esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Node Privacy Feature Decoupling Graph Autoencoder Based on Attention Mechanism
## 5 Method Details
### Model Training  
This paper adopts an alternating training approach to optimize the objective functions of both the privacy graph autoencoder and utility graph autoencoder, iteratively updating model parameters. In each training epoch, we first train the privacy graph autoencoder to extract the privacy latent representations \(Z_{p}\) and select top-\(k\) important nodes based on their importance scores to construct the privacy information data distribution. Next, we train the utility graph autoencoder by computing the cosine similarity between the utility latent representations \(Z_{u}\) of the selected \(k\) nodes and their corresponding privacy latent representations \(Z_{p}\). The HSIC penalty enforces orthogonality between the learned utility latent codes and the privacy latent codes, effectively achieving the decoupling of privacy information. During training, we implement a parameter freezing strategy: when the privacy encoder is being trained, we keep the utility encoder parameters \(\theta_{u}\) fixed;conversely, when training the utility encoder, the parameters of the privacy encoder \(\theta_{p}\) remain unchanged. This approach helps prevent model oscillation, allowing each encoder to focus on its specific objectives. The entire algorithm process is shown as Algorithm 2.  
```
0: Adjacency matrix \(A\), node features \(X\), utility autoencoder epochs \(epoch_{u}\), utility learning rate \(\mathrm{lr}_{u}\), privacy autoencoder epochs \(epoch_{p}\), selected node count \(k\)
0: Publishable privacy-preserving graph embedding \(Z_{pub}\)
1: Initialize network parameters \(\theta_{p}\), \(\theta_{f}\), \(\varphi_{p}\)
2:for\(i\gets 1\) to epoch\({}_{u}\)do
3:for\(j\gets 1\) to epoch\({}_{p}\)do
4: Compute node privacy importance scores \(\alpha\in\mathbb{R}^{n\times 1}\) via Algorithm 1
5:endfor
6: //Generate utility latent representation \(Z_{u}\leftarrow\theta_{f}(A,X)\)
7: //Reconstruct adjacency matrix \(\hat{A}\leftarrow\mathrm{Sigmoid}(Z_{u}Z_{u}^{T})\)
8: //Compute reconstruction error \(L_{rec}\leftarrow\frac{1}{N^{2}}\sum_{i=1}^{N}\sum_{i=1}^{N}A_{ij}\log(\hat{A} _{ij})\)
9: //Construct the privacy distribution \(Z_{p}^{k}\leftarrow\{z_{p1}^{(1)},z_{p2}^{(2)},\ldots,z_{pk}^{(k)}\}\)
10: //Construct the non-privacy distribution \(Z_{u}^{k}\leftarrow\{z_{u1}^{(1)},z_{u2}^{(2)},\ldots,z_{pk}^{(k)}\}\)
11: //Compute privacy decoupling loss \(L_{dec}\gets HSIC(Z_{u}^{k},Z_{p}^{k})\)
12: //Compute total utility autoencoder loss \(L_{tot}\gets L_{adj}-\beta L_{dec}-L_{reg}\)
13: //Update parameters via gradient descent \(\theta_{f}\leftarrow\theta_{f}-\eta_{f}\frac{\partial L_{tot}}{\partial\theta_ {f}}\)
14:endfor
15:return\(Z_{pub}\)
```  
**Algorithm 2** Dynamic Privacy Decoupling
# Gearbox Fault Diagnosis Based on Multi-Sensor and Multi-Channel Decision-Level Fusion Based on SDP
## 3 Fitting Fault Identification Based on SDP Technology
### Single-Channel Fault Diagnosis
Therefore, considering the accuracy of the diagnosis results, the model training time and the diagnosis time, and the sensitivity of the parameters, the VGG16 diagnosis model has a better performance than the S-T diagnosis model, and the VGG16 convolutional neural network algorithm was, therefore, selected for the fault diagnosis in this study. In the VGG16 diagnostic model, the learning rate was set at \(lr=0.005\) by adjusting the parameters, and the number of iterations was 1300. The accuracy and the loss of the training results of the three-sensor single-channel model are shown in Figure 12.  
Figure 12: Single-channel VGG16 model evaluation metrics. (**a**) Y-channel of sensor 1 under \(l=10\), \(\xi=35^{\circ}\); (**b**) X-channel of sensor 2 under \(l=6\), \(\xi=25^{\circ}\); (**c**) Y-channel of sensor 3 under \(l=6\), \(\xi=30^{\circ}\). From Figure 12, it can be concluded that the individual sensor models have been basically trained and were stable at 1300 iterations. In Figure 12a, the accuracy was 99.84% with a loss of 0.0058; in Figure 12b, the accuracy was 99.67% with a loss of 0.0015; and in Figure 12c, the accuracy was 99.91% with a loss of 0.0048.
