esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methodology
### Applied Techniques
#### 3.1.2 Spatial Autocorrelation (SPAC) Technique  
Spatial autocorrelation (SPAC) analysis is an array-based technique that allows for the use of simultaneous recording of ambient noise to calculate the dispersion curve of surface waves [38]. This technique is mainly based on the assumption that the wave field exhibits stochastic, stationary behavior in both spatial and temporal dimensions [38]. In theory, the SPAC method identifies a single-phase velocity for each frequency within a specified range by fitting the SPAC coefficient to a Bessel function. In the case of a circular array, the Bessel function represents the mean cross-correlation between station pairs as a function of their separation distance. The authors of [38] demonstrated that, at a given frequency, the SPAC coefficient aligns with the 0 th order Bessel function. The SPAC method is optimized using a circular array with a centrally placed sensor, as attested by [PERSON] [41]. However, a previous study [39] introduced a modification to this approach, enabling the SPAC method to be applied to arrays with non-circular configurations. This adaptation replaces fixed radius values with rings of finite thickness, thereby affording greater flexibility in array design. In the present work, the modified SPAC (MSPAC) technique proposed in [39] was utilized.
## 8 Discussion
### Challenges
#### 8.1.7 Security  
Since the Internet has become an increasingly significant part of people's lives and work, security has gotten a lot of attention. Despite the fact that DL has proved its effectiveness in threat detection [392], future communication services will necessitate context-aware security protection using a variety of data created in people's lives and work. Furthermore, when new strategies are developed to maintain message security, the choiceof security configuration will become an even more difficult problem, as security protection usually entails some loss of network QoS.
# Pursuing High Quality Phase-Only Liquid Crystal on Silicon (LCoS) Devices
## 5 Phase Precision
Either internal compensation using a chip circuit, or external compensation using an FPGA or Application Specific Integrated Circuit (ASIC) driver board are the better solutions to avoid extra load of computing the compensation algorithm in CGHs. In addition, it is possible to divide the active area into several unit blocks. The phase correction look-up table of each unit block (LUT(x, y)) can be performed individually to achieve high-precision modulation of the wavefront. To demonstrate the phase precision measurements, we compare three LCoS-SLM at \(\lambda\) = 633 nm, PCM-2-01-633, PCU-3-01-633 (prepared in house), and LETO (Holoeye), under the same digital driving scheme at a controlled temperature T = 35 \({}^{\circ}\)C. The phase linearity errors (APAE%) under the global LUT of the PCM-2-01 and PCU-3-01 are 1.08% and 1.24%, respectively. The phase linearity error (APAE%) of the LETO panel can be tuned from 2.20% to 0.71% by using our driving calibration instead of the default setting for production. The spatially varying phase responses (SVPR) of the three different LCoS-SLM panels are measured after the phase linearity error of the global phase response is adjusted to around 1.0%. On each LCoS-SLM panel, a phase shift is estimated from eight different sections (P-1 to P-8 as shown in Figure 5) covering the entire panel's active area, as shown in Figure 6. The mSTD of phase precision error from PCM-2-01-633 and LETO are 0.035 \(\pi\) radians (\(\lambda/57\)) and 0.043 \(\pi\) radians (\(\lambda/47\)), respectively. For the PCU-3-01-633 panel, it is 0.067 \(\pi\) (\(\lambda/30\)). The cell gap uniformity errors of PCM-2-01-633 and PCU-3-01-633 panels are 0.68% and 0.99%, respectively, which are evaluated at 12 points using white light spectroscopy (Lambda 950 from Perkin Elmer). This rules out the panel thickness variation issue of these two panels. The problem of uniformity in data addressing is further evaluated by DAC (digital-to-analog converter) and BGP (background pixel) data driving modes. The measurement of DAC mode bypasses dynamic data addressing in pixel memory. It only fixed the IC backplane electrode voltage (\(V_{0}\), \(V_{1}\)) and adjusted the voltage different of \(V_{ITO}\) (\(V_{ITO\text{-}H}-V_{ITO\text{-}L}\)). The DAC mode, in short, simply provides the \(V_{rms}\) of full panel as one large single pixel. The BGP mode is implanted in the linear global LUT data addressing in the SRAM. The phase precision errors from PCM-2-01-633 and PCU-3-01-633 panels are 0.035 and 0.039 \(\pi\) radians under DAC mode, but 0.035 and 0.067 \(\pi\) radians with BGP mode, respectively. It is clear that the non-uniform data addressing in pixel level is the cause of larger SVPR value in the PCU-3-01-633 LCoS-SLM panel.
