esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks
###### Abstract  
Fast convergence routing is a critical issue for Low Earth Orbit (LEO) constellation networks because these networks have dynamic topology changes, and transmission requirements can vary over time. However, most of the previous research has focused on the Open Shortest Path First (OSPF) routing algorithm, which is not well-suited to handle the frequent changes in the link state of the LEO satellite network. In this regard, we propose a Fast-Convergence Reinforcement Learning Satellite Routing Algorithm (FRL-SR) for LEO satellite networks, where the satellite can quickly obtain the network link status and adjust its routing strategy accordingly. In FRL-SR, each satellite node is considered an agent, and the agent selects the appropriate port for packet forwarding based on its routing policy. Whenever the satellite network state changes, the agent sends \"hello\" packets to the neighboring nodes to update their routing policy. Compared to traditional reinforcement learning algorithms, FRL-SR can perceive network information faster and converge faster. Additionally, FRL-SR can mask the dynamics of the satellite network topology and adaptively adjust the forwarding strategy based on the link state. The experimental results demonstrate that the proposed FRL-SR algorithm outperforms the Dijkstra algorithm in the performance of average delay, packet arriving ratio, and network load balance.  
LEO satellite networks; satellite routing; multi-agent reinforcement learning; distributed routing +
Footnote †: journal:  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks. _Sensors_**2023**, 23, 5180. [[https://doi.org/10.3390/s23115180](https://doi.org/10.3390/s23115180)]([https://doi.org/10.3390/s23115180](https://doi.org/10.3390/s23115180))
# Anti-Jamming Method and Implementation for GNSS Receiver Based on Array Antenna Rotation
###### Abstract  
Global navigation satellite system (GNSS) array antenna receivers are effective for suppressing wideband jamming. However, its anti-jamming performance decreases sharply when the number of wideband interference surpasses the number of array elements. Since a large number of jammers are often used in navigation countermeasures, it is crucial to keep array antenna receivers available in such conditions. Aiming at this issue, two main tasks were performed in this research and are presented in this paper: Firstly, the direction sensitivity of the sup-freedom anti-jamming performance is revealed and an anti-jamming method for array antenna receivers based on antenna rotation is proposed. Secondly, in order to determine the optimal rotation angle rapidly, a variable-step iteration algorithm based on gradient descent is proposed. Theoretical analysis and simulation show the effectiveness of the proposed anti-jamming method and the efficiency of the implementation algorithm. In a typical airborne scenario with a maximum azimuth difference of 90\({}^{\circ}\), the anti-jamming ability of the proposed method improved by 21-26 dB and 5-10 dB for arrays adopting the PI (power inversion) and MVDR (minimum variance distortionless response) algorithms, respectively. The iterative efficiency improved by 78.35-99.63% in comparison with a traversal of 0.1\({}^{\circ}\) search resolution. The proposed method and algorithm are not limited to airborne scenarios and they might be influential to anti-jamming algorithms in the data domain.  
anti-jamming; GNSS receiver; gradient descent; variable-step Article
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 3 Engineering Background and Database Construction
### Data Processing and Dataset Construction  
To ensure data quality, consistency, and suitability for model training, the raw data underwent a structured preprocessing pipeline before the final training dataset was assembled. The primary goal was to create a well-structured, standardized, balanced, and optimized dataset ready for input into the landslide prediction model. The resulting training dataset consists of 19,898 samples, each characterized by 16 optimized features, with balanced representation of landslide (positive) and non-landslide (negative) classes.
## 4 Results and Discussion  
This section provides detailed simulation results, where the objective is to demonstrate the performance of the proposed RLS-based ANC algorithms in comparison with the related existing algorithms: [PERSON]'s algorithm [24], Th-FxLMS [25], INSS-FxLMS [26], and the previous CCSS-NMFxLMS algorithm [39; 40]. Additionally, a slightly modified variant of the FxLMK algorithm [37], as presented in [40] is also included in the performance comparison.
# A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend
## 3 Establishment of Tower Tilt Calculation Model  
### Model Building  
#### 3.1.1 Camera Imaging Model  
Usually, the camera uses a lens to focus the light reflected from the object's surface onto the photosensitive plane of the camera [20]. This project analyzed the tower tilt by the camera and used monitoring devices to verify, as shown in Figure 7. It can be visualized how a point in the real world is projected onto the camera's light screen.  
