esa-sceva/satcom-chunk-collection huggingface dataset:
# Versatile Video Coding-Post Processing Feature Fusion:
## 1 Introduction
We leveraged a feature fusion mechanism that combines the benefits of local and global features by integrating earlier layer features into deeper layers, thereby preserving spatial information and preventing feature loss during network output processing. 2. The inclusion of the QP map as prior knowledge with the reconstructed images allows the CNN architecture to concentrate on identifying features that are most closely tied to compression artifacts, resulting in a more targeted and efficient feature extraction process. 3. We leveraged skip connections between the input reconstructed image and the network residuals, allowing the network to effectively reuse features from the input. This design choice is particularly beneficial for tasks where the output should closely resemble the input, but with enhanced quality. 4. In video coding, both random access (RA) and low delay (LD) modes rely on inter-frame prediction to enable efficient decoding and display. RA allows decoding from any frame, while LD demands rapid display after decoding. To overcome the reconstruction quality issues inherent in inter-frame prediction, the proposed network is employed to improve the accuracy and fidelity of the reconstructed video in both RA and LD scenarios. We designed a versatile, single CNN-based post-processing network capable of adapting to coding scenarios, including RA and LD, while accommodating a wide range of QP values, from low to high. The remainder of this paper is structured as follows: a review of recent study in traditional artifact removal techniques and deep learning-based in-loop filter and post-processing techniques is presented in Section 2. Our proposed approach is detailed in Section 3. The performance evaluation and analysis are discussed in Section 4, and the paper concludes with a summary of the study in Section 5.
# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 2 Application Scenario
### Satellite Relay  
A final case where inter-vehicle cooperation helps is when the distance between robotic explorers prevents direct or mesh communication. In this case, a network of satellites can facilitate communication between distant rovers. In addition, the satellites also serve as relays for communication back to Earth.  
Thus, in the envisioned scenario, the key aspect for efficient and effective Mars exploration is the collaboration between the rovers, drones, and satellites. The next sections dive deeper into how this can be facilitated.
# Detection and Tracking of Weak Exoatmospheric Target with Elliptical Hough Transform
## 4 Elliptical Hough Transform
### Detection and False Alarm Probabilities
Actually, the detection performance relates to many elements, such as the primary threshold \(\eta\), secondary threshold \(\eta^{\text{Hough}}\), SNR, resolution cell of discretization \(\Delta a\times\Delta b\times\Delta\theta\), total processing Plane-constrained integration \(N\Delta t\), radar measurement errors, and estimation accuracy of early-warning information, etc. Most of these statistics do not have analytical expressions and need massive Monte Carlo simulations to give a quantitative evaluation.
## 3 Testing Results and Analysis
### Triaxial UU Test
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**CU test**} \\ \hline  & mudstone & \multicolumn{2}{c}{ mudstone–cement} \\  & C = 0\% & C = 4\% & C = 8\% \\ \(\sigma\) & 85 & 147 & 167 \\ \(\phi\) (\({}^{\circ}\)) & 25.79 & 44.37 & 44.35 \\ \(\tau\)f (kPa) & 104.33 & 186.13 & 206.10 \\ \hline  & \multicolumn{3}{c}{**UU test**} \\ \hline  & mudstone & mudstone–cement & \\  & C = 0\% & C = 4\% & C = 8\% \\ c (kPa) & 70 & 154 & 256 \\ \(\phi\)(\({}^{\circ}\)) & 27.26 & 42.72 & 43.13 \\ \(\tau\)f (kPa) & 90.61 & 190.94 & 293.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Shear strength calculated by CU and UU test results. Figure 15: Cohesion values and internal friction circle of the samples for triaxial Unsaturated Unconsolidated Undrain tests: (**a**) mudstone; (**b**) mudstone–cement.
