esa-sceva/satcom-chunk-collection huggingface dataset:
# VidBlock: A Web3.0-Enabled Decentralized Blockchain Architecture for Live Video Streaming
## 3 System Design
### VidBlock Architecture
#### 3.1.1 Layer 1: Data Integrity and Verification  
Layer 1 ensures the integrity, security, and authenticity of all data within the VidBlock ecosystem. At its core is the blockchain network, which consists of components such as the node registry, node registry addresses, and relay registry. These components work collaboratively to maintain a decentralized infrastructure and securely handle essential metadata, including transaction logs, ownership information, and verification records.  
Layer 2 handles real-time data processing and user-facing operations, ensuring low-latency performance and a seamless streaming experience. It includes key components such as the ViB Creator Node* and ViB Consumer Node*, which manage user interactions and live streaming tasks. The P2P Protocol facilitates direct communication between nodes, enabling efficient and responsive peer-to-peer streaming.  
Figure 1: Overall architecture of VidBlock, the proposed system.  
To optimize the handling of large-scale video data, Layer 2 manages real-time streaming operations independently of the blockchain. This design ensures that high-performance streaming is maintained while relying on the blockchain only for critical metadata verification. By separating these responsibilities, Layer 2 supports scalability and provides users with a reliable and efficient streaming experience.  
Additionally, during real-time operations, Layer 2 temporarily organizes metadata, such as session details and user interactions, which is later transferred to Layer 1 for verification and secure recording. This division of tasks allows Layer 2 to focus on performance while leveraging Layer 1 to ensure data integrity and security.  
Communication between Layer 1 and Layer 2 is facilitated through the JSON RPC interface, which allows metadata generated during live streaming to be transmitted for verification and immutable storage on the blockchain. By delegating tasks like data validation and transaction recording to Layer 1, VidBlock ensures that critical operations are handled securely without impacting the performance of real-time streaming managed by Layer 2.  
This separation of responsibilities enables VidBlock to provide a robust framework where real-time operations and data security coexist seamlessly, ensuring scalability and trust across the platform.
# A Review of the High-Mix, Low-Volume Manufacturing Industry
## 9 Discussion
### Automation and Robotic Production in HMLV  
Question 5: \"How has research in automation and robotic production contributed to HMLV?\"  
The demands for mass customisation and market challenges have driven manufacturers to extend the levels of automation in HMLV production while being cost-effective and flexible. Flexible batch manufacturing is one of the strategies for HMLV, which aims to use multi-robot setups to suit the variation of tasks. New programming methods have also minimised the setup costs of the robotic system. Techniques, such as learning by demonstration, offline programming, and telemanipulation have shortened the robots' programming time and downtime. The robotics capability is also actively being researched to explore more possibilities to automate monotonous, dangerous, and un-ergonomic assembly steps.  
Human and robotic collaboration combines the advantages of human operators with robotics. Robots have good precision, repeatability, and strength, while human intelligence and flexibility are valuable under variable conditions. Work has been carried out to optimise the task allocation between the two with consideration of the task properties. Safety and trust of the worker working alongside the collaborative robots are also key concerns addressed by the research work.
# Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks
## 3 System Description
### System Model
#### 3.1.1 Social Proximity Layer  
The SPL characterizes the social attributes of users existing in the social networks. These attributes determine the possibility of pairing between two users for D2D communications. SPL can be modeled  
Figure 1: The D2D architecture with social and physical proximity layers.  
using the weighted directional graph GSPL = VSPL \(\epsilon\)SPL [14], where VSPL is the vertex set that represents the users (from A to H) and \(\epsilon\)SPL is the edge set that represents the social link between them as shown (with green double arrows) in Figure 1. Human users have different levels of social intimacy with each other, which affect their interaction and communication behavior. Therefore, the social proximity between them is incorporated in the D2D peer selection, which improves the user's willingness to cooperate with others and enhances the overall D2D performance. In the subsequent sections, we represent the transmitter DUE with \(Tx\) and the receiver DUE with \(Rx\).