## 2 The Orientation Compressed Map
### Quantizing and Encoding the Orientations  
Experts usually chose to consider image edge gradients because they are proved to be more discriminant than other forms of representations [16; 35]. To accelerate the matching process, the gradient direction is quantized and a binary string is employed to represent the quantized direction. As illustrated in Figure 1a, a semicircular space is divided into \(N\) parts (e.g, \(N=16\)) which are congruent with each other, and each part is represented by a binary string. In order not to be affected by whether the object is over a dark background or a bright background, we only consider the orientation of the gradient, as shown in Figure 1b, if the angle of the direction is less than 0, it will be mapped to the upper half of the circle. As shown in Figure 1b, the orientation is encoded by a binary string, and each individual bit of the string corresponds to one quantized orientation.  
In this paper, Canny operator is used for edge detection. After the non-maximum suppression, double thresholds are applied to obtain the edge maps. The low threshold \(T_{\mathrm{L}}\) and the high threshold \(T_{\mathrm{H}}\) of Canny detector are proportional to the maximum gradient \(G_{\mathrm{max}}\) of the tested image. In this paper, \(T_{\mathrm{H}}=0.1\cdot G_{\mathrm{max}}\) and \(T_{\mathrm{L}}=0.5\cdot T_{\mathrm{H}}\) reaches the best performance with respect to our experiments.
# Towards Client Selection in Satellite Federated Learning
## 3 System Model
### Model Assumption  
Our study focuses on comparing the SFL performance between a GS as the PS and an LEO satellite as the PS, verifying the effectiveness of CS for SFL. We ignore some conditions and constraints, differing from real physical systems. Therefore, we list our assumptions in our model as follows:  
(1) All satellites are run in the standard circular orbits and ignore perturbation.  
Figure 3: The access comparison between the satellite and GS acts as the PS. The horizontal axis represents the simulation time, and each horizontal line on the vertical axis represents the status of the connection between the client satellites and the PS.  
Figure 2: Comparison of the number of satellites-to-ground and satellites-to-satellites visits.  
2. We exclude the potential effects of the harsh space environment on satellites, such as satellite failures caused by cosmic rays. We assume that the satellite's communication and computing will not be adversely affected.
3. Inter-satellite links and ground-satellite links share the same communication channel model, which ignores most of the complex effects such as atmospheric absorption, antenna misalignment, and Doppler shift.
4. The queuing delay in communication is ignored.
5. The computation ability is constant for a satellite; it is not affected by radiation, overheat, low power, etc.
6. All satellites' hardware remain healthy, and always have enough energy to complete communication and computation tasks.
7. The time the PS takes to make decisions is ignored.
8. When the link is established, the communication parameters remain stable.
9. The PS knows all the clients' orbit information, and can forecast their position during the whole simulation.
## 3 Methodology
### Impact of Hop Penalty Value on the Performance of BATMAN  
Experiments in [34] show that a hop penalty higher than 15 would have an overall impact on \(TQ\), which will diminish the local link quality, and BATMAN as a routing protocol becomes a hop count rather than a composite metric. The variation in hop count also impacts the overall network throughput and delay performance. To better understand the impact of the hop penalty on the \(TQ\), consider Figure 5, where 10 hop-to-hop nodes are presented in a serial fashion. All nodes have a \(TQ\) value of 255 (the maximum as per BATMAN specifications) and analytical results are plotted with the impact of increasing hop numbers. The figure shows that increasing hop decreases the overall \(TQ\) quality by 42% after 10 hops; therefore, networks with a higher number of hops suffer more as compared to networks with fewer hops. Another example of delay impact on the network where nodes are considered symmetric in nature is presented in [35].
