esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Hand Gesture Spotting  
#### 3.3.1 Target Person Locking  
When multiple people are present, the targeted person is the one chosen to perform hand gesture during tracking. In this work, the Kinect V2 sensor can extract skeleton information for up to six people at once. Therefore, using the algorithm described above, the system can locate the fingertips of up to six people. However, in order to recognize the hand gesture, we need to identify the target person so as to eliminate the influence of the others. To accomplish this, we used a user-locking algorithm to solve the problem during hand tracking. The implementation is presented in Algorithm 1.  
In this algorithm, the given detected head joint coordinates and right-hand joint coordinates of multi-people in-depth image using the Kinect skeletal tracker, as shown in Figure 3, we define the target person based on the distance head-hand. The user who raises his hand over his head in 10 frames is the target user.  
```
begin Require User ID, head joint coordinates (x1, y1), right hand joint coordinates (x13, y13).
1: Calculation the distance between y1 and y13.
2: If y13 > y1 during 10 frames then process target user else redo 1 end begin
```  
**Algorithm 1** Target Person Locking
# AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting
## 3 Materials and Methods
### Semantic Analysis and Clustering  
The GIST-all-MiniLM-L6-v2 model [48] was integrated into the data pipeline by directly applying it to the pre-filtered and moderated data. After preprocessing, which involves filtering out hate speech, the tweets are converted into high-dimensional vector representations and stored in a vector database. These vectors store the semantic relationships between the tweets. In addition, a client for a persistent vector database was created to efficiently store the generated vectors and enable access for further steps.  
ODET begins to process data in chunks to divide large amounts of data into manageable sections. These chunks represent time slots that can be preselected. This makes it possible to view data from historical records as live data by examining only ever looking at the currently relevant time slot. The preprocessed, filtered, and vectorized tweets were stored in a vector database. Each chunk is added to a separate collection, with the name of the collection being generated dynamically (e.g., \"collection0\", \"collection1\"). If a collection does not yet exist, it is created by the client.  
This process involves adding vectors and IDs to the collections in the database to enable efficient and rapid searches for semantically similar content. This mechanism ensures that the data are structured and prepared for the subsequent semantic analysis. Finally, the file management process (handle_list_file) ensures that the information in the collections remains consistent and available for further analysis.  
After transforming the tweets into embeddings, a community detection algorithm was used to identify semantically similar groups of tweets. This is performed by calculating the cosine similarity between the vectors, which measures the degree of content similarity between tweets. Tweets with high content similarity generate a similarity value close to one, whereas those with less similarity generate lower values. To ensure that only relevant semantic relationships are captured, similarity values below a threshold of 0.1 are set to zero.  
This process is performed in small steps to keep the calculations memory-efficient. The calculated similarity matrix was converted into a sparse matrix and stored in a compressed format. This data structure allows efficient processing of large amounts of data because only the relevant connections between tweets are stored.  
A similarity matrix serves as the basis for the community detection algorithm, which groups semantically similar tweets and identifies and eliminates overlapping communities. Thus, significant topics in the data can be identified and used to generate situation reports. The minimum cluster size depends on the number of tweets analyzed. For smaller datasets, the minimum size can be reduced accordingly, whereas larger datasets require larger clusters.
# Blockchain-Based Multiple Authorities Attribute-Based Encryption for EHR Access Control Scheme
## 6 Scheme Analysis
### Security Analysis  
#### 6.2.1 Policy Hidden Security  
The patient uploads the encrypted data to the cloud server and replaces the attribute \(attr_{j}\) in the access policy matrix with an implicit bilinear mapping expression \(\sigma_{j}=e((g^{\delta})^{r},\)\(H(attr_{j}))\), which is embedded in the ciphertext using a one-way hash function. Only an authorized user with the key \(H(\omega)^{\delta}\) can compute \(e(g^{r},H(\omega)^{\delta})\). Since \(\delta\) is a random value, the cloud server and other users cannot guess the attribute \(\omega\) from the value \(e(g^{r},H(\omega)^{\beta})\). Without knowing the corresponding \(H(\omega)^{\delta}\), no one can compute \(e((g^{\delta})^{r},H(\omega))=e(g^{r},H(\omega)^{\delta})\) and hence cannot construct the attribute in the access policy.
