esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Data Preprocessing
#### 4.1.2 Normalization and Feature Engineering  
The following step of the presented methodology is data normalization. This step is, in the case of deep neural networks, extremely important. Engine data consists of data thatmight be of diverse types and ranges. Examples of data with diverse types and precision are single, double, integer, integer8, long, etc. For neural network data processing, it is important to normalize the data. Otherwise, the design model could fail to converge or result in poor network accuracy. In this case study, the standardized dataset has a mean of 0 and a standard deviation of 1, which allows data symmetry and distribution.
# Safety Control for Satellite Formation Flying via High-Order Control Barrier Functions
## 3 Safety Control Design  
This section mainly discusses how to solve the problems mentioned above and design a safety controller for the accompanying satellite. A high-order control barrier function is first designed based on the satellite formation flying model. Then, a quadratic programming based safety control problem is formulated to calculate the safety controller. The details are presented below.
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 5 Transition Modes Process Monitoring Method
### Hard Classification and Soft Classification  
Hard classification and soft classification are defined according to whether the sub-stage is strictly separated from the process by 0 or 1 in the transition data clustering membership. [PERSON] et al. [78] proposed a \"hard classification\" method to determine the transition process among steady-state modes. In this method, two adjacent sub-period models of the process are weighted to form an overall model and approximately describe the characteristics of the transition mode.  
To better classify the transition and stable regions, [PERSON] et al. [79, 80] further proposed a \"soft classification\" method with the batch process as the research background. In this method, the degree of 0-1 fuzzy membership is used as the weight coefficient of the two sub-period models adjacent to the transition modes and the sub-period is weighted and fused.  
Both hard and soft classification methods are poor in processing ability for multi-mode batch processes due to a lack of data information on transition modes.
## 1 Introduction
The normalized received probability of signal OAM modes carried by finite energy frozen wave is independent of the quantum number of OAM modes in weak seawater turbulence, and the higher normalized received probability achieved in the smaller received diameter of the receiver, the larger the transverse size and longer the signal wavelength and the signal OAM modes [17]. An HyGG vortex beam with smaller topological charge and longer wavelength has higher transmission quality [18]. Compared with the Laguerre-Gaussian beam and the Bessel-Gaussian beam, a diffraction- and attenuation-resistant random vortex beam is a more resistant to seawater turbulence and seawater absorption and the optical communication link, which consists of a long-wave length anti-diffraction and anti-attenuation random vortex carrier and a small receiving diameter, has high channel capacity [19]. More importantly, by designing a longitudinal profile of finite energy frozen beams, we can compensate the attenuation loss of the transmittance [20]. Moreover, the localized wave of Bessel-Gaussian amplitude envelope (or Bessel-Gaussian localized vortex) is a more suitable beam for the vortex mode communication than conventional vortex waves [21, 22]. The disturbance of turbulent seawater to the OAM modes of anti-diffraction and anti-attenuation random beam with a large truncated Gaussian aperture is less than the corresponding OAM modes carried by the Laguerre-Gaussian beam [23]. The position-power Gaussian vortex is a new kind of vortex beam, which is different from all the previous research. The disturbance of seawater turbulence when the OAM mode carried by the beam is transmitted in the underwater channel is also a problem worth discussing. Furthermore, most of these studies only focus on the transport of a single OAM vortex or multiple OAM vortices with the same rotation direction in turbulent seawater. When the vortex carrier carries two OAM modes with different amplitudes and rotation directions in the absorption of turbulent seawater, whether the transmission characteristics are different from the above research conclusions, that is, whether there are more factors that can optimize the communication system. However, as far as we know, there are no reports on the channel capacity of underwater communication links with a coaxial dual-position power vortex Gaussian carrier. In this paper, the influence of coaxial double-position power vortex Gaussian carrier parameters and seawater turbulence parameters on the channel capacity and bit error rate (BER) of the communication link is studied. The remainder of this paper is organized as follows. In Section 2, we derive the random coaxial double-position power Gaussian vortex (RCDPPGV) laser based on the Huygens-Fresnel diffraction integral formula and the Rytov approximation theory. We derive the BER of OAM mode and the channel capacity of the optical communication link with a RCDPPGV carrier in Section 3. In Section 4, we discuss the effects of the parameters of turbulent absorption seawater and beam structure on the channel capacity as well as BET. Section 5 provides the main conclusions of the paper.
