esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Global Key-Value Storage System Based on Kinetic Drives +
## 5 Design and Architecture
### Overview  
The goal of our work is to present a global key-value store system. We assume the entire system is divided into multiple regions worldwide. We believe the concept of regions is important, similar to regions (e.g., U.S. East, Asia Pacific) in Amazon Web Services (AWS) [43]. In each region, there is a group of Kinetic Drives that offers key-value pair storage and access. There are also many users dispersed globally in different locations, accessing key-value pairs stored in Kinetic Drives. Figure 5 shows the overall picture of the system with \(N\) regions.  
In order to manage this group of Kinetic Drives for each region, a metadata server is needed. This metadata server is also the first contact for users in this region accessing data. We assume all the metadata servers worldwide know the IP addresses of each other.  
In the real deployment, practically, there should be multiple synchronized metadata servers in each region for reliability and fault tolerance. In this paper, to better explain our design, we use a metadata server to represent these multiple ones in each region. In other words, we assume this one metadata server takes care of data management in its region. The data synchronization issues among metadata servers in a region are classical problems in distributed systems, and they are not unique for Kinetic Drives. In this paper, we focus on the coordination among metadata servers in different regions.  
For redundancy and fault tolerance considerations, each drive in a region is assumed to have two backup drives in two other regions. In other words, each key-value pair has three copies distributed in three regions. In the rest of this paper, we use the term _\"original data\"_ to represent the primary copy of the key-value pair. These _original data_ are initially stored by users. The term _\"original drive\"_ means the drive initially storing these _original data_ as the primary copies. Furthermore, the terms _\"Backup 1\"  
Figure 4: Data access with a cluster of Kinetic Drives [42].  
and _\"Backup 2\"_ are used to indicate the first and second additional backup copies in two other regions. These backup copies are replicated by the KVS system based on the _original data_.
## 4 Results and Analysis
### Assessing the Impact of Various Clustering Algorithms on the Cluster Analysis Locating Method
(684 out of 861) and 20.56% (177 out of 861), respectively. The other 17.61% (184 out of 1045) of the events could not be geographically pinpointed for two main reasons: firstly, due to a limited number of WEMPD-detected events that match the WWLLN observations within the specified time frame, which accounts for 40.76% (75 out of 184) of these events; and secondly, due to insufficient differentiation of the \(STEC_{i}\) using the CAL method, which represents 59.24% (109 out of 184) of the undetermined events. The two-dimensional locations of 81.92% (684 out of 835) of the TIPP events have been successfully determined using the MSDN-CAL method. Therefore, the three-dimensional locations of these TIPP events could be obtained by further estimating their occurrence height. When comparing the MSDN-CAL method's results for the two-dimensional locations with those of the GHMLLS-SCL method, 88.24% of the TDRD are found to be within a 300 km range. The respective arithmetic mean, geometric mean, and standard deviation of the TDRD are 176.26 km, 92.53 km, and 174.79 km, respectively. Additionally, for the altitude deviation (AD) of the locating results between the same methods, the arithmetic mean, geometric mean, and standard deviation are 2.08 km, 1.30 km, and 2.26 km, respectively (see Figure 7 for details). These levels of accuracy are substantial for the comprehensive analysis of the events' sources, occurrences, and distributions on a global scale, which could provide valuable insights into the phenomena under study.
