esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Security Analysis
### Comparison of Execution Time for Different Techniques  
Several existing works have proposed digital signature schemes optimized for various environments, such as vehicular ad hoc networks (VANETs) and IoT. Notable techniques include:  
* [PERSON] et al. (2023) [14] combined functional signatures and multiparty ECDSA signatures to create a multiparty functional signature for private blockchains. This paper used a personal computer (RedmiBook with AMD CPU Ryzen 5 5600H with Radeon Graphics @ 3.30 GHz with 16.0 GB RAM and Windows 10 Home OS) implementing the proposed scheme in Python 3.9.6. The number of nodes is 3.
* [PERSON] et al. [23] designed a multiparty collaborative signature scheme based on SM2 digital signature algorithm in the standard \"SM2 Elliptic Curve Public Key Cryptography\". The experimental PC operating system is win10 operating system, the processoris Intel(R) Core(TM) i7-5500U CPU @ 2.40 GHz 2.39 GHz, 8 G RAM; the large integer library gmp-6.2.0 is selected for implementation; the main programming language is C; the platform used to implement the algorithm is Visual Studio 2019. The number of nodes is 5.  
Table 1 compares the execution times of these schemes with the proposed HMPS. The computer base used for the testing of our algorithm was CPU: Intel Core i7-10610U 2.30 GHz. RAM: 16 GB DDR4, and storage: SSD. Operating system: Windows 11, and programming language: Python 3.10. The number of nodes is 3. In these experiments, we adopted Equation (6) for encryption and not Equation (7).  
The HMPS scheme outperforms these prior techniques in terms of execution time while maintaining robust security properties. Its hierarchical design and efficient aggregation process reduce computational and communication costs, making it an excellent choice for real-time, resource-constrained systems.  
In the context of signature generation techniques, a crucial metric for evaluating performance is the execution time or computation cost. As shown in Table 1, HMPS demonstrates a significant advantage in terms of efficiency when compared to recent alternatives in the literature.  
HMPS vs. [PERSON] et al. (2023): When compared to the technique presented by [PERSON] et al. (2014), which has a computation cost of 343 ms, our proposed method is dramatically faster, by more than 5 times. This significant improvement in computation time highlights the potential of our approach for real-time or resource-constrained environments where efficiency is paramount.  
The clear reduction in execution time shown by our proposed method indicates its suitability for applications requiring fast processing, such as in scenarios where multiparty signatures need to be generated or verified rapidly, while the other methods may provide additional functionalities or security benefits that come with a higher computational cost, the trade-off between efficiency and other features would need to be carefully considered based on specific application requirements.
# Cladding-Pumped Erbium/Ytterbium Co-Doped Fiber Amplifier for C-Band Operation in Optical Networks
## 3 Results and Discussion
The EYDFA parameters remain unchanged (7 m EYDF, 3 W, 975 nm, co-propagation), and the input optical power is set to \(-20\) dBm per WDM channel. The results in Figure 7 shows the power penalty below 0.1 dB at the reference level of BER = \(10^{-9}\) compared to the configuration without the amplification. Such distortion levels can be considered as negligible. However, its gain spectra should be flattened out, e.g., by using gain flattening filters or several amplification stages, before such amplifiers can be efficiently used for optical loss compensation in WDM transmission systems. However, this aspect deserves separate attention and thus will be addressed in future work.
## 3 Results
### Landslide Susceptibility Models  
Three landslide susceptibility models were produced from bivariate statistical analysis using 7 landslide conditioning factors (Figures 2 and 3). The weights calculated for these three models are listed in Table S1. In the first model (model 1), all the landslide points from the 2014 inventory were used and there was no spatial partitioning of the dataset (Figure 4a). The AUC of the SRC of this model is 0.91 (Figure 5a). The areas classified as very high and high susceptibility cover 7% and 9% of the city, respectively. These two susceptibility classes are concentrated in the northeastern, west-central, and southern parts of Kuala Lumpur; much of this area is not developed. About 7% of the area is classified as moderately susceptible. The moderately susceptible areas are distributed mainly in the vicinity of the two earlier classes. The low susceptibility areas (20%) are scattered around the earlier classes and within the very low susceptibility class. Covering 57% of the area, the very low susceptibility class is the largest. It is distributed mainly in the northern and eastern Kuala Lumpur, in relatively flat areas underlain by alluvium and mine tailings.  