# Research on Multiple-Axis Contour Error Suppression Method Based on Composite Layered Control
## 1 Introduction
Therefore, this paper proposes a multi-axis contour error suppression method based on composite layered control. While ensuring the accuracy of single-axis positioning, the 3D spatial machining contour is divided into upper and lower layers. After layering, different controller designs can be used for different contour layers. For the creation of a fuzzy PID controller for the XY lower contour, compared to traditional PID control methods, a fuzzy PID controller can adjust the three control parameters of the PID in real-time to cope with complex and strongly nonlinear multi-axis linkage servo control systems. Designing a simple and direct proportional controller for the upper contour further improves the overall contour accuracy. Because the hierarchical control method has certain analogies in multi-axis systems, the design method of composite hierarchical controllers can be further extended to six-axis or even seven-axis servo motion control systems. The composite layered controller design method dramatically improves the flexibility of controller design. For the planning and accuracy requirements of different trajectories in actual machining processes, different controller combinations can be designed to meet performance indicators. Moreover, the design of XY lower-level dual-axis controllers has been studied by a large number of scholars, and the contour error compensation methods for dual-axis control systems have become mature, which is more conducive to the controller design of multi-axis and multi-layer systems. In summary, the contour layered control method is of great significance for improving the machining accuracy of multi-axis CNC machining machines. In response to such problems, this paper designs a sliding-mode disturbance observation controller based on an improved S-type function, which improves the accuracy of single-axis positioning while suppressing the generation of tremors. Based on this, a composite layered cross-coupling controller design is proposed, which designs different controllers for different layers. The overall contour accuracy is further improved by improving the accuracy of a single plane contour. Finally, the effectiveness of the proposed method is verified through experiments.
# Insights into Bending Stiffness Modeling of Elementary Flexure Hinges
## 2 Nonlinear Bending of a Straight Beam
### Boundary Conditions  
A beam with a generic double symmetric profile along the axis direction \(x\) and a cross-section with a rectangular shape in the \(yz\) plane is considered.  
A natural consequence of such a choice is considering elastic deformations only within the \(xy\) plane. Given the double symmetry of the beam profile along its axis, it is convenient to place a reference system in the center of symmetry, as shown in Figure 4. It follows that \(\widetilde{x}\in[-1;1]\).  
In order to study Equation (10), boundary conditions must be imposed. The study case of a beam fixed at one end and free to move at the other end is proposed. These two conditions are summarized as:  
\[\begin{split}&\widetilde{\theta}(-1)=0\\ &\widetilde{\theta}^{\prime}(1)=\frac{x_{0}M_{0}\tilde{M}(1)}{EI_{ 20}\tilde{I}_{z}(1)}\end{split}\, \tag{13}\]  
where the second relation is obtained by applying a bending moment \(M(x)=EI_{z}(x)\kappa(x)\) with \(M(x)=M_{0}\tilde{M}(\tilde{x})\). From Equations (11) and (13), the analytical solution of a beam subjected to static loads is obtained:  
\[\widetilde{\theta}(\tilde{x})=\int_{-1}^{\tilde{x}}\left[\frac{x_{0}M_{0} \tilde{M}(1)}{EI_{20}\tilde{I}_{z}(\tau)}+c_{2}\frac{\tau-1}{\tilde{I}_{z}( \tau)}\right]d\tau. \tag{14}\]
## 4 Software
### Event Broker Tools Comparison
#### 4.2.4 Apache Kafka  
There are various advantages of Apache Kafka [46]. For instance, Apache Kafka allows senders and receivers to operate independently at separate times; supports with multiple programming languages, such as Java, C, Python, and front-end languages, such as HTML, CSS, and JavaScript; moreover, it employs data encryption both at rest and in transit, allows the processing of data from a variety of sources in real-time, and has minimal delay between input and feedback. In addition, Apache Kafka allows messages and data to integrate directly into applications using APIs, provides additional features and functionalities in the form of prepackaged integrations and plug-ins, and it actively monitors the status of work stations.  
Apache Kafka is capable of handling high-velocity and high-volume data, it has replication features that enable the persistence of messages on a cluster [46], and it has Low latency even when handling a large amount of messages [47]. Moreover, Apache Kafka is resistant to failures or crashes within a cluster, the messages become easily accessible to anyone with access [46], and it has capabilities, like replication and distributed partitioning, that allow Kafka's distributed architecture [47]. Finally, Apache Kafka is a scalable software product as it can handle an extremely large amount of messages [46].  
With the main strengths of Kafka presented, the disadvantages of Kafka vary, from the lack of management and monitoring tools, its high performance dependence on whether the message requires additional processing, that it can only match an exact topic name, and that it does not support wildcard topic selection, which can make many use cases unreliable. With the message size increase, the brokers and consumers will compress the messages, which leads to an increase of used memory. The tool performance may also be affected as the number of queues in a cluster increase. Finally, Kafka still lacks some message paradigms, such as request/reply or point-to-point queues [47,48].