## 3 VQA Model Design
### Membership Functions and Compact Rule Base
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Video CRF** & **Video FPS** & **Video SI** & **Video TI** & **Predicted MOS** & **Consequent Parameters** \\ \hline High & Medium & HighDetail & HighMotion & OutputFunction1 & (\(-\)0.87, 0.12, 0.73, 0.14, \(-\)34.83) \\ Medium & VeryLow & HighDetail & VeryHighMotion & OutputFunction2 & (\(-\)0.06, 0.42, 0.12, \(-\)0.03, 5.48) \\ Low & High & MediumDetail & HighMotion & OutputFunction3 & (0.07, 0.08, 0.1, \(-\)0.05, 20.53) \\ VeryHigh & Medium & MediumDetail & MediumMotion & OutputFunction4 & (\(-\)3.67, 0.13, 0.48, 0.11, 246.35) \\ High & VeryHigh & LowDetail & LowMotion & OutputFunction5 & (\(-\)0.52, 0.19, \(-\)0.31, \(-\)0.06, 31.72) \\ VeryHigh & VeryLow & HighDetail & VeryHighMotion & OutputFunction6 & (\(-\)0.49, 0.3, 0.14, \(-\)0.002, 25.79) \\ Medium & Low & LowDetail & MediumMotion & OutputFunction7 & (\(-\)0.12, 0.14, \(-\)0.49, 0.02, 31.62) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The fuzzy rules used in the ANFIS model. The header row lists the four input parameters and the model’s output, predicted MOS rating. Each rule represents a conjunction of input conditions (using the AND operator). For example, rule 1 indicates that _IF_ Video CRF is High _AND_ Video FPS is Low _AND_ SI is HighDetail _AND_ TI is HighMotion _THEN_ the MOS rating corresponds to the OutputFunction1 whose parameters are listed in the last column of the table (i.e., the \(\beta_{ij}\) coefficients used in Equation (4)). where \(c\) and \(\sigma\) are the center and spread of the Gaussian function, respectively. Layer 2 determines the firing strength of each fuzzy rule by applying the AND operator with the product method. For a rule \(i\) with \(q\) inputs, the firing strength \(w_{i}\) is calculated as:  
\[w_{i}=\prod_{j=1}^{q}\mu A_{ij}\big{(}x_{j}\big{)}, \tag{2}\]  
where \(\mu A_{ij}\big{(}x_{j}\big{)}\) is the membership degree of the \(j\)-th input in the \(i\)-th fuzzy set. In Layer 3, these firing strengths are normalized by dividing each \(w_{i}\) by the sum of all firing strengths (with \(m\) being the total number of rules):  
\[\widetilde{w}_{i}=\frac{w_{i}}{\sum_{k=1}^{m}w_{k}}, \tag{3}\]  
Layer 4 computes the crisp output for each rule using a linear function of the inputs:  
\[f_{i}\big{(}x_{1},x_{2},\ \ldots,\ x_{q}\big{)}=\sum_{j=1}^{q}\beta_{ij}x_{j}+ \beta_{i0}, \tag{4}\]  
where the \(\beta_{ij}\) coefficients represent the consequent parameters (see the last column in Table 3). Finally, Layer 5 aggregates these outputs through weighted average defuzzification:  
\[y=\sum_{i=1}^{m}w_{i}f_{i}\big{(}x_{1},x_{2},\ \ldots,\ x_{q}\big{)}. \tag{5}\]  
This layered process ensures that the entire fuzzy reasoning, including the embedded implication and aggregation steps characteristic of Sugeno-type systems, is efficiently synthesized into a single, interpretable output that can be directly compared with actual quality measurements. Figure 4: ANFIS structure of the VQA model.
## 2 Materials and Methods
### Data Preprocessing and Mapping  
The quality control of the raw FASTQ files was performed using FASTQC (v. 0.11.9) [28]. Next, sequences were aligned to the _dm6_ reference genome using STAR (v. 2.7.10a) [24; 29], which was run with default parameters. The resulting read count matrix was filtered by removing genes that were not expressed in any sample; the raw gene counts were normalized into Reads Per Kilobase per Million mapped reads (RPKM) values using the edgeR R package [30]. Normalized data were subjected to Principal Component Analysis (PCA) and the t-distributed Stochastic Neighbor Embedding (t-SNE) method to verify the correct clustering of samples into their respective groups based on their gene expression profiles. The number of principal components was chosen in order to explain 90% of the variance of the gene expression profiles of all samples.
# Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
## 3 Stripe Correction New Algorithm Based on Wavelet Analysis and Gradient Equalization  
The proposed algorithm includes the following three steps: (1) Wavelet decomposition of the original image to extract high-frequency components, and then the stripe noise is concentrated in the vertical component of the high-frequency component. (2) Small-window column equalization of vertical components. (3) Taking the approximate component as the guidance image, and the equalization vertical component as the input image, then using the guided filter for the second filtering, and finally carrying out the wavelet reconstruction. The complete flow chart of the algorithm is shown in Figure 2. The details of the proposed method are presented in the following subsections.
# A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response
## 2 Proposed Approach
### Level 1--Similarity-Based Correlation  
The first level is aimed at the aggregation, clustering, and context supplementation steps of the event correlation process defined. This model uses the similarity of event attributes for the correlation. The input to the first level is the stream of events. The output is a set of clusters that have been processed and have had contextual information added and carried through the process.
