esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Main Design
### Tuning \(\alpha\)  
As discussed above, we use a weighting parameter \(\alpha\) to calculate the integrated correlation metric. When \(\alpha\) is large, the history correlation is estimated with more weight; When \(\alpha\) is small, the instant correlation is estimated with more weight. If the packet is received/lost at both receivers, we mark the ground truth of link correlation as 1; if the packet is received at only one receiver, we mark the ground truth as 0.  
We record the ground truth and adaptively tune \(\alpha\) for more accurate link correlation estimation. The intuition is that the correlation value close to the ground truth should be more weighted: If the ground truth of packet link correlation is 1, we increase the weight of max (\(ch\), \(ci\)). If the ground truth of packet link correlation is 0, we increase the weight of min (\(ch\), \(ci\)). For example, with \(ch=0.9\), \(ci=0.5\) and \(\alpha=0.5\). If the received packets are the same, which means the instant correlation is 1 for the packet, we increase \(\alpha\) by \(inc\), since the \(ch\) value is closer to 1. Note that the increased value \(inc\) can also be tuned: \(\alpha\) with a large \(inc\) is more sensitive to the estimation results, while \(\alpha\) with a small \(inc\) is more accurate. Finding a theoretical optimal \(\alpha\) will be studied in future works.
These studies demonstrated the feasibility of using a DVC algorithm to process OCT images. However, the data format is often limited by the commercial software. [PERSON] et al. conducted a series of studies. They developed a DVC algorithm based on a differential evolution method for the 3D displacement measurement ([PERSON] et al., 2019), then proposed a VFM method, which is insensitive to rigid body motion, to recover the constitutive parameters of the optic nerve head ([PERSON] et al., 2019). This work provided a paradigm for combining OCT, DVC and VFM for the mechanical properties' measurement of heterogeneous tissue. However, using strains instead of displacements as the input for VFM calculation may be more accurate. Although there are not so many studies of combined OCT and DVC yet, it can be seen that it is completely feasible to combine them for the 3D displacement measurement. However, the noise insensitive method is required to calculate the strain tensors in dealing with the laser speckle noises in OCT images. In addition, when objects with complex structures are measured, a large amount of virtual displacements and large-scale linear equations maybe involved in VFM, which may result in unsolvable problems or wrong solutions. Thus, a method to simplify the VFM calculation is required when a nonhomogeneous sample is measured. This study is to develop a DVC and VFM based OCE method for the quantitative characterization of mechanical properties of layered biological tissue. As the DVC algorithm has been advanced in all aspects since it was first proposed in 1999 ([PERSON] et al., 2019), we picked out the steps that are insensitive to noise and integrated them into a 3D strain measurement method with OCT imaging. This included a zero-mean normalized sum of squared difference (ZNSSD) criterion ([PERSON] et al., 2019), a 3D inverse compositional Gauss-Newton (IC-GN) matching method ([PERSON] et al., 2019), and a local ternary quadratic polynomial fitting method ([PERSON] et al., 2019) for strain calculation. We then chose a linear VFM method which is noise resistance ([PERSON] et al., 2019). The automatic segmentation of the layers is proposed based on the strain gradient acquired by DVC and OCT, so that the [PERSON]'s modulus and Poisson's ratio of each layer can be solved individually by VFM. Strains are used as the input for the VFM calculation of material properties of double-layer materials under a compressive load. This quantitative 3D OCE method can obtain the elastic modulus of each layer of the specimen simultaneously. As many biological tissues, such as the skin and the vascular wall, are layered structures, this study will have a wide application potential in experimental biomechanics.
