esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The study elucidated the wound healing and hair regeneration properties of a conditioned medium prepared from the culture of human hair follicle mesenchymal stem cells (HFMSCs). The wound-healing effects of mesenchymal stem cell-conditioned medium (MSC-CM) were tested in vitro using scratch assays co-cultured with HaCaT keratinocyte and monitored through optical microscopy. The cell proliferation of HFMSCs and the HaCaT keratinocyte were observed in the presence of different kinds of drugs including UK5099, sodium L-lactate, lactate dehydrogenase-A, MSC-CM, caffeine, and caffeic acid. The hair regeneration properties were investigated in vivo by administrating the MSC-CM solutions to adult B6 mouse models. For quantification, hematoxylin and eosin staining were performed following euthanasia. In vitro results revealed that MSC-CM promotes dermal cell migrations and enhances proliferation of HFMSCs and HaCaT keratinocytes, demonstrating wound-healing properties. Moreover, when the MSC-CM solutions were applied to the shaved mouse skin, a dark area that expanded overtime was seen. Although no hair growth was found, histological analysis proved that a fat layer thickness increment was found under the mouse's skin, ultimately projecting the formation of new hair growth. MSC-CM promotes the migration and proliferation of dermal keratinocytes that are beneficial for wound healing and hair growth. It is believed that MSC-CM can potentially serve as the basis of alternative therapeutic applications for wound closure and skin regeneration as well as hair growth stimulation and hair loss prevention in alopecia.  
hair follicle; hair regeneration; mesenchymal stem cells; wound healing
# Fourier Neural Operator Network for Fast Photoacoustic Wave Simulations
## 1 Introduction
Neural finite element models are mesh-independent and closely resemble traditional finite element methods [25; 29]. It replaces the set of local basis functions in the finite element models with a fully connected neural network. It requires prior knowledge of the underlying PDE and is designed to solve for one specific instance of the PDE. The neural network needs to be retrained for new instances where the underlying PDE is parameterized with a different set of functional coefficients. FNO is a mesh-free approach that approximates the mapping between two infinite dimensional spaces from a finite collection of input-output paired observations [30; 31]. The neural operator is learned directly in the Fourier and image space using a CNN. The same learned operator can be used without retraining to solve PDEs with different discretization and parameterization. Fourier Neural Operators have been demonstrated to achieve state-of-the-art results for a variety of PDEs (e.g., Burgers' equation, Darcy Flow and Navier-Stokes) and outperform other existing deep learning methods [31]. To the best of our knowledge, this is the first paper to apply deep learning for solving the photoacoustic wave equation for simulating PAT. FNOs were chosen for this task given their flexibility in discretization and superior performance compared to other deep learning methods. Prior work with FNOs demonstrated solutions to the Navier-Stokes and Burgers' equations, which have relatively smooth spatio-temporal solutions [31]. Unlike these works, photoacoustic signals have high broadband frequencies and contain sharp transitions. Specifically, this paper highlights the following innovative contributions:  
* Adapting the FNO neural network and applying it as a fast PDE solver for simulating the forward and adjoint 2D photoacoustic operator. * Simulations from the FNO network and the widely used k-Wave toolbox for time domain acoustic wave propagation [16] were compared in terms of accuracy and computation times. Further experiments were also conducted to evaluate the generalizability of the FNO network beyond the training data and the impact of key hyperparameters on network performance and complexity. The remainder of the article is organized as follows. The forward problem and the inverse problem of PAT are described in Section 2. Acoustic wave simulation techniques for the PAT based on conventional methods and FNO networks are presented in Section 2. The data generation and training process of the FNO network and its detailed implementation are also given in Section 2. Simulation results of the FNO network on the different test set with different spatial grid sizes and hyperparameter optimizations are provided in Section 3. Furthermore, the simulation results of zero-shot super-resolution with the FNO network are also given in Section 3. The conclusion and discussion are presented in Sections 4 and 5, respectively.
# An Overview of Intelligent Transportation Systems in Europe
## 5 Key Features of ITSs
### Benefits of ITSs
#### 5.1.2 Improving Road Safety  
The main objective of ITS development is to ensure road safety, aiming to reduce the number and severity of crashes. eCall, which speeds up emergency responses after crashes, and C-ITSs, which facilitate communication between vehicles and infrastructure, are examples of current approaches [50]. These innovations are likely to contribute to the EU's goal of achieving zero road deaths by 2050 [51].