Figure 5: Mask the result.  
Figure 6: Mask the HSV result.  
This project simplifies the model by calculating the one-dimensional projection size, as shown in Figure 7b, and extending the results to a two-dimensional (2D) space. The similar relationship in Figure 7 can be simplified as Formula (8):  
\[\Delta\text{CAB}\cong\Delta\text{CDE}\rightarrow\frac{\text{DE}}{\text{CE}}= \frac{\text{AB}}{\text{CB}}\rightarrow\frac{\text{Poy}}{\text{CE}}=\frac{ \text{Oy}}{\text{CB}} \tag{8}\]  
In the equation, \(\text{Oy}\) represents the object's height, \(\text{Poy}\) represents the pixel height where the object is imaged, CB represents the object distance, and CE represents the image distance. In this way, 3D coordinate points in space can be converted to 2D coordinates under the camera coordinate system, allowing for subsequent distance calculations to be performed using 2D coordinates.
# Handover Decision-Making Algorithm for 5G Heterogeneous Networks
## 6 Conclusions  
This paper has proposed a handover algorithm that integrates the dwell time prediction technique and the TOPSIS method. The handover decision-making process takes MT speed, network cost, and various QoS parameters into consideration. Heterogeneous wireless networks consisting of LTE, WLAN, and 5G networks have been simulated. The results show that the proposed algorithm significantly reduces the number of handovers in the high-speed scenario while satisfying user preferences for cost preference and QoS preference up to 35.71% and 33.33%, respectively. The proposed handover algorithm outperforms the TOPSIS-based handover algorithm in terms of cost and throughput as well. It reduces the network connection cost and improves the throughput by up to 27.51% and 5.12%, respectively. The proposed algorithm could improve user experience in the new era of heterogeneous networks.  
This research focuses on a high-speed environment with the assumption of an MT crossing the small cell network in a straight line. The proposed handover algorithm can be further improved and tested in an ultra-dense scenario where the MT moves in random directions.  
Conceptualization, H.T.Y., A.K. and S.K.C.; methodology, M.I.G., A.I.M. and H.T.Y.; software, M.I.G., A.I.M. and H.T.Y.; validation, M.I.G., A.I.M., H.T.Y., M.K.H., A.F. and A.K.; formal analysis, M.I.G., A.I.M., H.T.Y., M.K.H., A.F. and A.C.; investigation, M.I.G., A.I.M. and H.T.Y.; resources, H.T.Y., A.K. and S.K.C.; writing--original draft preparation, M.I.G., A.I.M. and H.T.Y., writing--review and editing, H.T.Y., M.K.H., A.F. and A.C.; supervision, H.T.Y., A.K. and S.K.C.; project administration, H.T.Y. and S.K.C.; funding acquisition, H.T.Y. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Compared with TOPSIS–Based Handover Algorithm [11]** \\ \hline Network connection cost & \(-\)27.51\% \\ Throughput & \(+\)5.12\% \\ Number of handovers for cost preference based & \(-\)35.71\% \\ Number of handovers for QoS preference based & \(-\)33.33\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of the proposed algorithm.  
Figure 14: Number of handovers and unnecessary handovers for 100 loop simulation.  
**Funding:** This research was funded by the Ministry of Higher Education Malaysia, Fundamental Research Grant Scheme (FRGS) FRGS/1/2020/TK0/UMS/02/2. The APC was funded by Research Management Centre, Universiti Malaysia Sabah.  
**Acknowledgments:** The authors wish to express their appreciation to the Ministry of Higher Education Malaysia, Fundamental Research Grant Scheme, and Universiti Malaysia Sabah funded the research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Network Model Design
### Classification Layer  
The classification layer can be divided into the following steps:  
1. Fusing dual features: Multi-head attention is adopted to fuse time-frequency features and HOS features, resulting in a fused feature dimension of 256.
2. Linear transformation: Linear transformation is achieved through matrix multiplication and addition operations, mapping the 256-dimensional input features to 128 dimensions.