## 5 Discussions  
### Why Does GCTNRec Work?  
We proved the advantages of GCTNRec through experiments. Three advantages of GCTNRec which may explain its effectiveness in sequential recommendation are:  
1. **Modeling data from a heterogeneous perspective:** Most data scenarios in the real world are composed of multiple types of entities and various relationships between entities. Therefore, a heterogeneous graph used to describe each entity and the relationship between the entities contains rich information. Extracting the information from the heterogeneous graph will help us resolve the problem of sparse features of the entity itself.
2. **Better sequence understanding through deep learning:** Unlike traditional techniques, GCNTRec learns sequences with GCN and the transformer encoder. Characteristics of items in sequences, such as adjacent items in graphs and item orders in sequences, are considered in these models. Therefore, it can better evaluate the relevance of sequences and items to predict the next item.
3. **Degree coding further enriches the characteristics of items in sequences:** The degree of a node in a graph indicates the number of nodes adjacent to the node. The degree can reflect the influence level of a node in a graph. The higher the degree is, the more nodes are connected with the node. Therefore, the degree has a certain influence on the item feature representation for sequential recommendation tasks.
## 1 Introduction  
Lumbar surgery, primarily dominated by rigid fusion techniques, has been a cornerstone in treating degenerative spinal conditions [1]. However, this approach has been increasingly scrutinized due to the rising incidence of adjacent segment disease (ASD), a significant complication that compromises patients' long-term outcomes [2, 3, 4, 5, 6, 7, 8, 9, 10]. In spine surgery, ASD is defined as degeneration occurring in the mobile segments above or below the fused spinal segment, and it can result in adjacent segment Isthesis, instability, herniated nucleus pulposus, stenosis, hypertrophic facet arthritis, scoliosis, and vertebral compression fractures [11, 12]. While rigid fusion provides stability, it often does so at the cost of increased mechanical stress on adjacent segments, leading to ASD, as revealed in some biomechanical studies [13, 14]. Radiological ASD was reported to have an incidence rate of 42.6% beyond a five-year follow-up [15]. The incidence of clinically significant ASD, necessitating revision surgery, ranges from 4.7% to 27.4% [5]. This issue has prompted the exploration of motion-preserving fixation methods as alternatives. Dynamic stabilization methods, such as Dynesis, ISObar-TTL, and PEEK (polyetheretherketone) rods, which aim to preserve motion while offering stability, initially showed promise but faced setbacks mainly due to a high rate of mechanical complications [16, 17]. Semi-rigid fixation techniques, although less explored, offer a potential compromise. These methods strive for a balance between the rigidity needed for effective stabilization and the flexibility that is necessary to mitigate the mechanical impact on adjacent segments. Our study aimed to evaluate the long-term outcomes of semi-rigid fixation in lumbar surgery with a Nitinol spring rod (Bioflex) and an interbody cage. Nitinol is an alloy of nickel and titanium that belongs to a class of materials called shape memory alloys. Nitinol implants have high elasticity, high tensile force, and flexibility (below 10 \({}^{\circ}\)C), but they have rigidity (above 30 \({}^{\circ}\)C) and biological compatibility. This system has been used to preserve motion at the area of implantation. However, the standalone use of Bioflex and e-pedicle screws is not widely adopted due to concerns about mechanical failure [18]. We specifically hypothesized that the use of posteriorly placed Nitinol spring rods combined with the stability provided by an anterior cage would not impede the achievement of fusion and would result in a lower incidence of adjacent segment disease (ASD) compared to fusion surgery using rigid rods based on the reported outcomes of fusion surgery using rigid rods. Through documenting and analyzing the long-term outcomes of using this method, our aim is to contribute to the ongoing discussion on spinal surgery techniques and potentially redefine surgical strategies to improve patient outcomes in treating lumbar degenerative diseases.