## 3 Method
### Calculate SIF Anomaly Index  
SIF data from 2001 to 2020 were selected; long-time series data are beneficial for monitoring drought. Firstly, the mean SIF for each year is calculated.  
\[SIF_{\text{mean},i}=(SIF_{1}+SIF_{2}+\ldots\ldots,+SIF_{n})/n \tag{1}\]  
where \(SIF_{\text{mean},i}\) represents the average value of SIF. \(n\) equals 12, representing the whole year.  
Then, the overall mean SIF from 2001 to 2020 is calculated.  
\[\overline{SIF}=\sum_{i=1}^{n}\frac{SIF_{\text{mean},i}}{n} \tag{2}\]  
where \(\overline{SIF}\) is the 20-year average of SIF, \(n\) is 20.  
Finally, \(SIF_{anomaly,i}\) is calculated using the following equation:  
\[SIF_{anomaly,i}=\frac{SIF_{\text{mean},i}-\overline{SIF}}{\overline{SIF}} \tag{3}\]  
where \(SIF_{anomaly,i}\) is the SIF anomaly index for the \(i\) year.  
After calculating the SIF anomaly index, the SIF anomaly index image was masked and cropped based on the administrative map of Henan Province in the study area. Simultaneously, the regional average of the SIF anomaly index for the current year was computed as the mean value across all pixels in the image. The spatiotemporal variation in drought in Henan Province from 2001 to 2020 was analysed based on the SIF anomaly index.
## 2 Biochemistry and Genetic Regulation of Aroma and Flavor Compounds in _C. sativa_ L.  
The sensory diversity of _C. sativa_ L. arises from a complex interplay of phytochemicals, including terpenes [13], flavonoids [12; 39], and other volatile and non-volatile compounds [25; 26]. These compounds not only define the aroma and flavor profiles of cannabis, but also influence consumer preferences [1] and potentially contribute to therapeutic effects [11]. The intricate interactions among these metabolites create the unique sensory complexity that characterizes different cannabis varieties.  
The biosynthesis and accumulation of cannabinoids, terpenes, and flavonoids in _C. sativa_ L. (Figure 1) are highly tissue-specific, with the highest concentrations found in the glandular trichomes of female flowers [40]. These specialized structures, particularly those on the bracts and calves, serve as the primary sites for the production and secretion of resin containing these bioactive compounds. The small leaves surrounding the flowers, known as sugar leaves, contain moderate amounts of phytochemicals due to the presence of trichomes, whereas the larger fan leaves have mostly non-glandular trichomes, contributing minimally to cannabinoid synthesis, but instead serving as secondary sources of flavonoids [39; 41]. The stems and stalks contain few glandular trichomes, resulting in low levels of these metabolites, although they may retain trace amounts of certain terpenes and flavonoid derivatives. In contrast, roots lack trichomes entirely and do not actively produce cannabinoids or terpenes, though they may contain other secondary metabolites with potential biological activity [41]. This tissue-specific distribution plays a crucial role in determining the phytochemical composition of different plant parts.  
Advancements in cannabis genomics have greatly enhanced biochemical research in this area by elucidating the genetic basis of cannabinoid and terpene biosynthesis. Early efforts, such as the 2011 draft genome of _C. sativa_ L. [42], provided foundational insights, while in 2020, the high-quality reference genome of wild _C. sativa_ by [PERSON] et al. [42] and another by [PERSON] et al. [43] refined our understanding of its genetic architecture [44]. In 2024, [PERSON] et al. published a chromosome-level haploid assembly of _C. sativa_ L. cultivar Pink Pepper, improving genome resolution and enabling precise gene annotation [45].  
These genomic resources assist in exploring genetic diversity, improving breeding programs for optimized biochemical profiles, and supporting integrative studies combining transcriptomics, proteomics, and metabolomics. By studying flower terpene, flavonoid, and volatile organic compound biosynthesis, researchers can better understand the molecular basis of cannabis sensory diversity. These findings contribute to cannabis variety development and broaden the scientific understanding of plant secondary metabolism.