3. Nonlinear activation: The GELU activation function is applied to perform the nonlinear transformation on the output of the linear transformation, introducing nonlinearity to enable the model to learn more complex features.
4. Dropout regularization: During training, a dropout layer is introduced to randomly set a portion of input units to zero, preventing the model from overfitting.
5. Final classification: Linear transformation is implemented through matrix multiplication and addition operations, mapping the 128-dimensional input features to 8 dimensions to obtain the final classification results.  
The parameter table for the entire classification layer is presented in Table 1.
## Article
### MDSA: A Dynamic and Greedy Approach to Solve the Minimum Dominating Set Problem
The proposed algorithm extends the idea of Malatya centrality and makes it consistent with the given domain in the minimum dominating set problem. Based on our experimental findings, this perspective effectively saves time and resources by providing optimum or near-optimum answers that facilitate finding a fast and feasible solution for the MDS problem. The main contributions of the proposed method are as follows:  
* Inspired by the Malatya centrality algorithm, this concept was applied to the MDS problem and is the first study in the literature using it. * A greedy search strategy application that applies the same steps in each run, produces the same result, and provides robust, efficient, and effective solutions. * Optimization in terms of time and resource complexity thanks to a dynamic search strategy that updates the graph at each step. * It is easily applicable to large graphs since it is designed to have polynomial time and resource complexities. * Increasing its preference rate in application areas since it produces optimum or nearly optimum solutions for the MDS problem. * It is suitable for a wide range of application scenarios since it has the ability to find minimum dominant sets for all memory allocated graph structures. In this study, comprehensive experimental evaluations were conducted to assess the effectiveness and performance of the proposed algorithm. The algorithm was validatedusing DIMACS, BHOSLIB, SNAP, and network repository datasets. It consistently achieved optimal dominating set results with notably short execution times across these datasets. Comparative results with algorithms from the literature showed that the proposed algorithm excels particularly in terms of speed on large datasets. The consistent performance of our algorithm, especially on large graphs, underscores its practicality for real-world applications. These findings indicate that the proposed algorithm provides an effective and efficient solution for both small and large graphs. This article is structured as follows: Section 2 reviews active studies in the literature addressing the dominating set problem. Section 3 discusses the definition of the dominating set problem and its NP-hard nature. Section 4 provides a detailed explanation of the proposed Malatya Dominating Set Algorithm. Section 5 presents the implementation results of the algorithm and its comparison with competitive methods. Finally, Section 6 concludes the article.
# Image Steganography Using LSB and Hybrid Encryption Algorithms
## 3 Method
### The Hybrid Approach with LSB  
A hybrid Image Encryption Using Cryptography Steganography (Image Steganography using LSB and Hybrid Encryption Algorithms (Algorithm 1)) combines two techniques: Least Significant Bit (LSB) steganography and hybrid encryption. LSB steganography is a commonly used technique for hiding secret messages within images. It works by replacing the least significant bits of the cover image with the secret message bits. The least significant bits of an image is the least important bits of the colour values of each pixel, and changing them is unlikely to cause a noticeable change in the image. This makes LSB steganography a good option for hiding secret messages within images without altering the appearance of the cover image. Hybrid encryption is a method of encryption that combines two or more encryption techniques to provide a higher level of security. In this research, using LSB steganography, the hybrid encryption approach is used to encrypt the secret message before it is hidden within the cover image. This means that the secret message is encrypted using a combination of encryption algorithms, providing higher security than when using only one encryption algorithm. For example, we consider a scenario where an individual wants to send a secret message to a friend. The individual can use LSB steganography to hide the secret message within a cover image and then use hybrid encryption to encrypt the hidden message. The individual can send the cover image to a friend, who can then use the decryption key to decode the encrypted secret message and extract it from the cover image using the LSB steganography technique. The LSB approach would replace the least significant bits of the cover image with the encrypted secret message. For example, if the pixel values of a colour  
Figure 2: Flow diagram of embedding images. image are represented in an 8-bit format, the least significant bit could be replaced with the corresponding bits of the encrypted secret message. Figure 3 depicts the hybrid approach with LSB. The hybrid approach with LSB encryption is given below. The resulting stego image appears visually similar to the cover image while concealing the secret message within the least significant bits. To further improve the security of the hidden message, AES and Blowfish algorithms could be used in a hybrid encryption scheme. The secret message is first encrypted using AES, and the resulting ciphertext is then encrypted using Blowfish. The final encrypted message is then hidden within the LSBs of the cover image. This hybrid approach offers the advantages of both AES and Blowfish, with AES providing strong encryption for sensitive data and Blowfish providing fast and secure encryption for large amounts of data. The process is similar to a grey-scale image, with the difference being that the pixel values are represented in an 8-bit or a 16-bit format, depending on the image resolution. The encrypted secret message is then hidden within the LSBs of the grayscale image. Figure 3: Hybrid approach with LSB encryption and Embedding image process.