# Network Slicing for mMTC and URLLC Using Software-Defined Networking with P4 Switches
## 5 Conclusions  
This paper proposes an SDNPS with an INT data packet format to simultaneously support mMTC and URLLC by using NS. The method satisfies the opposing service requirements of both mMTC and URLLC in 5G and alleviates the control load on the SDN controller. In SDNPS, INT data are recorded in data packets forwarded by P4 switches every sampling period, and these data are collected by an INT data collector module in the application plane. Before forwarding a packet with an INT header added by the ingress switch, each P4 switch along a flow path in the data plane records its current status from this header. The INT data comprise the queue occupancy, link throughput, and processing delay. When a packet carrying INT data arrives at the egress switch, the INT data are duplicated and sent to the application plane to determine the status of each switch along the flow path. The network slice controller is responsible for changing paths in each slice and informing the reroute module of these path reconfigurations. The P4 switches are instructed to update their forwarding rules as necessary. The SDNPS is verified through a simulation to substantially improve 5G NS performance for supporting mMTC and URLLC in the average throughput, packet loss, and packet delay, compared with a conventional SDN with dynamic or static routing. The proposed SDNPS is expected to improve the core network used in 5G NS. We intend to use SDNPS with a RAN to completely realize E2E 5G NS.  
Conceptualization, Y.-J.W.; Data curation, C.-Y.S.; Formal analysis, Y.-J.W.; Funding acquisition, W.-S.H.; Investigation, C.-Y.S.; Methodology, Y.-J.W.; Project administration, W.-S.H.; Resources, W.-S.H.; Software, C.-Y.S. and Y.-Y.C.; Supervision, Y.-J.W.; Validation, C.-Y.S. and Y.-Y.C.; Writing--original draft, Y.-J.W.; Writing--review and editing, W.-S.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by Taiwan Ministry of Science and Technology under Grant No.: MOST 110-2221-E-158-001 and MOST 110-2221-E-992-029-MY2.  
The source cord of this work and data obtained via the simulation are available from the corresponding author upon request.  
We thank the anonymous reviewers for their constructive comments, which improved the quality of this paper.  
The authors declare no conflict of interest.
###### Abstract
In general, PSG monitors several factors such as: breathing airflow, breathing events, snoring, blood oxygen saturation (SpO2), electrooculography (EOG), electroencephalography (EEG), and electrocardiography (ECG). However, the main drawbacks of the PSG method are: (i) PSG needs continual, hands-on supervision for patients during the examination process since each patient should wear many wearable devices (i.e., sensors), (ii) PSG needs a high level of recording systems, and (iii) the cost of PSG is between $3000 to $6000 [15]. To reduce the time and cost of apnea screening, we proposed a new CAD system that helps doctors to discriminate between apnea or normal respiration using ML methods. Building robust CAD systems can enhance the overall performance of the diagnosis process. To investigate this hypothesis, this study's motivation is to investigate the performance of ML classifiers to detect OSA based on beat-to-beat interval traces, medically known as RR intervals, into apnea versus non-apnea. We used a notch filter to remove the collected ECG signal's noise before extracting the most valuable feature. Moreover, this study highlights the performance of the hyper-parameter method for ML classifiers while the learning process. In practice, users can directly apply the proposed system to diagnose the OSA or normal via the ECG signals recorded from the patients. Unlike other models, the proposed system not only filters and extracts the important information from the signals but also keeps the significant information when diagnosing the OSA, which helps in reducing the complexity and improving the diagnosis system. The rest of this paper is organized as follows: Section 2 explores the related works of sleep apnea and CAD. Section 3 presents the proposed methodology that is used in this paper. Section 4 explores the public ECG dataset used here. Section 5 presents the obtained results with analysis. Finally, Section 6 explores the conclusion and future work on this paper.