## 5 Conclusions  
The complexity of EEG signals, along with the limitations of hand-operated feature extraction, presents significant challenges to the classification of EEG signals. To overcome this challenge, we introduce a parallel structure that utilizes CNN with CWT technology,thereby enabling more comprehensive feature extraction and improving classification accuracy. Notably, our work accomplishes a mean accuracy of 84.1% on the BCI Competition IV 2b dataset for MI purposes. Upon comparing the classification outcome values obtained from various pre-processing methods, it is evident that CWT is better suited for integration with CNN for analyzing MI-EEG signals compared with CSP, FFT, and STFT. We replace the softMax classifier commonly used by CNN with SVM and DT, and experiments prove that SVM is more suitable for small datasets such as BCI Competition IV 2b. Moreover, our method compares favorably with other state-of-the-art approaches. We envision that this technique will be of value in different AI and MI analytical undertakings. We are eager to explore its potential further. Our future plans involve the creation of a specialized inference accelerator tailored for purposed CNN that is seamlessly compatible with adaptable devices like field programmable gate arrays (FPGAs). Nevertheless, our objective raises numerous layout hurdles, specifically concerning the parallel minor CNN. Furthermore, the implementation of CNN and CWT demand considerable computational resources, which can lead to memory limitations or sluggish inference speed on the hardware employed. To overcome these obstacles, we must devise inventive solutions that enable efficient resource utilization while ensuring performance.  
Conceptualization, Y.X. and S.O.; methodology, Y.X.; software, Y.X.; validation, Y.X.; formal analysis, Y.X.; investigation, Y.X.; writing--original draft preparation, Y.X.; writing--review and editing, Y.X. and S.O.; supervision, S.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Dataset available on request from the authors.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CNN & Convolutional Neural Network \\ CWT & Continuous Wavelet Transform \\ FPGA & Field-Programmable Gate Arrays \\ DL & Deep learning \\ MI & Motor Imagery \\ EEG & Electroencephalography \\ CSE-UAEL & Covariate Shift Estimation and Unsupervised Adaptive Ensemble Learning \\ SR & Sparse Representation \\ CSP & Common Spatial Pattern \\ FFT & Fast Fourier Transform \\ STFT & Short Time Fourier Transform \\ BCI & Brain-Computer Interface \\ CEMD & Conditional Empirical Mode Decomposition \\ ERD & Event-related Desynchronization \\ ERS & Event-related Synchronization \\ ReLUs & Rectified Linear Units \\ SVM & Support Vector Machine \\ DT & Choice Decision Tree \\ MFESF & Multi-domain feature extraction and sparse feature fusion \\ CSSD & common spatial subspace decomposition \\ PSD & Power Spectral Density \\ \end{tabular}
# Mesenchymal Stem Cell Applications in Spine Disorders: A Comprehensive Review
### Clinical Outcomes
The 18 million MSCs group scored best in terms of the VAS and ODI at all follow-ups (12, 24, and 36 months), obtaining results better than the other treatment arms (6 million MSCs and HA). However, no significant radiographic improvement was observed in any of the studied cohorts. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Author** & **Year** & **Journal** & **Study Design** & **N. of Patients** & **Source of MSCs** & **N. of MSCs** & **Outcomes of Interest** & **Follow-Up** \\ \hline
1 & [PERSON] et al. & 2006 & _Stern Cells Dev._ & Prospective & 10 & Hemmopoietic stem cells from link & / & VAS requirement for & 1 year \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline
2 & Yoshidaawa & 2010 & _Spive_ & Case series & 2 & Hemmopoietic stem cells from link & / & X-rays, CT, MRI & 2 years \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline
3 & [PERSON] et al. & 2011 & _Transplantation_ & Prospective & 10 & Hemmopoietic stem cells from link & \(23+5\times 10^{6}\) & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline
4 & [PERSON] et al. & 2015 & _Stern Cells_ & RCT & 26 & Hemmopoietic stem cells from link & / & \\  & & & & & & & & \\  & & & & & & & & \\ \hline
5 & [PERSON] et al. & 2016 & _J. Transl. Mod._ & Case series & 5 & Hemmopoietic stem cells from link & / & Strength, mobility, MRI & 4-6 years \\  & & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & & \\ \hline
6 & [PERSON] et al. & 2017 & _Int. Orthoy._ & Prospective & 26 & Hemmopoietic stem cells from link & \(2\times 10^{6}\) / \(4\times 10^{7}\) & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline
7 & [PERSON] et al. & 2017 & _Stern Cell Res. Thr._ & RCT & 10 & Adopere derived such cells from & \(2\times 10^{6}\) or \(4\times 10^{7}\) & & ODI, VAS, MRI, X-rays & 1 year \\  & & & & & & & & & \\  & & & & & & & & \\ \hline
8 & [PERSON] et al. & 2017 & _Transplantation_ & RCT & 24 & Allogenic hematopoietic stem cells & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline
9 & [PERSON] et al. & 2020 & _PM R_ & Case report & 1 & / & / & / \\ \hline
10 & [PERSON] et al. & 2020 & _SMC: Macalistit._ & & Retrospective & 33 & Hemmopoietic stem cells from link & / & VAS, ODI, SF-36 & 1 year \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline  & & & & & & & & ODI, VAS, MRI, SF-36 and & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline  & & & & & & & & \\ \hline  & & & & & & & & \\ \end{tabular}
\end{table} 
Table 1: Summary of the characteristics of the available studies. MSC: mesenchymal stem cells; VAS: visual analogic scale; ODI: Ownestry Disability Index; SF-36: Short-Form 36; MRI: magnetic resonance imaging: CT: computed tomography.