###### Abstract  
Slope deformation monitoring is the prerequisite for disaster risk assessment and engineering control. Terrestrial laser scanning (TLS) is highly applicable to this field. Coarse registration method of point cloud based on scale-invariant feature transform (SIFT) feature points and fine registration method based on the k-dimensional tree (K-D tree) improved iterative closest point (ICP) algorithm were proposed. The results show that they were superior to other algorithms (such as speeded-up robust features (SURF) feature points, Harris feature points, and Levenberg-Marquardt (LM) improved ICP algorithm) when taking the Stanford Bunny as an example, and had high applicability in coarse and fine registration. In order to integrate the advantages of point measurement and surface measurement, an improved point cloud comparison method was proposed and the optimal model parameters were determined through model tests. A case study was conducted on the left side of the K146 + 150 point at S236 Boshan section, Shandong Province, and research results show that from 14 August 2018 and 9 November 2019, the overall deformation of the slope was small with a maximum value of 0.183 m, and the slope will continue to maintain a stable state without special inducing factors such as earthquake, heavy rainfall and artificial excavation.  
terrestrial laser scanning (TLS); slope deformation monitoring; point cloud registration; improved point cloud comparison method +
Footnote †: journal: Applied  
Article
# Application of Terminal Audio Mixing in Multi-Bandwidth End-to-End Encrypted Voice Conference +
## 2 Homomorphic Encryption
### Homomorphic Mixing  
Once the server receives the encrypted data from all three users, it performs an additive homomorphic operation to combine the ciphertexts. This results in the mixed encrypted audio data E(110110100110011100100111101).
# Comparison of Thermal Response for RF Exposure in Human and Rat Models
## 4 Discussion
The thermal time constant obtained here would also be useful to interpret the experimental data discussing the thermal and non-thermal effects of RF fields, because the temperature elevation may not always be monitored in the measurements [58; 59; 60].
# Development of Vibration Control Structure on Suspended Ceiling Using Pulley Mechanism
## 5 Simulation Analysis  
### Simulation Model  
With the aim of observing the verification of the structural modeling using equivalent truss elements for the wire-cable, a simulation analysis was carried out and the modeling results were compared with the experimental results. The 3D-frame simulation model of the experimental test specimen was constructed by using the STructural Earthquake Response Analysis 3D (STERA 3D Version 10.8) software, which is a finite element-based program developed by one of the authors considering the material and geometric nonlinearities [33]. In the model, as illustrated in Figure 20, the steel beam element is presented by a line element and two nonlinear flexural springs at both ends. The steel column element is presented by a line element with nonlinear vertical springs in both end sections to consider nonlinear axial-moment interaction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**PGA** & **Case C** & **Case CW0** & **Case CW60** & **Case CWD0** & **Case CWD60** \\ \hline
1 m/s/s & 0.29 & 0.1 & 0.11 & 0.17 & 0.13 \\ \hline
2 m/s/s & 0.39 & 0.13 & 0.18 & 0.19 & 0.14 \\ \hline
3 m/s/s & 0.46 & — & 0.27 & 0.2 & 0.17 \\ \hline Average & 0.38 & 0.11 & 0.19 & 0.19 & 0.15 \\ \hline \end{tabular}
\end{table}
Table 4: The response ratio of maximum acceleration to the Case CB under major PGAs.  
Figure 19: Amplification factor for different PGAs.  
Figure 18: Acceleration response of the SCS surface under simulated wave: (**a**) Maximum acceleration for different PGAs; (**b**) acceleration time histories under PGA 2 m/s/s.  
Figure 21 shows the simulation model in the STERA 3D. The ceiling panel is suspended 1500 mm from the supporting beams, and the model was analyzed applying the fixed-ended connection to all the joints. In addition, recorded acceleration data at the shake table surface during the excitation were used as the input seismic motions.  
Figure 22 shows the mechanical model of the bilinear and non-linear viscous damper. The friction force, \(F_{f}\), generated on pulley-wire contacts, was modeled as one bilinear model, and the yield strength, \(F_{fy}\), was estimated by multiplying the number of pulleys and the friction coefficient, using Equation (10). The initial and second stiffnesses of the bilinear hysteresis are \(K_{0}\) and \(K_{1}\), respectively. The hysteresis behavior of the fluid viscous damper is modeled based on the element test results in Figure 10. The damping force \(F_{d}\) is quantified in proportion to an exponential \(\alpha\) of the velocity \(\dot{d}\) (Equation (11)).  
