esa-sceva/satcom-chunk-collection huggingface dataset:
# Measuring Ocean Surface Current in the Kuroshio Region Using Gaofen-3 SAR Data
###### Abstract  
The Kuroshio is the strongest warm current in the western North Pacific, which plays a crucial role in climate and human activities. In terms of this, the accurate acquisition of ocean surface current velocity and direction in the Kuroshio region is of great research value. Gaofen-3 synthetic aperture radar (SAR) provides data support for the study of ocean surface current measurements in the Kuroshio region, but no relevant experimental result has been published yet. In this paper, four available stripmap mode SARs' data acquired by Gaofen-3 in the Kuroshio region are used for measuring the ocean surface current field. In general, the Doppler centroid anomaly (DCA) estimation is a common method to infer ocean surface currents from single-antenna stripmap data, but only the radial velocity component can be retrieved. In order to measure current vectors, a novel method combining the sub-aperture processing and the least squares (LS) technology is suggested and demonstrated by applying to the Gaofen-3 SAR data processing. The experiment's results agree well with model-derived ocean current data, indicating that the Gaofen-3 SAR has the capability to accurately retrieve the ocean surface current field in the Kuroshio region and motivate further research by providing more data.  
oean surface current; Gaofen-3 SAR data; the Kuroshio region; sub-aperture processing; the least squares +
Footnote †: journal: Applied Sciences
###### Abstract
decoded scattered spectra beyond the diffraction limit by using a deep learning algorithm. They achieved an accuracy of 95.84% by adjusting the noise in the training sample to 20% [26]. Other studies have reported the effectiveness of ANN-based auxiliary methods in coding optical information. In this study, we propose a novel three-dimensional (3D) sloppy nanostructure as an optical storage device. The bottom surface of the sloppy structure forms a non-perpendicular angle (slope angle) with the sides. By changing the slope angle as well as the azimuth of the structure, different optical signals can be obtained. A specific combination of two angles represents a coding structure, this strategy can enhance the storage density under the condition of fixed spatial resources. We can obtain different reflectance spectra based on the variation of the slope angle and the azimuth angle of the nanostructure. Accordingly, we propose wavelength-based reflectance spectra as a readout recording strategy to stabilize the information readout given the high sensitivity of the spectra. In this paper, the optical spectra obtained by changing the slope angle are used as the input to the ANN, and the encoded data sequence is used as the output of the ANN. Due to the excellent image recognition function of ANN, the mapping between data sequences and the spectra can be trained. A spectrum corresponds to a structure encoded by one data, so it can be used as a medium for encoding and decoding. We added 20% noise to the ANN training set samples to simulate realistic manufacturing and measurement errors. The readout accuracy was found to be 99.86% for all 120 data sequences. We reduced the azimuthal variation interval of the slope angle and the whole nanostructure to achieve higher-density optical storage and found that the readout accuracy can still reach 97.25%.
## 2 Materials and Methods
### System Integration  
The concept and practice of openness has many components. We are most familiar with open-source software and open data, but no less important are open hardware, open standards, open education, and, consequently, open science. Open-source geospatial software includes a broad range of libraries, tools, applications, and platforms developed and released under different Open Source Initiative (OSI) licenses, where most used those that are part of the Open Source Geospatial Foundation (OSGeo) software ecosystem [20,21]. OpenStreetMap (OSM) is the most widely known example of a global open geospatial dataset, collaboratively maintained and expanded through a global community of contributors. OSM was inspired by restrictions on the use and availability of geospatial data all over the world.  
Due to the specific situation in the country, problems with acquiring software licenses, and legal procedures in procurement, the synergy between open-source geospatial software and open geospatial data together with official data could make a good working community composed of academia, civic society, the private and public sectors [20,21].  
There are many examples of frameworks for integrating cloud computing with GIS through architecture, workflow, and a case study in the health sector [22] which show promise in being valuable in solving resource distribution and disease tracking.  