## Nomenclature  
\(E_{W}\) Wind power utilization  
\(C_{GL}\) System operation cost  
\(C_{gen}\) Generation cost of thermal generators  
\(C_{SL}\) Operation cost of shiftable loads  
\(C_{HL}\) Operation cost of high-energy loads  
\(n,N\) Index and set of scenarios  
\(G\) Thermal generation units  
\(W\) Wind farms  
\(SL\) Shiftable loads  
\(HL\) High-energy loads  
\(T\) Time horizon (e.g., 24 hours).  
\(\Delta T\) Time span of each period \(t\).  
\(L\) Transmission lines  
\(N_{W}\) Total number of wind farms  
\(N_{S}\) Total number of shiftable loads  
\(N_{H}\) Total number of high-energy loads  
\(N_{C}\) Total number of thermal generation units  
\(\lambda_{Gj}\) Start-up and shut-down cost for thermal unit \(j\)  
\(n_{j},m_{j},l_{j}\) Operating cost factors for thermal generator \(j\).  
\(\lambda_{S4}\) Cost factors for shiftable load \(b\) to regulate a unit load  
\(\lambda_{Hb}\) Cost factors for high-energy load \(b\) to regulate a unit load  
\(P_{L}^{t}\) Total amount of system load over time period \(t\)  
\(P_{Gj}^{\max}\) Maximum output of thermal generator \(j\).  
\(P_{Gj}^{\min}\) Minimum output of thermal generator \(j\).  
\(R_{L}^{t}\) Amount of spinning reserve needed for the system over time period \(t\).  
\(\gamma\) Reserve capacity factor of wind power  
\(R_{Gj}^{qp}\) Ramp-up output limit of generator \(j\).
# DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes
## 2 Preliminary
### SC Flipping Decoder  
The SC flipping decoder initiates a SC decoder to generate the estimated codeword \(\hat{u}_{1}^{N}\). If \(\hat{u}_{1}^{N}\) passes the CRC detector, the decoding is successful. Otherwise, the flipping decoder is given \(T\) attempts to flip the first error bit. Algorithm 1 presents the original SC flipping algorithm, and the flipping set is denoted as \(\mathcal{V}\). There are two reasons for decoding failures: channel noise and error propagation. The SC flipping algorithm improves error-correction performance and restrains error propagation. However, the decoder can only eliminate the error propagation caused by the first error bit, but there may be other error bits caused by noise or follow error bits in the subsequence. The information bits \(\{9,13\}\) in Figure 2, for example, are error bits caused by channel noise. The SC flipping decoder tries to corrects the first error bit \(\{9\}\) caused by channel noise, and the error propagation phenomenon of \(\{10,11\}\) disappears, lowering the BER. However, \(\{13\}\) is still an error and the block error rate (BLER) is not lower. Therefore, we consider flipping the second error bit \(\{13\}\) to lower the BLER and BER in this paper.  
```
0:\(y_{1}^{N},\mathcal{A}\), \(T\)
0:\(\hat{u}_{1}^{N}\)
1:\(\{\hat{u}_{1}^{N},\,\text{LLR}\}\leftarrow\text{SC}\ (y_{1}^{N})\)
2:if\(\hat{u}_{1}^{N}\) fail CRC and \(T>1\)then
3:\(\mathcal{V}\gets i\in\mathcal{A}\) of \(T\) smallest \(|\) LLR \(|\)
4:for\(j\gets 1\) to \(T\)do
5:\(\hat{u}_{1}^{N}\leftarrow\text{SC}\ (y_{1}^{N},\mathcal{V}(j))\)
6:if\(\hat{u}_{1}^{N}\) pass CRC then
7: break
8:endif
9:endfor
10:endif
11:return\(\hat{u}_{1}^{N}\)
```  
**Algorithm 1** The SC flipping algorithm.
# MTGNet: Multi-Agent End-to-End Motion Trajectory Prediction with Multimodal Panoramic Dynamic Graph
## 4 Experimental Results and Analysis
### Ablation Test  
For the different modules in our proposed model, we conducted ablation experiments to evaluate their effectiveness.  