## 8 Conclusions  
This article proposes an active congestion avoidance transmission rate control method based on network node bandwidth allocation, establishes a joint rate control mechanism between network nodes and sending nodes, designs an initial value allocation algorithm for the transmission rate, a rate adjustment algorithm that adapts to network changes, and a substream rate allocation algorithm. The initial value algorithm for the transmission rate has a constant time complexity \(O(1)\). The rate adjustment algorithm must traverse all adjustable transmissions with a time complexity of \(O(N)\), where \(N\) is the number of transmissions the node passes. The subflow rate allocation algorithm must traverse all subflows belonging to the same transmission with a time complexity of \(O(M)\), where \(M\) is the number of subflows included in the transmission. Through simulation, it was found that in terms of the start-up speed, the transmission rate control scheme proposed achieved a link BDP faster than that for a slow start-up speed and therefore had better throughput performance even when the transmission data volume was small. In terms of congestion control, compared with controlling the transmission rate at the sending node, the network nodes could more accurately grasp the network congestion status. Therefore, the transmission rate and delay of the proposed scheme could remain stable without periodic fluctuations. Moreover, in the face of link packet loss, NewReno and Cubic can misjudge network congestion and cause throughput degradation, especially in high-latency and bandwidth networks where performance loss is severe. The proposed scheme can maintain good throughput performance. In terms of fairness, the proposed scheme not only maintained fairness in bandwidth allocation between single-path transmissions but also avoided subflows from encroaching on the bandwidth of other transmissions through shared bottlenecks during multipath transmission.  
Conceptualization, H.L., H.N. and R.H.; methodology, H.L., H.N. and R.H.; software, H.L.; writing--original draft preparation, H.L.; writing--review and editing, H.L. and R.H.; supervision, H.N. and R.H.; project administration, H.N. and R.H.; funding acquisition, H.N. and R.H. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Strategic Priority Research Program of Chinese Academy of Sciences: Information Collaborative Service and Data Sharing (Grant No. XDA031050100).  
The data presented in this study are available on request from the corresponding author.  
We would like to express our gratitude to [PERSON], [PERSON], [PERSON], and [PERSON] for their meaningful support for this work.  
The authors declare no conflict of interest.
## 3 Proposed Solution to Predict the AoA
### Curvature-Adaptive Kernel for Non-Linear Mobility
#### 3.3.5 Enhanced MUSIC AoA Estimation with Predictive Window Constraints  
In standard implementations, MUSIC-based AoA estimation requires scanning the entire angular domain, which introduces high computational latency, particularly in distributed O-RAN systems. To overcome this challenge, our framework uses the fused AoA prediction \(\hat{\theta}_{pred}\) from the DE-ACSW-R pipeline to dynamically restrict the angular search region for MUSIC.  
Specifically, the enhanced MUSIC algorithm scans only a localised angular window, defined as follows:  
\[\Theta_{local}=\{\theta|\theta\in[\hat{\theta}_{pred}-w,\hat{\theta}_{pred}+w].\} \tag{24}\]  
where \(\hat{\theta}_{pred}\) is the current fused prediction (from LR and SVR fusion) and \(w\) is a tunable half-width (e.g., 10\({}^{\circ}\)).  
Within this window, the MUSIC pseudo-spectrum is computed as follows:  
\[P_{music}(\theta)=\frac{1}{a^{H}(\theta)\ E_{n}E_{n}^{H}\ a(\theta)} \tag{25}\]  
where \(a(\theta)\) is the array steering vector at an angle theta and \(E_{n}\) is the noise eigenvector matrix.  
By limiting the search to \(\Theta_{local}\), rather than the entire angular range, the proposed method significantly reduces computation time while retaining the high-resolution characteristics of MUSIC, especially when the user's mobility is smooth or predictable. If the system detects high uncertainty or abrupt mobility, it can revert to a broader or full-range search as needed.