Because of the very sudden and unplanned inception of the ELIS project as well as the time-critical nature of the epidemic, we had to act swiftly to create a reliable ICT infrastructure. Cloud computing was the only possible solution in a such short period of time available for implementation of the system. It made handling a large amount of data and services, enabling users to scale, customizing and providing computing resources (software, storage, and development platforms), and giving access to the data and services possible, without time or location dependency. Case data can include sensitive private information about citizens like name or address, which is not suitable to record in public cloud resources.
# A Trustworthy Framework for Skin Cancer Detection Using a CNN with a Modified Attention Mechanism
## 4 Results and Discussion
### Pros and Cons of the Proposed Model  
The proposed model modification, integrating a ConvNeXt-based CNN architecture with modified channel and spatial attention mechanisms, offers distinct advantages tailored to the specific challenges of skin cancer detection. The inclusion of attention mechanisms enhances the model's ability to focus on relevant regions of dermatoscopic images, leading to improved diagnostic precision and reduced false positives. By emphasizing critical areas in the data, the model achieved a fine balance between accuracy and interpretability, enabling clinicians to better understand the reasoning behind predictions. The ConvNeXt backbone further contributed to the model's efficiency, leveraging its advanced feature extraction capabilities to handle high-dimensional image data while maintaining a relatively low computational overhead compared to more resource-intensive transformer architectures. This makes the model particularly suitable for specialized medical imaging tasks where both precision and resource optimization are critical.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline Solo-ConvNeXtBase & 96.30 & 96.70 & 96.49 & 96.40 \\ ConvNeXtBase + channel attention & 96.55 & 96.80 & 96.67 & 96.52 \\ ConvNeXtBase + spatial attention & 96.88 & 97.15 & 97.01 & 97.00 \\
**ConvNeXtBase + channel + spatial attention** & **97.00** & **97.57** & **97.10** & **97.57** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ablation study of the proposed model by integrating various attention modules. All the results are given as percentages (%).  
However, the model's design also introduces tradeoffs that must be considered. While it excels in focusing on image-based tasks, its unimodal nature limits its ability to incorporate complementary data sources, such as patient history or genetic information, which multimodal models like CLIP and LLAVA can efficiently handle. Multimodal approaches offer greater flexibility and generalization across various tasks, enabling broader applications but often at the cost of higher computational demands and the need for extensive training datasets. Additionally, the added complexity of attention mechanisms, while beneficial for feature prioritization, can increase the model's training and inference time, potentially posing challenges for real-time deployment in resource-limited settings. Thus, while the proposed model strikes a balance between accuracy, efficiency, and interpretability in a specific domain, its scope may need to be expanded or adjusted to compete with the versatility and broader applicability of multimodal frameworks.
# Trusted Data Access Control Based on Logistics Business Collaboration Semantics
## 4 Construction of Access Control Model
### Optimization of Access Control Policies  
The optimization of access control policies is crucial to ensure that these rules are both efficient and conflict free. This process involves identifying and resolving conflicts and redundancies in rules. The Jena inference engine, an open-source framework for processing and reasoning over RDF data, plays a pivotal role in this optimization process. Jena provides a suite of tools and APIs that facilitate various inference tasks on RDF data, including ontology-based inference, rule inference, and data integration, making it an ideal choice for optimizing access control policies within the context of logistics business collaboration. The main steps for optimizing access policies using the Jena inference engine are as follows:  
1. Initialization and Parsing The first step is to initialize Jena and parse the OWL file, which is a description file automatically generated by the system after creating the access control ontology in Protege as described in Section 4.2. This file contains essential information such as ontology names and class relationships (e.g., parent-child relationships). Parsing this file enables the Jena inference engine to comprehend the ontology's structure and semantics. The content structure of the parsed OWL file is shown in Figure 4.
