esa-sceva/satcom-chunk-collection huggingface dataset:
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 6 Evaluation
### Performance of Query Scheme  
To verify the feasibility of the whole scheme and to simplify the whole process of querying description files, we visualized the area responsible for each fog node as a 10 \(\times\) 10 matrix area, with one POI in each small area. Considering the practical situation, we designed 3 \(\times\) 3 of matrix regions, which means that there were 9 fog nodes and 900 different small regions (for simplicity, we specify that each small region has only one POI attribute). The regions are shown in Figure 11.  
We set the range of areas that the user is allowed to query to be a rectangular area of 5 \(\times\) 5, locations beyond this range are not considered part of the query result. Since we used the k-anonymity algorithm to protect the privacy of the querying user, the query area was expanded accordingly. We set k to 9, which expanded the corresponding query area to a rectangular range of 7 \(\times\) 7. The region surrounded by the red line in Figure 11 was the true area of the LBS user, and the region surrounded by the green line was the expanded area.  
Figure 11: Region area.  
Figure 10: Blockchain incentive.  
Regarding the size of the verification objects, only when the number of counters was more than 9585, the false positive was less than 0.01 [9]. So we set the number of counters in the fog node verification object to 10,000. To verify the impact of the verification and padding regions of the description file verification objects on the description file query, we designed two sets of comparison tests, i.e., the verification regions of the counting Bloom filter of the description file verification objects were designed to be 10,000 and 20,000, and the padding regions were designed to be 2500 and 5000. As mentioned above, the size of the fog node verification object was approximately 5 KB (10,000 \(\times\) 4 bits) and the sizes of the description file verification objects were approximately 6 to 12 KB (12,500 \(\times\) 4 bits to 25,000 \(\times\) 4 bits).  
We conducted 100 experiments for each POI type data query, with each query location randomly selected. The results of the experiments are shown in Figure 12.  
Figure 12a shows the time proportion of the preparation works of our scheme, including the construction of verification objects and the storage process. The time is about 1828 ms (100 description files). Figure 12b shows the time proportion of the one query. For most of them, it involves the incentive time for the fog node to reach the storage rewards. It takes about 14 s, which was determined by the time the blockchain generated a block. Indeed, the time the user obtained the required description files after querying and verifying was only about 307 ms.  
From the above experimental results, it can be seen that the average query time for each description file is within acceptable limits. The experiments show that the scheme proposed in this paper is practical and feasible.
## 4 Conclusions  
The nanostructured core@shell composites, in which the inner noble metal nanoparticle is encapsulated and protected from agglomeration, adsorption, or chemical reactions by the outer shell, attract a lot of attention due to their unique physical, chemical, biological, and catalytic properties. The thickness of the outer shell plays a huge role in the formation of the functional properties of nanocomposites and, therefore, the nanometer scale, which can be implemented using carbon materials with one or several layers, is of particular interest. The use of two successive processes during ablation, that is the formation of noble metal nanoparticles and subsequent carbon deposition, allows to obtain noble metal nanoparticles in a homogeneous and ultrathin carbon layer. In the present work, the surface of Ag-Au alloys colloidal nanoparticles, obtained by laser ablation in water was characterized by XPS and the formation of Ag-C bonds was established. We found low-energy chemical shift for the Au 4f doublet which has previously been observed for Au NPs on different substrates. Despite the existence of various interpretations of such a shift in the literature, we assume that low-energy shift of Au 4f levels in (Ag-Au)@LCC could be attributed to a size-induced gold-LCC charge transfer. It is shown that agglomeration can be eliminated by the interaction of colloidal nanoparticles with linear carbon chains and formation of (Ag-Au)@LCC nanostructures.  
