esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of the Art
### Deep Learning Approaches in CSI-Based Sensing  
Recent research has applied deep learning (DL) techniques to enhance performance across various sensing tasks. DL models like Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and hybrid CNN-LSTM architectures can effectively learn spatial-temporal patterns from CSI data. SenseFi [4], for instance, provides a benchmark dataset and DL framework that outperforms traditional methods. [PERSON] et al. [16] leveraged a CNN and LSTM for CSI-based indoor localisation, while [PERSON] et al. [17] and [PERSON] and [PERSON] [18] proposed Fresnel-based and sparse recovery models, respectively. [PERSON] et al. [19] and [PERSON] et al. [20] further contributed by incorporating orientation-aware features and compiling DL-focused surveys. These methods also extend to gesture recognition and biometric authentication. However, high resource demands limit deployment on lightweight IoT devices, underscoring the need for efficient model design.
# Data Compression with a Time Limit
## 4 Lossy Compression
### Test 1  
The dataset \(D\) to be compressed consisted of 1000 images, for a total size of 138.2 MB (138,243,000 bytes). Single runs of the compression tools with the indicated quality setups yielded the following results shown in Table 8:  
From Figure 9, it is possible to observe that the fastest tool was WEBP10 while the slowest was JPEG_NO_OPTIMIZE15.  
The best tool for output size was WEBP11, while the worst was JPEG_NO_OPTIMIZE3.  
Figure 10 shows the useful setups.  
Figure 11 shows the optimal mix. If the time limit is 3530 ms and the SSIM quality limit is set to 0.933483, the optimal mix is given by the mix of the WEBP10 and WEBP11 setups.  
It is possible to observe how the optimum is perfectly intersected with the convex hull.  
The number of images to be compressed with WEBP10 is equal to 750 with quality 24, while for WEBP11 it is 250 with quality 23.  
Compressing D as suggested by the algorithm resulted in a compression time of 3683 ms, while the output size in bytes is equal to 9,433,432 bytes with a quality indicated by an SSIM index of 0.931679.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tool** & **Download** & **Configuration** \\ \hline JPG & [[https://github.com/](https://github.com/)]([https://github.com/](https://github.com/)) & JPEG lossy optimized \\  & LuaDist/libjpeg & \\ WEBP & [[https://github.com/](https://github.com/)]([https://github.com/](https://github.com/)) & WebP lossy \\  & webproject/libwebp & \\ JPEG NO OPTIMIZE & [[https://github.com/](https://github.com/)]([https://github.com/](https://github.com/)) & JPEG lossy not optimized \\  & LuaDist/libjpeg & \\ SSIM & [[https://imagemagick.org/](https://imagemagick.org/)]([https://imagemagick.org/](https://imagemagick.org/)) & SSIM \\  & index.php & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Compression tools.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tool** & **Size of Output in Bytes** & **Time in ms.** &
\begin{tabular}{c} **Quality** \\ **Parameter** \\ \end{tabular} & **SSIM** \\ \hline JPEG1 & 60,837,185 & 3652 & 95 & 0.987475 \\ WEBP2 & 43,938,844 & 4894 & 95 & 0.988174 \\ JPEG-NO OPTIMIZE3 & 63,865,873 & 5745 & 95 & 0.987482 \\ JPEG4 & 16,576,161 & 5636 & 40 & 0.942975 \\ JPEG5 & 15,107,663 & 5632 & 34 & 0.940295 \\ JPEG6 & 14,934,171 & 5754 & 33 & 0.939170 \\ JPEG7 & 14,607,030 & 5693 & 32 & 0.937845 \\ WEBP8 & 12,025,946 & 3791 & 40 & 0.949340 \\ WEBP9 & 9,976,364 & 3560 & 27 & 0.937786 \\ WEBP10 & 9,481,598 & 3524 & 24 & 0.934067 \\ WEBP11 & 9,313,614 & 3548 & 23 & 0.932677 \\ JPEG-NO OPTIMIZE12 & 17,732,502 & 5888 & 40 & 0.942941 \\ JPEG-NO OPTIMIZE13 & 16,365,984 & 6046 & 34 & 0.940250 \\ JPEG-NO OPTIMIZE14 & 16,206,014 & 6030 & 33 & 0.939123 \\ JPEG-NO OPTIMIZE15 & 15,901,162 & 6172 & 32 & 0.937797 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Test 1.  