## 2 Framework of RUL Prediction Based on TCNMABG  
### Overall Framework  
The overall framework of RUL prediction based on TCNMABG for rolling bearing is shown in Figure 1, which includes data acquisition and preprocessing, feature extraction and selection, and RUL prediction. Firstly, reduce the noise of the vibration signals of rolling bearing based on the dung beetle algorithm optimization-based variational mode decomposition (DBOVMD). Secondly, extract the multi-domain mixed features by integrating the features of the time domain, frequency domain, and entropy. Then, the isometric feature mapping algorithm reduces dimensionality and compresses the extracted features to get the health indicator curve effectively, and the kernel-based fuzzy C-mean clustering method is introduced to determine the first prediction time (FPT) of the rolling bearing. Finally, construct the TCNMABG prediction model, and train the model with the goal of minimizing the loss function to obtain the RUL prediction results for rolling bearing.
## 5 Classification of Smart Grid Cyberattack
### Data Integrity Attacks
#### 5.3.3 Time Synchronization  
Precise timing information is necessary for many smart grid functions, including fault detection and event location estimation. Due to this, a time synchronization attack (TSA) can be launched to target the smart grid's timing data. It is highly possible to attack the measurement system by faking the GPS (global positioning system) because many smart grid applications use synchronous measurements and the majority of measurement equipment include a GPS for exact timing [105].  
In addition, TSA may target the wide-area measurement systems (WAMSs) in smart grids. The monitoring equipment is dispersed throughout the entire power grid network,where the control center receives their measurement data with different transmission delays. The control center must time-synchronize all acquired measurements in order to obtain an accurate picture of the state of the system operation. GPS-based time synchronization monitoring devices have been widely used in smart grid monitoring systems because the GPS signal is highly accurate and reliable for timing without the need for additional communication infrastructure [106].
## 4 Implementation of Policy Hiding in CP-ABE Using Logical Connective
### Policy Hiding (\((A,\ \rho),\ \mathcal{T})\to HV\)  
In this process, we derived the hidden value \(HV\), which is the location of the attribute value that has been encrypted. The policy hiding algorithm adopts an access policy (\((A,\ \rho),\ \mathcal{T}\)) as an input. It first extracted the attribute values' set associated with access policy (\(\mathcal{T}\)) and then got the exact location \((x,y)\) of each attribute value. Each location is converted to ciphertext via operation \(\oplus\) and \(\odot\). Finally, our CP-ABE solution produced the output of ciphertext and hidden value in (\(CT\), \(HV\)) and outsourced it to Cloud servers. Algorithm 2 depicts the entire Policy Hiding Algorithm.  
Figure 4: Example of the access. matrix \(Att_{(x,y)}\).  
```
Input: Access policy \(((A,\ \rho),\ \mathcal{T})\) Output: Hidden Access Policy \((HV)\) Begin \(AV_{x}\) of \(\mathcal{T}\), \(\forall_{x}\) = index of \(AV\).  Extract location \((\mathsf{X}_{x},\ \mathsf{Y}_{\mathsf{x}})\)  Convert location \((\mathsf{X}_{x},\ \mathsf{Y}_{\mathsf{x}})\) String to Binary  Compute \(\alpha_{x}=\mathsf{X}_{x}\oplus\mathsf{Y}_{\mathsf{x}}\)  Compute \(\beta_{x}=\alpha_{x}\odot\mathsf{Y}_{\mathsf{x}}\)  Convert Binary to Hexadecimal for each \(\beta_{x}\), store as \(=HV_{x}\) End the process
```  
**Algorithm 2** Policy Hiding Algorithm
# PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring--A Comprehensive Review
## 3 Results
### Temporal Scope and Distribution  
The majority of the reviewed articles (approximately 70%) were published within the last decade, with a noticeable increase in research activity between 2015 and 2024. This trend underscores the growing interest and rapid development in the field of wearable health monitoring technologies, particularly those utilizing PPG and EBI methods.  
Finally, the synthesis of these articles has provided a comprehensive overview of the current state of wearable heart rate monitoring technologies, highlighting both the successes and the areas in need of further innovation. The next sections will delve deeper into the biological signals used in these devices, followed by an exploration of the algorithms and methodologies that address the identified challenges.