The technical parameters for the one set of the pulley-damper system in the simulation model are summarized in Table 5. The damping factor in the SCS was predicted using a free-vibration range during shake table excitation, and 1.5% was selected for the inherent damping of the SCS by the logarithmic attenuation method.
# TRAP: A Three-Way Handshake Server for TCP Connection Establishment
## 5 Evaluation
### Discussion  
The performance overhead occurs during the time a client receives a command, and the TCP connection being redirected to the specified server. Compared to regular TCP connection establishment, a TRAP-enabled server requires the TCP three-way handshake process one more time. This may be affected by network latency, which we are unable to measure in our evaluation.  
The redirection process affects all new future connections, and all new TCP connections towards the specified resource are automatically being handed off to the server. Hence, this overhead only happens once per client. This one-time overhead averages to 179.49 ms.  
As for the evaluation performed while undergoing a simulated DDoS attack, the results are much more promising. While it takes a long time for regular servers to transport a file to a client (2935 ms), a TRAP-enabled server and client only needs 253 ms for the connection redirection.
# The Interaction between the LEO Satellite Constellation and the Space Debris Environment
## 3 Risk Analysis of Constellation Collision  
When describing the risk of collision between the space debris environment and the constellation, it is necessary to evaluate the debris size threshold that can cause catastrophic collisions. The Energy-to-Mass ratio (\(EMR\)) is used as a measure of catastrophic collision events, which is defined as  
\[EMR=\frac{M_{imp}v_{rel}^{2}}{2M_{tar}}, \tag{1}\]  
where \(M_{imp}\) describes the mass of the debris, \(v_{rel}\) is the velocity between satellite and target debris, \(M_{tar}\) is the satellite mass. If the \(EMR\) exceeds the 40,000 J/kg, the collision is considered to be catastrophic [13], which means the satellite is completely broken. Through the analysis of the historical collision event, taking the average velocity 10 km/s, it is calculated that the mass of the target debris in the catastrophic collision is 0.210 kg, assuming that the target debris to be sphere [14], and the density is 2.8 g/cm\({}^{3}\), which is similar to that of the aluminum material. The diameter threshold of the catastrophic collision is about 6 cm, which is smaller than the detectable threshold, 10 cm. Therefore, it is impossible to use the available anti-collision technology to avoid it.  
Based on the flux F calculated in the MASTER model [15], determine the average number of collisions of the satellite [16], which is defined as  
\[N=FAT, \tag{2}\]  
where \(T\) is the running time range of the satellite, and \(A\) is the collision cross-sectional area, which can be calculated according to the radius of the satellite and the target debris  
\[A=\pi(r_{tar}+r_{imp})^{2}. \tag{3}\]  
According to the average number of collisions, Poisson distribution is used to calculate the probability distribution of \(n\) collisions  
\[P_{n}=\frac{N^{n}}{n!}e^{-N}. \tag{4}\]  
The probability of no impact (\(n=0\)) is  
\[P_{nP}=e^{-N} \tag{5}\]The probability of one or more impact is  
\[P=1-P_{nP}. \tag{6}\]
## 4 Data Set Generation and Processing
### GNSS Signal Model and Types of Interference  
At the GNSS receiver front-end, the GNSS signal from the different satellites is downconverted to Intermediate Frequency (IF), sampled into a digital sequence, and finally sent to the acquisition and tracking blocks. The received signal is expressed as follows:  
\[r(t)=\sum_{i=1}^{N}s(i)+w(t)+q(t) \tag{5}\]where \(N\) is the overall number of satellites considered in this study, \(i\) indicates the ith satellite signal, _w(t)_ is the zero-mean white Gaussian noise with variance \(\sigma^{2}\), and \(q(t)\) is the interference signal. The desired GNSS signal from the \(i_{th}\) satellite is given by  
\[s_{i}(t)=\sqrt{P_{i}}D_{i}(t-\tau_{i})\cdot C_{i}(t-\tau_{i})\cdot e^{(i(2\pi(f_ {IF}-f_{d,i}t+\phi_{i})} \tag{6}\]  
where \(i\) denotes the index of in-view satellites; \(P_{i}\) is the received power; \(C_{i}()\) and \(D_{i}()\) are the \(\pm 1\)-valued pseudorandom spreading code and navigation message, respectively; and \(\tau_{i}\), \(f_{IF}\),\(f_{d,i}\), and \(\phi_{i}\) are the code delay introduced by the channel, \(IF\) used by the receiver front-end, carrier Doppler frequency, and carrier phase, respectively. Three type of interferences are considered. Wideband and narrowband chirp interferences are represented by  
\[q(t)=\sqrt{P_{j}}e^{2\pi f_{imt}t+\pi\frac{f_{max}-f_{min}}{i_{exp}}t^{2}+\phi _{j}} \tag{7}\]  
where for wideband \(f_{max}\) and \(f_{min}\) are 1.57542 GHz (i.e., \(f_{init}\)) \(\pm\) 5 MHz and \(T_{swp}\) is 5 us. For narrowband interference, \(f_{max}\) and \(f_{min}\) are 1.57542 GHz (i.e., \(f_{init}\)) \(\pm\) 20 KHz and \(T_{swp}\) is 1 ms.  