The next two models were used for spatial cross-validation, where the study area is partitioned into a series of two alternating linear zones (zones A and B) trending east-west. In the initial partitioned model (model A), the susceptibility map is generated using landslide points in zone A as the training dataset and landslide points in zone B as the test dataset (Figure 4b). The role of landslide points in zones A and B are reversed in the next model (model B) (Figure 4c). The distribution of the very high to moderate susceptibility classes of these two models are similar to the unpartitioned model (model 1), which obtained using the entire dataset of the inventory (Figure 4a-c). Compared to model 1, the low susceptibility areas of both models A and B are higher (27%), and the very low susceptibility areas are lower (48%).
## 6 Conclusions  
In this work, we proposed and developed a cooperative task execution of an edge-assisted object detector system and presented how to optimal deliver (offload) batches of images to minimize the E2E latency or maximize the execution accuracy of the system. Our purpose was to design an edge-assisted object detection that is tailored around the needs of the user to minimize the E2E execution time or to maximize the accuracy. After presenting our two optimization problems to solve them, we followed an experimental approach by testing all the possible configurations of the parameter which affect the system performance (execution time, accuracy). After testing all the possible configurations of the parameters which are presented in the Pareto plot, we found the optimal trade-offs of our parameters, which solve the two optimization problems \"manually\". It is concluded that the parameters which affect the E2E execution time and accuracy of the system are the encoding rate \(q\) (compression value) and the batch sizes (splitting decisions), which are sent to every end device. The key contribution of this study is that we highlight the optimal trade-offs of our parameters, which can be applied according to the needs of the user to minimize E2E latency or maximize the execution accuracy of the system. We demonstrated that offloading the workload on multiple end devices based on different splitting decisions and compression values can improve the system's performance to respond in real-time conditions without the need for a server or cloud resources.  
Conceptualization, D.K. and P.A.; methodology, D.K., G.I. and A.N.; software, P.A.; validation, G.I., T.L. and D.K.; formal analysis, D.K., T.L. and P.A.; data curation, P.A.; writing--original draft preparation, P.A. and D.K.; writing--review and editing, G.I., T.L. and A.N.; visualization, D.K. and P.A.; supervision, D.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Multi-User Scheduling for 6G V2X Ultra-Massive MIMO System
## 3 Ultra-Massive MIMO Multi-User Scheduling Based on BD Precoding
### Multi-User Scheduling Based on BD Precoding
The expression \(coor_{ks}\) represents the Pearson coefficient between the user \(k\) in the candidate user set and the selected user set \(\hat{\mathbf{H}}\). The specific steps of the above scheduling algorithm are shown in Algorithm 1:  
```
Input: channel matrix \(\mathbf{H}\), number of users \(K\), number of users' antennas \(N_{r}\), number of users by the base station \(R\), number of current iterations \(i=1\). Output: scheduling user set \(\hat{\mathbf{H}}\), the number of final scheduling users of the base station \(M\). Step1: Establish candidate user set \(\mathbf{S}=\{1,\ 2,\ \dots,\ K\}\) and selected user set \(\hat{\mathbf{H}}=\varnothing\); Step2: Traverse the candidate user set \(\mathbf{S}\), select \(\mathbf{s}_{1}=\underset{k\in S}{\mathrm{argmax}}(C_{k})\). Update the candidate user set \(\mathbf{S}=\mathbf{S}-\{\mathbf{s}_{1}\}\) and the selected user set \(\hat{\mathbf{H}}=\hat{\mathbf{H}}+\{\mathbf{s}_{1}\}\), the number of selected users is \(M=1\) with \(C_{k}=B\log_{2}\det\left(\mathbf{I}_{N_{r}}+\frac{P}{{R^{\alpha}}}\mathbf{H} _{k}\mathbf{H}_{k}^{H}\right)\); Step3: Calculate the