## 2 Related Works
### Electricity Theft Detection
#### 2.2.2 Anomaly Detectors  
Unlike supervised detectors, anomaly detectors are built solely utilizing benign data. Then, malicious data are detected by observing their deviation from the normal pattern. In [11], [PERSON] et al. trained an OC-SVM utilizing benign samples from the ISET dataset. They conducted experiments to compare OC-SVM and binary SVM for detecting electricity theft, and the results showed the advantage of binary SVM over OC-SVM in detecting known forms of electricity theft cyberattacks. Nevertheless, they did not compare the performance of the two models against zero-day attacks. In [35], [PERSON] et al. examined the application of Principal Component Analysis (PCA) to detect potential theft cases. PCA helps reduce the dimensionality of electricity usage data by identifying principal components. During testing, an anomaly score is assigned to each sample based on these components. These scores are then evaluated against a set threshold to identify instances of theft. In [36; 37], [PERSON] et al. utilized ARIMA to identify potential theft. They introduced assessments of both mean and variance, resulting in a 77.46% reduction in the quantity of electricity attackers stole.  
Apart from previous works, the authors of [16; 34] explored anomaly-based electricity theft detectors using DL algorithms. In [34], the authors investigated a deep autoencoder-based anomaly detector using a fully connected feed-forward architecture. They compared the performance of the autoencoder with other anomaly detectors based on OCSVM and isolation forest (IF). The simulation results demonstrated the autoencoder's superiority over both OCSVM and IF. Moreover, [PERSON] et al. [16] explored using deep support vector data description (DSVDD) to detect potential theft cases. They proposed converting electricity consumption samples into images and then training a DSVDD model with these images. The simulation results in [16] demonstrated DSVDD's ability to distinguish malicious samples from benign ones. However, none of the existing works have explored electricity theft anomaly detection using the CAT approach for efficient data transmission.
## 2 Results and Discussion
### The Results of UPGMA Analysis Using 22 Highly Polymorphic Markers among 22 Allium Accessions  
The application of genic SSR markers to genetic diversity analysis and fingerprinting has recently been reported in several plant species [25, 38, 39, 40]. In this study, The UPGMA (unweighted pair-group method with arithmetic average) cluster analysis divided the 22 _Allium_ accessions into three groups with Nei's similarity coefficients ranging from 0.37 to 0.99 (Figure 2).  
Group I only contained \"ZhongYingTeDa\", but Group II had three subgroups: Group IIa, containing only \"WuYeQi\"1; Group IIb, consisting of \"WuYeQi\"2, \"WuYeQi\"3, \"ErShengZi\", and \"Zao134\";  
Figure 2: Dendrogram of 22 _Allium_ accessions derived from UPGMA analysis of 22 highly polymorphic SSR markers.  
and Group IIc, comprising \"Qi110\", \"SongBenYiBenTaiCong\", \"ZhongHuaJuCong\", \"7722\", and \"LiaoNingChaoYangLinCong\". In addition, Group III also had three subgroups: Group IIIa, consisting of \"Shou1105\", \"SanYu\", \"ShanXiYunChengKeXingYiHao\", \"HeBeiJinXian-DaCong\", and \"XiYeWanShengCong\"; Group IIIb, comprising \"GaoBaiQingCaiCong\", \"ShanXiFenCong\", and \"YeFu YiHao\"; and Group IIIc, containing \"ZhangQiuDaCong\", \"HanLvDaZhu\", and \"Yuanzang\".  
In recent years, transcriptome sequencing has proven to be one of the most efficient ways of developing genic SSR markers in many plant species, and many SSR markers based on transcriptome sequencing have been widely utilized in genetic diversity analysis, chromosome mapping and gene-based association studies [19,25,41,42]. Based on the results of our experiments, 22 genic SSR markers divided the 22 _Allium_ accessions into three groups and seven subgroups using UPGMA cluster analysis. Although the resulting dendrogram could not sufficiently explain the genetic relationships of the accessions, it demonstrated the effectiveness of the 22 genic SSR markers. A possible reason for the inaccurate dendrogram was that the Welsh onion possesses various local cultivars and a complex genetic background and, furthermore, useful molecular markers from the Welsh onion transcriptome were lacking. Therefore, developing more and better molecular markers from the Welsh onion transcriptome is very important and valuable for the further analysis of _Allium_ phylogenetic relationships.