2. Reading Policy Files Following the ontology parsing, Jena also needs to read a policy file with a '.rules' suffix, containing all the access control rules defined in the SWRL language. Part of the content in the parsed rules file is shown in Figure 5. This operation allows Jena to reason over the rules set against the defined ontology. 3. Detecting Conflicts and Redundancies Jena's reasoning results are then leveraged to detect conflicts and redundancies, and further to correct them. For instance, in the access control rules, logistics delivery per sonnel are granted access to the delivery address. However, in actual operation, users define a rule that \"does not allow logistics delivery personnel to access the delivery address\", resulting in rule conflicts. Upon detecting conflicts or redundancies, manual intervention is required to resolve these issues. While Jena can identify problems, it lacks the capability to autonomously resolve them. Therefore, adjustments to the rules must be made manually to ensure they are logical and consistent. When dealing with rule conflicts, it is essential to prioritize rules based on their importance. Typically, user-defined rules are considered to have higher priority than pre-defined rules. 4. Rule File Management The Jena inference engine allows users to place rules in a separate file for program calls. This enables the division of logistics rules into different files, which can be imported as required. This approach effectively prevents the issue of slow system response speed caused by a large rule library. Furthermore, because rule files are independent of the application, they can be dynamically added, deleted, or modified without impacting the overall system. <l-.
# Chemotherapy Regimen Optimization Using a Two-Archive Multi-Objective Squirrel Search Algorithm
## 4 Experimental Setup and Results Analysis
### Results and Analysis  
#### 4.4.1 Comparison of Overall Performance  
The performance metrics hypervolume (\(HV\)) and Spacing Metric (\(SP\)) are used to compare the overall performance of the TA-MOSSA and other algorithms in terms of the Pareto front obtained. Figure 8 displays the Pareto fronts of all algorithms. To mitigate the impact of the inherent randomness of meta-heuristic optimization algorithms on the experimental results, each algorithm is independently executed 30 times, and the mean HV and SP values across all runs are taken as the final experimental results. The optimal results are highlighted in bold in Table 5.  
Analysis of Figure 8 reveals that the Pareto front generated by the TA-MOSSA exhibits the widest coverage among all compared algorithms. It closely aligns with the Pareto fronts of Two_Arch2 and C-TAEA-II in the objective space, indicating that algorithms featuring a dual-archive mechanism excel in chemotherapy optimization. Conversely, the Pareto fronts obtained by MOPSO and the MOSSA are notably higher and exhibit narrower coverage, suggesting suboptimal optimization performance. Notably, in the objective of minimizing bone marrow cell damage, the disparity in damage between the optimal solutions of MOSSA and the TA-MOSSA exceeds \(1\times 10^{10}\) cells, underscoring the substantial performance enhancement achieved by the TA-MOSSA over the MOSSA's utilization of a single external archive mechanism.  
Moreover, according to Table 5, the TA-MOSSA outperforms other algorithms across both performance metrics. While MOPSO demonstrates the highest SP value, the MOSSA exhibits the smallest HV value among all algorithms, aligning with the characteristics illustrated by the Pareto front visualized in Figure 8. A higher HV value signifies that the Pareto front obtained by the TA-MOSSA boasts superior diversity, capturing a broader spectrum of trade-offs between conflicting optimization objectives. This offers a richer array of candidate chemotherapy regimens for medical decision-making, showcasing the TA-MOSSA's robustness in the search process. Conversely, a smaller SP value indicates a  
Figure 8: The Pareto front of all algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metrics** & **TA-MOSSA** & **NSGA-II** & **MOPSO** & **Two\_Arch2** & **C-TAEA-II** & **MOSSA** \\ \hline HV & 0.85182 & 0.83819 & 0.72816 & 0.82386 & 0.82174 & 0.70734 \\ SP & 0.23518 & 0.30864 & 0.35115 & 0.27281 & 0.29388 & 0.33472 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance metrics of Pareto fronts.  
more uniform distribution of non-dominated solutions on the Pareto front, indicating the TA-MOSSA's thorough exploration of the objective space.