# Multi-Band Unmanned Aerial Vehicle Antenna for Integrated 5G and GNSS Connectivity
## 3 Conclusions  
A dual-port FSS-based MIMO antenna was developed for outdoor 5G-GNSS unmanned aerial vehicle communication. An inverted T-shaped single-patch element was orthogonally placed to avoid mutual coupling and achieve self-decoupling. A square-ring FSS was implemented using an array of 4 \(\times\) 11-unit cells. The presented 1 \(\times\) 2 array  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Antenna** \\ **Type** \\ \end{tabular} } & \multirow{2}{*}{**Substrate Material**} & \multirow{2}{*}{\begin{tabular}{c} **Resonant** \\ **Frequency** \\ **(GHz)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **S\({}_{11}\) (dB)** \\ **Gain (dB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Aial** \\ **Ratio** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **ECC (S-** \\ **Parameter)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **(Radiation** \\ **Pattern)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **DG** \\ **MEG** \\ \end{tabular} } & \multirow{2}{*}{**MEG**} & \multirow{2}{*}{**VSWR**} \\ \cline{3-3} \cline{5-10}  & & 1.28 & & & 2.2871 & 1.509 & - & - & - & 1  
antenna operates in the dual-frequency bands of 1.28 GHz (GNSS) and 4.58 GHz (5G) with dual polarization. It achieved a reflection coefficient S\({}_{11}\) < \(-\)10 dB and a mutual coupling S\({}_{21}\) < \(-\)20 dB. At a lower frequency, the MIMO + FSS antenna enhanced the gain by 5.4 dBi, with an AR of 1.10 dB, and operated under circular polarization. The higher frequency achieved a gain of 6.43 dBi with an AR of 4.4 dB, achieving linear polarization. The MIMO parameters, such as the ECC < 0.5, DG < 9.9, MEG < \(-\)3 dB, and TARC < \(-\)10 dB, were within the limits. The measured and simulated results of S\({}_{11}\) were good; therefore, this antenna is suitable for outdoor UAV applications.  
Conceptualization, S.G.; data curation, M.C.; methodology, R.A.; software, K.S.; validation, M.C. and R.A.; visualization, S.S.; writing--original draft preparation, S.G.; writing--review and editing, R.A. All authors have read and agreed to the published version of the manuscript.  
We would like to thank and acknowledge the IIT Tirupati Navavishkar I-Hub Foundation (IITTNiF), the National Mission on Interdisciplinary Cyber-Physical Systems (NM-ICPS), and the Department of Science and Technology (DST), India, for their financial support (SASTRA-IITTNiF/5 GNP/2024-25) and SASTRA Deemed University, India, for providing the necessary resources and support.  
The authors declare that any data related to this research will be made available upon request.  
The authors confirm that there are no known conflicts of interest associated with this publication and that there was no significant financial support for this work that could have influenced its outcome.
## 3 Results and Discussion
### Formation Mechanism  
Based on the above experimental results, it can be clearly concluded that the plasma electrolysis sprayed Al\({}_{2}\)O\({}_{3}\) coating was able to improve the tensile strength and thermal conductivity of the quartz fiber. The formation mechanism of the Al\({}_{2}\)O\({}_{3}\) coating has not been fully understood yet. We proposed that the extremely strong potential between the anode and the cathode led to the formation of Al(OH)\({}_{3}\) by AlCl\({}_{3}\) electrolysis, and the Al(OH)\({}_{3}\) gradually evolved into the Al\({}_{2}\)O\({}_{3}\) under the thermal effect from the plasma arc.  
Figure 6 shows the formation mechanism of the Al\({}_{2}\)O\({}_{3}\) coating on the surface of the quartz fiber fabric. When the working voltage was gradually increased, a hydrogen evolution reaction occurs on the surface of the cathode to release a large amount of H\({}_{2}\). When the working voltage reaches a critical voltage, the breakdown of the vapor envelope causes the plasma to discharge. Electrolyte electrolysis forms a large amount of Al(OH)\({}_{3}\). As the voltage continues to increase, a stable, uniform, continuous plasma arc was formed, along with the electrolyte ejected from the nozzle. After mechanical compression and thermal compression of the plasma arc, Al(OH)\({}_{3}\) dehydrates to form Al\({}_{2}\)O\({}_{3}\) on the surface of the quartz fiber. The reactions involved are suggested as follows:  
Electrolysis of AlCL\({}_{3}\) and H\({}_{2}\)O  
\[\rm{AlCl}_{3}\rightarrow\rm{Al}^{3+}+\rm{Cl}^{-} \tag{1}\]  
\[\rm{H}_{2}O+2e^{-}\rightarrow\rm{H}_{2}+2 OH^{-} \tag{2}\]  
Formation of hydroxides  
\[\rm{Al}^{3+}+\rm{OH}^{-}\rightarrow\rm{Al}(OH)_{3} \tag{3}\]  
Al\({}_{2}\)O\({}_{3}\) formation  
\[\rm{Al}(OH)_{3}\stackrel{{\rm{plasma}}}{{\rightarrow}}\rm{Al}_{2 }\rm{O}_{3}\,+\,\rm{H}_{2}O \tag{4}\]  
Figure 5: The tensile strength (**a**) and the thermal conductivity (**b**) of the bare and the coated quartz fibers.  
