esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Classification of Customer Groups
#### 2.1.2 Sample Customer Clustering Based on AAP Algorithm
Here, the responsibility \(r(R_{i},R_{j})\) represents the degree to which customer \(R_{j}\) is suitable as the cluster center for customer \(R_{i}\), and the availability \(a(R_{i},R_{j})\) represents the suitability of customer \(R_{i}\) selecting customer \(R_{j}\) as the cluster center. \[r(R_{i},R_{j})\ =\ s(R_{i},R_{j})-\max\{s(R_{i},R_{j})\}. \tag{8}\]  
\[a(R_{i},R_{j})\ =\ 0. \tag{9}\]  
Step 2: Update the responsibility and availability of customer profiles for the new iteration. The results of the \(N\)-th iteration are shown as follows:  
\[r_{N}(R_{i}  
Step 4: If \(N\) reaches the predefined maximum number of iterations \(D_{\text{max}}\), proceed to Step 5. Otherwise, set \(N=N+1\) and return to Step 2.  
Step 5: Obtain the cluster center set \(C_{center}\), and use Equation (12) to determine whether customer \(R_{j}\) is a cluster center.  
AAP clustering algorithm is based on the traditional AP clustering algorithm, where in each iteration, the self-similarity of the users is updated, and the step size is dynamically adjusted according to the number of clusters, as follows:  
\[s(A_{j},A_{j})\ =\ s(A_{j},A_{j})-s_{\rho}\ =\ s(A_{j},A_{j})-\frac{\mu_{m}}{10 \sqrt{Q+50}}, \tag{13}\]  
where \(s_{\rho}\) is the update step size, \(\mu_{m}\) is the median similarity between \(R_{j}\) and other user profiles, and \(Q\) is the number of clusters. Based on the clustering results obtained from each iteration, the clustering quality metric \(Z_{Q(av)}\) is calculated as follows:  
\[Z_{Q(av)}\ =\ \frac{1}{I}\sum_{i\,=\,1}^{I}Z_{Q}(R_{i})\ =\ \frac{1}{I}\sum_{i\,=\,1}^{I} \frac{d_{out}(R_{i})-d_{in}(R_{i})}{\max(d_{out}(R_{i}),d_{in}(R_{i}))}, \tag{14}\]  
where \(Z_{Q}(R_{i})\) is the tightness of user \(R_{i}\) with its corresponding cluster center when the number of clusters is \(Q\), \(d_{out}(R_{i})\) is the average inter-cluster distance, representing the average distance between user \(R_{i}\) and user profiles from other clusters, and \(d_{in}(R_{i})\) is the average intra-cluster distance, representing the average distance between user \(R_{i}\) and other user profiles within the same cluster. Compare the clustering quality metric \(Z_{Q(av)}\) for different numbers of clusters. The number of clusters that corresponds to the maximum value of \(Z_{Q(av)}\) is considered the optimal number of clusters. The detailed flowchart of the AAP-based customer clustering method is shown in Figure 2.  
Figure 2: Flowchart of sample customer clustering method based on AAP algorithm.
## 1 Introduction  
With the proliferation of readily accessible high resolution satellite imagery, many researchers have focused their efforts on multi-temporal imagery analysis. [PERSON] and [PERSON] astutely observe that the temporal aspect of spatial data has become an increasingly important component for analysis applications [1], including image to image change detection. One example of such a system is the Geospatial Change Detection and Exploitation System (GeoCDX), a fully-automated system for large-scale change detection in high resolution imagery [2], which was recently published in a Special Issue on multi-temporal analysis of remote sensing data [3]. Other approaches for high resolution change detection include using neural networks [4], hierarchical clustering [5,6], expectation maximization level sets [7], morphological attribute profiles [8] and segmentation [9]. However, in many cases, simply identifying the changes that have occurred is not sufficient.  
Several change detection approaches focus the identification of change for very specific purposes. A sampling of these includes mapping land cover patterns for urban growth modeling [10], identifying areas in need of vegetation cover rehabilitation [11] and estimating seismic risk [12]. In this manuscript, we are interested in permanent, anthropogenic changes; a more detailed description is provided in Section 3.1.  
We propose an approach for automatically identifying relevant change using a classifier that has been trained with user-identified examples of relevant change. If a user views exemplar regions of a pair of multi-temporal images and provides an assessment of whether or not a relevant change occurred, then we should be able to train a system to then classify other regions within the image.  
In previous work, we developed a query-by-example (QBE) system for content-based image retrieval (CBIR) [13, 14, 15] that could identify imagery in a database that matched a given query image. In [16], [PERSON] and [PERSON] developed semantic models using genetic optimization of low-level image features. Other examples of applying data mining algorithms to remote sensing imagery include mining temporal-spatial information [17] and using association rules to extract information from the gaze patterns of individuals viewing satellite imagery [18].  