# USD-YOLO: An Enhanced YOLO Algorithm for Small Object Detection in Unmanned Systems Perception
## 5 Experiments
### Comparison Experiment  
On the Citypersons dataset, we compared the USD-YOLO algorithm with SOTA algorithms, including mainstream YOLO series algorithms (YOLOv5, YOLOv7, YOLOv8, YOLOv9, and YOLOv10), YOLO-based improved small object detection algorithms (TPH-YOLO, SPD-YOLO), two-stage algorithms (Faster R-CNN), and single-stage algorithms outside the YOLO series (SSD). The YOLOv5, TPH-YOLO, YOLOv7, YOLOv8, YOLOv9, YOLOv10, Faster R-CNN, and SSD algorithms were obtained from the open-source platform GitHub. The SPD-YOLO algorithm was sourced from reference [26].  
As depicted in Table 4, compared with other SOTA algorithms, the USD-YOLO algorithm significantly outperforms them in terms of Recall, mAP50, and mAP50-95 metrics with a fast speed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**YOLOv8s** & **SCConv** & **CGAM** & **APE** & **Recall (\%)** & **mAP50** & **mAP50-95** \\ \hline ✓ & & & & 54.4 & 64.9 & 37.7 \\ ✓ & ✓ & & & 54.5 & 65.1 & 37.9 \\ ✓ & & ✓ & & 54.1 & 63.9 & 38.3 \\ ✓ & & & ✓ & 55.1 & 70.1 & 45.8 \\ ✓ & ✓ & ✓ & & 54.6 & 64.6 & 38.3 \\ ✓ & ✓ & & ✓ & 55.7 & 70.4 & 45.8 \\ ✓ & & ✓ & ✓ & 54.9 & 70.3 & 46.4 \\ ✓ & ✓ & ✓ & ✓ & 56.7 & 70.8 & 46.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiments on the Citypersons dataset.  
In terms of precision, USD-YOLO achieves a significant improvement of 6.8-7.9% and 8.9-10.8% on mAP50 and mAP50-95, compared with small object detection algorithms improved by other researchers (TPH-YOLO, SPD-YOLO). Compared with the baseline, USD-YOLO improves mAP50-95, mAP50, and Recall by 8.5%, 5.9%, and 2.3%, respectively. USD-YOLO is also clearly superior to other SOTA algorithms, including YOLO series detectors, the single-stage detector SSD and the faster two-stage detector R-CNN. In terms of network parameters and GFLOPs, USD-YOLO has fewer parameters than YOLOv8m, SPD-YOLO, and SSD, and significantly fewer than Faster R-CNN. This indicates that USD-YOLO has a relatively lightweight structure, requiring fewer computational resources. In terms of FPS, USD-YOLO outperforms most algorithms, although it is 3.5 FPS lower than the baseline YOLOv8s. This suggests that USD-YOLO sacrifices a small amount of speed after the improvements, but it remains fast in the field of object detection.  
These advantages demonstrate that USD-YOLO significantly improves the precision of small object detection while maintaining a fast detection speed. Unmanned systems demand high precision and efficient utilization of limited computational resources. Consequently, USD-YOLO is well-suited for deployment on unmanned devices, meeting these critical requirements. However, based on the comparison experiments, the two-stage detector Faster R-CNN exhibits significant drawbacks, including a large number of parameters, high computational complexity, and extremely low detection speed. These limitations make it entirely unsuitable for the detection requirements of unmanned systems. Therefore, Faster R-CNN will not be included in subsequent generalization experiments.
## 4 FE Model Nonlinearities  
According to the conventional FE assessment, there could be three nonlinearities for the FE modeling scheme, which are as follows: (1) geometrical nonlinearity, (2) material nonlinearity, and (3) boundary nonlinearity.  
As the TIRF is based on the linear superposition method, a series assessment for the effect of the nonlinearities on the ultimate strength assessments is required. However, the effect of material nonlinearity is excluded from this study as the major strength member of the NO96-type LNG cargo containment system is a typical brittle material.  
A box-type LNG cargo containment system (GTT NO96) consists of the primary box and the secondary box, which are connected through an elastic coupler system. Therefore, the boundary conditions between the bottom of the primary box and the top of the secondary box are in surface-to-surface contact, similar to the typical nonlinear boundary condition (A in Figure 3a). In addition, there are discontinued panels in the top plate of the primary box owing to the tongue (B in Figure 3a) and primary membrane connection purpose, as shown in Figure 3.  