## 2 Data Collection and Curation Process
### Data Collection and Parameters  
The first database includes recompiled information on antioxidant compounds (AC-DB), while the second corresponds to wall materials (WM-DB). The compounds for the AC-DB were extracted from Phenol-Explorer: Database of Polyphenol Content in Foods [24]; their selection was narrowed based on the number of data found for each compound. Compounds were selected by assigning a value of 30 or higher to the number of discovered data. On the other hand, the wall materials were extracted from multiple books, such as Bioactive Carbohydrate Polymers [25] and Materials for Encapsulation. Chapter 7. Food Processing and Encapsulation Technologies for Active Food Ingredients and Wall Material Selection for Spray Drying [26].  
For selecting the appropriate parameters, it is crucial to remember that the wall material must form a cohesive film with the core material and be chemically compatible and non-reactive with the core material [7]. In this regard, RDKit, an open-source cheminformatics toolkit, was utilized to collect the parameters that will shape the model [27]. First, the compounds' canonical smiles (CS) were extracted, and then, using the CS, molecular descriptors could be determined by implementing RDKit in Python. These molecular descriptors represent the physical and chemical properties of a molecule mathematically. There are various types of molecular descriptors, including unidimensional (1D), bi-dimensional (2D), and three-dimensional (3D) descriptors.  
The molecular descriptor's complexity determines these; similarly, the 1D descriptors are the simplest to calculate. They represent the information calculated with the molecular formula of the compound, such as the type of atoms, functional group, and molecular weight. The 2D descriptors include information regarding the molecule's size, shape, and electronic distribution. The 3D descriptors, such as the Polar Surface Area (PSA) and intramolecular hydrogen bonding, are the most complicated and require the 3D structure of the molecule to be calculated. In this manner, the following molecular descriptors were extracted: chemical formula, molecular weight, logP, Hydrogen Bond Donor (HBD), Hydrogen Bond Acceptor (HBA), Polar Surface Area (PSA), number of heavy atoms, number of carboxyl groups, number of carbonyl groups, number of hydrogen groups, and number of acyclic groups [28; 29].  
Before data curation, it is necessary to analyze the recompiled information and determine the superficial relationships between the parameters of ACs and WMs.Understanding the parameters' meaning and other pertinent information is essential. For this purpose, bibliographic research was conducted for each parameter. See Appendix A.1.
# Mobile Augmented Reality Serious Game for Improving Old Adults' Working Memory
## 2 Literature Review
### Adopting AR/VR Technology  
In a serious game, it is important to motivate the players to hold their attention so that they can focus on the game itself rather than their physical limitations. The use of interactive AR/VR technology can help them do this. There are a number of studies on serious games for pain relief and rehabilitation treatment using AR/VR technologies [36; 37; 38; 39; 40; 41; 42; 43]. Among them, studies related to upper limb rehabilitation therapy for adult stroke patients have been actively conducted, and guidelines were presented in [44].  
For example, in 2008, [PERSON] and [PERSON] developed a game-based exercise to encourage physical exercise in virtual reality using a head-mounted display (HMD) for patients with upper limb motor disorder. In the case of VR movement therapy, the framework was designed by adjusting various factors so that the game could be played according to the patient's ability [39].  
In addition, an experiment was done to measure and evaluate the effects of natural interaction technology on the rehabilitation process [45]. Studies using AR/VR technologies have been performed not only in the field of rehabilitation for the improvement of physical functions but also in the field of psychotherapy. Studies aimed at the treatment of mental disorders, such as alcoholism, anorexia, anxiety disorder, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), etc. have been actively conducted [45; 46; 47; 48; 49; 50; 51; 52; 53].  
Whereas the purpose of the above-mentioned studies is psychological treatment, studies that proposed educational and auxiliary tools in relation to dementia and Alzheimer's disease (AD), which are representative diseases of the elderly, were also conducted. [PERSON] designed a tablet-based game for measurement of the cognitive functions of the elderly using augmented reality and evaluated the effectiveness of the game [54], and, in a study by [PERSON] et al., a multi-step action was constructed to support the daily activities of Alzheimer's disease patients using the HoloLens from Microsoft [55].  
However, in advanced studies on the improvement of cognitive function for the elderly, games were developed with an HMD device; however, their supply and activation are still in the beginning stages. Therefore, it is considered to be difficult to access and use the games in daily life, and continuous practice cannot be guaranteed.  
This study proposes a cognitive training system for the elderly made by combining a working memory measurement test and mobile augmented reality with the topic of serious games applied to the field of rehabilitation. This system was developed with mobile augmented reality technology that can accompany the minimum physical activity, which can be practiced with a smartphone with high penetration rates in all age groups. In addition to the areas targeted by this system, it can help with training on cognitive functions in other areas, such as attention concentration and spatiotemporal cognitive ability.