channel capacity of the selected user set at this time \(C_{\mathrm{max}}=B\log_{2}\det\left(\mathbf{I}_{N_{r}}+\frac{P}{{R^{\alpha}}} \mathbf{H}_{k}\mathbf{H}_{k}^{H}\right)\); Step4: If the number of iterations is \(i<R\), perform the following steps, otherwise skip to Step10; Step5: Traverse each user in the candidate user set \(\mathbf{S}\) and calculate the scheduling factor \(\rho_{k}\) of each user and the Pearson coefficient \(coor_{kl}\) between the user and the selected user set \(\mathbf{S}\); Step6: Select users who meet \(\mathbf{s}_{i}=\underset{k\in S}{\mathrm{argmin}}_{k\in S}{coor_{kl}}\) and calculate the system capacity \(C_{m}\) currently; Step7: If the current system capacity meets \(C_{m}>C_{\mathrm{max}}\), the algorithm continues, otherwise skip to step 10; Step8: Update candidate user set \(\mathbf{S}=\mathbf{S}-\{\mathbf{s}_{i}\}\), selected user set \(\hat{\mathbf{H}}=\hat{\mathbf{H}}+\{s_{i}\}\), maxi mum system capacity \(C_{\mathrm{max}}=C_{m}\), and user scheduling number \(M=M+1\); Step9: \(i=i+1\), jump to Step4; Step10: The algorithm ends, the scheduling user set \(\hat{\mathbf{H}}\) is obtained. ```  
**Algorithm 1** Multi-User Scheduling Based on BD Precoding for UM-MIMO System
# Research on Radionuclide Identification Method Based on GASF and Deep Residual Network
## 3 Establishment of Neural Network Model
### Evaluation  
The neural network model features a complex structure and is widely applied. In practical applications, the quality of the network's training results becomes the primary focus.  
Figure 4: ResNet18 network model: (**a**) overall structure of the ResNet18 network; (**b**) CONV BLOCK structure; (**c**) ID BLOCK structure.  
Figure 3: Structure diagram of traditional neural network and residual block: (**A**) traditional neural network; (**B**) residual neural network.  
Evaluation metrics provide a quantitative benchmark for model performance, effectively assessing its capabilities in specific tasks and helping to understand the model's accuracy, stability, and robustness. Nuclide recognition based on deep learning can be viewed as a multi-class classification task. In cases of imbalanced classes, a single accuracy measure often fails to reflect the true performance. Therefore, this paper introduces additional evaluation metrics, including precision, recall, F1 score, and loss, which are derived from neural network evaluation standards. These metrics offer a more comprehensive understanding of the model's performance across different classes, thereby enhancing the recognition capability of minority classes. The calculation methods and interpretations of each metric are presented in Table 1.  
Loss quantifies the discrepancy between the model's predicted outcomes and the true labels, typically represented by a numerical value. A smaller loss indicates a more accurate prediction by the model. In deep learning-based nuclide recognition tasks, the loss is used to measure the difference between the predicted probabilities of each nuclide category and the actual category labels. In this context, TP refers to the number of correctly identified target nuclides, FP denotes the number of non-target nuclides incorrectly identified as target nuclides, TN indicates the number of correctly identified non-target nuclides, and FN represents the number of target nuclides that could not be identified.
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 4 Experiments  
### Dataset  
We evaluated our model on the KITTI dataset. The KITTI dataset contains 61 outdoor scene images that were collected using cameras and laser scanners on moving vehicles. The outdoor scenes often have significant depth variations and contain nearby objects such as homes, pedestrians, cars, etc., as well as far away objects such as the sky. The resolution of the RGB images is 1242 \(\times\) 375 pixels. Each RGB image has a corresponding depth map. We referred to [2] to split the dataset into training and testing sets. The training set had 32 scenes with 23,488 images and the testing set had 29 scenes with 697 images.