## 4 Experiments
### Ablation Study
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**Method** & **DK** & **Depth** & **Pose** & **Line** & **Metric** & **00** & **02** & **03** & **04** & **05** & **06** & **07** & **08** & **09** & **10** & **Avg** \\ \hline Ours1 & ✓ & & & & \(t_{rel}\) & 2.56 & 3.76 & 4.87 & 1.07 & 1.90 & 7.24 & 1.00 & 7.66 & 2.95 & 5.58 & 3.86 \\  & & & & & \(r_{rel}\) & 0.31 & 0.27 & 0.17 & 0.24 & 0.26 & 0.17 & 0.28 & 0.28 & 0.29 & 0.23 & 0.25 \\ \hline Ours2 & ✓ & ✓ & & & \(t_{rel}\) & 2.13 & 4.07 & 2.76 & 2.88 & 2.12 & 3.03 & 1.38 & 3.13 & 2.75 & 4.27 & 2.85 \\  & & & & & \(r_{rel}\) & 0.79 & 1.06 & 1.46 & 0.91 & 0.58 & 1.15 & 0.41 & 0.66 & 0.53 & 0.62 & 0.82 \\ \hline Ours3 & ✓ & ✓ & ✓ & & \(t_{rel}\) & 1.98 & 3.70 & 2.58 & 3.53 & 2.15 & 2.57 & 1.59 & 3.03 & 2.67 & 4.27 & 2.81 \\  & & & & & \(r_{rel}\) & 0.63 & 1.01 & 1.43 & 1.16 & 0.54 & 1.07 & 0.69 & 0.65 & 0.51 & 0.62 & 0.83 \\ \hline Ours4 & ✓ & ✓ & ✓ & ✓ & \(t_{rel}\) & 2.09 & 3.83 & 2.63 & 3.21 & 2.27 & 2.91 & 1.18 & 2.82 & 2.28 & 3.99 & **2.72** \\ \cline{3-12}  & & & & & & \(r_{rel}\) & 0.80 & 1.02 & 1.62 & 1.00 & 0.56 & 1.12 & 0.41 & 0.63 & 0.43 & 0.58 & 0.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The ablation study into the key modules in our DSL-SLAM system.
## 2 Materials and Methods
### Methods  
#### 2.3.1 Unification of Data Spatiotemporal Resolution  
The resolutions of different datasets used in this study varied in spatial and temporal scales. To minimize the errors caused by the mismatch in spatial and temporal resolution, we unified all variables at 0.05\({}^{\circ}\) spatial resolution and monthly temporal resolution to maintain consistency with GOSIF data resolution. ArcGIS 10.7 software was used to interpolate the monthly mean air temperature and accumulated precipitation data to 0.05\({}^{\circ}\) grid points to match GOSIF for further collaborative analysis.
## 4 Discussions
### Initial Position Dependency  
This algorithm relies on an initial alignment position determined by fitting the \(y\)-axis stratification to the center of the sphere. If the initial position is inaccurately estimated, multiple local optimal solutions may exist in the search space. This dependency is especially noticeable in complex scenes or when the quality of the point cloud data is poor. To solve this problem, one can try to introduce a multiple random initialization strategy to reduce the effect of the initial position dependence by running the algorithm multiple times and taking the optimal result. In addition, using population intelligence algorithms such as Particle Swarm Optimization (PSO) or Differential Evolution (DE) to optimize the initial positions can also provide better starting solutions.