Figure 6: The schematic diagram of the formation mechanism of the Al\({}_{2}\)O\({}_{3}\) coating on quartz fiber fabric, (**a**) H\({}_{2}\) bubble and micro arc formation, (**b**) stable plasma jet formation, and Al\({}_{2}\)O\({}_{3}\) coating deposited on the quartz fiber.
## 3 Results and Discussion
### Silver Loss from PVA/BC/C-Ag Composite Membranes  
The silver bleed curve of the PVA/BC/C-Ag composite membrane is presented in Figure 6. According to the national drinking water standard (GB5749-2006, China), the content of silver in drinking water should not exceed 0.05 mg/L. After 30 days of continuous immersion of the composite membrane, silver concentrations in the water remained lower than the national drinking water standard (Figure 6a). Immersion was repeated 10 times, and the silver loss remained below the standard after each use (Figure 6b). The PVA/BC/C-Ag composite membrane therefore appears highly suitable for practical antibacterial water treatment.  
Figure 5: Effect of BC (**a**) and C-Ag (**b**) content on antibacterial ability of composite membranes.
## 3 Results and Discussion
### Dial Vertical Migration
The maximum average velocity speed of upward vertical movement was 5 mm s\({}^{-1}\) at the center of the SSL. It started moving downward slowly at sunrise and reached the peak at \(-\)5 mm s\({}^{-1}\). This slow movement of zooplankton in the observed area was due to their small size. Mean vertical velocity at sunset was slower than at sunrise. The dashed line inside the echogram in Figure 10 indicates the center of the SSL and the average of vertical velocity speed. During the migration period, the average MVBS value ranged between \(-\)75 to \(-\)70 dB with vertical velocity varied between 2 to 5 mm s\({}^{-1}\) with downward movement at sunrise and 2 to 5 mm s\({}^{-1}\) with upward movement at sunset. The vertical velocity profiles were slightly lower on April 16 throughout the day, which was related to the lower MVBS value. It was assumed that the zooplankton moved to another area outside the detection area of moored ADCP. During 11-17 April 2016, sunrise and sunset occurred between 5:37 and 17:43 (UTC +8), respectively. Diel vertical migration started during this time. A typical DVM pattern of the acoustic scattering layer is seen in the echogram at 11.5-16 m of depth with upward vertical movement to shallower depth  
Figure 10: The dashed line shows the center of the sound scattering layer on the echogram and the vertical velocity average at the vertical velocity profile at depths 11.5–16 m.  
sunset at 17:43, while downward vertical migration to a deeper depth about 20 min before sunrise at 5:37. The duration of DVM was repeated every day based on the MVBS echogram and vertical velocity profile, and it related to the diurnal cycle. Based on this analysis, it was assumed that DVM was triggered by the sunlight penetration to the water column, depending on solar irradiance. However, the irradiance data in the observed area were not measured directly, and temporal average solar irradiance data was used instead, obtained from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resource (POWER), close to the ADCP position. The all significant MVBS peaks are related to the minimum value of irradiance. The day-time MVBS at 13 m and irradiance are shown in Figure 11. This result probably indicates that the MVBS variation correlated with irradiance which triggered the DVM.
# Semi-Direct Point-Line Visual Inertial Odometry for MAVs
## 4 Experimental Results and Analysis
### Computational Performance  
To evaluate the computational performances of SDPL-VIO, the average times taken for tracking and marginalization were measured and analyzed.  
Figure 12: The comparison of translation errors on the MH01, MH05 and V202 datasets.
