esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Coverage-Preserving Control Scheduling Scheme
### Determination of Redundant Nodes
Algorithm 1 is described as follows:  
```
Input: initialize the sensor node set \(S\) = {\(N_{1}\),\(N_{2}\),...,\(N_{i}\),...} and target area \(R\); Require: redundant node set \(RS\), maximum independent set \(MIS\), temporary redundant node set \(TS\), temporary active node set \(AS\). Output: minimum connected cover set (\(MCS\)) of \(R\). 1. \(RS\) = \(AS\) = \(\varnothing\);
2. \(TS\) = \(S\);
3. for each node \(N_{i}\) \(\in\)\(IS\) do
4. calculate the node redundancy degree;
5. broadcast the HELLO message to its neighbors, and receive the reply;
6. update the neighbor nodes list;
7. obtain the categories of the neighbor nodes;
8. end for;
9. if (\(R\cup_{N_{i}\in MCS}\rho\left(N_{i},R_{i}\right)\) then
10. return \(MCS\) = \(\varnothing\);
11. end if;
12. while (TS \(\
eq\varnothing\))
13. \(MIS\) = \(\varnothing\);
14. obtain the maximum redundancy degree {\(max_{N_{i}\in TS}\{E[\phi_{i}]\}\)};
15. for each node \(N_{i}\) do
```  
**Algorithm 1**Determination of redundant nodes
###### Abstract  
The description and assessment of braided river architecture are usually limited by the paucity of real geological datasets from field observations; due to the complexity and diversity of rivers, traditional evaluation models are difficult to apply to braided river systems in different climatic and tectonic settings. This study aims to establish an architectural model suitable for the study area setting by introducing a hierarchical analysis approach through well-exposed three-dimensional outcrops along the Second Songhua River. A micro-macro four-level hierarchical framework is adopted to obtain a detailed anatomy of sedimentary outcrops: lithofacies, elements, element associations, and archetypes. Fourteen lithofacies are identified: three conglomerates, seven sandstones, and four mudstones. Five elements provide the basic components of the river system framework: fluvial channel, laterally accreting bar, downstream accreting bar, abandoned channel, and floodplain. Four combinations of adjacent elements are determined: fluvial channel and downstream accreting bar, fluvial channel and laterally accreting bar, erosionally based fluvial channel and laterally accreting bar, and abandoned channel and floodplain. Considering the sedimentary evolution process, the braided river prototype, which is an element-based channel filling unit, is established by documenting three contact combinations between different elements and six types of fine-grained deposits' preservation positions in the elements. Empirical relationships are developed among the bankfull channel depth, mean bankfull channel depth, and bankfull channel width. For the braided river systems, the establishment of the model promotes understanding of the architecture and evolution, and the application of the hierarchical analysis approach provides a basis for outcrop, underground reservoir, and tank experiments.  
Songliao Basin; braided river; hierarchical approach; architecture; bankfull channel width; bankfull channel depth
# Hybrid Beamforming in Massive MIMO for Next-Generation Communication Technology  
[PERSON]  
1 School of Electronics and Electrical Engineering, Lovey Professional University, Phagwara 144411, India; [EMAIL_ADDRESS] (S.H. ); [EMAIL_ADDRESS] (A.G.)  
[PERSON]  
1 School of Electronics and Electrical Engineering, Lovey Professional University, Phagwara 144411, India; [EMAIL_ADDRESS] (S.H. ); [EMAIL_ADDRESS] (A.G.)  
[PERSON]  
1 School of Electronics and Electrical Engineering, Lovey Professional University, Phagwara 144411, India; [EMAIL_ADDRESS] (S.H. ); [EMAIL_ADDRESS] (A.G.)  
[PERSON]  
2 Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India; [EMAIL_ADDRESS]  
[PERSON]  
3 Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications, and Information Technology, National University of Science and Technology Politechnica, 061071 Bucharest, Romania 3*  
and open the door to a wide range of informational content. In this way, 5G is paving the way for fully digitizing and linking the world. As a result, telecom networks are conducting research into key 5G and beyond technologies for essential certification and confirmation, as stated by the authors of [2] in their study, where they outlined the requirements for a 5G network, such as high-frequency spectra with a wide bandwidth, base station densification, and a significant number of antennas to facilitate efficient data transfer [3]. Reduced device power consumption, path loss, better coverage area, greater data rates, increased spectral efficiency, and interference suppressing are a few of the design considerations in 5G. To achieve these requirements, various algorithms and methodologies have been designed and are being used, for example, the cutting-edge access techniques Beam Division Multiple Access (BDMA), which is the technique where a broader beam is divided into multiple narrower beams via user scheduling and Non- and Quasi-Orthogonal or Filter Bank Multicarrier (FBMC) Multiple Access and many other hardware architectures for practical implementation, along with the above-mentioned technologies, massive MIMO has a primary position in techniques used to achieve the above-mentioned requirements. Massive MIMO is a technique where the base station is employed with hundreds of antennas which, in turn, employs the beamforming. By precisely balancing the magnitude and phase of each antenna signal, beamforming uses several antennas to control the direction of wavefronts. Some of the beamforming technologies and their contributions are briefly mentioned in Table 1.  