To further validate the effectiveness of each component in MTGNet, an ablation study is conducted, as shown in Table 3. When only employing the Transformer encoder and fully-connected layers (neither \"Dynamic partition scene\" nor \"Fully connected \(\rightarrow\) GCN\" is adopted), the brier-minFDE (k = 6) value is 2.24. This baseline result reflects the performance of a conventional structure without these two optimizations. When \"Dynamic partition scene\" is adopted alone, the value drops to 2.15. This demonstrates that dynamic scene partitioning enables the model to focus on relevant scene elements, effectively filtering out irrelevant information and improving performance. When \"Fully connected \(\rightarrow\) GCN\" is adopted alone, the value is 2.20. This indicates that replacing fully-connected layers with GCN introduces a more structured interaction modeling, yet without dynamic scene partitioning, its performance improvement is restricted due to handling unnecessary global interactions. Notably, when both \"Dynamic partition scene\" and \"Fully connected \(\rightarrow\) GCN\" are adopted, the brier-minFDE (k = 6) reaches the lowest value of 1.99. This outcome highlights that the synergy of dynamic scene partitioning and GCN-based interaction modeling significantly enhances the model's capability to capture critical features and minimize prediction errors, and verifies the indispensable role of these two components in optimizing the model's prediction accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method \& Rank** & **Brier-minFDE (k = 6) \(\downarrow\)** & **\#Param** \\ \hline MTGNet (ours) & **1.94** & **2148 K** \\ HiVT [4] & 2.01 & 2546 K \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of prediction accuracy (brier-minFDE (k = 6) \(\downarrow\)) and parameter count between MTGNet and HiVT (fully-connected) on the Argoverse 2.0 validation set. The best results are in bold.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Dynamic Partition Scene** & **Fully Connected \(\rightarrow\) GCN** & **Brier-minFDE (k = 6) \(\downarrow\)** \\ \hline MTGNet & \(\surd\) & & 2.24 \\  & \(\surd\) & & 2.15 \\  & \(\surd\) & \(\surd\) & 2.20 \\  & \(\surd\) & \(\surd\) & 1.99 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative evaluation of ablation experiments. K is the number of predicted trajectories for calculating the evaluation metrics. “\(\downarrow\)” mean that the larger the better.
# Common Nearest Neighbor Clustering--A Benchmark
## 4 Results
### Benchmark
#### 4.1.4 Cluster Overlap  
As shown in the former section, the CNN algorithm fails if overlapping clusters of different data-point density are clustered. Therefore, it is of interest to investigate the influence of the overlap for clusters of equal data-point density. Increasing cluster overlap was tested using the S sets [13]. The datasets with a small overlap (\(S1\) and \(S2\)) were clustered correctly. In the datasets \(S3\) and \(S4\), CNN identified 16 instead of 15 clusters, as in the reference solution. The additional cluster, however, had a similar data-point density compared to the 15 reference clusters. From the viewpoint of density-based clustering, there was thus no reason to declare the data points in the additional cluster as noise. To further evaluate how the overlap influences the clustering results (depending on the chosen parameter set), the \(G2\) datasets [44] were also tested. The \(G2\) datasets consisted of two Gaussian-distributed clusters in 1-1024 dimensions. For every dimension, 10 datasets were present, where the standard deviation differed between 10 and 100. The distance between the maxima was \(\sqrt{dimensionality}\cdot 100\). The CNN algorithm was probed on the datasets of 2, 8 and 128 dimensions. To gain useful information out of these 30 datasets, three parameters were recorded: The distance cutoff \(R\) (0.9 of the position of the first maximum in the distance distribution), the minimal value of \(N\) needed to separate the two clusters, and the maximal relative cluster size that could be obtained with the resulting parameter set (the maximal cluster size for each cluster was 50%). The results are summarized in Figure 7a. Figure 7a shows that with an increasing standard deviation (i.e., increasing overlap), the chosen cut-off parameter \(R\) also increased as the maxima of the distance distribution merged, as shown in [39]. The CNN algorithm could separate the clusters in two dimensions until a standard deviation of 60 was reached. For higher standard deviations, no separation could be observed using this algorithm. Additionally, with increasing overlap, the density parameter \(N\) had to also be increased, resulting in a decrease of the maximal cluster size from 50% (\(\sigma=10\)) to 10% (\(\sigma=60\)). The effects of increasing dimensionality are discussed in the next paragraph.