# A Platform of Unmanned Surface Vehicle Swarms for Real Time Monitoring in Aquaculture Environments
## 2 Related Work
In Reference [18] the authors present a decentralized platform of Autonomous Aquatic Crafts (AACs) for research on swarm algorithm, that comprises two networks, one internal and another external to monitoring the state of the AACs. Both networks are defined based on an _ad-hoc_ wireless technology with Xbee. Several structures have been proposed to maintain the connectivity within a team. Approaches like leader-follower [19; 20; 21; 22] and the virtual structure approach [23; 24; 25] are categorized as centralized control. Moreover, behavior-based structure approach [26; 27] is categorized as decentralized control. Compared to these works, our approach is able to deliver an end-to-end solution, with a heterogeneous communication approach, motion planning based on the networks requirements in a swarm environment, in a self-organized way, coordinating both missions and data collection in a dynamic approach and providing a user-based web platform for visualization and coordination.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 8 Challenges and Opportunities
### Interoperability and Standards
#### 8.3.1 Universal Standards  
Establishing universal standards for the metaverse is another formidable challenge. Standards are essential to ensure consistency, security, and compatibility across various platforms and technologies that constitute the metaverse [96]. However, reaching a consensus on these standards involves coordination among numerous stakeholders, including tech companies, developers, regulatory bodies, and users. Each group may have different priorities and interests, making it difficult to agree on a unified set of rules and protocols. Additionally, rapid technological advancements and the evolving nature of the metaverse further complicate standardization efforts. The metaverse risks becoming a collection of isolated ecosystems without robust interoperability and standardized frameworks rather than a seamless, interconnected digital reality. This major challenge is divided into two parts: fragmentation and data compatibility.
## 3 Dataset and Methods  
This study mainly utilizes the drilled-well data of four wells, namely Q1, Q2, N1, and Z1 from structures Q and N (Figure 1a), as well as the analysis and testing data of 35 core samples related to these four wells. The completed test analyses in this study mainly include scanning electron microscope (SEM) analysis, carbon isotope (\(8^{13}\)C\({}_{1}\)) analysis, and fluid inclusion analysis, etc. Among them, the SEM analysis was completed at the Jiangsu Oilfield Experiment Center. The illite K-Ar dating shows that the oil and gas charging time was 25.14 Ma. Meanwhile, the Central Anticline Belt structural area is covered by high-resolution 3D seismic data, which were collected in 2018, with an area of approximately 300 km\({}^{2}\). They were collected by dual-vessel, three-dimensional, and ten-streamer acquisition, with a coverage of 54 times and a cable spacing of 75 m. In addition, the location of cross-well seismic profile A-A\({}^{\prime}\) used in this study is shown in Figure 1.  
This paper summarizes the development and enrichment characteristics of natural gas reservoirs in different structures of the Central Anticline Belt through a comprehensive research method of drilling-wells data and 3D seismic data. Combining the evolution  
Figure 1: (**a**) Regional geological maps of the Central Anticline Belt in the Xihu Sag in the East China Sea Shelf Basin, including the major anticline structures, various compression zones, cross-well seismic profile A–A\({}^{\prime}\), and drilled wells used in this study. (**b**) Stratigraphic map of the Xihu Sag, showing the lithology, sea-level curve, seismic interfaces and ages, formation, and tectonic movement.  
characteristics of different structures of the Central Anticline Belt, integrated with the analysis of oil and gas transport capacity of faults and the geochemical characteristics of oil and gas in this study, we try to compare the oil and gas accumulation process of various structures (Q, N, Z) and propose different natural gas accumulation models of the Central Anticline Belt.
## 5 Conclusions  
Linear encoders are often used in applications for the precise displacement measurement of a moving unit to control positioning and speed. In order to properly accomplish these tasks, encoder parameters such as accuracy and resolution are critical. In practice, a SDE in an optical encoder is unavoidable. The magnitude of this error is a major factor that limits maximal resolution and causes a speed ripple. When analog encoder signals are interpolated without any error correction or compensation approach, it is very important to know the limitations of the used encoder. The method described in this paper presents a way to determine the magnitude and trend of the linear encoder SDE.  