Conceptualization, E.Z.K. and L.D.; methodology, I.S.Z. and L.D.; formal analysis, I.S.Z., E.Z.K. and L.D.; investigation, I.S.Z., M.C., S.O.C., A.S.B. and E.F.; resources, S.O.C. and L.D.; writing--original draft preparation, E.Z.K. and I.S.Z.; writing--review and editing, I.S.Z., E.Z.K., E.F. and L.D.; supervision, S.O.C., E.Z.K. and L.D.; funding acquisition, I.S.Z. and E.Z.K. All authors have read and agreed to the published version of the manuscript.  
This study was supported by Russian Science Foundation (Project 19-72-00001). E.Z.K. and S.O.C. thank Ministry of Science and High Education of Russia (Theme \"Electron\" No. AAAA-A18-118020190098-5 and Project FEUZ-2020-0060) for financial support.  
Data Availability StatementData is contained within the article.  
The authors declare no conflict of interest.  
Figure 6: XPS C 1s spectra of Ag-Au and (Ag-Au)@LCC.
## 2 Numerical Modeling
### Parameters for Ice Modeling  
The parameters for the contact model were the particle radius (\(r\)), density (\(\rho\)), Poisson ratio (\(\
u\)), friction coefficient (\(\mu\)), and restitution coefficient (\(e\)). Some parameters, namely, Poisson ratio (\(\
u\)), friction coefficient (\(\mu\)), and restitution coefficient (\(e\)) for the contact model, were less crucial to approximate the strength and stiffness of the continuous material with the inter-particle bonds [23; 24]. The parameters of the bond model considered in this study were the bond Young's modulus (\(E_{b}\)), width (\(2R_{b}\)), length (\(l_{b}\)), thickness (\(h_{b}\)), ratio of the normal to shear stiffness (\(\lambda_{ns}\)), normal strength (\(\sigma_{b}\)), and shear strength (\(\tau_{b}\)). The normal and shear bond strengths were applied to be identical [31; 32; 33]. In the bending problem, the effect of the contact point of the load was not significant [25]. Within a particle assembly, all particle properties were assumed to be distributed uniformly. The distance between particles was entirely covered by the bond. Thus, the bond length (\(l_{b}\)) and the bond width (\(2R_{b}\)) were regarded as the particle diameter (\(d=2r\)). The radius factor (\(\lambda_{R}\)) was 1. For both the three-point bending tests and the uniaxial compressive tests, the particle diameters were set at \(d=2.5\), 5, 10 and 20 mm. The particle size effect was studied using the relative particle size ratio (\(h/d\)). Here, \(h\) indicates the cross-sectional height of the ice specimen in the three-point bending test and uniaxial compressive test as shown in Figure 2. \(h/d\) is proportional to the number of particle layers of the ice specimen. The parameters related to the contact and the bond of ice modeling are shown in Table 1.
## 6 Error Analysis and Correlation Matrix for Aerosol Retrieval
### Correlation of Surface and Aerosol  
The error correlation matrices for elements of the state vector are shown in Figure 7. Our main objective is to study the correlation of surface and aerosol parameters (coefficient of vegetation fraction and AOD) and to analyze the strength of the correlation between surface reflection and aerosol scattering. The larger the correlation coefficient, the harder it is to separate the radiative effect from surface and aerosol. For all aerosols, the correlation coefficients between the vegetation fraction and AOD are not less than 0.6, indicating that surface reflection has some influence on aerosol retrieval. However, the correlation coefficients are positive for dust and soot, whereas negative for sea salt and sulfate. If the vegetation fraction is large, the surface albedo decreases, thereby resulting in a diminution of the radiance at TOA. When AOD increases, more scattering for sea salt and sulfate can lead to an enhancement of the radiance at the TOA. Thus, the vegetation fraction and AOD for non-absorbing sea salt and sulfate correlate negatively. When comparing nadir and multi-angle  
Figure 6: Four kinds of retrieval errors for multi-angle retrieval in three viewing angles and five viewing angles respectively. Others are the same as Figure 4.  
retrieval, the correlation of AOD and the vegetation fraction decreases to less than 0.4 for dust in multi-angle measurements. However, there is no obvious distinction for the other three types of aerosols. This proves that multi-angle retrieval can reduce the correlation between surface reflection and aerosol scattering for a strong scattering aerosol.