In this manuscript, Section 2 presents a high-level overview of the GeoCDX change detection system, as this serves as the source of the imagery features and change annotations used in the prediction of relevant change. Our definition of relevant change is given in Section 3 along with a description of the classification algorithms used. Section 4 describes the experiments performed to evaluate the change prediction algorithms and discusses the meaning of the results. Finally, Section 5 provides a conclusion and a brief description of future directions of research.
# Secured Big Data Analytics for Decision-Oriented Medical System Using Internet of Things
## 4 Simulations
Figure 9 illustrates the performance of the SBD-EC model against other solutions for second scenario. It can be seen from the experimental results that the SBD-EC model decreases the ratio of retransmitted packets by 37% and 45% compared to other models. This is due to the fact that the SBD-EC model decreases the probability of choosing non-optimal routes for forwarding healthcare data even in the case of mobility of medical edges. The incorporation of latency values increases the ratio of routes that can decrease the delivery ratio, thus the SBD-EC model is continuously searching for another route until it finds the optimal performance. Moreover, a properly secured and mutual authentication-based solution is offered by the SBD-EC model, which strengthens the communicating nodes in terms of packet delivery performance with the least transmission distance. Figure 8: Packet disturbance and mobility. Figure 7: Normalized overhead and iterations. Figure 10 depicts the performance of the SBD-EC model for second scenario in the comparison with other solutions. Based on the experimental results, it is revealed that it increases the packet delivery ratio by 13%, and 17%. This is due to the fact that the SBD-EC model minimizes the number of hops and data latency in forwarding the nodes' data. Also, the weighted cost values give uniform contributions from each parameter and optimize the performance. In the SBD-EC model, the mobile medical edge performs a key role in data collecting and forwarding toward cloud services. Moreover, due to the mobility factor, the SBD-EC model decreases the data retrieval rate from nodes and offers a significant improvement in message delivery. Furthermore, with proper management of crowding control on the selected route, the SBD-EC model maintains the well-timed distribution of health data to medical servers. Figure 11 illustrates the performance analysis of the SBD-EC model compared with other solutions in the case of breach attack. It can be seen from the experimental results that the SBD-EC model decreases the malicious attacks by 41%, and 49% and increases the trustworthiness among nodes. The generation of security keys by high computing nodes guarantees secure transmission in real-time scenarios even in the existence of intruders. Moreover, mathematical and related keys are utilized by directly connected nodes for data confidentiality and mutual authentication. Furthermore, the sequence of unique hashes gives little time to intruders for destruction and compromising the sensors' data. The SBD-EC model sets up the routing paths with data security along with reliable delivery, thus increases the trustworthiness for medical data without ignoring the resource constraints. Communication routes are such that energy consumption is balanced. Figure 10: Delivery ratio and mobility. Figure 9: Retransmitted packets and mobility.
## 3 Experiment and Discussion
### UAV Experiment  
To evaluate the model's performance on UAV data, we collected several samples to train this model. The results of the super-resolution experiments are presented in Figure 10.  
In Figure 10, each sample group consists of the low-resolution image, the reconstruction, and the high-resolution result, from left to right. Upon examining these image groups, we observed that DSpix2 pix still demonstrates super-resolution capabilities for UAV data, but the issue of style deviation is more pronounced compared to satellite data. In some cases, such as in Figure 10a, the network is able to generate style-consistent reconstructed images. However, this is not the case in many other instances. For example, as shown in Figure 10b,c, the ground that should be brown exhibits a tendency to shift toward a greenish tone, while the grass that should be green shows a drift toward a brownish pavement, as seen in Figure 10d. The issue likely arises from the conflict between the high resolution of the UAV data and the working mechanism of the StyleGAN generator. StyleGAN decouples the style information of input images through its Mapping Network, allowing for perfect style fitting. This mechanism enables StyleGAN to account for the various styles present in  
Figure 10: UAV super-resolution results. (**a**) is railways; (**b**) is train station; (**c**) is road; (**d**) is airports.  
Figure 9: Example of results at the 1024 scale for the 2\(\times\) super-resolution task with or without additional style injection.  
the dataset. However, different land cover types often have distinct styles, and in trying to balance these styles, the problem of style deviation emerges. While we mitigated this issue in satellite images by incorporating Style 2, the higher resolution of UAV data introduces a greater challenge. The style differences between samples in UAV data are much more pronounced than in satellite imagery, making it harder to maintain style consistency. Even with the addition of style components, the results using StyleGAN remain suboptimal. In the future, we will explore more effective methods for controlling image styles, such as extracting the style from sample images and feeding it into the mapping network.  