## 2 Materials and Methods
### Model
#### 2.3.2 Model Training and Evaluation  
The hyperparameters of Geoman were optimized and determined using the grid search method [61]. All data from the 18 sites were fed into the model for training, without considering the interactions between sites. The performance of Geoman was evaluated using a sample-based 10-fold cross-validation strategy in which the observation data were randomly assigned 10 folds. Each fold contained approximately 10% of the data and was used consecutively for model training, validation, and testing to ensure independence of the three datasets [60]. According to this partition principle, the entire data were divided into non-overlapping training, validation, and test data at a ratio of 8:1:1. The cross-validation process was repeated 10 times. The estimated RE from all 10 folds were compared with the observed RE.  
Three frequently used statistical indicators, i.e., the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE) were selected as evaluation metrics to analyze the performance of the model. These metrics were calculated as follows:  
\[R^{2}=\left(\frac{\sum_{i=1}^{n}(y_{i}-\overline{y})\Big{(}y_{i}^{\prime}- \overline{y^{\prime}}\Big{)}}{\sqrt{\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}} \sqrt{\sum_{i=1}^{n}(y_{i}^{\prime}-\overline{y^{\prime}})^{2}}}\right)^{2} \tag{4}\]  
Figure 2: The structure of Geoman adapted from [PERSON] et al. [44]. LSTM: long short-term memory networks. Attn: attention. Concat: concatenation layer. \(\hat{y}_{t}\): the predicted value at time \(t\). C\({}_{t}\): the context vectors at time \(t\). \(h_{0}\): the initial state of encoder. VT: vegetation type. Ta: temperature. PAR: photosynthetically active radiation. EVI: enhanced vegetation index. PRE: precipitation. SOCD: soil organic carbon density. SoilTex: soil texture.  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\bigl{(}y_{i}-y^{\prime}_{i}\bigr{)}^{2}} \tag{5}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\bigl{|}y_{i}-y^{\prime}_{i}\bigr{|} \tag{6}\]  
where \(y_{i}\) and \(y^{\prime}_{i}\) are the observed and estimated RE, respectively. \(\overline{y}\) and \(\overline{y^{\prime}}\) are the means of \(y_{i}\) and \(y^{\prime}_{i}\), and \(n\) is the number of observed samples.  
When all parameters were optimized, the trained Geoman model was applied to predict eight-day RE at all spatial grids, using environmental data with 1 km spatial resolution and eight-day temporal resolution. The grid numbers for each grassland subtype are shown in Table 2. The DEM, SOCD, and SoilTex data were set unchanged at all time steps due to their limited availability.
# Underwater Electromagnetic Sensor Networks--Part I: Link Characterization +
## 2 Experimental Section
### Setup Procedures
#### 2.4.2 Measuring Procedure  
One of the most important tasks when attempting underwater channel characterization is conducting noise floor measurements, as they are strictly necessary to assess possible interferences and to discuss sensitivity issues. In this work, they were performed with each type of antenna prior to signal transmission. It was then that reliable measurements of attenuation could be carried out.  
It is important to explain how attenuation is understood in this context, as power dissipated (P\({}_{\text{out}}\)) by a load divided by power transfer (P\({}_{\text{in}}\)) from the generator (Figure 6). Therefore, this attenuation involves antenna, cable and device losses. In short, it refers to all losses suffered by signals between the output of the signal generator and the input of the spectrum analyzer.  