# Aerospace
## 5 Single Small Satellite to Venus Orbit as Secondary Payload  
The high-level mission objective is to insert a small satellite into a low-circular polar orbit at Venus as a secondary payload on an orbiter mission. Venus has a much higher gravity well than Mars but a similar atmospheric extent, resulting in a much smaller aerocapture entry corridor compared to Mars. This imposes more stringent constraints on the allowable EFPA error for the small satellite at the entry interface than at Mars. Due to its bigger gravity well and thicker atmosphere, both entry speed and aerothermal heating are considerably higher at Venus compared to Mars. The more demanding entry conditions require increasing the allowable deceleration to 10\(g\) and the peak heat rate to 240 W/cm\({}^{2}\).
# A CSAR 3D Imaging Method Suitable for Edge Computation
## 2 BP 3D Imaging  
### Imaging Geometric Model of CSAR  
The CSAR imaging system's geometric model is shown in Figure 1a. In this system, the SAR is carried by an SRUAV in a circle around the observation scene at a fixed height. The beam center of the SAR antenna always points to the center of the imaging scene. The instantaneous slant range from the SAR platform to the point target P in the movement process is as follows [8]:  
\[\begin{array}{ll}R_{\rm P}(\varphi)&=\sqrt{\left(R_{\rm xy}\cos\varphi-r_{\rm P }\cos\theta_{\rm p}\right)^{2}+\left(R_{\rm xy}\sin\varphi-r_{\rm P}\sin \theta_{\rm p}\right)^{2}+\left(H-z_{\rm p}\right)^{2}}\quad,\varphi\in[0,2 \pi)\\ &=\sqrt{R_{\rm xy}{}^{2}+{r_{\rm p}}^{2}+\left(H-z_{\rm p}\right)^{2}-2R_{\rm xy }r_{\rm P}\cos(\varphi-\theta_{\rm p})}\end{array} \tag{1}\]  
where \(R_{\rm xy}\) is the radius of the circular trajectory; \(\left(R_{\rm xy}\cos\varphi,R_{\rm xy}\sin\varphi,H\right)\) is the position coordinate of the carrier platform flying at point A, and \(\varphi\in[0,2\pi)\) is the angle with the positive half of the X axis as the reference direction; incident angle \(\psi=\arctan(R_{\rm xy}/H)\) is the angle between the center of the SAR beam and the negative half axis of the Z axis; and \(\left(r_{\rm P}\cos\theta_{\rm p},r_{\rm P}\sin\theta_{\rm p},z_{\rm p}\right)\) is the coordinate of any point target P in the observation scene.  
The instantaneous slant range of point target P measured in CSAR mode changes with the position changes in the SAR platform, as shown in Figure 1b.
# Soil Moisture Retrieval from Multi-GNSS Reflectometry on FY-3E GNOS-II by Land Cover Classification
## 3 Methods
### Correction for Vegetation Attenuation  
As the GNSS reflected signals are absorbed by the vegetation canopy, the observed reflectivity can be corrected to the reflectivity of the soil surface [31]:  
\[\Gamma_{soil}(\theta)=\ \Gamma(\theta)/\gamma \tag{5}\]  
where \(\Gamma\) is the observed reflectivity calculated by Equation (4); \(\Gamma_{soil}\) is the reflectivity of soil surface; \(\theta\) is the incidence angle; \(\gamma\) is the two-way attenuation of biomass canopy derived as  
\[\gamma=\exp(-2\cdot b\cdot vwc/\cos(\theta)) \tag{6}\]  
where \(vwc\) is the vegetation water content derived from the SMAP soil moisture product at a resolution of 9 km; \(b\) is a constant parameter depends on signal wavelength, polarization, and vegetation classification. For a wavelength of about 20 cm, the parameter \(b\) ranges from 0.09 to 0.15 under different land cover classifications [32]. In this study, an IGBP look-up table was employed for IGBP classification, and the value of \(b\) was set according to the SMAP ATBT document for each IGBP class, as indicated in Table 3[33].