# A Novel Spectral-Spatial Classification Method for Hyperspectral Image at Superpixel Level
## 2 The Proposed HSI Classification Method at Superpixel Level
### Complexity  
By localizing k-means clustering procedure, the improved SLIC avoids multiple times of calculating redundant distance. In fact, there will be no more than eight clustering centers around one pixel [42]. It means that the complexity of improved SLIC is O (_NBI_), where \(I\) is the number of iterations. Assuming that the maximum superpixel volume is about \(s^{2}\) in the case of the pre-specified segmentation scale s. After the superpixel segmentation is completed, the proposal first requires O (\(s^{2}B+2s^{2}log_{2}s\)) to calculate the similarity from a pixel to a superpixel. It is supposed that \(h\) (\(h<<\text{N/s}^{2}\)) superpixels are marked. Then, the complexity of computing the similarity from unmarked superpixels to labeled superpixels is approximately \(O\) (\(h(N/s^{2}-h)s^{2}\times Bs^{2})\approx O\left(hNBs^{2}\right)\). Finally, in the classification stage, the minimal similarity from each unmarked superpixel to L marked superpixels is calculated with \(O\) (\(h(N/s^{2}-h)\)). Hence, the computational complexity of the proposed method is about \(O\) (\(hNBs^{2}+NBI\)).
## 3 Federated Learning for Space-Air-Ground Integrated Networks
### Privacy-Preserving Federated Learning
#### 3.5.3 Global Model Aggregation at the Central Server
The global model aggregation formula is as follows:  
\[\theta_{\text{global}}^{t+1}=\sum_{k=1}^{K}w_{k}\cdot\theta_{\text{local},k}{} ^{t}+\lambda_{\theta}\cdot\Delta_{\text{domain}} \tag{59}\]  
where \(w_{k}\) represents the weight of edge node \(k\), \(\theta_{\text{local},k}{}^{t}\) represents the local model update of edge node \(k\), \(\lambda_{\theta}\) is the cross-domain transfer correction coefficient used to adjust the influence of cross-device differences on the global model, and \(\Delta_{\text{domain}}\) is the cross-domain transfer correction term used to mitigate data distribution differences between devices. \(\Delta_{\text{domain}}\) is defined as follows:  
\[\Delta_{\text{domain}}=\frac{1}{K-1}{\sum_{j\
eq k}\|\theta_{\text{local},k}- \theta_{\text{local},j}\|_{2}^{2}} \tag{60}\]  
where \(\theta_{\text{local},j}\) and \(\theta_{\text{local},k}\) are the local models of different edge devices \(j\) and \(k\). \(\Delta_{\text{domain}}\) measures the model update differences between devices, reducing problems caused by data distribution differences between devices, making the global model aggregation more stable and consistent.
## 4 Experiment
### Evaluation Indicators  
This paper uses the F1 score as an evaluation metric. Precision (\(P\)) refers to the ratio of the number of correctly identified named entities to the total number of identified named entities. Recall (\(R\)) refers to the ratio of the number of correctly identified named entities to the total number of entities. Precision is the ratio of the number of correctly predicted samples to the total number of samples, and \(F1\) is the harmonic mean of precision and recall. The calculation formula is:  
\[P=\frac{TP}{TP+FP} \tag{17}\]  
\[R=\frac{TP}{TP+FN} \tag{18}\]  
\[F1=\frac{2 TP}{2 TP+FP+FN} \tag{19}\]  
Among them, \(TP\) represents the number of samples predicted as positive and actually positive, \(FP\) represents the number of samples predicted as positive but actually negative, \(FN\) represents the number of samples predicted as negative but actually positive, and \(TN\)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Parameters** \\ \hline Operating System & CentOS 7.7.1908 (Core) \\ CPU & Intel(R) Xeon(R) Gold 6271C \\ GPU & Tesla V100 \\ Memory & 128 G \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment environment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline batch\_size & 8 \\ epoch & 10 \\ max\_seq\_length & 512 \\ learning\_rate & \(1\times 10^{-5}\) \\ dropout & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment parameters.  
represents the number of samples predicted as negative and actually negative. Accuracy and \(F1\) score can comprehensively evaluate the performance of the NER model.