Figure 3: Schematic of the structural boundary nonlinearity in LNG CCS application. (**a**) Sectional view of the LNG cargo containment system; (**b**) Onboard condition.  
Figure 2: Calculation process for the TIRF method.  
Furthermore, the nonlinear boundary effect between the bottom of the LNG cargo containment system and hull part was studied (C in Figure 3b).
# Utilizing Trusted Lightweight Ciphers To Support Electronic-Commerce Transaction Cryptography
## 5 Security and Performance of Proposed E-Commerce Applications
### Attacks Analysis on Our Protocol
Our protocol applies encryption and decryption procedures to all network devices (buying, selling and server), namely, our protocol resists these attacks. * **Zero day** The attacker tries to exploit security vulnerabilities in e-commerce software or applications to use them to carry out malicious attacks on network devices and applications. In commerce institutions, an attacker tries to obtain sensitive transaction information or execute fraudulent threats. After the attacker carries out this attack, he/she tries to exploit the documents, files and information to apply his/her attack on the entire network. When detection of this attack is delayed, this can give the attacker time to apply their attack on a larger scale. If we assume that the attacker penetrates one of the network devices (buyer, seller or server device) and obtains requests for buying, selling or invoices, then he will not benefit from these files because they are all encrypted on the network devices. In addition, all security parameters are hidden (suchas \(Pr_{k}\)) on network devices. Therefore, our protocol supports robust protection of e-commerce applications and controls zero day attacks.
## 5 Conclusions  
In this study, the main aim was to implement and explain how a convolutional neural network (CNN) with a one-dimensional architecture could be applied to tree species classification with OHS-1 hyperspectral images. This model has an end-to-end complete architecture and does not require separated feature extraction and classification stages. In order to explore the potential of this model in tree species classification, popular random forest (RF) classifiers were also used to classify tree species based on the same training samples. The results demonstrated that the application of this model produced high recognition performance on different tree species. The overall classification accuracy of the Conv1D-based classifier was 85.04%, higher than that of the RF classifier (80.61%), and the kappa coefficient was 0.81 and 0.75, respectively. Moreover, although some broadleaf species such as Manchurian walnut and aspen have similar spectral signatures, the classification accuracy of these two tree species based on the Conv1D-based classifier reached 87.15% and 89.10%, respectively. In general, compared with traditional classification algorithms, deep learning algorithms have better classification performance in tree species classification. Although this study initially explores the tree species classification based on the Conv1D-based classifier, it still needs to try more deep learning methods and further improve the CNN performance, scalability, and incorporation of spatial and temporal information.  
[PERSON] calculated and analyzed the data in addition to writing the paper. [PERSON] designed the research project and analyzed the data in addition to writing the paper. [PERSON] calculated and analyzed the data in addition to writing the paper. [PERSON] optimized the algorithm and gave suggestions for the whole study. [PERSON] processed hyperspectral data in addition to writing the paper. [PERSON] and [PERSON] gave suggestions for the whole study. [PERSON] contributed to the field survey and gave suggestions for the whole study.  
This study is supported by the National Key Research and Development projected of China (No. 2016 YFC0500300), the Jilin Scientific and Technological Development Program (No. 20170301001 NY) and Strategic Planning Project from Institute of Northeast Geography and Agroecology (IGA), Chinese Academy of Sciences (Y6H2091001).  
We thank the National Earth System Science Data Center for providing geographic information data.  
The authors declare no conflict of interest.
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 1 Introduction
This innovative method merges global features derived from the convolutional decoder and the Transformer feature space, resulting in a thorough representation that enhances classification. This work represents a significant advancement in remote-sensing-based flood detection and monitoring. It tackles the limitations of current models through a novel architecture and tokenization strategy.