# Performance Prediction of Composite Marine Propeller in Non-Cavitating and Cavitating Flow
## 2 Methodology and Results
### Structural Modeling
#### 2.3.3 Stacking Method  
Although the FE analysis model should be applied to consider the internal lamination structures [21], designing a propeller using a real FE analysis need a long computation time. Therefore, the 3D 8-node continuum shell element of Abaqus was applied proportionally to lamination properties of a composite material in the thickness direction [14]. For example, if an element thickness and ply sheet thickness are known, ply layers could be stacked in proportion to 'one' element. Figure 5 shows the comparison of a material beam between the real FE model and simple ply stack model.  
In this study, the ply sheet thickness is 0.13 mm and the number of ply layers can be calculated based on the element thickness of each model. The experiment was performed on only model 1, and model 2 to 5 were designed with different ply angles from model 1. Among them, model 5 showed the greatest difference in terms of deformation compared to model 1. In the case of models 1 and 5, the lamination reference lines were rotated 30\({}^{\circ}\) and \(-\)60\({}^{\circ}\), respectively, in the propeller rotation direction on the propeller generator line. Based on this, the internal lamination angle was 90\({}^{\circ}\), which is the same in all layers as shown in Figure 6. Ply angles were decided to examine more obvious relationship between lamination angle and displacement of composite propellers and minimize the manufacturing error when performing experiments scheduled for the next step.  
Figure 5: The comparison of a material beam between (**a**) the real FE model and (**b**) simple ply stack model. (Reprinted with permission from Ref. [14]. Copyright 2017 [PERSON]).
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 3 Digital Twins for Solar Photovoltaics  
From previous sections, it can be observed that DT utilization covers various applications and processes. In the context of PVs, we consider the application of DTs to solar cell production, module manufacturing, physical system design, integration, operation, and maintenance. We look at the current trends in published works using keyword analysis of the 'Web of Science' and 'Scopus' databases, as shown in Figure 3. The search from Section 2, using \"Digital twin and energy\" in the abstract, title, and keywords for articles, journals, and conference proceedings, was refined to limit answers to only files that contain the following keywords: \"photovoltaic cells\", \"photovoltaic power\", solar panels, photovoltaic systems, solar power generation, solar energy, solar power, solar PV, solar concentrators, photovoltaic and photovoltaic power generation. Booleans were applied to assist with this refinement. It was observed that most of the publications within solar photovoltaics are related to PV'system','management', 'performance', 'efficiency', and the type of'machine learning' approach used. While this can be expected, in this work, we take a step further to provide an in-depth analysis of the current approaches and applications.  
It should be noted that, prior to the introduction of the digital twin concept for PV systems, there existed traditional digitized systems used to monitor performance [27; 28]. However, in line with the definitions of the DT concept (Section 2), these traditional platforms are highly limited in their capabilities, as they were often designed for specific applications. For example, traditional systems gather and store basic monitoring data for manual interpretation or integration with other tools for deeper insights, while a DT is  
Figure 2: Global distribution of (**a**) published works, (**b**) publication count, and (**c**) energy sectors.  
designed to gather the information, process it using advanced algorithms, and interpret and take action via external controllers where required. A traditional DT designed for data monitoring will also not be optimized for logging faults and maintenance issues or actions taken for rectification. Furthermore, DTs are highly scalable to accommodate future functionalities and controls compared to traditional systems.  
To clarify the contributions of this work, we compare recent literature [29; 30; 31] that has provided some perspectives on PV-DT research and identify the overlap. As indicated in Table 2, this paper aims to provide a holistic view of the system-level implementation of a PV-DT across the PV life cycle, with a focus on post-construction monitoring and maintenance. This includes the use of autonomous platforms (aerial robots and ground robots), as well as other system-level components which are not covered elsewhere. In the next section, we provide a more detailed description of DT applications in the various PV developmental stages, architectures, technologies, and industry trends.