# Application of Label Correlation in Multi-Label Classification: A Survey
## 4 Applications of Label Correlation in MLC Methods
### Global MLC Methods Considering Only Negative Label Correlation  
[PERSON] et al. [73] were the first to explore the application of negative label correlation in MLIC. Their extensive experiments on real-world visual classification tasks demonstrated that incorporating negative label correlation significantly enhances classification performance. Subsequently, many researchers have begun to investigate the potential for further integrating negative label correlation in the field of MLC. [PERSON] et al. proposed the LELR [73] method, which constructs a weighted graph \(G=<V,E>\) to describe the mutual exclusivity between labels, where the edge set \(E\) represents the mutual exclusivity between pairs of nodes. The weight matrix \(W\) associated with graph G is set according to the following rule: if labels \(l_{i}\) and \(l_{j}\) do not appear together in any training image, then \(W_{ij}\) is set to 1; otherwise, it is set to 0. Then, the graph shift method is used to find dense subgraphs in graph G, where each set of nodes in a dense subgraph is considered as a mutually exclusive label group. The LELR method employs a linear representation approach with a sparse-induced regularizer for MLIC. Given a multi-label query image and learned mutually exclusive label groups, the LELR method assigns each group's labels mutually exclusively to the query image. This dense subgraph search method automatically learns mutually exclusive label sets from the training data. However, this method does not account for the more frequently occurring positive label correlations, which somewhat limits its classification performance.
## 5 Performance Evaluation
### Simulation Results
#### 5.2.4 Changes in Duty Cycle vs. Packet Collision  
As the duty cycle or active time of a node's radio increased, the probability of multiple nodes waking up simultaneously also increased. This phenomenon can increase the number of packet collisions. These collisions could occur between data packets, data packets and ACK packets, or ACK packets. A comparison of the packet collision performances of the protocols is shown in Figure 10. Based on the figures, the number of collided packets also increased with the duty cycle. With increasing duty cycles, the proposed FCM-OR protocol exhibited fewer collisions compared with the existing protocols (ORW and ORR). The simulation was executed at a high node density.  
Figure 10: Comparison of the number of collisions per transmission with varying duty cycles in highly dense sensor fields (number of nodes = 900).  
Figure 9: Comparison of the network lifetime with varying non-uniform densities and duty cycles (for a single-packet transmission).
## 2 Omni-Directional SGM with Reliable Cost Propagation
### Omni-Directional SGM
#### 2.2.2 Integrate Results from Multiple Directions  
Our method performs cost aggregation along tree structures in four directions, since we have four outputs for each pixel, namely \(L_{T}^{k}\) with \(r^{k}\in\{(1,0),\)\((-1,0),\)\((0,1),\)\((0,-1)\}\), \(k=1,2,3,4\). The final aggregated cost of our omni-directional SGM, \(L^{\text{ad}}\), is the sum of outputs in four directions. For pixel \(\mathbf{p}\) at disparity \(d\), we have  
\[L^{\text{ad}}(\mathbf{p},d)=\sum_{k=1}^{4}L_{T}^{\text{rl}}(\mathbf{p},d). \tag{10}\]  
Figure 3e presents pixels contributing to the aggregated cost of root node in our method. We can see that root node gains supports from all pixels in the reference image and any pixel in the image contributes to the output of root node only once. Figure 3f-h illustrate the ways of information propagated in SGM variants. Figure 3f describes traditional SGM along eight directions. As explained in Ref. [30], two adjacent pixels are loosely related for that they only share the pixels on the same scan line. When matching costs on this line are weak, different disparities could be generated for multiple passes, resulting in streak artifacts in the disparity image. However, all pixels in the reference image contribute to the aggregated cost of root node, and a huge number of pixels are shared by neighboring root nodes, enhancing the reliability of aggregated cost in homogeneous area, since streak artifacts can be reduced in our result. Figure 3g shows the simple tree structure in Ref. [33]. The 1D optimization is performed along rows at first and then along columns. Although they utilized two tree structures which are complementary with each other, streak artifacts still appear in the disparity image. Figure 3h presents the minimum tree structure used in Ref. [34]. Neighboring pixels may have large distance on the MST, so that useful information cannot effectively propagate across the entire image, resulting in noisy disparity image.