The time budget was used entirely. The very small margin of error, about 153 ms, is, certainly negligible and can depend on many factors strictly related to the system (CPU usage, RAM, file reading/writing, etc.).  
Figure 10: Useful setups.  
Figure 9: Setups.
## 4 c-di-AMP Binding Molecules
### KtrA Homolog Protein, CabP  
CabP was identified in _S. pneumoniae_ by c-di-AMP affinity chromatography of bacterial cell lysates [25]. This protein belongs to the potassium ion transporter protein family, Trk, which is involved in potassium ion transport. CabP has a molecular mass of 24 kDa and is 33% identical to the KtrA protein by amino acid sequence, both of which form stable octamers in solution [17; 25]. The presence of genes encoding CabP and the KtrB ortholog, SPD_0076, in the same operon in _S. pneumoniae_ further supports the involvement of CabP in potassium uptake [25]. Indeed, deficiency of either CabP or SPD_0076 leads to impaired potassium uptake and decreased survival of the bacterium [25]. Using an _E. coli_ two-hybrid system coupled with coexpression of the c-di-AMP synthesis enzyme DisA (as _E. coli_ does not normally produce c-di-AMP), c-di-AMP was shown to inhibit the interaction of CabP with SPD_0076 and thereby lead to the reduction of potassium uptake [25]. CabP has a high affinity for c-di-AMP, and the presence of other nucleotides such as cAMP, c-di-GMP, AMP, and pApA does not decrease the CabP:c-di-AMP binding rate. In the above-mentioned study, [PERSON] et al. isolated several proteins from the crude cytoplasmic extract of _S. pneumoniae_ and CabP was the most abundant. This suggests that more c-di-AMP binding proteins still await discovery.
## 5 IoRT Systems Based on Space-Borne NTN Networks
### Enhancement of Energy Efficiency
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **NTN Architecture** & **Description** \\ \hline
[73] & LEO & Energy optimization utilizing the repetition factor of data symbols on multiple subcarriers of the transmitted OFDM signal and the mean number of coded packets required to satisfy dynamic requirements. \\ \hline
[74] & LEO & A novel random access preamble design and detection mechanism for IoT devices in LEO satellite-based NTNs that addresses the energy-intensive nature of random access. \\ \hline
[75] & LEO & A method for synchronizing sleep and wake-up periods with satellite availability to substantially enhance the battery life of IoT-NTN devices. \\ \hline
[76] & LEO & Combination of uplink signals from multiple LEO satellites to improve SNR and reduce power consumption for energy-constrained terminals. \\ \hline
[77] & LEO & Novel MEC framework for terrestrial-satellite IoT accompanied by an energy-efficient computation offloading and resource allocation algorithm named E-CORA. \\ \hline
[78] & LEO & Development of DeepCA, a deep reinforcement learning-based method for dynamic and energy-efficient channel allocation. \\ \hline
[79] & Generic & Development of a novel power control algorithm for IoT terminals in satellite-based networks that optimizes user distribution and SINR demand. \\ \hline
[80] & Generic & Development of a scheduling algorithm for multi-connectivity in multi-orbit NTNs to maximize uplink data rates while minimizing user terminal energy consumption. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: IoRT systems based on space-borne NTN networks: **enhancement of energy efficiency**.
## 4 Multi-Modal Multi-Task Graph Spatiotemporal Network
### GCN Model for Wind Power Prediction  
According to the analysis before, the NWP contains the future meteorological information and it can be found that there is a good correlation with the power. Therefore, the multi-modal learning is used to combine the temporal and spatial characteristics of the historical wind power and NWP. The multi-task learning is used for the prediction of each wind farm. The proposed multi-modal multi-task graph spatiotemporal network (M2 GSNet) model for wind power prediction is as shown in Figure 5:  
Figure 4: Hard parameter sharing for multi-task learning.  
From Figure 5, we can see that the whole structure is an encoder-decoder framework. The historical wind power and NWP data are encoded into a latent spatial and temporal feature space by using graph convolutional network and multi-modal learning. The features are decoded into the wind power of different wind farms by using multi-task learning and fully connected layers. The model consists of the following modules.  