Finally, we will explain the resource allocation during the training. The proposed DSpix2 pix network has a model weight size of 432 MB (float32). Training time is approximately the same as pix2 pix, with a slight increase of 6-8 h due to the inclusion of style encoding and style convolution layers. The GPU memory requirement for training is 10 GB, which is sufficient for completing the process. This indicates that the model can run efficiently on common consumer-grade GPUs (e.g., NVIDIA RTX 3080 or RTX 3090), without requiring high-performance computing platforms. This trade-off in training time is justified by the significantly improved quality of super-resolution images. Future optimizations, such as mixed-precision training or pruning redundant computations, could further reduce computational costs while maintaining quality.
## 3 Experiment Results and Discussions
### Optimization of Weld Bonding Conditions
Figure 5: Relationship between the nugget diameter of the welding condition and the tensile shear strength after a salt spray test (1920 h). manually, so there was variation in the thickness of the adhesive, which is thought to have affected heat generation and the resulting nugget sizes. The important point is that the highest corrosion resistance was observed in Specim 4, which had the roughened surface. It is well known that the tensile shear strength of spot welds increases as the nugget diameter increases [22; 23]. Specim 4 had the smallest nugget diameter, even though the welding conditions were the same as with Specim 3, which is assumed to be due to the variation in contact resistance and adhesive thickness and the resulting change in the amount of heat generated. The tensile shear strength of Specim 4 also decreased as the salt spray test time elapsed, but the strength drops were not significant after 1280 h. This is believed to be due to the fact that it could not prevent the decrease in strength. Due to corrosion on the outer surface of the specimen by the corrosion solution, penetration into the faying interface could be prevented for a longer period of time, compared with Specimens 2 and 3. It was confirmed that the corrosion resistance increased by applying the adhesive, and the effect became greater when the adhesive was applied by increasing the roughness at the interface. As a result of measuring the roughness by 3-D optical contour mapping on the specimen surface, as shown in Figures 6 and 7, the average surface roughness of the roughened Specim 4 (with 60 grit SiC abrasive paper) increased by about three times compared with that of the plain AA6061-T6 specimen. Figure 6: Measured surface roughness (**a**) before and (**b**) after roughening of the AA6061-T6 aluminum alloy specimen.
# Candidate Waveforms for ARoF in Beyond 5G
## 3 Candidate Modulation Formats for MmWave Over UDWDM-PONs
### Single Carrier Amplitude and Phase  
This solution supposes higher order modulation than the waveforms explained above. Its main features are the same as for a single carrier waveform. The modulation formats employed are based on amplitude and phase. Therefore, high spectral efficiency can be achieved. However, these systems are, in general, weaker to phase noise.  
One of the most popular amplitude and phase modulation formats is QAM. Based on it, amplitude and phase shift keying (APSK) was proposed and, as QAM, it is considered a combination of amplitude shift keying (ASK) and phase shift keying (PSK), but without being restricted to quadrature constellations. Namely, APSK is more flexible than QAM and, thus, there are many APSK constellation designs for different channels [12] with remarkable importance for those focused on achieving a channel capacity very close to the Shannon limit.
## 5 Conclusions and Future Study
Future studies should focus on resolving the limitations of the method by using the geometrical information of the buildings for localization of signals at unlearned positions or domain adaptation techniques for better knowledge transfer. In addition, although the CNN-based approaches showed their generalizability, the datasets are only suitable for the designed tasks. To deal with tasks with a high degree of freedom, the data are expected to be updated considering many different scenarios for better generalization, e.g., change of the receiver's position and orientation. Conceptualization, W.S. and H.Y. ; methodology, H.C., W.S. and H.Y. ; software, H.C.; validation, W.S. and H.Y. ; data curation, H.C., W.S. and H.Y. ; writing, H.C., W.S. and H.Y. ; supervision, W.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Research Foundation of Korea (NRF) with a grant funded by the Korea government (MSIT) (No. NRF-2017R1E1A2A01078766) and (No. NRF-2019R1F1A1058794) and supported by the Agency for Defense Development in Korea under Contract No. UD190005 DD. Not applicable. The authors thank Sangkyum An and Monseuk Park for providing the experimental sites. The authors declare no conflict of interest.
