity which includes multiple 2D slices (Figure 3) in an Imaging and Communications in Medicine (DICOM) format. DICOM format is a standard format of medical imagery (i.e., Xray, PET, CT scan) [37]. Moreover, each CT scan is labeled as with cancer or without cancer by pathology diagnosis which contains a different number of images.  
Since the main purpose of this study is to apply 3D-CNN architecture in order to classify lung CT scan images, the essential stage is preparing a 3D dataset.  
Each CT scan consists of slices by 512 \(\times\) 512 sizes with a variable number of slices (z dimension) based on the resolution of the scanner machine. Hence, in order to use the 3D dataset, reconstruction of the data by reading all slices of each CT scan is necessary. Therefore, the first integer pixel values of each slice are converted to Hounsfield Units, which are standard for radio density measurement; [38] afterwards each slice is normalized to the (0,1) range. Eventually, 2D slices are stacked into 3D volume in the appropriate sequence to produce 3D inputs. To have the same input, 3D images in terms of dimensions, zero padding is applied on all 3D scans along each dimension to prepare a dataset of 512 \(\times\) 512 \(\times\) 512 volume. According to the memory usage of GPU, the input has been rescaled to a 227 \(\times\) 227 \(\times\) 227 volume.  
By applying and comparing the state-of-the-art architectures on lung CT scan datasets, it has been proved that the appropriate architecture can be the customized architecture of CNN. Hence, in this study, the most significant purpose was to propose appropriate 3D-CNN models. Furthermore, in order to evaluate the performance of proposed 3D-CNN models (straight 3D-CNN and hybrid 3D-CNN), AlexNet and GoogleNet, two well-known architectures of CNN, are applied on the prepared CT scan images. Since the prepared CT scan image dataset contains 3D images, both AlexNet and GoogleNet architectures have been altered to 3D architectures to be suitable architectures in order to classify the 3D images. In this section, the proposed 3D-CNN architectures and both 3D-AlexNet and 3D-GoogleNet architectures are explained as well. Furthermore, Python was used to prepare the dataset and open-source python-friendly library Tensorflow was used to create the 3D learning architectures. The models ran over a computer with Nvidia GeForce Titan X Pascal 12 GB GPU.
# Dual-Frequency Signal Enhancement Method of Moving Target Echoes for GNSS-S Radar
## 2 Methodology  
### Composition of the GNSS-S Radar System  
The schematic diagram of the GNSS-S radar system proposed in this paper is shown in Figure 1. It mainly includes four parts: the antenna module, the RF module, the sampling module, and the processing module. The antenna module includes the direct receiving antenna and the scattering receiving antenna, which are both dual-frequency antennas and can simultaneously receive B1 and B3 signals of BDS. The RF module implements the amplifying, filtering, and downconversion of the RF signals and divides the dual-frequency signals into four channels. The sampling module carries out the AD sampling of the four signals. Finally, the GNSS-S detection algorithms are carried out in the processing module.  
The physical diagram of the GNSS-S radar system is shown in Figure 2, and the main technical parameters are shown in Table 1. The direct receiving antenna, which is an omnidirectional receiving antenna that points towards the zenith, receives direct signals from GNSS satellites [27]. The scattering receiving antenna, which is a high-gain directional receiving antenna that points towards the detected target, receives the scattering echo of the target [28]. Both the direct and scattering signals are input to the processing host to realize the simultaneous sampling of the four signals and execute the processing algorithm.  
Figure 1: Schematic diagram of the GNSS-S radar system.  
Figure 2: Composition of the GNSS-S radar system.
## 4 Experimental Results
### Verification and Analysis of Measurement Results
#### 4.1.2 Stability Analysis of Measurement Values on the Standard Platform  
To evaluate the stability of the measurement system, one-way ANOVA was applied to measurements from the standard platform. The system was configured to continuously collect 100 sets of data, with each set including measurements at three points and four repeated measurements per points, yielding a total of 1200 data points. Whether repeated measurements introduced any systematic bias in the measurement results was determined.  
The ANOVA results indicated that the developed system made repeated measurements with nonsignificant differences (\(F\) = 0.025, \(p>0.05\)), as shown in Table 3. This finding confirms that the system does not introduce bias due to repeated sampling, demonstrating consistent measurement stability.
