esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative Filtering
## 3 Combining Knowledge Graph and Collaborative Filtering Recommendation Algorithm  
The algorithm described in this article proceeds as follows: Initially, it analyzes course information and constructs a course knowledge graph. Using the knowledge graph embedding method, it converts chapter content into vector representations and calculates the similarity between chapters. Subsequently, implicit user data are collected, and preference rules are applied to calculate chapter similarity. Finally, the algorithm combines collaborative filtering and knowledge graph similarity to achieve accurate and personalized recommendation results.  
The algorithm's framework, depicted in Figure 2, consists of three main parts.  
The first part focuses on the knowledge graph. It begins by gathering course information and constructing a knowledge graph for the course. This knowledge graph is used to vectorize chapter content through knowledge graph embedding, ultimately determining the similarity of the knowledge graph section.  
The second part is dedicated to collaborative filtering. It starts by collecting implicit user data within the course and performing data cleaning to remove redundant information. An implicit feedback model is then established based on preference rules. This part concludes with a data matrix to calculate the collaborative filtering section's similarity.  
The third and final part involves fusion. It combines the similarities obtained from the previous two sections, applying different weights for Top-K recommendations.
The present study found the top ten hub genes and their essentiality in tumor growth by implementing publicly available a gene expression omnibus (GEO) dataset, GSE161533, which contains DEGs across 28 tumors and 28 normal tissues of the esophagus from ESCC patients. At the first top 250 DEG's, they were predicted from the GEO dataset GSE161533 based on the \(p\)-value GEO2R analyzer. We constructed a protein-protein interaction (PPI) network for the top 250 DEGs using STRING 2.0 with hierarchical clustering and functional enrichment analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. We identified HGsT10 with the help of CytoHubba. Likewise, we have analyzed the finding essentiality in expression at various stages, mutation and its effect on overall survival rate and promoter methylation with its histology. Furthermore, the miRNAs that targeted the HGsT10 were also predicted to play central roles in cancer development via the regulation of its target genes. On the other hand, mutation-based studies often focus on a single gene or cancer type and do not contain extensive assessments of possible gene interactions that might predict survival. The overall survival analysis for HGsT10 and its variant was performed using the Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and University of California, Santa Cruz (UCSC) Xena databases. Our study combined the multi-omics analysis platform, which included gene expression profiling microarrays, comparative analysis with various stages, functional enrichment, promoter methylation, wild and mutated type overall survival analysis, miRNA regulation. We suggested that our data provided detailed biological information about differentially expressed novel hub genes. These findings may be used to improve our understanding of ESCC to derive novel diagnostic methods and anti-cancer drugs against ESCC.
# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 4 Experimental Results
### Relevance to This Study  
The proposed CS-SISR (convolutional self-attention block-based single image super-resolution) method aligns with similar principles, emphasizing high-frequency information to enhance medical image quality. Experimental results using the ISIC 2020 dataset demonstrated a 1-2% improvement in PSNR and SSIM metrics compared to conventional super-resolution methods such as VDSR and EDSR. This indicates that the proposed method effectively restores fine details in melanoma images, validating its efficacy for clinical applications.
# A Miniaturized Circularly-Polarized Antenna for In-Body Wireless Communications
## 2 Antenna Design and Simulation
### Geometry of the Proposed Circularly-Polarized Antenna  
The configuration of the proposed implantable antenna is demonstrated in Figure 2, the dimensions of the patch are fixed to 9.0 mm \(\times\) 9.0 mm with a ground plane of 9.2 mm \(\times\) 9.2 mm. To achieve the miniaturization, the antenna is manufactured on a Rogers 3010 substrate with a high dielectric constant of \(\varepsilon_{r}\) = 10.2 and a low loss tangent of tan \(\delta\) = 0.0035, covered by a layer of superstrate with the same material as the substrate, each with a thickness of \(H\) = 0.635 mm. The superstrate is utilized to separate human tissues from the conducting patch of the proposed antenna and to enhance the matching with the around inner tissues. In order to avoid shorting and to relieve mismatching, the proposed antenna should be wrapped by a thin film of biocompatible materials alumina (\(\varepsilon_{r}\) = 9.2, tan \(\delta\) = 0.008). The 50-\(\Omega\) coaxial cable feeding point is welded at the position (\(d\), \(d\)) along the center of diagonal of the patch. Two small triangle patches are embedded on the upper and lower sides of the proposed antenna and connected with the main patch through two high impedance lines. Additionally, V-shaped slots are embedded into the left and right side of the proposed antenna. It should be noted that perturbation slots are cut to optimize the axial ratio (AR). Table 2 lists the detailed values of the geometrical dimension after the optimization with the aid of ANSYS HFSS.