## 3 Proposed BMI System
### Design of Adaptive Direction Tracking (ADT) Filter  
The aim of the proposed adaptive direction tracking (ADT) filter is to extract features along the boundary direction. Shadow boundaries can be extended to almost every possible direction as shown in Figure 3a. However, four directions can be set in the modelling method: \(0^{\circ}\) in horizontal direction, \(90^{\circ}\) in vertical direction, \(45^{\circ}\) and \(-45^{\circ}\) in diagonal directions as shown in Figure 3b. The direction of boundaries can be calculated through coordinates of adjacent pixels using canny edge detection algorithm. The shape of ADT filter depends on its function and can be customized by users. In this work, the shape of the filter is designed as shown in Figure 4.  
The ADT filter is developed into a rectangle or a square shape, in accordance with the following rules. The rectangle ADT filters applied on images are sized \(\tau\times 3\) or \(3\times\tau\), where \(\tau\) represents the length of the filter, while square filters are sized \(\tau\times\tau\). Furthermore, the ADT filter is designed as shown in Figure 5. Features are evaluated on both sides of each boundary. It is to be noted that each layer of ADT filter shares the same weight coefficients. When the ADT filter moves along the boundary, the information of the extracted feature will be automatically collected from one side of the boundary through the positive parts with values of 1, such as the upper part of filter in Figure 5a. The same operation is performed to collect information on the other side of the boundary by rotating the filter by \(180^{\circ}\).  
Figure 2: (**a**) Original image. (**b**,**c**) Canny edge detection with parameters computed by the default approach (**b**) and with threshold values of 0.08, 0.2 and standard deviation of the Gaussian filter \(\sigma=3\) (**c**).  
Figure 4: Shapes of ADT filter: (**a**) \(0^{\circ}\) filter in horizontal direction (**b**) \(90^{\circ}\) filter in vertical direction (**c**) \(45^{\circ}\) or \(-45^{\circ}\) filter in diagonal directions. The blue squares indicate the input images and the arrows in blue square indicate the boundaries found by canny method. The pink quadrilaterals indicate the shape of filters. Rectangle filter refers to horizontal and vertical direction, square filter refers to sloped direction.  
Figure 5: ADT filter coefficients for a three layer-image. Note that the number of layer for ADT filter is the same as those for image to be processed. (**a**) \(0^{\circ}\) in horizontal direction, (**b**) \(90^{\circ}\) in vertical direction, (**c**) \(-45^{\circ}\) and (**d**) \(45^{\circ}\) in diagonal direction.  
Figure 3: (**a**) Boundaries detected with threshold values of 0.08, 0.2 and standard deviation of the Gaussian filter \(\sigma\) = 3. As an example, the red rectangle indicates an area of interest which shows four directions of boundaries. (**b**) Shadow boundaries with four possible directions for the local region of interest.
# Ransomware Detection Model Based on Adaptive Graph Neural Network Learning +
## 2 Materials and Methods
### Ransomware Detection Methods  
In the realm of ransomware detection, a blend of deep learning techniques with dynamic or static detection methods has been employed by several researchers. EldeRan [19], for instance, is a dynamic analysis and classification approach that leverages machine learning to combat ransomware. This method scrutinizes a set of operations executed within the initial 30 s of running the analyzed application. It employs Mutual Information (MI) and standard Logistic Regression (LR) to anticipate impending ransomware attacks. When evaluated on a dataset comprising 582 ransomware applications from 11 families and 942 benign applications, EldeRan achieved an Area Under the ROC Curve of 0.995.  
[PERSON] [20] analyzed samples submitted to UNVEIL, tracked I/O access, and calculated image difference scores before and after execution to monitor system activity for abnormal files.  
Presently, there are numerous methods for detecting ransomware. One such method automatically extracts ransomware features from behavior logs generated by various benign and ransomware analysis reports using the Cuckoo Sandbox tool [21]. This tool monitors and categorizes suspicious software activities, such as alterations to files and folders, memory dumps, network traffic, and the handling of API calls. Subsequently, it employs Term Frequency-Inverse Document Frequency (TF-IDF) to automatically identify and rank the most significant ransomware features in the recorded data. Feature frequency can also be utilized for ransomware detection [22]. This method, based on the dynamic analysis of 1412 ransomware and 379 benign software applications from 16 families, calculates the frequency of ransomware behavior for specific suffixes, paths, and API calls and combines it with memory behavior to construct features. A ransomware detection model is then established using a parameter-optimized Random Forest algorithm. Moreover, by monitoring the real-time behavior of program operations and extracting features from API calls and registry operations, supervised learning is utilized to detect ransomware [23]. Similarly, the dynamic detection method using TextCNN treats APIs as text languages, constructs graph convolutional networks, and preserves word order information by enhancing pooling layers [24].  