# Deep-Feature-Based Approach to Marine Debris Classification
## 2 Materials and Methods
### Deep Convolutional Architectures
#### 2.2.5 DenseNet121  
[PERSON] et al. in [54] address the vanishing gradient problem of deep convolutional networks by introducing new, dense connectivity pattern in _Densely Connected Convolutional Network_ (_DenseNet_) architecture. The main idea is to connect all layers directly with each other in a feed-forward manner to improve the information and gradient flow between layers as illustrated in Figure 10. The \(m^{th}\) layer \(L_{m}\) receives the original input \(\mathbf{x}_{0}\) and outputs \(\mathbf{x}_{1},\dots,\mathbf{x}_{m-1}\) of all preceding layers \(L_{1},\dots,L_{m}\) as input and outputs \(\mathbf{x}_{m}=f_{m}([\mathbf{x}_{0},\mathbf{x}_{1},\dots,\mathbf{x}_{m-1}])\), where \([\mathbf{x}_{0},\mathbf{x}_{1},\dots,\mathbf{x}_{m-1}]\) denotes the depth-wise concatenation of volumes \(\mathbf{x}_{0},\mathbf{x}_{1},\dots,\mathbf{x}_{m-1}\) (it is assumed that all volumes have the same width and height). If each layer outputs \(g\) feature maps and if \(d\) denotes the depth (number of channels) of input, then layer \(L_{m}\) receives the input volume \([\mathbf{x}_{0},\mathbf{x}_{1},\dots,\mathbf{x}_{m-1}]\) of depth \(d+(m-1)g\) and forwards a total of \(d+mg\) feature maps to the next layer.  
Figure 8: Inception-ResNetV2 modules.  
Figure 9: Inception-ResNetV2 architecture.  
The architecture of densely connected network with 121 weight layers (excluding batch normalization layers), _DenseNet121_, is shown in Figure 11.  
Four dense blocks are comprised of 6, 12, 24, and 16 smaller building blocks each comprised of \(1\times 1\) convolution, which reduces the number of the input features (added for computational efficiency), and \(3\times 3\) convolution, which produces \(g=32\) feature maps and concatenates it to the original input volume as illustrated in Figure 12. Transition layers following dense blocks aim to reduce the depth and spatial size of the input volume.  
Reuse of features learned in earlier layers encourages classifier to use features of all complexity levels, removing the need to learn the redundant features and results with narrower architecture requiring fewer parameters.
## 3 Model Verification
### Theoretical Comparison of Erosion by Single Abrasive Particle  
The model employed by [PERSON] and [PERSON] [20; 21] was chosen to verify our model for the following reasons. Because it is based on energy conservation, their model and that developed in this study are similar in that the erosion rate of rock-like material is calculated based on single particles. Their work was also suitable for comparison and verification of the theoretical and numerical results because plastic deformation and crack deformation occur simultaneously [43], as explained in the numerical analysis results obtained in this study.  
For different kinetic energies, combinations involving different densities (\(A\): 0.00379 g/mm\({}^{3}\) and \(B\): 0.00702 g/mm\({}^{3}\)) and diameters (0: \(\phi\) 0.09 mm, 1: \(\phi\) 0.18 mm, 2: 0.264 mm, and 3: 0.344 mm) of the spherical abrasive particles were used, resulting in a total of eight analytical cases (Table 2). The density and diameter of the abrasive particle were obtained from the commercial specifications for garnet and steel balls. The parameters used for theoretical verification, including the pump pressure and the water and abrasive flow rates, are given in Table 2, along with the abrasive and target material properties (e.g., diameter, density, and hardness). Under these conditions, the Reynolds number of the abrasive waterjet was in the range 18,600-20,000 [9; 44]. The drag coefficient for smooth spheres under this condition was used to solve Equation (11) [36]. The exit velocity in each case was calculated (Equation (18)). The exit velocities of the abrasive particles ranged from 335 m/s to 352 m/s depending on their diameters and densities.  