## 2 Phenomenological Description
### Ultrasound Radiation and Reception  
The sound radiation and reception is based on the interaction of fluid and solid waves. Especially the flexural wave, also known as the first asymmetric Lamb wave mode (A\({}_{0}\)-mode), can be efficiently used for sound radiation due to the high out-of-plane displacement amplitudes. An exemplary interaction between the waves in the plate and acoustic waves is shown in Figure 1.  
The radiation of the Lamb waves into the adjacent fluid takes place at an angle \(\varphi\) which results from the ratio of the wavelength in the fluid \(\lambda_{F}\) to the wavelength of the Lamb wave \(\lambda_{P}\). This angle can be calculated either from the wavelengths or from the velocities of the waves using the following equation:  
\[\phi=\arccos\frac{\lambda_{F}}{\lambda_{P}}=\arccos\frac{c_{F}}{c_{P}} \tag{1}\]  
wherein \(c_{F}\) is the speed of sound in the fluid and \(c_{P}\) is the phase velocity of the flexural wave in the plate. Since the phase velocity is not only a function of the mechanical material properties but also of the excitation frequency, the radiation angle can be set by modifying the excitation signal characteristics  
Figure 1: Radiation of the ultrasound wave from the functionalized Textile-reinforced thermoplastic composites (TRTC) plate at an angle \(\varphi\).  
of the structure-integrated actuators. In order to scrutinize the procedure to set the radiation angle a case study employing the mechanical properties of the analyzed material is presented below.  
Glass fiber-reinforced polyamide 6 (GF/PA6) plate with the thickness of 2 mm is studied. The dispersion curves--describing the relation between the wave speed and the excitation frequency--can be calculated analytically based on mathematical relation presented in various textbooks (see e.g., [21]) by assuming isotropic material properties ([PERSON]'s modulus E = 20 GPa, mass density \(\rho\) = 1800 kg/m\({}^{3}\), [PERSON]'s ratio \(\
u\) = 0.3). Figure 2 shows the dispersion curves with the phase velocities and the group velocities of the A\({}_{0}\)- and the S\({}_{0}\)-modes.  
The wavelength calculated from the phase velocity of the A\({}_{0}\)-mode is shown in Figure 3A as a function of the excitation frequency. For the radiation in the air (\(\epsilon_{\mathrm{F}}\) = 340 m/s), the radiation angle is shown in Figure 3B.  
For example, at a frequency of 35 kHz the A\({}_{0}\) Lamb wave phase velocity is 640 m/s and the resulting wavelength of the flexural wave is 18.2 mm. The wavelength in air is 9.7 mm which leads to a radiation angle of 58\({}^{\circ}\). As Figure 3 shows, this angle increases with increasing frequency. The angle can be adjusted to specific applications either by adapting the material and the thickness of the plate or by changing the excitation frequency.
# One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
## 7 Proposed 1D CNN Regression for Cutter Wear Prediction
### ReLU Function  
The rectified linear unit (_ReLU_) is a linear activation function with zero thresholds as in Equation (4). The convergence of the gradient descent can be accelerated by applying _ReLU_[49, 50]. The _ReLU_ function is used for negative values in the dataset to return 0.  
Figure 14: Proposed 1D CNN model architecture diagram.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Output Shape** & **Parameters** \\ \hline Conv\_1 (1D) & (None, 1532) & 128 \\ MaxPooling1D & (None, 1532) & 0 \\ Conv\_2 (1D) & (None, 1364) & 6208 \\ MaxPooling1D & (None, 1364) & 0 \\ Conv\_3 (1D) & (None, 11,128) & 24,704 \\ MaxPooling1D & (None, 11,128) & 0 \\ Conv\_4 (1D) & (None, 9256) & 98,560 \\ MaxPooling1D & (None, 9256) & 0 \\ Flatten & (None, 2304) & 0 \\ Dense\_1 & (None, 512) & 1,180,160 \\ Dense\_2 & (None, 128) & 65,664 \\ Output (Dense) & (None, 1) & 129 \\ Trainable Params & 1,375,553 & \\ \hline \hline \end{tabular} \({}^{*}\)None\({}^{*}\) refers to the batch size of the model.  