The very near future of wireless technology is 6G, with some important characteristics [14] presented in Table 2.
## 1 Introduction
### Contributions  
In summary, we contribute to medical imaging and display in the following ways:  
* We propose a video player that allows the user to selectively convert a portion of a long-shot McSL video into free viewpoint images to minimize time and data until playbacks.
* We propose a multi-frame multi-view feature matching strategy to estimate intrinsic and extrinsic camera parameters from a set of McSL video frames. This process is always required after the independent movement of internal cameras (e.g., when surgeons touch the surgical light during the operation). We also analyze the number of frames to achieve stable calibration results using real surgical videos.
* With the robustly estimated camera parameters, we demonstrate a 3D mesh and a recently emerged 3D multi-layer reconstruction. The latter enables disocclusion rendering to remove foregrounds in the generated 3D scene representation for better surgical field visibility.
* We reviewed comments from surgeons to discuss the differences between those 3D representations with respect to medical usage.
# Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China
## 2 Method
### Data Description
#### 2.1.4. SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 7  
Previous studies show that GNSS signals reflected from the surface are sensitive to changes in soil moisture, and the soil moisture also changes the SR [22]. This soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. The data are on a regular latitude/longitude grid, with predictable disparities in space and time. We used the soil moisture data to plot a time series of mean snow depth with SMAP mean soil moisture with different land cover types on the Tibetan Plateau. For more detail, please see the following. By analyzing the variations in soil moisture over the same period, the effect of SR changes due to soil moisture can be excluded; thus, we believe that the variations of SR are due to the changes of snow coverage.
## 3 WSAN System Models
### Performance Metrics
This problem occurs in a wireless network when two nodes that are not visible to each other transmit their packets to a third node that is visible to the formers. This will lead to the loss of the packet [11]. For example, when the CBR message of node 7 and the RREP message of node 9 are delivered to node 8 at the same time, the packet collision occurs due to the hidden node problem. Hence, the source node tries to re-establish the route by sending another RREQ message until the RREP returns to the source. When the CBR rates are increased from 1 to 2.25 Mbps, the number of successfully received packets is getting decreased. Higher CBR rates indicate higher chance of packet collisions. The source node continues retrying to re-establish the route; although, the successful data packets are lower when the CBR is higher. When the CBR rate is higher than 2.25 Mbps, the upper path becomes faster than the lower path. The AODV changes to select the upper path more often. There is no CBR traffic flow on this upper path; consequently, the communication performance can be recovered on this path. In addition, we have found that on increasing the CBR rates from 2.25 to 3.25 Mbps, CBR packet loss due to buffer overflow at the node ID 7 has occurred at time approximately 2,500 s, 2,000 s, 1,250 s, 500 s, and 500 s  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**CBR Rate** & **Number of Packets Sent** & **Number of Packets Received** \\ (**Mbps**) & **at the Source** & **by the Sink** \\ \hline
0.00 to 0.50 & 107 & 107 \\
0.75 & 107 & 105 \\
1.00 & 107 & 104 \\
1.25 & 99 & 93 \\
1.50 & 82 & 71 \\
1.75 & 69 & 54 \\
2.00 & 40 & 21 \\
2.25 & 14 & 9 \\
2.50 & 78 & 73 \\
2.75 & 94 & 91 \\
3.00 & 104 & 103 \\
3.25 respectively. The buffer is full due to the excessive transmission rate, the queue can manage. When the first packet is dropped, all packets arrived after the queues are also dropped. As a result, the amount of CBR packets transmission is largely decreased between node IDs 7 and 8. This will lead to reduce in the packet collisions due to the hidden node problem, especially at node IDs 7, 8, 3 and 9.
## 2 Materials and Methods
### Statistical Analysis  
Alpha diversity indices Chao1, Abundance-based Coverage, Dominance, Equitability, Goods Coverage, observed OTUs, Shannon and Simpson were calculated using a PAST software package (v.4.09 [[https://www.nhm.uio.no/english/research/resources/past/](https://www.nhm.uio.no/english/research/resources/past/)]([https://www.nhm.uio.no/english/research/resources/past/](https://www.nhm.uio.no/english/research/resources/past/)) (accessed on 3 June 2022)) to reflect the diversity and richness of the fungal communities in the different samples (L1, L2, L3 and L4).  