Experimental investigation of the standard optical linear encoder SDE under different scanning speeds was accomplished according to the presented methodology. Deeper analysis of the determined errors helped to reveal their physical nature and the limits of the encoder. On the basis of the obtained results, it is possible to specify the weak points of the tested device to help to improve its performance. Mapped and approximated SDE at different speeds could be used as a multivariable function for positioning-error compensation. This is our future research direction.  
collected and processed the data; [PERSON] supervised the research. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:
## 4 Discussion
Specifically, previous study found that Fgf9 produced from the wound promotes dermal Wnt activation and induces fibroblast Wnt expression, eventually leading to WIHG [37]. The overexpression of Wnt7 by MSC effectively promotes wound healing and at the same time, induces hair follicle regeneration through the acceleration of the biological progression of hair follicles from the telogen (resting) to anagen (growth) phase [20]. In the course of hair development, hair follicles undergo cyclic regeneration throughout its life, namely the anagen, catagen (regression), and telogen phases [38]. It has long been recognized that the adjacent tissue-layers including nerves, hair follicles, interflicular epidermis, blood vessels, melanocytes, muscles, adipocytes, and fibroblasts participate and have a direct link in the hair cycle and formation [38; 39]. Accordingly, in this study, we used the thickness of various skin layers including epidermal thickness, cortical thickness, muscle thickness, hair follicle number, and fat thickness as hair regeneration indicators. Given the limited number of samples analyzed, we could not establish a significant difference between the treated and control mice except for the fat layer thickness. Nevertheless, a previously published study found that there was a positive correlation between the thickness of the adipocyte layer with hair growth. When the hair follicles are in the anagen phase, the thickness of the adipocyte layer will rapidly increase [40]. In our study, the significant increment of the fat layer thickness found under the mouse's skin might indicate the potential growth of the hair follicle after the MSC-CM treatment. In the near future, MSC-CM may prove a promising therapeutic tool to improve wound healing and hair regeneration as well as in other regenerative applications. Specifically, the induced proliferation of stem cells present in the wound might be beneficial for the formation of new hair or prevent hair loss in alopecia patients.
## 8 Challenges, Future Directions, and Lessons Learned
### Challenges
* Commercialization challenge: various factors can hinder the commercialization or deployment of the 5G network, which include inadequate optical fiber penetration, lack of civil infrastructure, lack of electrical power supply, cost of spectrum and equipment, etc., in some regions or countries, particular in Nigeria where the 5G network is yet to fully deployed [186]. This poses a great challenge to the 5G network for both the users and the telecommunication operators, as the need to provide these amenities in the regions or countries can further increase the cost of deployment, which can lead to an increase on the part of the users of the network. Thus, there is a need to design the network such that the cost of using the network would be minimal.
## 3 Results and Discussion
### Imaging of PAN/Biochar Fibers though the Preparation Process
#### 3.2.3 EDS Analyses of \"as-Spun\" Fibers  
As expected, PAN fibers were formed mainly by carbon, nitrogen, and oxygen. EDS analysis is incapable of detecting hydrogen, but it is known that it was also present due to the polymer composition.  
Figure 7 presents the results of the analysis. While the biochar percentage grew, the percentage of detected inorganics grows as well. It can be noticed that potassium in 90/10, 75/25, and 50/50 solutions grew continuously, and the calcium content increased as well in the 75/25 and then in the 50/50 samples. Aluminum and gold derived from the metallization process for the ESEM analysis [36]. Nitrogen and oxygen probably derived from PAN. The 50/50 sample showed various other elements that probably belonged to the biochar ashes. It can be seen how potassium and calcium were the most prevalent; these  
Figure 5: ESEM imaging of the “as-spun” fibers: (**a**) 100% PAN; (**b**) 90% PAN-10% biochar; (**c**) 75% PAN-25% biochar; (**d**) 50% PAN-50% biochar.  
elements came from the initial biomass composition. In this work, the biomass used to produce the char consisted in vineyard pruning wood pellets. This fuel is known to have a significant K and Ca content [37].  
Figure 6: ESEM imaging of the carbonized fibers: (**a**) 100% PAN; (**b**) 90% PAN-10% biochar; (**c**) 75% PAN-25% biochar; (**d**) 50% PAN-50% biochar.  