## 7 Conclusions  
In this paper, we presented a novel algorithm designed to detect spoofing signals and accurately predict the ToA of a spoofer in IoT environments. Our algorithm is robust against spoofer attacks and offers significant improvements with respect to the effectiveness and efficiency of location spoofing signal detection and localization systems. The approach begins by estimating the spoofer's ToA based on the received power at the AP. It then utilizes a feature extraction method using a single AP to detect and predict the spoofer's ToA. This allows for the estimation of the distances at different points relative to the predicted ToA. Additionally, we collaborated with multiple APs to determine the positions of the spoofed UAVs in 3D space.  
The results of our study demonstrate the effectiveness of our proposed model in detecting and localizing spoofing signals. By accurately detecting the presence of spoofing signals and predicting the spoofer's ToA, we can estimate distances at various points with respect to the predicted ToA. This research contributes to the ongoing efforts to combat location spoofing attacks in IoT-based applications. Our proposed method offers a reliable and robust solution for detecting and localizing spoofing signals, thereby enhancing the security and reliability of location-based services in IoT environments. By addressing the challenges posed by location spoofing attacks, we can ensure the integrity and trustworthiness of IoT systems, thus safeguarding their crucial applications and services.  
This research work was funded by the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia through the project number (IF2/PSAU/2022/01/23134).  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through project number (IF2/PSAU/2022/01/23134).  
The authors declare no conflict of interest.  
Figure 18: The XYZ coordinates estimated by MLP and LSTM. The coordinates predicted by the LSTM model are represented by the triangle, square, and circle dashed lines, while the solid line depicts the coordinates predicted by the MLP model.
# Efficient Preamble Design Technique for Millimeter-Wave Cellular Systems with Beamforming
###### Abstract
Therefore, a new preamble design technique is needed for mmWave cellular systems that can provide a larger number of CIDs and permit a CBID detection with a lower computational complexity. In this paper, a CDM-based preamble design technique for the mmWave cellular communication systems is proposed using a preamble sequence constructed by a combination of two ZC sequences. It is shown that the proposed preamble design technique can increase the number of CBIDs significantly, compared to the previous technique generated using a single ZC sequence. It is also shown that the proposed technique can reduce the computational complexity in the CBID detection without a noticeable performance degradation, using a specific mapping rule and a parameter selection method. Because of the special structure used in preamble generation, the proposed preamble is shown to have a low PAPR characteristic similar to the one obtained using a single ZC sequence. The rest of this paper is organized as follows: in Section 2, a preamble design technique using two ZC sequences is proposed for OFDM-based mmWave cellular systems with beamforming. In Section 3, an efficient technique for the CBID detection is described for the proposed preamble. Parameter selection methods are also discussed to avoid the performance degradation caused by a preamble sequence constructed using a combination of two ZC sequences. The performance of the proposed technique is evaluated by computer simulation in Section 4. The conclusions are drawn in Section 5.
# Design and Simulation of Low-Orbit Satellite Broadcast Signal Receiving and Processing Terminal
## 4 Simulation Results
### Digital Beamforming Simulation

Table 2: Sidelobe level reduction and gain of several typical beam angles after optimization (G/T value is \(-9\) dB/K). Figure 19: The (\(\theta\), \(\varphi\)) = (0\({}^{\circ}\), 45\({}^{\circ}\)) beam characteristics before and after optimization. of 1 dBic at \(\pm 35^{\circ}\) and \(\pm 80^{\circ}\), and nulls are formed in the remaining directions. Since the sidelobe sizes in the four directions are all greater than 0 dBic and the energy is strong, the beamforming effect is not so good; after using Taylor distribution to optimize the beamforming directivity diagram, the main lobe at the desired direction \(\varphi=0^{\circ}\) is slightly weakened, from the original 15.3 dBic to 15.0 dBic, but the sidelobes at \(\pm 35^{\circ}\) are well suppressed to form nulls, and the sidelobes at \(\pm 80^{\circ}\) are also well suppressed, with the strength reduced from 1 dBic to \(-5\) dBic. It can be seen that the use of Taylor distribution can improve the beamforming directivity diagram to a certain extent. Similarly, it can be seen from Figure 19 that before optimization, for the directivity diagram at the desired direction of \(\varphi=45^{\circ}\), the signal strength in the main lobe direction is 13 dBic, and there is a sidelobe with an intensity greater than 0 dBic in the \(0^{\circ}\) and \(90^{\circ}\) directions, and no null is formed well in other directions, and the sidelobe energy is strong; after optimization using the Chebyshev distribution, although the main lobe is slightly weakened compared to before optimization, the energy drops from the original 13 dBic to 10 dBic, but nulls can be well-formed in other sidelobe directions, and the sidelobe energy is greatly weakened compared to before optimization. It can be seen that the use of Chebyshev distribution can improve the directivity diagram of beamforming to a certain extent.