The community structure was analyzed based on the relative abundance at different classification levels. The relative abundance of individual taxa within each sample was estimated by comparing the number of sequences assigned to a specific taxon with the number of total sequences obtained for that sample. For this purpose, Microsoft(r) Excel(r) 2016 software was used to process the raw OTU table, and to analyze and visualize the results. Due to the high number of taxa present in each sample, only the 20 most abundant were detailed in the graphs.  
Beta diversity analysis was performed using principal coordinate analysis (PCoA) ordination, based on the Bray-Curtis's dissimilarity matrix, generated from the relative abundance of fungal genera, and the unweighted pair-group method with arithmetic means (UPGMA) clustering were both performed using a PAST software package v.4.09. Using the same software, differences between the community structures within a site were tested by permutational multivariate analysis of variance (PERMANOVA) and further analyzed through a SIMPER test.  
Between samples, a Venn analysis was constructed to identify unique and common fungal genera using an InteractiVenn online tool [54]. Prevalence within each sample for the 20 most abundant genera was evaluated in a Clustvis web tool through a Heatmap [55].
## 4 Experiment and Results
### Test Results and Analysis at Typical Frequency Point  
The bandwidth of the most sensitive frequency offset of tested navigation receiver is \(\pm 2\) MHz. Therefore, two different criteria are used to measure the sensitivity twice, respectively, at the typical frequency points within the working bandwidth. The output power of navigation signal simulator is set as \(-90\) dBm and \(-110\) dBm separately. The theory working power of \(-130\) dBm is not selected to test the stability of proposed measurement method because the working state of the navigation system is extremely unstable under this weak power. The signal is emitted by a signal simulator could not be received by the receiver very well when the working power is less than or equal to \(-120\) dBm without any interference.  
When the loss of positioning duration is not taken into account, the critical interference power is shown in Table 3. Where measurement 1 and measurement 2 are the two critical interference powers measured at different moments, respectively, which are according to the configuration in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Main Component** & **Specifications and Models** \\ \hline Signal simulator & CETIC-NS8400 \\ Signal generator & RIGOL DS6821 9 kHz\(-2.1\) GHz \\ Power amplifier & Ceyear 80,224 50 MHz\(-3\) GHz, 35 dB \\ Navigation receiver & UNICORECOMM UM220 \\ Transmitting/Interference antenna & BBHA9120D 1 GHz-18 GHz \\ Receiving antenna & Special circular polarization antenna for navigation receiver \\ \hline \hline \end{tabular}
\end{table}
Table 2: The specifications and models of main components in experiment system.  
Figure 3: Configuration of sensitivity testing on navigation receiver.  
When it takes the \"sensitivity phenomenon occurs within the first 4 s and the loss of positioning lasts for 30 s\" as the sensitivity criterion, two measurement results at typical frequency points in band are shown in Table 4.  
From Table 3, we can see that the error of the two measurement results at the same frequency point is up to 3 dB, which indicates that the sensitivity criterion of the navigation receiver is less accurate without regard to the loss of positioning duration. The error between the measured value 1 and value 2 in Table 4 basically remain at about 1 dB. The measured values of critical interference power are all greater than the measured results in Table 3. This conforms to the phenomenon in Table 1. It shows that the sensitivity threshold range of measurement is large when the loss of positioning duration is not taken into account. It also shows the measurement results are unstable once again. However, the results measured by the proposed sensitivity criteria and measurement method are relatively stable.
## 5 Case Studies: Validating the CILS Framework
### Case Study Two: HelloTalk
#### 5.2.5 Conclusions  
The successful application of the CILS within HelloTalk advances language learning technology and sets a benchmark for future innovations in the field. This partnership has illustrated the transformative potential of integrating advanced AI capabilities with social learning platforms to enhance language education.  
Learners with linguistic skills and cultural insights are better prepared to thrive in a globalized world. The HelloTalk and CILS integration shows that technology can play a pivotal role in this process, making language learning more accessible, effective, and engaging. The positive outcomes of this collaboration provide a strong foundation for further research and development in AI-driven language education, suggesting a promising direction for combining technological advancements with educational methodologies [40].  
The HelloTalk case study serves as an inspiring model for other educational platforms aiming to incorporate AI to improve learning outcomes. It encourages continuous innovation in the educational technology sector, pushing the boundaries of what is possible in language learning and cultural education. This endeavor enhances individual learning experiences and contributes to the broader goal of fostering global communication and understanding.