## 4 Proposed COA Protocol
### Coati Optimization Algorithm-Based Cluster Head Selection
#### 4.1.3 Fitness Function  
The fitness function is used to optimize the candidate solution for an objective problem. It considers both the Residual Energy (RER) and distance between nodes (Distance) to find an optimal solution. In COA, the fitness function determines whether the coat reaches the prey location. The coat evaluates the fitness value in each iteration to find the prey. If a coat reaches the best position, that node is considered the CH in that iteration. The CH selection process is presented in Algorithm 1.  
```
Input: Number of nodes 'n' Output: Best position of coat acts as CH
1: initialize the position of nodes using Equation (4)
2: For z = 1 to Z do
3: prey position is updated based on best member position //exploration phase
4: For z = 1 to [Z/2]
5: the new position of coat is calculated using Equation (7)
6: update position of \(i\)-th coat using Equation (10)
7: END for
8: For z = 1 + [Z/2]: Z
9: prey random position is computed using Equation (8)
10: coat new position is computed using Equation (9)
11: updated position of \(i\)-th coat using Equation (10)
12: END for //exploitation phase
13: For z = 1 to Z
14: Update the position of the ith coat using Equations (11) to (13)
15: END for
16: compute the fitness value using Equation (16)
17: If coat reaches best position, then
18: Best coat acts as CH
19: else
20: Go to step 1
21: END for
22: return optimal CH
```  
**Algorithm 1**COA-based CH Selection Algorithm  
The sensor nodes are equipped with limited battery life, affecting the network's longevity. The node with the highest RER is considered the CH in the network for long-term network sustainability before replacing the node with others. The RER metric plays an essential role in a WSN, which can be used to determine the difference between the initial and available energy after some operation time [33]:  
\[RER(n)=\frac{Energy_{avail}}{Energy_{initial}} \tag{14}\]  
* Computation of Distance  
A shorter distance between CH and CM may reduce communication overhead and energy consumption for transmitting data over the network. The distance parameter plays a significant role in real-time scenarios where nodes are equipped with limited battery power. The distance between a sensor node \(n_{i}\) and a sink node is computed by utilizing the Euclidean distance, as stated in reference [34]:  
\[dis(n_{i},sink)=\sqrt{\sum_{i=1}^{n}(sink-n_{i})^{2}} \tag{15}\]  
The present position of the coat's fitness function \(C_{i(fitness)}\) is determined through Equation (16).  