Figure 7: EDS analyses of “as-spun” fibers, and mass percentages of the detected elements: (**a**) 100% PAN; (**b**) 90% PAN-10% biochar; (**c**) 75% PAN-25% biochar; (**d**) 50% PAN-50% biochar.
# Experimental Study on the Dynamic Characteristics of Fractured Coal Under Cumulative Impact
## 3 Results and Analysis
### Dynamic Compressive Strength Analysis
The plot reveals a robust linear correlation: dynamic compressive strength decreases proportionally with wave velocity reduction, enabling strength prediction via wave velocity measurements given known fracture inclination. Furthermore, increased disturbance impact energy correlates with progressive deterioration in dynamic compressive strength of  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Cumulative Impact Effect Times**} & \multicolumn{4}{c}{**Fracture Inclination Angle**} \\ \cline{2-5}  & **No Fracture** & **30\({}^{\circ}\)** & **60\({}^{\circ}\)** & **90\({}^{\circ}\)** \\ \hline
0 & 59.74 & 44.43 & 49.10 & 57.69 \\
1 & 56.24 & 39.25 & 44.92 & 53.63 \\
3 & 49.13 & 34.24 & 39.45 & 46.92 \\
5 & 43.92 & 27.35 & 35.49 & 41.78 \\ \hline \end{fractured coal specimens. This finding reveals that sustained energy absorption accelerates internal damage propagation within the material matrix, ultimately leading to strength degradation. Through data fitting, it is found that there is also a strong linear relationship between dynamic compressive strength and disturbance impact energy. It should be noted that the above relationship is based on the application mode of \"lower single disturbance energy and more disturbance times\", and different input modes of disturbance impact energy may lead to different results.
# Pilot Sequence Allocation Schemes in Massive MIMO Systems Using Heuristic Approaches
###### Abstract  
This paper presents a comparison of different metaheuristic approaches applied to the pilot sequence allocation problem in Massive Multiple-Input Multiple-Output (MIMO) systems. A modified version of the Genetic Algorithm (GA) as well as different versions of the Particle Swarm Optimization (PSO) Algorithm are used to maximize the system spectral efficiency under an inter-cell interference regime. The metaheuristic parameters were optimized and computational simulations under different scenarios parameters were conducted to verify the system performance impact in terms of system spectral efficiency, minimum and maximum spectral efficiency per user and the cumulative distribution function (CDF) of the users spectral efficiencies. The main contributions of this work are: the creation of a public available dataset; heuristic parameters tuning; findings related to the impact of sub-optimal pilot sequence allocation to the users in terms of maximal and minimal achievable user spectral efficiency and the robustness of some algorithms in scenarios with different system loadings.  
p 2022 12 2022 10.3390/app1205117
## 4 Reactive Power Incentive Method
### Profit Obtained by Customers  
Once \(v_{q}\) is determined, customers can calculate their own obtained profit \(v\) following the order from DisCo. The \(\kappa\) is disclosed in Figure 4, and the profits obtained by the customers can be derived using Equation (22).  
\[v_{i}(t)=Q_{PVinv,i}^{\prime}(t)\times v_{q}\times P_{base}\ \ [\text{JPY}] \tag{22}\]  
In this paper, \(P_{base}\) is 5000 kW. \(Q_{PVinv,i}^{\prime}(t)\) is the reactive power incentive considering the contribution factor \(\kappa\), and it is described by the following equation:  
Figure 5: Sigmoid contribution factor of reactive power incentive. (**a**) Sigmoid contribution function for hourly powerand each node and (**b**) contribution region of the sigmoid and linear function.  
\[Q^{\prime}_{PVing,i}(t)=|Q_{PVinv,i}(t)|\times\kappa_{t,i}(t),\ \ \ \ \ \ \mathbf{f}\in\left\{lin,sig\right\}. \tag{23}\]  
Therefore, customers can obtain a maximum profit by supplying reactive power output close to \(Q^{*}_{PV}\). Since all customers should carry out the command for maximizing the profit, DisCo can maintain the voltage using this incentive.