# Construction of Power Fault Knowledge Graph Based on Deep Learning
## 1 Introduction
### Power System Fault Processing Technology Based on Deep Learning Technology
In addition, there is not much research on text processing in this field, and there is still a lack of universal text data sets, which need to be made by ourselves. Due to the diversity of entities in power equipment fault defect text, the relationships between entities will be more complicated, which greatly increases the difficulty of accurately judging the relationship between entities. A log system usually contains a large variety of information related to faults and a log analysis helps researchers collect fault information and perform association analysis. Therefore, making full use of the fault prior knowledge in the power field, integrating the abnormal logs generated in the operation of the power system, and constructing a perfect and practical power system fault knowledge graph is of great significance to improving the power system fault handling capacity and level of intelligence. At present, the construction of the power grid fault knowledge graph is mainly based on the distribution network equipment account data, fault handling data, dispatching regulation data, and distribution network defect data as objects, most of which are unstructured data written in Chinese. In the process of Chinese knowledge graph construction, the word segmentation system can directly realize the boundary division of most entities and concepts, so the performance of the word segmentation system plays a key role in the construction of a knowledge graph. The performance of Chinese word segmentation systems in standard data sets (such as news corpus) is nearly perfect, but the performance of the Chinese word segmentation system in other specific fields without annotations is not satisfactory, especially in some professional fields, because there are a large number of unknown words. In order to solve this problem, this paper introduces logs in the system as the source of fault data. Logs are semi-structured data with a large quantity of rich information, which is convenient for automatic processing. A new BBIGRU-GA-CRF model is proposed. The model firstly identifies and extracts the power equipment entities from the pre-processed logs and Chinese technical literature, and then extracts the semantic relationships between the entities using the relationship classification method based on dependency parsing. Finally, the extracted knowledge is stored in the form of triples in the Neo4j database and visualized in the form of graphs. Through the above steps, the Chinese knowledge graph of power equipment fault is established.
## 2 Materials and Methods
### Workflow  
The workflow developed for the validation and the evaluation of the TPS can be summarized as follows: (i) phantoms creation, (ii) simulation of the SPECT projections, (iii) attenuation map generation and tomographic reconstruction, (iv) creation of the input file for the MC simulation, (v) conversion of the GATE output file in absorbed dose images, (vi) calculation of absorbed dose images by convolution of voxel S-values (the latter calculated independently from the TPS tested here) for comparison and validation purposes. The software packages (and respective references) used for each step are reported in the corresponding subsection.
# Analysis and Correction of Antenna Pattern Effects in AMAO Spaceborne SAR Images
## 4 Correction Method  
In this section, the method of the antenna pattern correction is improved to normalize the 2D radiometric variation in AMAO SAR images. The proposed method accounts for the insensitivity to errors in antenna patterns.  
The antenna pattern has a 2D effect on the radiometric variation in AMAO SAR images. A direct approach to radiometric normalization is a numerical evaluation of the integrated energy for each resolution unit or coarser grid, taking into account the full 2D antenna pattern. From Equation (14), the relative correction factor of each given resolution cell can be obtained by integration  
\[K_{ij}=\int_{t_{1}}^{t_{2}}\frac{G_{ij}^{2}(t)}{R_{ij}^{4}(t)}\mathrm{d}t\,, \tag{21}\]  
where \(i\) and \(j\) are the index variables in azimuth and range, respectively. This approach requires integration operations for each cell, leading to high computational costs. To improve efficiency, according to Equation (18), the SAR image can be corrected first using the reference antenna pattern, followed by additional corrections for the residual radiometric variation. From Equations (18) and (20), the additional correction factor can be obtained by  
\[K_{ij}^{\prime}=1+b_{j}(\mu_{1}+i\delta)\,, \tag{22}\]  
where \(\delta\) is the azimuth size of the cell. Since the normalization is applied to image intensities after image formation, it has no effect on the nonideal signal weighting induced by the 2D antenna pattern.  
Figure 5: Two-dimensional effect of antenna pattern in the image.  