\[C_{i(fitness)}=0.5\times(1-RER(C_{i})+0.5\times(1-dis(C_{i})) \tag{16}\]
## 2 Related Work
Experimental results show that BagMPMODE significantly improves the search efficiency on 20 out of 22 multi-optimization problems compared to MPMODE. The ensemble learning (EL) based on Adaboost is adopted in [33] for a dynamic multi-objective optimization evolutionary algorithm (DMOEA). Multiple base models are used to predict new populations using a shared population, and the weight associated with each base model is determined based on the prediction error. A strong model is defined based on the weights determined as follows: a base model that has a higher weight has a higher chance of being incorporated into the strong model. At the end of an iteration, the strong model generates a new, improved population used in the next iteration. The proposed EL-DMOEA algorithm combines different strategies and benefits from an improved convergence. In [34] are proposed different strategies for hybridizing a Genetic Algorithm (GA) with a Genetic Programming (GP) algorithm. The population of GP is regarded as a pool of base classifiers (i.e., arithmetic trees) that are improved during the GP search. However, at different iterations of GP, the authors choose to sample the current population of GP to create multiple subpopulations of a given size. Each subpopulation in GP is regarded as an ensemble of base classifiers and is coded as a single chromosome in GA. The GA search procedure aims to find an ensemble with the best combination of base classifiers. Experimental results show that the proposed hybridization approach and its different strategies provide ensembles of classifiers that significantly outperform the standard GP. It is also interesting that the authors observe degraded performance when ensembles contain a larger number of classifiers (greater than seven), possibly due to increased ensemble complexity. Four individual niching genetic algorithms are used in [35] to form an ensemble. The authors choose two instantiations of the restricted tournament selection (RTS) and two of the clearing (CLR) algorithms as niching algorithms. The basic principle is to use four parallel populations, where a particular niching algorithm owns a population. Although a specific algorithm handles a population, a collaborative strategy between populations is achieved using a shared pool of newly created offspring from each niching algorithm. The parallel populations are iteratively evolved until a maximum number of function evaluations is reached. Experimental results show that the proposed ensemble scheme locates more optima than any of the individual niching algorithms in most cases. In [36], an approach that adapts evolution strategies for evolving an ensemble model is presented. A subset of sensor data represents the input data of each model in the ensemble, and neuroevolution is used to optimize the architecture and hyperparameters of each model. Other studies can also show the advantages of using ensemble learning with evolutionary computation [37; 38; 39].
# A Real-Time Automated System for Dual-Aperture Common-Path Interferometer Phase-Shifting
###### Abstract  
We report a novel fully real-time automatized optomechatronic dual-aperture common-path interferometer system for obtaining the phase difference between two interferograms by using the technique of phase-shifting interferometry. A motorized system is used to shift an additional phase transversally to the optical axis by ruling translation. For each high-resolution ruling displacement step of 0.793 \(\upmu\)m, an interferogram is recorded by a CCD camera. The phase difference between the two successive recorded interferograms is then automatically calculated by computational self-calibrated algorithms. The proposed device provides more accurate measuring than typically used manual processes. Real-time phase differences are obtained from a robust low-cost optomechatronic system. Analytical calculation of the phase is performed automatically without the requirement of additional or external tools and processes, reducing the significant rework delay. A set of 47 interferograms were captured in real time then recorded and analyzed, obtaining an average phase shifting of 2.483 rad. Analytic explanation and experimental results are presented.  
generalized phase shifting interferometry; dual-aperture common-path interferometer; real-time optical instrumentation; optomechatronic systems 2021117438. [[https://doi.org/10.3390/app11167438](https://doi.org/10.3390/app11167438)]([https://doi.org/10.3390/app11167438](https://doi.org/10.3390/app11167438))
## 2 Materials
### Data Set
#### 2.2.1 Satellite Images  
A total of 662 satellite images covering the study area in 1999-2013 were collected from different platforms, including Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Mapper (ETM+), HJ-1A/B Charge Coupled Device (CCD), CBERS CCD, IRS-P6 Advanced Wide-Field Sensor (AWiFS), IRS-P6 Linear Imaging Self-Scanning Sensor (LISS) and Beijing-1 CCD. The spatial resolution of these satellite images varies from 10 m to 56 m. To assess the locations of layer aquaculture regions, 50 images taken from 1999 to 2013 were used (Table 1), while 160 images taken from December 2008 to August 2010 were employed for DEM construction including six images were used for determination of aquaculture regions as well. All satellite images used are of high quality and show the layer aquaculture regions quite clearly.  