\end{table}
Table 5: Proposed 1D CNN model summary.  
Furthermore, the _ReLU_ function was utilized to obtain easier training and high model performance.  
\[\begin{array}{c}ReLU(x)=\max(0,x)\\ \frac{d}{dx}(x)=\{1\;if\;x>0;0\;otherwise\}\end{array} \tag{4}\]
## 1 Introduction
In the proposed ADN model, gated recurrent unit (GRU) and convolutional networks are used to encode the inputs, and attention and fully connected networks are combined to enhance the decision-making ability. * We construct a Markov decision process (MDP) for AEOSSP with multi-ATW, in which the multi-VTW information is included in two proposed direct attributes so that the sequence length of inputs for all tasks is the same. * A start-time-shift-based local search is proposed to improve the solution quality of the ADN model. By adjusting the start times of the tasks in the observation plan, feasible unscheduled tasks are inserted into the original plan, resulting in a better solution. * Comprehensive experiments are conducted to verify the efficiency of the proposed method. Our method is compared with other advanced algorithms (DRL [39] and ALNS [25]) and proved to be effective and efficient. The remainder of this paper is organized as follows: Section 2 describes the AEOSSP in detail. Section 3 presents the proposed methods, followed by the experimental results and discussions in Sections 4 and 5, respectively. Finally, the conclusions are presented in Section 6. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Literature** & **Single VTW** & **Multi-VTW** & \begin{tabular}{c} **Fully Connected** \\ **Networks** \\ \end{tabular} & \begin{tabular}{c} **Complex** \\ **Networks** \\ \end{tabular} & \
\begin{tabular}{c} **Training Method** \\ \end{tabular} \\ \hline [PERSON] et al. (2019) [38] & \(\surd\) & & \(\surd\) & & Asynchronous Advantage \\  & & & & Actor-Critic (A3C) \\ \hline [PERSON] et al. (2019) [41] & \(\surd\) & & \(\surd\) & Actor-Critic (AC) \\ \hline [PERSON] et al. (2020) [42] & \(\surd\) & & \(\surd\) & Policy Gradient \\ \hline [PERSON] et al. (2021) [44] & \(\surd\) & & \(\surd\) & Deep Q-network (DQN); \\  & & & & \(\surd\) & Deep Deterministic Policy \\  & & & & Gradient (DDPG) \\ \hline [PERSON] et al. (2023) [45] & \(\surd\) & & \(\surd\) &
\begin{tabular}{c} Proximal Policy \\ Optimization (PPO) \\ \end{tabular} \\ \hline [PERSON] et al. (2022) [39] & & \(\surd\) & \(\surd\) & DQN \\ \hline This study & & \(\surd\) & \(\surd\) & Soft Actor-Critic (SAC) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of this study and previous studies.
# Design of a Computed Tomography Automation Architecture
## 2 Computed Tomography Setup and Initial Methods
### Non-Circular Scanning Trajectories  
[PERSON] and [PERSON] [9] had the goal of reducing CT acquisition time for direct use in product lines by using a few-views CT inspection. They used robotic systems to allow for the inspection trajectory to not be limited to one acquisition plane, reducing the necessary projections for CT reconstruction. Instead of using the classical circular trajectory for CT, they used a partial spherical trajectory. The use of this complex 3D trajectory required the use of 3D iterative reconstruction algorithms. They focused on using the simultaneous algebraic reconstruction technique (SART) and the discrete algebraic reconstruction technique (DART) (integrates prior knowledge on the object) algorithms. The X-ray generator source and detector were attached to the end of two robots that were controlled via master-slave technology. The acquisition/source points were distributed on a spherical surface equidistant from the sample and on opposite sides of each other to keep a constant magnification. A partial spherical trajectory was defined by fixing a limited angular range for two angular parameters. Their results concluded that an angle out of the circular plane (40\({}^{\circ}\) for their experiment) improved the reconstruction of object horizontal edges.  