For the erosion model calculations, the Knoop hardness was used to describe the resistance to erosion. The Knoop hardness, also called microhardness, is often used for microscale analysis. The Knoop hardness is usually estimated based on the Vickers hardness and is suitable for evaluating microhardness-related characteristics such as microfractures or microsettlements. Therefore, the Knoop hardness was used to experimentally analyze the cutting performance of the AWJ [9; 45].  
In Figure 4, the erosion depth and volume, which were determined using the erosion model developed in this study (Equations (28) and (30)), are compared to those determined using the elastoplastic model (Equation (1) [20; 21]). The erosion depth calculated using the suggested model was slightly smaller than that obtained from the elastoplastic model (Figure 4a): this can be attributed to the characteristics of microcracking in quasi-brittle erosion. Even with microcracks, the two sets of results corresponded well because of the high hardness of the target, resulting in considerable plastic erosion and small cracks. The erosion volume results obtained using the proposed model (this study) and those obtained using the elastoplastic model also matched well (Figure 4b). The results from the proposed model seemed to match better for smaller abrasive particles.
## 3 Data Processing & Methodology
### Receiver Clock Offset w.r.t GPS Time
#### 3.1.2 Septentrio Data Processing  
The Septentrio receiver records the data in standard CGGTTS format. The data are recorded for different constellations with an identifier. Since only GPS constellation is being considered for this paper, only the CGGTTS file with GPS identifier will be processed. For Septentrio receivers, the receiver can be steered to an external reference clock. For  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Location** & **Calendar Year** & **MJD** \\ \hline NTU & 3 February 2024 to 5 April 2024 & 60343–60405 \\ Stirling Rd & 5 July 2023 to 1 September 2023 & 60130–60188 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data used for processing.  
Figure 4: Map of Singapore showing the location of the receivers: Septentrio receiver at Time Lab in FP2 (in green triangle), Ublox receiver at NTU (in gold star), and Ublox receiver at Stirling (in orange star). The diagram also shows the flow of post-processing steps for a Ublox receiver.  
the experiments conducted for this paper, the Septentrio receiver will have an external reference, which is the precision timing generated at FP2.  
An experiment was run to check on the data from the Septentrio receiver to study its performance in the presence of the external reference. For this experiment, a free-running cesium atomic clock was chosen as the external reference. Figure 5 shows the data with and without the external reference. The red dots are when the Septentrio receiver is running by its internal reference and the blue dots are when the external reference (from the FP2 Lab) is connected to the receiver. Almost the same number of days were considered for the comparison; however, there were some missing data for the case when the receiver is run by its internal clock. This is because there was a period when the instruments were shifted due to the relocation of the labs. The case was soon identified, and the devices were again working fine with no missing data after.  
Figure 5 clearly shows that the distribution of the data with time is very much affected by the presence of an external reference. As expected, with the external reference, which is very precise, the data have a lesser range and less variation when compared to the case with the internal clock only. Also, there is a clear separation between the two when the amplitudes of the data are compared. The Refsy values in the presence of the external reference are more than 20 times smaller. For applications where the receiver clock bias errors are critical, like positioning, such differences can cause a huge impact.
# Design of Time-Synchronized Switch Based on Zynq
## 4 Clock Synchronization Module Design
### AXI to Wishbone Bridge (AXI-WB Bridge)  
The various modules in the PL domain communicate via the Wishbone bus. Since the PS and PL domains of the Zynq communicate through the AXI bus, an AXI-to-Wishbone bridge is required to facilitate communication between the PS domain and the modules in the PL domain.  
Data can be directly transmitted between the AXI bus and the Wishbone bus without the need for additional registers. The PL domain is connected to Zynq's AXI interface as an AXI IP core, and the actual address of each module is the base address of the AXI interface (0 \(\times\) 8000000) plus the offset address of that module.  
When the AXI bus's already signal is 0 and the arvalid signal is 1, the read process is initiated. Data can only be transferred from the Wishbone bus to the AXI bus when the Wishbone bus's stall signal is 0 and the ac signal is 1. When the AXI bus's awvalid signal is 1, the write process begins, and when the AXI bus's wvalid signal is 1, the data will be sent from the AXI bus to the Wishbone bus. The specific process is illustrated in Figure 15.  
Figure 14: The structure of the SoftPLL.