# Concurrent Validity of the Inertial Measurement Unit _Vmaxpro_ in Vertical Jump Estimation
## 4 Discussion
However, the existence of a random error similar to that of other previously validated accelerometers and the existence of a correlation between both devices suggests that the IMU has acceptable levels of validity as long as the error due to underestimation of _Vmaxpro_ is taken into account and comparisons are not made by exchanging data obtained by other systems. The estimation error of an IMU used to measure jump height can impact the accuracy of athletic ability monitoring in a few ways. Firstly, if the error is too high, it can make it difficult to accurately evaluate absolute performance and compare it to benchmarks or standards. Secondly, even if the error is relatively low, it can still affect the accuracy of tracking trends and improvement over a long training period. This is because small errors can accumulate and potentially mask or exaggerate true trends in performance. Therefore, it is important to consider and minimize estimation errors in order to accurately monitor and analyze athletic abilities. In any case, the characteristics of _Vmaxpro_ make it possible to obtain data from large groups and a multitude of VJHs, such as Abalakov jumps, squat jumps, drop jumps, multiple jumps, loaded jumps, and a long variety of sports-specific jumps, with slight modifications in the configuration of the software used by _Vmaxpro_. This versatility contrasts with laboratory instruments, such as force platforms or systems based on photogrammetry, which require greater logistics and also imply a more laborious pre- and postintervention treatment or a high cost to perform analysis of equivalent magnitude. Finally, to the authors' knowledge, this is the only study that addresses the validity of a sample composed only of high-level female athletes, and therefore the possible effect that the gender variable may have on biological variability has not been studied before as a possible source of error enhancement or attenuation that could affect the validity of the instrument. Therefore, more research is needed in the future comparing validity values for the same apparatus and protocol between sexes.
## 5 Conclusions  
The 5G network slicing technology is a good solution to the complex service needs of grid systems. The network slicing technology enables the isolation of electric power services from other services whilst also meeting specific service requirements and quality of service guarantees. This paper combines 1D CNN and BILSTM to extract data features of traffic services and encode the data, which proposes a neural network-based power 5G slicing service carrying method to achieve electric power services classification matching and dynamic traffic prediction. Through simulation verification, we can show that, based on the 1D CNN neural network model, the features of electric power services are extracted, classified, and matched. The recognition accuracy can reach up to 95% when the type of dataset is 10, and the recognition can be carried out separately for the services in different grid areas, which can improve the accuracy of dataset recognition and meet the mapping deployment of 5G power network slicing. Meanwhile, combined with the BILSTM-based service traffic prediction, the model training loss value is small, the value of each evaluation index is optimized compared with the BILSTM model, and the error of more than half of the prediction values is less than 10%. This enables the scheduling configuration of services at the edge nodes and meets the service-carrying requirements of 5G network slicing.  
Conceptualization, Y.H. and LG.; methodology, Y.H. and L.G.; software, Y.H. and X.L.; validation, Y.H. and R.Z.; investigation, Y.H. and H.L.; data curation, Y.H. and R.Z.; writing--original draft preparation, Y.H., L.G. and X.L.; writing--review and editing, Y.H. and R.G.; visualization, Y.H.; supervision, L.G.; project administration, L.G.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by State Grid Corporation Headquarters Management Science and Technology Project Grant: Research and Application of Power 5G Network Slicing Device Development and Integrated Control Technology (5700-202040380A-0-00).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Experiments and Discussions
### Experimental Setup  
The explainability of predictions (KPI = Up/Down) is critically important for systems owned/managed by experts. AI recommendations must be transparent and trusted, allowing experts to validate and adopt the solutions. Explainable AI (XAI) has many powerful methods with internal (built-in) and external interpretability capabilities. These can reveal the important set of features and sometimes also unveil the ranges of those features, providing a more detailed understanding of specific predictions. These recommendations, given as a set of rules, are known to provide the highest level of explainability that can be easily consumed by domain experts. Modern rule-learning methods contain several powerful classifiers. Two well-known classical approaches known as C5 rules and RIPPER are the state-of-the-art rule induction. RIPPER is especially powerful for large and noisy datasets as it scales linearly with the number of observations. However, we can equally apply both approaches as our datasets are rather small. In those cases, they show similar performance. The selection of RIPPER is simply connected with its efficient implementation in WEKA 3 machine learning software ([[https://www.cs.waikato.ac.nz/ml/weka/](https://www.cs.waikato.ac.nz/ml/weka/)]([https://www.cs.waikato.ac.nz/ml/weka/](https://www.cs.waikato.ac.nz/ml/weka/)), accessed on 29 November 2023). The corresponding implementation is known as JRip (Java RIPPER).  