The amalgamation of dynamic and static methods effectively enhances detection accuracy. The multi-classifier ensemble method based on features such as permissions and program intentions [25] and the set detection model based on Random Forests and Decision Trees [26] have effectively improved the system's detection accuracy, yielding satisfactory detection results. Building upon previous research, we propose ADC-TextGCN. This method also integrates dynamic analysis and static analysis for text graph construction to augment detection accuracy.
###### Abstract  
This in vitro study aims to compare the trueness of digital impressions obtained using two intraoral scanners (IOS) and one photogrammetry device for full-arch implant-supported rehabilitations. According to the Carames Classification I, three models were produced with Straumann implants arranged in different spatial distributions: Option A with six implants and Options B and C with four implants each. The three models were scanned using a 12-megapixel scanner to create digital master casts. For each reference model, 30 digital impressions were acquired: 10 with the 3 Shape Trios 3 intraoral scanner, 10 with the Medit i500 intraoral scanner, and 10 with the PIC Dental photogrammetry device. Trueness was assessed through best-fit superimpositions between the digital master casts and the corresponding virtual models. The Shapiro-Wilk test was applied to assess the normality of the data distribution, and [PERSON]'s test was used to evaluate the homogeneity of variances. The non-parametric Kruskal-Wallis test was employed to compare group differences, with post hoc adjustments made using the Bonferroni correction. A significance threshold of \(p=0.05\) was adopted for all statistical tests. Statistically significant differences were observed in the root mean square values among the three devices. The Medit i500 demonstrated the highest trueness, with a median (interquartile range) deviation of 24.45 (18.18) \(\upmu\)m, whereas the PIC Dental exhibited the lowest trueness, with a median deviation of 49.45 (9.17) \(\upmu\)m. Among the implant distribution, the Option C showed the best trueness, with a median deviation of 19.00 (27.83). Considering the results of this in vitro study, intraoral scanners demonstrated comparable trueness, whereas the photogrammetry-based system exhibited lower trueness values. Additionally, a smaller number of implants and reduced inter-implant distances were associated with improved trueness in digital impressions for full-arch implant rehabilitation.  
digital impressions; implants; full arch; intra oral scanner; photogrammetry; trueness +
Footnote †: journal: Applied sciences
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 3 Materials and Methods
### Computation and Evaluation of Estimation Error  
The root-mean-square error (RMSE) between the estimated coordinates and the actual radio source coordinates is computed as the distance error (DE). Given that \(p_{i}\) and \(p_{i,\text{target}}\) represent the estimated coordinates and the target coordinates, respectively, the distance error is computed using the norm as shown in Equation (16).  
\[DE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(\|\mathbf{\hat{p}}_{target}-\mathbf{p}_{ training}\|_{2}^{2})} \tag{16}\]  
The distance error (DE) is computed for all 144 transmission sources. In this study, the computed DE is evaluated based on the mean value and the 90 th percentile value of the cumulative distribution function (CDF) as key indicators of localization accuracy.The mean DE provides an overall estimate of the system's accuracy, whereas the CDF 90% value assesses how frequently large errors (outliers) occur in localization results.
## 2 Biosensor Application of Electrodeposited Nanoparticles  
The modification of electrodes with metallic nanoparticles, which are most frequently gold, provides an increase in surface area, and for enzymes, it often promotes direct electron transfer between the metal surface and the enzyme active site [1]. Approaches that do not involve electrodeposition involve the immobilization of preformed gold nanoparticles onto the electrode surface, which is often modified with the terminal amine group presenting self-assembled monolayers (SAMs) by adsorption from solution [4; 5]. The immobilization of preformed nanoparticles can also apply to the nanoparticles of other metals, such as silver [6; 7]. The direct formation of the gold nanoparticles on the supporting electrode by the reduction of most commonly HAuCl\({}_{4}\) dissolved in solution is an attractive and convenient way to directly produce immobilized gold nanoparticles on an electrode surface [8]. The direct formation of nanoparticles of other metals requires the use of the appropriate precursor [9], and for alloy nanoparticles, a mixed solution of precursors for each metal [10]. Adjustment of variables and conditions can result in different outcomes for electrodeposition in terms of particle size, the surface density of particles, and morphology. The supporting electrode material, composition of the solution, temperature, and addition of structure-directing agents may all be varied. The electrochemical method that is used (fixed potential, swept or cycled potential, constant current) can be varied along with the time, potential limits, and scan rates.
# SIoT: A New Strategy to Improve the Network Lifetime with an Efficient Search Process
## 3 Preliminaries
### Parameters of a Network  
There are a set of parameters that are computed to measure the efficiency of a network. In the following, we explain the most important parameters for the undirected network as discussed below.  