It is noted that common medium resolution satellites such as Landsat-5, have a revisit interval of 16 days [48]. The temporal resolution is incapable to meet the requirement of a seasonal scale DEM construction thus, we were unable to carry out a seasonal pattern analysis using these satellites. The HJ-1 satellite constellation, consisting of HJ-1 A and B (launched in 2008) and HJ-1 C (launched in 2012), revisits China and its surrounding areas in intervals of less than 48 h [49], which greatly improved the possibility of seasonal scale DEM construction using optical images. A total of 392 optical images covering the study area recorded by the HJ-1A/B satellites were obtained. Of these, 141 images met the requirements for constructing a DEM, accounting for 35.97% of the total number of collected images. Among the usable images, 98 were considered high quality as the images were cloud-free and the waterlines were distinguishable. In view of 43 images with medium or poor quality, an additional 19 images were added to nearly achieve an average of 23 images per season. The spatial and temporal accuracy of our DEM was guaranteed based on the combination of satellite images from multiple sources.
###### Abstract
Novelty detection or anomaly detection methods have been also largely adopted; some examples are provided in [16; 17; 18; 19]. A recent review paper on the emerging artificial intelligence methods applied in structural engineering was written by [PERSON] and [PERSON] [20]; it concluded that the use of artificial intelligence in feature extraction is a powerful tool in SHM [21]. The current approaches reveal that large datasets are needed to monitor a structure. In particular, to detect a damage, structural information from when the damage was not present is required. This reflects that the knowledge of at least one previous state of the system is needed to check for a change in structural behavior, which can be interpreted through the approaches previously illustrated. Obviously, this is not always possible since, usually, monitoring starts after the appearance of a defect on a structure. This evidence suggests two major problems. On one side, to encompass all the possible types of damage (location, type, and extent), a consistent amount of information related to a large number of monitored structures is required. On the other, adopting unsupervised learning approaches requires long time records on a single structure. The present paper proposes a hybrid approach that helps to address these problems. The method allows for numerical modeling to simulate all the possible damage configurations on a structure and for supervised learning to interpret the records of sensors on a structure. Exploiting just a small part of the power of machine learning algorithms, in the present paper, shallow neural networks (NN) [22] are adopted as learning machines for the analysis. To be implemented in a real case, the fact that the measurements can be affected by errors must be included in the analysis. Thus, random errors are included in the analysis.
# FSOpt_k: Finding the Optimal Anonymization Level for a Social Network Graph
## 6 Experimental Results
### Information Loss Metrics
#### 6.2.1 Optimal Value of k  
In this section, the proposed algorithm is evaluated to determine the optimal value of k for a specified utility value. For this purpose, we compare the proposed algorithm with the UMGA algorithm. As discussed in Section 2, the UMGA algorithm uses a partitioning graph to find the best node partitioning for a value of k. This partitioning graph yields an anonymous degree sequence that is slightly different from the graph degree sequence. In this paper, the value of the desired utility is changed, and the obtained k value by the FSopt_k algorithm is investigated. The higher value for k denotes the algorithm is stronger in anonymizing the degree sequence of the graph. In order to evaluate UMGA, the value of k is increased from 2 to n, where n is the number of nodes in the graph, and then the value of k that resulted in the desired utility was selected. Table 5 represents the evaluation results of the two algorithms on the datasets. As it is seen in this table, FSopt_k resulted in the values of k that are close to the k values by UMGA. However, the k values are lower than UMGA. Our goal in this paper is to reduce the algorithm runtime while preserving other properties of the algorithm. Therefore, it is reasonable to have a little reduction in the graph's utility. However, this amount is not a lot.