## 4 Experiments
### Ablation Study
The best results are in bold font. Figure 5: Visualization of features on the penultimate layer. There are 9 classes of samples represented in different colors which are randomly selected from the test set of CIFAR100. Figures (**a**–**e**) show the results of various methods using ResNet-18 as the backbone. (3) _Intra-class feature variance._ We used Mstd to quantify the average intra-class variance across all the classes, with smaller values indicating that features within the same class are closer to each other. As Table 4 shows, the Mstd of label-smoothing, CS-KD, BAKE, and CSD is much smaller than the baseline, indicating that inter-sample self-distillation can indeed reduce intra-class variation. The Mstd of CSD is similar to label-smoothing across almost all the datasets, which can further corroborate our view that label-smoothing can be regard as a special case of CSD. Another difference between CSD and the previous approaches is that, as shown in Equation (5), CSD generates soft targets by offsetting the class centers. This results in a slight increase in Mstd, but it helps the model to retain some intra-class feature variations, thus effectively preventing the overfitting caused by the density of intra-class features and improving the model's generalization. (4) _\(\lambda\) variance._ Based on Table 5, we can observe a clear trend: as the value of lambda increases from 0 to 0.9, the Top-1 error rate consistently decreases and Mstd grows. With \(\lambda=0\), the error rate is \(24.71\pm 0.24\%\), indicating the baseline performance of the ResNet-18 model without any modification or additional regularization corresponding to lambda. The error rates are presented with standard deviations, indicating the variability of the model's performance across different training runs or data subsets. The decreasing error rate with increasing lambda suggests that the modification or regularization technique associated with lambda positively impacts the model's ability to generalize to new, unseen data, thus reducing the likelihood of incorrect predictions. This trend suggests that the parameter \(\lambda\) plays a crucial role in enhancing the model's generalization capabilities, possibly by controlling overfitting or by encouraging the model to learn more robust features that are invariant across the CIFAR-100 dataset's diverse set of images. Such findings are particularly relevant for deep learning researchers and practitioners aiming to optimize model performance on challenging classification tasks, as they highlight the importance of tuning hyperparameters like lambda to achieve lower error rates.
# BDCOA: Wavefront Aberration Compensation Using Improved Swarm Intelligence for FSO Communication
## 2 BDCOA Based Optimum Control Signal
### Model of SLAO System  
The working module of the SLAO of FSO is given in Figure 2, which comprises of Charge Coupled Device (CCD), beam splitter, lenses, controller, and a Deformable Mirror (DM). As illustrated in Figure 2, the laser carrier signal with WA is divided into two signals by utilizing the beam splitter.  
Here, one signal is used to perform the aberration compensation while the remaining signal's intensity is recorded in the CCD. The compensation phase \(C(r)\) made by DM is used to fix the wavefront aberration \(A(r)\). Equation (1) represents the residual phase \(R(r)\) computation based on the \(A(r)\) and \(C(r)\).  
\[R(r)=A(r)-C(r) \tag{1}\]  
The minimization of the mean square of \(R(r)\) is used to obtain the optimum voltage or control signal \(v=\{v_{1},\ v_{2},\ldots,v_{32}\}\). The compensation phase \(C(r)\) is computed by using the optimal control signal.
# A Dynamic Risk Assessment and Mitigation Model
## 4 Methodology  
Our methodology (Figure 1) comprises four modules designed to provide a dynamic and proactive approach to cybersecurity risk assessment. The first module is responsible for collecting all necessary data from publicly accepted, open-source cybersecurity-related repositories. It begins by detecting all vulnerabilities (CVEs) within the target environment. Using this data, it collects additional information, such as CVSS 3.x metrics (exploitability and impact subscores), which are then normalized to a scale of 1. Other critical data sources include EPSS scores, CWE, CAPEC, and confirmation of whether the identified CVEs are listed in the CISA KEY Catalog.  
Figure 1: Proposed methodology flowchart.  
The second module focuses on the generation of risk scenarios by integrating the identified CVEs, the target network topology, and the attacker's perspective. This module primarily considers the number of vulnerabilities identified on a single asset and the potential pathways an attacker could exploit to reach an asset. Specifically, for assets with multiple vulnerabilities, individual risk scenarios are generated for each detected vulnerability. Furthermore, if an attacker can access an asset through multiple distinct pathways within the network, the methodology produces separate risk scenarios for each pathway.  
The third module constitutes the core of the methodology. This module integrates the normalized exploitability scores from the first module with the identified risk scenarios using a Fuzzy Cognitive Map (FCM) model to generate dynamic vulnerability severity values for each risk scenario. Additionally, a Bayesian network (BN) model incorporates EPSS scores and the identified risk scenarios to provide dynamic and proactive threat assessments for each scenario. The dynamic and proactive risk calculator combines the outputs of the FCM (dynamic vulnerability severity values) and the BN (dynamic and proactive threat scores) with the normalized impact to generate dynamic and proactive risk estimates for each risk scenario.  
After estimating the dynamic and proactive risk, the fourth module provides additional insights for each identified CVE. These include whether the vulnerability is listed in the CISA KEV Catalog and mitigation recommendations based on CAPEC framework, offering actionable guidance to reduce the estimated risk for each scenario.