(1) GCN Module: GCN part includes two GCN modules for the historical wind power and NWP data, respectively. Each GCN module includes two layers of standard GCN and the adjacent matrix is based on distance. It is used to extract the spatial feature of the wind farm.  
(2) Multi-Modal Learning Module: It is used to concatenate the spatial and temporal feature of NWP and historical wind power. It makes M2 GSNet an effective hybrid prediction method by combining the advantage of model-based prediction and data-driven prediction.  
(3) Multi-Task Learning Module: The fully connected layer is used to map the fused spatial-temporal feature of each wind farm into the wind power. Each wind farm has a specific layer.  
The historical wind power and NWP data are the input of the model.The output is the ultra-short-term power sequence of each wind farm. The regional wind power can be calculated by adding together the power of each wind farm.
# Large Language Models Meet Next-Generation Networking Technologies: A Review
## 4 Open Challenges and Opportunities
### Language Server Protocol with Large Language Models  
The Language Server Protocol (LSP) is a critical standard in modern software development, designed to decouple the language-specific features of development environments from the editors or Integrated Development Environments (IDEs) that use them. Thisprotocol enables the creation of a single language server that can provide features like auto-completion, go-to-definition, and linting to multiple IDEs, simplifying the development process and improving the consistency of language support across different tools. LSP works by allowing the language server to communicate with the development tools using a common protocol, ensuring that features like syntax highlighting and code completion are uniformly applied, regardless of the editor being used. This decoupling facilitates easier maintenance and enhancement of language features and enables developers to use their preferred tools without sacrificing functionality.  
In AI-assisted programming [196; 197], integrating LLMs with the LSP marks a significant advancement. In particular, LLMs can enhance LSP by providing more sophisticated and context-aware programming assistance. For instance, LLMs can offer intelligent code suggestions, automated documentation generation, and advanced error detection and correction, thereby significantly boosting productivity and reducing time spent on routine coding tasks. Integrating LLMs with LSP can also improve the handling of networking protocols and configurations within development environments. Networking tasks often require precise and complex configurations, such as those involved with TCP/IP stacks. By embedding LSP within the networking stack, LLMs can assist in configuring and debugging network protocols, making it easier for developers to manage networking tasks. This integration has the potential to transform network programming by providing developers with intuitive tools that streamline building and maintaining network applications.  
The potential for LLMs to enhance LSP goes beyond mere coding assistance. With their ability to understand and generate human-like text, LLMs can facilitate better communication and documentation within development teams. These models can automatically generate detailed explanations and summaries of code, making it easier for team members to understand complex implementations and collaborate more effectively.
# Content-Based Video Big Data Retrieval with Extensive Features and Deep Learning
## 4 Proposed Method
### Distributed and Parallel Processing Model for Video Feature Extraction  
It is time-consuming work to extract features in large-scale datasets for content-based video retrieval systems. Apache Spark extends the MapReduce model providing the flexibility to persist data records, either in memory, on disk, or both. Spark favors the iterative processes met in machine learning and optimization algorithms. Therefore, we propose the video feature extraction in a distributed and parallel processing model with Spark, as illustrated in Figure 6. This not only saves the extraction processing time but also reduces the training time for deep learning models.  
A Spark cluster consists of a manager (master) and workers (slaves). The manager node running Spark Job is responsible for scheduling, assigning, and monitoring tasks to worker nodes, which run the actual Spark tasks. The workers execute tasks and send the status of the tasks to the manager. As a result, the extracted features from videos are stored and indexed in HDFS. It is available for querying by content-based video retrieval systems. The pseudocode algorithm to extract the video content is described in Algorithm 1.  
Figure 5: The precision/recall curve.  
```
0: Video Dataset Output: Video content features are extracted and indexed Begin
1: Initialize Spark Context
2: Browse videos in dataset
3:for each video in dataset do
4: Initialize \(N\) worker nodes
5: Split videos into frames, which are then fed into the worker nodes
6: Data pre-processing
7: Extract video features in text format consisting of subtitles, speeches, and objects.
8: Encrypt the extracted features using MD5 algorithm and then store to HDFS.
9:endfor
10: Indexing the HDFS database containing the video content features
11:End.