Depending on the type of antennas tested and the separation between them, different power ranges were used. Low power (0-10 dBm) was employed for short distances when testing loop antennas, in order to avoid saturation of electronic equipment, as strong signals were received in this case, whereas 23 dBm was the maximum power supplied by the signal generator, and it was just employed for dipoles as the last resource to get the maximum feasible distance. The usual power was 18 dBm to avoid harmonic distortion. Regarding transmitted frequencies, three bands were tested, especially the first two:  
* 10-100 KHz;
* 100 KHz-1 MHz;
* 1-20 MHz.  
During preliminary frequency measurements, frequency sweeps were performed up to 20 MHz to reach the maximum frequency that could be used. Nevertheless, no signal was received in such a high band under our measurements conditions. This is the reason why the upper limit for characterization was established in 1 MHz. To obtain higher resolution, the spectrum below 1 MHz was divided into two bands 10-100 KHz and 100 KHz-1 MHz.  
In the frequency response measurement procedure, a sinusoidal signal was transmitted and constantly swept between a range of frequencies. At the same time, data was retrieved from the spectrum analyzer in raw recordings of all sweeps in a time-frequency matrix of power data. This enabled easy post-processing and graph generation. Looking for maximum received power in each frequency, we were able to obtain attenuation curves.  
Time variation measurements were carried out with antennas in a fixed position, placed on the seabed and separated 4 m. A programme was employed to vary sine frequency to 40, 46 and 90 KHz each 10 s over a period of two hours.  
Figure 6: Attenuation description.
## 5 Results and Analysis
### Urban (Scenario I)
#### 5.1.1 Urban 5G Greenfield (Scenario I, Case I)  
To fulfill the coverage and capacity constraints, we calculated the estimated RRUs in the urban area as 70. Figure 3 shows a randomly generated population of 10,000 with the random initial deployment of 70 RRUsm for simulation in 2 km \(\times\) 2 km, i.e., 4 km\({}^{2}\) urban area. We implemented the metaheuristic algorithms PSO, GWO, SSA, WOA, ALO, and MPA simultaneously for proper placement of RRUsm using initial parameters as lower bound = 0, number of search agents = 30, and maximum number of iterations = 3000, upper bound = 2 and the number of RRUs = 70. Figure 7 shows the convergence of all algorithms and Figure 8 shows time execution for all algorithms. Figure 9 shows that MPA performed best to obtain the best-optimized locations among all algorithms, and Figure 10 shows the best-optimized locations and their coverage with hexagonal cell coverage.  
Figure 7: Convergence urban 5G.
## 5 Illustrative Example
### Scenario 4: Dispose  
Defective components or used materials need to be disposed of properly. The cobot assists by disposing of these items in designated waste bins. This is carried out using the following three steps.  
1. Initial Setup: - Waste disposal bins are identified and labeled on the workstation map. - The cobot's control system stores the coordinates for these bins.
2. Task Execution: - Worker: \"Cobot, use trajectory \"2\" to dispose of this defective capacitor in Waste Bin C\". (Figure 2) - Cobot: \"Disposing of defective capacitor in Waste Bin C\". - The cobot identifies the capacitor and moves its arm above the table, picks up the defective capacitor, and disposes of it in Waste Bin C. - Cobot: \"Defective capacitor disposed of in Waste Bin C\".
3. Follow-up: The worker continues assembling the electronic board, confident that defective parts are properly disposed of, maintaining a safe and clean workspace.  
Throughout the task, the human worker and cobot maintain a continuous, clear line of vocal communication. The cobot's predefined responses and actions ensure that the worker is always aware of the cobot's status and actions, fostering a collaborative environment.  
This integration not only improves task efficiency but also enhances safety and organization in the workspace. This detailed illustrative example demonstrates how vocal communication strategies can effectively coordinate human and cobot interactions in a manufacturing setting. By optimizing the pick and place, fetch, return, and dispose scenarios, the collaboration between human workers and cobots can significantly enhance productivity, safety, and overall operational efficiency [1; 2; 3; 4; 5; 6; 72; 73; 74; 75; 76; 77; 78]. This framework serves as a guide for implementing similar collaborative tasks in various industrial environments.