* **The giant component:** it is a connected component of a network that contains a significant proportion of the entire nodes in the network.
* **The average path length:** it measures the efficiency of information or mass transport on a network and it is calculated as follows: \[\mathrm{l_{av}}=\frac{1}{\mathrm{N(N-1)}}{\sum_{i\
eq j}}\mathrm{L_{\bar{ij}}}\] (1) where N is the number of nodes and \(\mathrm{L_{ij}}\) is the shortest path length between two nodes i and j. The shortest average path length boosts the rapid transmission of data and reduces costs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Mining** \\ \hline N & Number of nodes \\ \(\mathrm{P_{j}}\) & Property j \\ \(\mathrm{v_{P_{ij}}}\) & Value of property j for node i \\ \(\mathrm{c_{i}}\) & Capability of node i \\ \(\mathrm{c_{k}^{(g)}}\) & Capability of group k \\ \(\mathrm{r_{k}^{(g)}}\) & Centrality of group k \\ \(\mathrm{n_{i,k}^{(g)}}\) & Popularity of group k for node i \\ \(\mathrm{AC}\) & The agree capability \\ \(\mathrm{L_{x}^{(f)}}\) & List of friends of node x \\ \(\mathrm{L_{x}^{(n)}}\) & List of neighbors of node x \\ \(|\mathrm{X}|\) & Cardinal of X \\ \(\mathrm{\epsilon^{(f)}}\) & Sorting factor \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations used in the rest of this paper.  
Figure 2: A simple example of a social network.  
* **The average degree:** The degree \(\text{d}_{\text{i}}\) of the node i is the number of its neighbors/connections. The average degree of a network is calculated as follows: \[\text{d}_{\text{av}}=\frac{\sum\limits_{\text{i}=1}^{\text{N}}\text{d}_{\text{i}}} {\text{N}}\] (2) The average degree increases with the increase of the number of connections among nodes.
* **The local clustering coefficient:** It measures how close the neighbors of a node are to make a complete graph, and it is calculated as follows: \[\text{cl}(\text{i})=\frac{2*\text{e}_{\text{i}}}{\text{k}_{\text{i}}*(\text{k}_ {\text{i}}-1)}\] (3) where \(\text{e}_{\text{i}}\) is the number of edges among the neighbors of the node i, and \(\text{k}_{\text{i}}\) is the number of neighbors of the node i. The average local clustering coefficient of a network is computed as follows: \[\text{cl}_{\text{av}}=\frac{\sum\limits_{\text{i}=1}^{\text{N}}\text{cl}( \text{i})}{\text{N}}\] (4)
* **Diameter of a network:** It is the shortest path length between the two most distant nodes in a network. The giant component, the average degree, and the local clustering coefficient values of a social network are always fewer than or equal to those of its initial network. Whereas, average path length and diameter values in social network are always greater than or equal to those of its initial network.
# Technology for Green Hydrogen Production: Desk Analysis
## 4 Results and Discussion
### Current Technologies Used for Green Hydrogen Production
#### 4.2.2 Proton Exchange Membrane Electrolyzers
SOEs can be integrated with heat-generating technologies to enhance system efficiency, as the energy demand for water electrolysis decreases with increasing temperature due to Joule heating. This allows for the use of external heat sources, such as industrial waste heat or concentrated solar power, to drive the water-splitting reaction. A notable renewable application is the coupling of SOEs with concentrated solar power, which can provide both the electricity and heat required for the electrolysis process. SOEs offer benefits like using inexpensive electrodes and waste heat for increased efficiency, but face challenges in scaling up and maintaining performance due to material degradation, sealing difficulties, and system integration complexities.
# Ultra-Thin Metamaterial Beam Splitters
###### Abstract  
Here, we present blueprints for three types of ultra-thin beam splitters based on versatile fishnet metamaterial structures at the 1.55 \(\upmu\)m optical communication wavelength. The thicknesses of the designed polarizing beam splitter and partially polarizing beam splitter are 1/26 of the free-space wavelength, while the thickness of the non-polarizing beam splitter is 1/13 of the free-space wavelength. Numerical simulations show that, compared to other miniaturization approaches including popular dielectric metasurfaces, metal-based metamaterial approach can provide much thinner beam splitters with reasonable performance. Such beam splitters can enable miniaturization of conventional and advanced quantum photonic systems towards higher density, scalability, and functionality.  
metamaterial; beam splitter; polarization Article Ultra-Thin Metamaterial Beam Splitters