## 5 Results of Application of Image Pseudo-Coloring  
### \"Hot\" and \"Radar\" Palette Colors  
At the beginning of the analyses, the images presented in Figure 10 were colorized using typical color palettes available in the rasters discussed in Section 2. These palettes are named: \"Hot I\", \"Hot II\", \"Hot III\", \"Radar I\", \"Radar II\", and \"Radar III\" (Figure 13a,d). In these cases, the modification of the LUT coefficients concerns only the narrowing of the range of pixel amplitude values for which the reconstructed image can be colored. Other modifications proposed herein (different width \(A_{E}\) of the exponential function building the coefficients of the LUT and different position of \(B_{E}\) on the axis of the pixel amplitudes of the original image, regardless of the color R, G, and B), in relation to the discussed rasters, are not available, which significantly reduces the effectiveness and practicality of using pseudo-coloring in the process of electromagnetic infiltration. The obtained results of using this type of image coloring for a full and limited range of changes in the amplitude values of the pixels that make up the image are shown in Figures 15 and 16.  
The use of typical commercial color palettes of raster generator solutions does not give the expected results in the form of highlighting the sought data and improving their perception. It is necessary, as presented at the beginning of the article, to propose a method that allows changing the parameters of the color palettes used. The process of determining the parameters of the exponential function \(E(x)\) is not a simple process and requires an individual approach to each analyzed image, as shown in Figures 15 and 16. This is due to the fact that the structures of the reproduced images are not the same. Thus, the values of the amplitudes of the pixels building the sought data are not comparable. In addition, for each image the range of changes in these values is different, which results directly from the impact of Side Channel Attack on the form of the compromising emanations [36; 37; 38; 39; 40].  
Hence, it is necessary to use pseudo-coloring algorithms, which can also act as filters, allowing for the selection of the data sought.  
Figure 15: Cont.  
Figure 16: _Cont._  
Figure 15: The result of applying the pseudo-coloring algorithm to the images shown in: (**a**) Figure 10a, (**b**) Figure 10b, (**c**) Figure 10c, (**d**) Figure 10d, (**e**) Figure 10e, (**f**) Figure 10f, using the “Hot” color palette with the parameters of the exponential function \(E(x)\) specified in Table 1.
# Energy Blockchain for Public Energy Communities
## 3 Field-Test Implementation and Results
### LEC Analysis and Management  
The availability of transparent data related to the energy consumption and production data of public buildings permits economical and environmental analyses, which can be performed by research institutions or stakeholders. The application of the methodologies described in Section 2.2 to the Carloforte dataset can be seen as an example of the opportunities created by the approach suggested in this paper. In order to calculate the KPIs, lifetime and installation costs of PV generators have been considered 20 years and 1500 \(\copyright\)/kWp, respectively. In addition, BESS lifetime and installation costs have been set as 5 years and 300 \(\copyright\)/kWp and 200 \(\copyright\)/kWp.  
As a first result, the KPIs regarding current energy supply costs and emissions of the public buildings have been calculated for the whole year on the base of the raw readings data extracted from the blockchain register. In order to understand the impact of the existing renewable sources, the KPIs have been saved on the blockchain and compared with a benchmark scenario without renewable generation. These results are shown in Table 2.  
Furthermore, the economic and environmental impact of the individual buildings with the current installed capacity has been compared with the same indicators in the presence of an LEC. This has been done by comparing the respective KPIs defined in  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Scenario with No Renewable Sources**} & \multicolumn{2}{c}{**Current Scenario**} \\ \hline
**Building** & **Emissions [\(\mathbf{fCO_{2}}\)]** & **Costs [\(\mathbf{k\copyright}\)]** & **Emissions [\(\mathbf{fCO_{2}}\)]** & **Costs [\(\mathbf{k\copyright}\)]** \\ \hline Library & 19.28 & 1.66 & 15.04 & 1.22 \\ Sports club & 10.16 & 0.92 & 8.45 & 0.52 \\ City hall & 115.00 & 9.82 & 93.37 & 7.99 \\ Primary school & 7.59 & 0.72 & 4.92 & 0.16 \\ Police building & 32.77 & 2.76 & 32.77 & 2.76 \\ \hline
**Total** & 184.80 & 15.88 & 154.55 & 12.65 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of economic and environmental Key Performance Indexes (KPIs) between the current and the absence of installed renewable sources scenario.  
Figure 6: Energy community total generation and consumption.  
Equations (1) and (3) for the different tested scenarios. The results as saved on the blockchain, are shown in Figure 7. As shown, the existing PV production sources are already causing an emission reduction of 16.37% and an economic gain of 20.28% with respect to the absence of renewable energy scenario. However, if the buildings federate into an LEC, this profit would reach a value of 27.24% and an emission reduction of 27.09%.