```  
**Algorithm 1** Distributed video feature extraction
## 7 Conclusions  
As a result of the study, a new method for localizing LoRa mobile wireless nodes indoors with division into sectors was proposed. The use of the extended Kalman filter (EKF)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **Method** & **Machine Learning** & **Localization Error** & **WSN** \\ \hline
[38] & Fingerprint & ANN, LSTM, CNN & 1.27 m & LoRa \\ \hline
[36] & Fingerprint &
\begin{tabular}{c} BPNN \\ (Back-Propagation \\ Neural Network) \\ \end{tabular} & 0.5971 m & LoRa \\ \hline
[35] & Fingerprint & RF & 0.82 m & LoRa \\ \hline
[36] & Fingerprint & RNN & 0.12 m & LoRa \\ \hline
[32] & Trilateration & N/A & 1.6 m & LoRa \\ \hline
[62] & Trilateration & N/A & 0.846 m and 1.534 m & LoRa \\ \hline
[63] & Fingerprint & LSTM, SVM & 0.9 m and 1.1 m & Wi-Fi \\ \hline
[64] & \
\begin{tabular}{c} Adaptive signal \\ model fingerprinting \\ (ASMF) \\ \end{tabular} & kNN & 0.712 m and 0.939 m & Zigbee \\ \hline Proposed & Fingerprint &
\begin{tabular}{c} GRU, MLP, kNN, RF, \\ SVR, BPNN and RNN \\ \end{tabular} & 0.384 m & LoRa \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative analysis of technologies for localization of mobile nodes.  
for localizing mobile wireless nodes indoors does not provide significant improvements. The new method of dividing into sectors minimizes the impact of noise and nonlinearity of signal propagation. In addition, in this work, the test sample and training sample data were not parts of the same dataset, which increases the validity of the results and the purity of the experiment.  
The best results were shown by GRU with division into 256 sectors with an accuracy of 94.54% for the first scenario, 91.02% for the second scenario and 85.12% for the third scenario. The method based on division into 1024 sectors also shows good results using GRU: the first scenario--93.54%, the second--89.02%, the third--84.12%. The cumulative error distribution function analysis showed that the average localization error was 0.384 m, and the average absolute localization error was 0.246 m, which is a good result in indoor multiple reflection conditions.  
As part of our future work, we plan to focus on real-time node localization to en-hance the system's applicability and provide a real-time estimation, expand the experimental area, and explore hybrid approaches for more accurate localization of mobile nodes.  
Conceptualization, M.N. and A.S.; methodology, A.B.; software, E.Y. and B.Z.; validation, K.K. and E.Y.; formal analysis, A.K. and G.D.; investigation, N.K. ([PERSON]) and G.D.; writing--original draft preparation, M.N. and A.B.; writing--review and editing, B.Z., A.S. and K.K.; visualization, N.K. ([PERSON]) and S.O.; supervision, A.S.; project administration, M.N. All authors have read and agreed to the published version of the manuscript. This research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant AP19678552). The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors. The authors declare no conflicts of interest.
## 4 The Structure Fidelity Data Collection Framework
### Cluster Construction
In LEACH [10], authors indicate that there exists an optimal percent \(\alpha\) of nodes that should be cluster heads for a suitable energy dissipation consideration. \(\alpha=0.05\) for LEACH. In general, the predefined parameter \(max\_dis\) is less than the communication radius of sensor node, where we only consider the senor nodes with the same communication ability for the homogeneous network. In Algorithm 1, we first put the nodes close to each other in term of spatial distance together, and then sort them in order of the Pearson's distance. Only the nodes within the communication range \(R_{CH}\) of the cluster head join in the new cluster. The algorithm outputs a set of clusters in which all nodes are covered. The cluster head selection process is described in next section.
# High-Resolution and Wide-Swath 3D Imaging for Urban Areas Based on Distributed Spaceborne SAR
The feasibility of the proposed distributed SAR HRWS system and the effectiveness of the range ambiguity resolution method are verified by using the airborne array tomographic SAR data, and the HRWS 3D imaging results are obtained. The rest of this paper is outlined as follows. The detailed methodology is introduced in Section 2, and the experimental results and evaluation are provided in Section 3. Section 4 discusses the results and limitations of the method, as well as some potential applications, and finally, Section 5 concludes this paper.