For the normalization of radiometric variation and signal weighting, a novel approach is proposed according to the characteristics of the antenna pattern effect. It implements linear effect correction after range compression and before image formation. There are two necessary steps: the first is to derive the elevation-dependent linear coefficient in signal magnitude,  
\[b^{\prime}(\theta)=\frac{\partial}{\partial t}\left(\frac{G_{\text{eff}}(\theta, t)}{R^{2}(\theta,t)}\right). \tag{23}\]  
The second step is to remove the 2D antenna pattern from the range-compressed signal  
\[\bar{s}(t)=\frac{R_{ref}^{2}(\theta)}{G_{ref}(\theta)}\,\frac{s(t)}{1+b^{ \prime}(\theta)t}\simeq A\sqrt{\sigma}\,G_{az}(t)\,. \tag{24}\]  
The following step is image formation. This method improves both the radiometric precision and impulse function. The normalization is very stable because the correction of the linear effect is insensitive to the antenna pointing error and Doppler estimation error.
# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
###### Abstract  
In complex indoor and outdoor scenarios, traditional GPS-based ranging technology faces limitations in availability due to signal occlusion and user privacy issues. Wireless signal ranging technology based on 5G base stations has emerged as a potential alternative. However, existing methods are limited by low efficiency in constructing static signal databases, poor environmental adaptability, and high resource overhead, restricting their practical application. This paper proposes a 5G wireless signal ranging framework that integrates mobile edge computing (MEC) and crowdsourced intelligence to systematically address the aforementioned issues. This study designs a progressive solution by (1) building a crowdsourced data collection network, using mobile terminals equipped with GPS technology to automatically collect device signal features, replacing inefficient manual drive tests; (2) developing a progressive signal update algorithm that integrates real-time crowdsourced data and historical signals to optimize the signal fingerprint database in dynamic environments; (3) establishing an edge service architecture to offload signal matching and trajectory estimation tasks to MEC nodes, using lightweight computing engines to reduce the load on the core network. Experimental results demonstrate a mean positioning error of 5 m, with 95% of devices achieving errors within 10 m, as well as building and floor prediction error rates of 0.5% and 1%, respectively. The proposed framework outperforms traditional static methods by 3\(\times\) in ranging accuracy while maintaining computational efficiency, achieving significant improvements in environmental adaptability and service scalability.  
5G network; environment perception service; mobile edge computing (MEC); signal fingerprint +
Footnote †: journal:  
Article  
Rui Lu  
1 National Computer Network Emergency Response Technical Team/Coordination Center of China,  
Beijing 100029, China; [EMAIL_ADDRESS] (R.L.); [EMAIL_ADDRESS] (Z.D.)  
2 Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100085, China  
3
## 2 Related Work
The two models are the contactless measuring method of thermal comfort based on saturation-temperature (NIST) and the contactless measuring method of thermal comfort based on partly saturation-temperature (NIPST). [PERSON] [22] proposed a contactless measuring method based on human poses. A total of 12 poses of thermal comfort were defined and Kinect was adopted to estimate human skeleton and poses. Further, [PERSON] [23] also used Kinect to predict human thermal sensation and dynamic metabolic rates. With the development of machine learning (ML) and computer vision (CV), some thermal comfort perception methods based on ML and CV were proposed. Support Vector Machine (SVM) are often used for analyzing existing databases (RP-884) and captured environmental parameters [24; 28; 29]. Further, [PERSON] [26] use unsupervised and supervised learning to predict occupants' behavior, applied to three types of offices which are single person offices, multi-person offices, and meeting rooms. The results show that the average energy savings for the entire space is 21% in experimental condition. [PERSON] [30] proposed a fuzzy model to predict thermal sensation, skin temperature and heart rate considered as objective parameters. For avoiding overheating, [PERSON] [31] extracted data from multiple local body parts and analyzed them with four kinds of machine learning algorithms, including SVM, Gaussian process classifier (GPC), k-neighbors classifier (KNC) and random forest classifier (RFC). The kinds of machine learning adopted in references [24; 26; 28; 29; 30; 31] are traditional algorithms. In recent years, the use of deep neural networks is on the rise [32; 33]. In addition, a kind of subtleness magnification technology was presented [34; 35]. These provide new directions and opportunities for the measurement of human thermal comfort. Based on this technology, a novel contactless measuring method was generated which will be introduced as follows.