## 2 Related Work
In [26], the authors proposed a DL detection model using artificial neural networks (ANNs), RNNs, and CNNs, leveraging a real-world phishing corpus from Nazario. Their approach addressed data imbalance effectively, achieving 99.51% accuracy. Other studies explored different approaches, such as [13], which compared Gaussian naive Bayes and LSTM models on the SpamAssassian dataset. LSTM achieved a significantly higher accuracy of 97.18% compared to naive Bayes. Similarly, ref. [23] applied BERT and DistilBERT pre-trained transformer models to address traditional feature extraction limitations, achieving 99% accuracy. In [14], CNN and LSTM models were evaluated on a custom dataset of phishing and legitimate emails. CNN with word embeddings achieved 96.34% accuracy, while Naive Bayes demonstrated the fastest computation time with 75.72% accuracy. A graph convolutional network (GCN) model for phishing detection was proposed in [34], trained on a publicly available fraud dataset, and achieved an accuracy of 98%. Similarly, the authors in [15] employed various ML classifiers on a spam dataset, with SVM achieving the highest accuracy of 98.77%. Finally, the authors in [35] introduced THEMIS, a recurrent convolutional neural network (RCNN) model utilizing multilevel vectors and attention mechanisms. THEMIS demonstrated excellent performance, achieving 99.848% accuracy on an anti-phishing shared task dataset. While significant research has been conducted on phishing email detection, key gaps persist: (1) many studies use a single or limited number datasets, restricting generalizability; (2) advanced architectures, such as transformer-based models, are insufficiently examined for phishing email detection; and (3) lack of a unified framework for consistent model evaluation across datasets. To bridge these gaps, our work introduces a comprehensive evaluation framework incorporating diverse datasets and advanced ML and DL models to enhance phishing detection accuracy, reproducibility, and generalizability.
## 3 Personalized Waiting Time
### Central Subsystem
#### 4.4.1 Personalized Dynamic Scheduling Determination Module  
This module provides kernel computing to the personalized dynamic scheduling function of the TPTS system. Specifically, this module is responsible for calculating the personalized waiting times and recommended session times of attractions, comparing and finding the attraction with the shortest waiting time among the attractions, and finding the most popular (\"hottest\") attraction according to the visit counts of attractions recorded in the database.  
Under all three strategies, this module needs to determine the personalized waiting time and the recommended session time prior to performing the personalized dynamic scheduling. The two values are required to inform the tourist how long he/she may wait when arriving at the attraction and which session to take. Moreover, in the Shortest Waiting Time First strategy, the personalized waiting times of a group of attractions are required to determine which attraction has the shortest personalized waiting time. To conclude, the calculation of personalized waiting times is crucial to the personalized dynamic scheduling function.  
Recall that the central subsystem in the TPTS system supports the personalized dynamic scheduling function including three strategies. When receiving a personalized dynamic scheduling request from the mobile app subsystem, the central subsystem determines which strategy the tourist chooses and proceeds accordingly. The steps that the central subsystem performs for three strategies are, respectively, as follows.  
* Closest First strategy
* 1. Calculate the personalized waiting time and recommended session time of the closest attraction based on its moving time found in the received personalized dynamic scheduling request.
* 2. Send the personalized waiting time and recommended session time of the closest attraction to the mobile app subsystem.
* Shortest Waiting Time First strategy
1. Calculate the personalized waiting time and recommended session time of each of attractions based on their moving times found in the received personalized dynamic scheduling request.
2. Find the attraction with the shortest personalized waiting time.
3. Send the attraction ID/name that has the shortest personalized waiting time as well as its personalized waiting time and recommended session time to the mobile app subsystem.
4. Hottest First strategy
5. Find the most popular (hottest) attraction (the implemented prototyping TPTS system regards the attraction with the highest number of visits as the hottest attraction).
6. Calculate the personalized waiting time and recommended session time of the hottest attraction based on its moving time found in the received personalized dynamic scheduling request.
7. Send the attraction ID/name that is the most popular as well as its personalized waiting time and recommended session time to the mobile app subsystem.  
The case study for the functionality of the scheduling determination module is presented in Section 5.2.2.