The Gaussian band interference (random noise characterized by its Gaussian normal distribution) is represented by  
\[q(t)=\sqrt{P_{j}}\sum_{i}a_{i}g(t-iT_{a})cos(2\pi f_{j}t+\phi_{j}) \tag{8}\]  
where \(g(t)\) represents the impulse response of shaping filter, \(a_{i}\) is the pseudorandom code that takes the value \(-1\), \(+1\), and \(T_{a}\) is the code duration time.  
These types of interference have been used because they are adopted in [3] and for the following reasons. Gaussian noise is introduced to simulate the background noise present in real-world environments, such as electromagnetic interference from natural and man-made sources. Wideband chirps mimic interference sources such as radar systems or broadband communication signals. Narrowband chirp interference poses a unique threat to GNSS receivers by potentially disrupting the timing synchronization process.
## 2 Problem Formulation
### UWA Communication Model  
The UWA communication model in noisy environment can be written as [28; 29]:  
\[y(i)=\sum_{j=0}^{N-1}x(i-j)h(j)+w(i),\quad i=0,\ldots,M-1, \tag{1}\]  
where \(x\), \(y\), \(w\), and \(h\) are the transmitted signal, the received signal, the sea noise, and the CIR, respectively. Thus, (1) can be rewritten by a matrix representation as:  
\[\mathbf{y}=\mathbf{X}\mathbf{h}+\mathbf{w}, \tag{2}\]  
where the received signal \(\mathbf{y}\) and the noise \(\mathbf{w}\) are the size of \(M\times 1\) in the vector form and the UWA CIR \(\mathbf{h}\) is a size of \(N\times 1\). The matrix \(\mathbf{X}\in\mathbb{C}^{M\times N}\) is the measurement matrix with a size of \(M\times N\) which is made by the transmitted signal, as follows:  
Figure 1: The sample of experimental UWA channel with a block sparse time-varying structure.  
\[\mathbf{X}=\begin{pmatrix}x(0)&x(-1)&\cdots&x(-N+1)\\ x(1)&x(0)&\cdots&x(-N+2)\\ \vdots&\vdots&\ddots&\vdots\\ x(M-1)&x(M-2)&\cdots&x(M-N)\end{pmatrix}. \tag{3}\]  
Hence, to find the sparse solution of the UWA channel \(\mathbf{h}\) in (2), the CS algorithm can be applied to reconstruct the UWA channel \(\mathbf{h}\) from the received signal \(\mathbf{y}\) for a UWA communication system.
## 3 Methodology
### SHI Construction
#### 3.2.3 SHI Assessment  
The evaluation of SHI performance is mainly considered from two aspects. Firstly, whether the change characteristics of SHI are consistent with the changing trend of the function indicator (while considering the overall trend and local characteristics); the second is to calculate the correlation coefficient of SHI and functional indicators.  
[PERSON]'s rank correlation coefficient has the widest application range and does not need the variables to conform to normal distribution [27]. Therefore, it is selected to measure the correlation between SHI and functional indicators.  
For a data sample of size \(n\), the SHI values \(A_{i}\) and function indicator \(B_{i}\) are converted to ranks \(rg(A_{i})\) and \(rg(B_{i})\), \(rg\) is an arrangement method from largest to smallest. The \(r_{s}\) is described as follows:  
\[r_{s}=1-\frac{6\sum d_{i}^{2}}{n(n^{2}-1)}, \tag{10}\]  
where \(d_{i}=rg(A_{i})-rg(B_{i})\) is the significant difference between the two ranked variables. The value of \(r_{s}\) is from \(+1\) to \(-1\). If the absolute value of \(r_{s}\) is closer to 1, it indicates a strong correlation.