Let us show how JRip provides the recommendations. We apply it to the first dataset (CaSA) and obtain a set of rules, as in Figure 8. The first two rules explain the positive class (KPI is Down), and the final one labels all the remaining observations from the negative class (KPI is Up). RIPPER rules have hierarchical importance. The first has the largest coverage. After removing the observations (negative and positive) that fire the first rule, we can use the second rule to explain the labels of the remaining instances. Then, we remove the observations that fire the second rule, and all the remaining instances are labeled from the negative class. Our experiments showed only the first rules, although the others can be stored as relevant. We see that RIPPER is very fast for such small datasets. It generated those three rules in 0.03 s.  
JRip's output also reveals the classification measures in Figure 9. The accuracy is 86.2% for the first dataset. The corresponding evaluation is very important, as we cannot rely on the rules that eventually result in small accuracy. Similar analysis we performed for all three datasets evaluates the models' performance, calculates the precisions and recalls of the rules, and enables an expert validation if the corresponding scores are acceptable. We cannot control the number of rules. These rules can be simple or complex. Moreover, the experts can reject even very confident rules if the conclusions are frustrating or if they cannot validate them.
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 5 Performance Evaluation
### Results and Analysis
#### 5.2.1 Comparison with Other Methods  
To explore the optimal model architecture for emergency communication network deployment tasks, this study integrates two classical value-based reinforcement learning algorithms--the DQN and DDQN--and incorporates two mainstream deep neural network structures in the design of the value network: the stacked convolutional modules of VGG and the residual-connected ResNet modules. By constructing four distinct algorithm-network combinations and conducting performance comparisons under identical experimental conditions, the study aims to identify the most suitable model configuration for the given task.  
Meanwhile, we compared the proposed framework with a widely adopted baseline method--the greedy algorithm. The greedy strategy extends the classical greedy algorithm, which prioritizes locally optimal decisions at each deployment stage without considering long-term consequences. Specifically, during each site selection process, this method chooses the location with the highest immediate reward value (i.e., the optimal point under current network conditions) for deployment. This approach aligns with the greedy heuristic of selecting the \"best\" option available at each step, though it may not guarantee globally optimal solutions.
# S-Band Doppler Wave Radar System
## 4 Results Comparison and Analysis
### Significant Wave Height  
Figures 12 and 13 illustrate a comparison between the significant wave height measured by MORSE and the buoy from 00:00 22 March to 19:00 30 March 2015.  
The data points of both instruments are consistent. The correlation coefficient is 0.9238, the mean error is 0.0437 m, the standard deviation error is 0.1979 m, and the root mean square error is 0.2024 m. The scatter plot also shows a tight distribution along the diagonal, which supports the accuracy of the results. It is also shown in the scatter plot that those data points with low wind velocity distribute horizontally, which indicates a less satisfying performance of MORSE under low-wind-velocity condition.  
Figure 11: Wind velocity and direction during March 22 and March 30. The blue shade indicates the occurrence of rain.  
Figure 12: Significant wave height measured by MORSE and the buoy.