# Research and Application of Semi-Supervised Category Dictionary Model Based on Transfer Learning
## 3 Experiments and Results Analysis
### Model Settings  
For the sake of convenience, and following the conventions in references [13], S, V, and L are used to represent the SUN09, VOC2007, and LabelMe datasets, respectively. Meanwhile, the symbol S\(\rightarrow\)V denotes the cross-domain transfer learning task. Considering the characteristics of each dataset, the transfer learning tasks are established with the following settings:  
1. When LabelMe is the source domain, 65 labeled samples are randomly selected per category, resulting in a total of 302 samples. Sun09 and VOC2007 serve as target domains where three labeled samples are chosen at random, constituting an aggregate of 15 samples. The remaining samples in the target domains are used as the test set, and the dictionary size for each category is Im = 35;
2. When Sun09 serves as the source domain, 30 labeled samples are randomly selected per category, totaling 140 samples. LabelMe and VOC2007 act as the target domains, with the allocation of labeled samples and test sets remaining identical to the aforementioned settings. The dictionary size for each category is Im = 20;
3. When VOC2007 is employed as the source domain, 65 labeled samples are randomly selected per category, amassing 325 samples in total. LabelMe and Sun09 function as target domains, maintaining the same configuration of labeled samples and test sets as previously described. The dictionary size for each category is Im = 35. Utilize 1-NN [25], SRC [26], TSC [4] and LSDT [27] as the comparative models.  
The 1-NN model is an example of supervised learning, while SRC models involving coefficient feature extraction, followed by supplementary classification strategies, also fall under the category of supervised learning. Additionally, LSDT, TSC, TCA, and GFK are  
Figure 4: Bearing vibration data under inner ring failure (without load).  
all considered domain adaptation models. Due to their transfer learning nature, these models migrate models applicable to one data domain to a new data domain. Since some one-to-one correspondence needs to be confirmed during the migration process, these models are considered to be semi-supervised during the migration process.  
For our model (SSDT), \(\lambda_{1}=0.05\), \(\lambda_{2}=0.04\), \(\lambda_{3}=0.01\), the number of category magnitude dictionaries is 150, the number of augmented samples for each category in each iteration \(\widehat{N}_{m}^{\text{{\tiny{Lit}}}}=3\), and the maximum number of iterations is set to 8. The specific flow of the model is shown in Figure 5.
## 2 Materials and Methods
### Improved CenterNet  
#### 2.3.1 Multi-Layer Feature Aggregation  
CenterNet uses backbone networks with small sampling rates, and the module's backbone feature extraction networks, such as ResNet [33], Inception, and MobileNet [34] only use multiple deformable convolutions [35] and deconvolution to up-sample one-quarter of the input image for detection and do not achieve reuse of multi-layer features. The module is prone to missing some small pedestrians in the image and loses a large amount of pedestrian texture and location information contained in the shallow feature maps, which is essential for tracking algorithms. Therefore, multi-layer feature aggregation [36] is helpful to reduce identity switches with the one-shot algorithm due to its improved ability to handle scale variations.  
As a result, we incorporated the FPN into CenterNet, as shown in Figure 2. The shallow feature map has a smaller sensory field and contains more location and feature information of the obscured pedestrians compared to the deep feature map, so its introduction makes the network directly incorporate the semantic information of obscured pedestrians, which is beneficial for extracting their features. The fusion of feature maps of different layers of the FPN can effectively improve the accuracy of the network for obscured pedestrian detection.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 4 Sender Authentication
### Inadequacy of TESLA for Low-Bandwidth High-Latency Communication Media
#### 4.2.1 Wormhole Attacks against TESLA  
Suppose that the TESLA epochs are shorter than the propagation delay for some receivers located far from the sender, and assume that the authentication delay parameter used in the network is \(d=2\). We shall consider an adversary creating a \"wormhole\" connecting distant nodes, affording them a fast communication channel faster than the regular channel used by legitimate nodes. In the underwater setting, this can be achieved by using surface transmitters connected by cable to the UUVs (see Figure 4). In particular, we suppose that for legitimate nodes the propagation time from one end of this wormhole to the other is more than one epoch.  
Assume that the sender has sent some packets very close to the end of epoch \(i\); the adversarial node intercepts the packet and waits until the beginning of epoch \(i+2\). The seed \(s_{i}\) is disclosed by the sender, and the node can now change the intercepted message and generate the correct authentication code. The manipulated message is sent through the wormhole channel, catching up to or even overtaking messages from epoch \(i\) reaching the destination area. In this way, the adversary may flood an isolated network area with forged messages. Note that the adversary does not have to intercept the original packages, as it is suffices for the malicious node to learn \(s_{i}\) transmitted in the regular way.