Helical CT scanning is useful when attempting to scan objects that do not fully fit in your imaging area. [PERSON] et al. [10] used a helical trajectory to perform X-ray CT for correlative imaging. They used helical CT because they had long objects that exceeded the dimensions of the detector. Using helical CT shortened the scanning time over having to take multiple classical circular CT scans and stitching them together. It is important to note that different artifacts are seen in helical trajectories than in circular trajectories. Some artifacts present in a circular CT scan of a sample will not show up at all in its helical scan. Phantoms designed with the focus on the oil and gas industry were used in this study, and it was determined that the image quality of the top sample surface was improved with helical CT. This was because artifacts present in the circular scan performed had a larger presence on or near the top surface of the sample.  
Other trajectory optimization methods are presented in several studies that are based on prior object information. [PERSON] [11] determines the scan trajectory based on the CAD model of the object, optimizing their algorithm based on the same foundations described in Section 3.2[12].
## 4 Materials and Methods
### Developing the Model
#### 4.1.8 Mesh Generation and Calibration
To optimise the accuracy and computational time of the analysis, a fine mesh size of 20% of the bolt diameter under investigation was used for all tests. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Fine Mesh Seed Size (\%)** & **Maximum Shear Force (kN)** & **Computational Time (s)** \\ \hline Experimental & 795.50 & N/A \\
10 & 625.77 & 1746 \\
15 & 631.81 & 1025 \\
20 & 647.92 & 618 \\
25 & 652.12 & 431 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Computational times of different fine meshes in static testing. Figure 14: Force vs. displacement graph comparing fine meshes of static model. Figure 13: Overall model of mesh. To determine an optimal mesh size for the spherical load cell in dynamic testing, a seeding size of 1, 2, 5 and 10% of the sphere's diameter were tested, as shown in Figure 15. The finer mesh of the concrete box and steel bolt were kept at 20% of the bolt's diameter. Dynamic Model Sphere Mesh Comparison  
The computational time of each test was recorded in Table 11.  
A mesh size of 5 and 10% of the sphere's diameter had low computational times, however, it displayed excessive fluctuations in the produced force vs. displacement graph above. This indicated greater inaccuracy, thus a finer mesh was required. A mesh seed size of 1% of the sphere's diameter was too fine, as the computational time was excessive. Hence, a mesh size of 2% of the sphere's diameter was nominated for dynamic testing. The seeding sizes of all geometric parts of the calibrated model have been summarised in Table 12 below.
# Enhancing Data Integrity in Blockchain Oracles Through Multi-Label Analysis
## 2 Key Concepts  
### Data Integrity  
Data integrity refers to the data's accuracy, consistency, reliability, and completeness throughout their lifecycle. This definition is based on the following four characteristics:  
* Accuracy: Data must be correct and accurately represent the state of reality it describes. Any error in the data can lead to incorrect decisions and affect trust in the information.
* Consistency: Information should be coherent across different databases and systems. Data should not show discrepancies when compared in other instances, ensuring a single source of truth.
* Reliability: Data must be trustworthy and come from verifiable sources. It implies that they should be collected and stored to minimize the risk of unauthorized alterations.
* Completeness: Data should be complete and have essential elements that could affect its use. Missing information can lead to misinterpretations and poorly founded decisions.  
Data integrity is crucial for ensuring that information is valuable and trustworthy, especially in contexts where automated decisions are made or where data is essential for critical processes [16], such as databases [17], financial systems, and smart contracts in blockchain [2].