## 5 Experimental Results and Analysis of the Prediction Model  
### Prediction Model Results  
In this experiment, seismic magnitudes were categorized into 30 classes within the range of 0-3.0, in intervals of 0.1. A total of 300 waveform images were selected for prediction, with 10 waveform samples randomly selected from each class. All prediction images underwent the same preprocessing procedures as the training and test sets. Each waveform image was annotated with the arrival times of P-waves and S-waves, as well as the end time of the waveform, to support subsequent magnitude classification.  
Accuracy is defined as the proportion of correctly predicted labels in the entire test dataset. The ResNet model achieved high accuracy on the seismic magnitude waveform test dataset, indicating its strong performance in magnitude classification. This confirms that the shortcut connection mechanism in ResNet effectively mitigates performance saturation in deep networks.  
The bar chart as shown in Figure 10 provides a clear visual comparison of accuracy across different magnitude classes. Notably, the classification performance for magnitudes greater than 2.0 was excellent, with accuracy exceeding 93% for all such classes. The overall classification accuracy reached 96.3%, outperforming other models in both accuracy and effectiveness.  
Figure 9: An index map for the classification and evaluation of the CBAM-ResNet model.
# Evaluation of the Urban Weather Generator on the City of Toulouse (France)
## 4 Discussion
### UWG Performances  
As shown in Figures 6 and 7, as well as Table 6, the air temperature estimates are globally consistent. Quantitative values are indeed acceptable to represent the diurnal cycle of the temperatures and the global behavior of UWG estimations is coherent with respect to night/day variations. Following our quantitative preliminary study in [58], UWG performs better in densely constructed city centers. In addition, as shown in Figure 12, no outliers were estimated (better estimations being for hours in the middle of the day). When looking into details, we observe that the models tend to overestimate at night and underestimate during daytime, as particularly remarkable in Figure 11. This is in accordance with conclusions from [32; 33] where in general, an overestimation of urban temperature in summer was detected.  
From the two last sub-sections, it can be noted that UWG is consistent in estimating urban air temperatures but, following the low influence of surface parameters and the dominance of atmospheric parameters suffers from a lack of spatial variability to estimate correctly spatial variations. To cope with this issue, authors in [60] suggested a spatialized version of UWG by adding a statistical model of boundary layer height and better management of the advection downstream of the city. The first results are interesting, but not generalizable. In one paper [62], the authors developed the Vertical City Weather Generator (VCWG) which overcomes many limitations of UWG by _(i)_ resolving vertical profiles of climate variables in relation to urban design parameters, _(ii)_ including a building energy model and _(iii)_ considering the effect of trees on the urban climate. Here again, the associated model performs better but is dependent on additional parameters that are difficult to access in practice.
## 4 Experiments and Results
### Evaluation Metrics
#### 4.3.5 Computational Complexity  
We evaluated the time consumed to encrypt an image using the proposed scheme and compare it with full encryption. In full encryption, the image is divided into non  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Images & **Aerial** & **Lena** & **Cameraman** & **Jet** & **Lake** & **Tank** \\ \hline _NPCR_\% & 99.6454 & 99.6515 & 99.7855 & 99.6652 & 99.7544 & 99.8711 \\ \hline _UACI_\% & 33.3288 & 32.9722 & 33.7811 & 30.1776 & 32.5110 & 30.4588 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sensitivity to differential attacks—_NPCR_ and _UACI_.  
Figure 14: Plain image sensitivity: (a) lake; (b) encrypted lake; (c) lake SSIM = 0.9989; (d) encrypted lake.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Images** & **Aerial** & **Lena** & **Cameraman** & **Jet** & **Lake** & **Tank** \\ \hline Enrtopy & 7.9114 & 7.9059 & 7.9178 & 7.9453 & 7.8938 & 7.9068 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Entropy results for encrypted images.  
overlapping blocks to which SVD decomposition is applied. All singular values are then rearranged in a matrix to be encrypted. In contrast, in the proposed method, only the first SV of each block is encrypted. The results of our experiments are shown in Table 6.