# Anti-Multipath Performance Improvement of an M-ary Position Phase Shift Keying Modulation System
## 4 Simulation and Numerical Results
### Time Delay Estimation
In the case of transmitting the same information, adjusting the modulation parameter scheme saves spectrum resources and improves frequency band utilization.
## 4 Improved Algorithms
### I-RRT Algorithm
#### 4.3.1 Path Collision Detection  
The classic RRT algorithm performs collision detection for nodes but does not conduct collision detection along the path. If the path results in a collision with obstacles, the generated path does not meet the actual requirements. In this paper, collision detection is performed on the path in the Collision_check_line() function. The specific method is to pass between two nodes in the random tree, calculate the Euclidean distance between the two nodes in the three-dimensional space, and set the division rate (Discretepoint). The number of divided points is equal to the distance between the two points divided by the division rate. The path is divided into many points, and whether these points collide with obstacles is evaluated.
## 2 The Simplified Frequency-Domain Feedback Active Noise Control Algorithm
### The Proposed Algorithm  
Figure 3 illustrates the configuration block diagram of the SFDFBANC algorithm. Differentiated from Figure 2, the internal synthesis input signal is denoted as \(x(n)\), \(W(k)\) expresses the frequency-domain adaptive controller, \(\hat{C}^{*}(k)\) can be obtained using the FFT transform of the estimated total delay, \(\hat{R}^{*}(k)\) represents the frequency-domain filtering reference signal, \(E(k)\) is the FFT transform of error signal \(e(n)\), variable factor \(k\) denotes the time exponent in the frequency domain, and the symbol \({}^{*}\) represents the conjugate.  
The overlap storage method, which has a 50% overlap rate, is adopted [36]. Fourier transform (FFT) can be utilized by the two consecutive data blocks with length \(N\) of the internal synthesized signal to generate the frequency domain signal \(X(k)\), which is expressed as follows:  
\[X(k)=\text{diag}\big{\{}\text{FFT}[x(kN-N)\quad\ldots\quad x(kN)\quad\ldots \quad x(kN+N-1)]\big{\}} \tag{11}\]  
where \(k\) is an integer, and it denotes the frequency-domain index.  
Figure 3: Block diagram of SFDFBANC algorithm.  
\(\hat{R}(k)\) is the filtered reference signal in the frequency domain, and it is denoted as follows:  
\[\hat{R}(k)=X(k)\hat{C}^{*}(k) \tag{12}\]  
\(\hat{C}(k)\) refers to the Fourier transform result of the estimated total direct sound delay \(\Delta\) with a length of \(2N\), with \(\Delta\) being the summation of the direct sound delay and the electric delay of circuit [36].  
To avoid the \"winding\" value introduced when calculating linear convolution with cyclic convolution, the frequency-domain error signal \(E(k)\) with a length of \(2N\) is expressed as follows:  
\[E(k)=\text{FFT}\begin{bmatrix}0\\ e(k)\end{bmatrix} \tag{13}\]  
where \(0\) denotes an all-zero column vector of length \(N\), and \(e(k)\) can be denoted as follows:  
\[e(k)=[e(kN),\,e(kN+1),\ldots,e(kN+N-1)]^{\text{T}} \tag{14}\]  
\(W(k)\) is the adaptive filter coefficient, and it is updated in the frequency domain as follows:  
\[W(k)=W(k-1)-\mu\hat{R}^{*}(k)E(k) \tag{15}\]  
where variable \(\mu\) is a positive value that denotes the step size, \(W(k)\) is \(2N\) in length, and \(k\) takes the value of \(0,1,\ldots,2N-1\).  
The updated formula of the time-domain adaptive filter coefficients \(w(n)\) is as follows:  
\[w(n)=\text{IFFT}[W(k)]_{+} \tag{16}\]  
where \(w(n)\) is a column vector of length \(N\) and \"+\" denotes the value of \(\text{IFFT}[W(k)]\) from \(0\) to \(N-1\).  
The derivation process of Formulas (1)-(6) and Formulas (11)-(16) above is called the simplified frequency-domain feedback active noise control algorithm (SFDFBANC).