## 5 Discussion
### About the Analysis of the Modal Damping Ratio
#### 5.5.2 Possible Correction of Values  
If the results are precise, as happened in the E-1 structure, it is also advisable to study the possible correlation of the damping factor with other variables in an analogous manner to the case of natural frequencies. However, in this case, the conclusions are not so obvious or immediate.  
As an example, the in-depth analysis of the case of the E-1 structure showed the following [28]:  
* Although, a priori, the mechanical properties of the materials could vary depending on the temperature, in the E-1 structure, no relationship was observed between the variations recorded in the modal damping ratio neither with the ambient temperature nor the arch temperature.
* On the other hand, some correspondence was seen (coefficients of determination \(R^{2}\) of the order of 0.20 and 0.30) in the case of two of the natural frequencies and the average wind speed. The justification for this fact may be that the modal damping ratio has an aerodynamic component, although this contribution is usually very small.
* Finally, the relationship with the maximum vertical and transverse accelerations was also analysed since it is a measure of the degree of excitation of the structure. However, in this case, no clear relationships were obtained.  
As can be seen, although the conclusions are not as clear as with the natural frequencies, the possibility that there may be relationships between the damping factor and environmental variables should not be ignored in order to implement SHM systems. Therefore, it would be desirable to study in each case if there is a relationship between damping and environmental parameters.
# Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec
## 3 The Effect of Index Distribution
### Codewords Distribution Features  
CNV-QIM steganography changed the search range of codewords. When steganography is not performed, the split vector searches for an optimal codeword in one codebook. After steganography, the codebook is divided into two subcodebooks. The split vector searches for the optimal codeword in its corresponding subcodebook. The corresponding codeword index will also change. If N speech frames in G.723.1 are input, the quantization index sequence (QIS) in this speech output can be expressed as Equation (3).  
\[QIS_{i}=\big{\{}S_{i,1},S_{i,2},\cdots,S_{i,j}\cdots,S_{i,N}\big{\}},S_{i,j} \in[0,255],i=1,2,3,j\in[1,N] \tag{3}\]  
In Table 1, each column of data represents three QIS of one frame of speech output, and each row of data represents the value of the QIS\({}_{i}\) of N-frame speech.  
Steganography will change QIS of speech. In order to show the change of QIS more intuitively, we select 100 frames of speech. The \"cover\" sample represents the carrier sample before steganography, and the \"stego\" sample represents the carrier sample after speech steganography. We take QIS\({}_{1}\) as an example to demonstrate the results are shown in Figure 2.  
It can be seen that the index values of the \"cover\" and the \"stego\" are not completely coincident. The points with the same position indicate that the optimal code word for both is the same, and the points with different positions indicate that the optimal code words are different. Some points have small differences, and the location of some points is very different. This represents the degree of difference in the output index values.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **1** & **2** & \(\cdots\) & **N** \\ \hline QIS\({}_{1}\) & \(S_{1,1}\) & \(S_{1,2}\) & \(\cdots\) & \(S_{1,N}\) \\ QIS\({}_{2}\) & \(S_{2,1}\) & \(S_{2,2}\) & \(\cdots\) & \(S_{2,N}\) \\ QIS\({}_{3}\) & \(S_{3,1}\) & \(S_{3,2}\) & \(\cdots\) & \(S_{3,N}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantized index sequence.  
Figure 1: Binary codebook flow chart.  
In Figure 2, the changes in the QIS are manifested in following two aspects.  
1. The probability of each index value between 0 and 255 has changed on the vertical axis.
2. From the view of the horizontal axis, the neighbour frames have a certain correlation duo to the speech signal has short-term invariance.  
Through the statistical knowledge, the distribution characteristics of the index values are quantified. The QIM steganography can be analysed.