## 2 Graph Notions
### Minimal separation, chordal graphs, and minimal triangulation
We say that vertex \(x\) of number \(i\) by \(\alpha\) generates minimal separator \(S\) w.r.t \(\alpha\) if \(S\) is equal to the set of neighbors of \(x\) of higher number._  
Thus, to compute the set of minimal separators of \(H\), it is sufficient to compute a peo \(\alpha\) of \(H\) and to select the vertices that generate a minimal separator of \(H\) w.r.t. \(\alpha\). This is easy to accomplish with a special kind of peo defined by algorithms such as MCS [5], and will be used for our implementation in Section 4. **Minimal triangulations:** A _triangulation_ is a chordal completion of a graph: a set \(F\) of edges (called the fill edges)is added to the graph in order to obtain a chordal graph. **Definition 2.6**: _Let \(G=(V,E)\) be a graph. A chordal graph \(H=(V,E+F)\) is called a triangulation of \(G\), and \(F\) is called the fill. The triangulation is said to be:_  
* _minimal if for no proper subset_ \(F^{\prime}\) _of_ \(F\)_,_ \(H^{\prime}=(V,E+F^{\prime})\) _is chordal._
* _minimum if no other minimal triangulation has less fill edges._  
**Example 2.7**: _Graph \(H\) from Figure 4 is a minimal triangulation of Graph \(G\) from Figure 1 (see Figure 5)._  
Computing a minimum triangulation is NP complete [6], but computing a minimal triangulation can be done in \(O(nm)\) time (or a little less for dense graphs [7,8]). This problem has given rise to several recent papers, so there are now many minimal triangulation algorithms (see _e.g._[9, 10, 11, 12]). We will use one of them (MCS-M) in Section 4 where we describe an implementation. **Minimal elimination orderings:** One way to compute a triangulation of a graph \(G=(V,E)\) is to force the graph into having a perfect elimination ordering: define an ordering \(\alpha\) on \(V\), repeatedly pick the next vertex by \(\alpha\), add any edges missing in its neighborhood (in the subgraph induced by the not yet removed vertices), and remove it, until no vertex is left. This will yield a triangulation \(G^{+}_{\alpha}=(V,E+F)\) whose fill \(F\) is the set of all edges added in this process. Figure 5: \(H\) is a minimal triangulation of \(G\). The fill edges are represented by dotted lines. An ordering \(\alpha\) on the set of vertices of \(G\) is a _minimal elimination ordering (meo)_ of \(G\) if the triangulation \(G^{+}_{\alpha}\)of \(G\) obtained is a minimal triangulation of \(G\). Algorithm LEX M [13] was devised to compute an meo. In Section 4, we will use a simpler version of LEX M, MCS-M [9]. Both these algorithms yield a minimal triangulation \(H\) of the input graph \(G\) and an ordering \(\alpha\) that is both a meo of \(G\) and a peo of \(H\), and makes it easy to compute the set of vertices generating the minimal separators of \(H\).
# A Finite Element Analysis of Tunnel Lining Demolition by Blasting for Subway Tunnel Expansion
## 4 Blasting Mechanical Characteristics
### Results Analysis
#### 4.2.2 Failure Mechanism of the Vault of Tunnel Lining
The calculation formula of the fracture zone in the surrounding rock caused by blasting with the relevant blasting parameters is shown in Table 6, and it can be used to determine the fracture zone produced by blasting in the tunnel lining. The parameters are substituted into Equations (3)-(6), and the theoretical radius of the blasting fracture zone in the tunnel lining is calculated to be 0.86 m, which is greater than 0.7 m (the thickness of the lining structure). The results show that the blasting load leads to penetrating cracks in the tunnel lining, which are beneficial for dividing the tunnel \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cement Density** & **Detonation Velocity** & **Explosive Roll** & **Poisson’s Ratio \(\mu\)** & **Lateral Stress** & **Tensile Strength** \\ \(\rho_{s}\) (kg/m\({}^{3}\)) & \(D\) (m/s) & **Radius \(r_{c}\) (mm)** & **Poisson’s Ratio \(\mu\)** & **Coefficient \(\psi\)** & \(\sigma_{t}\) (MPa)** \\ \hline
2400 & 5000 & 18 & 0.25 & 0.33 & 2.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Blasting fracture zone calculation parameters. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Measuring Points**} & \multicolumn{2}{c}{**Vault**} & \multicolumn{2}{c}{**Left Arch Footing**} & \multicolumn{2}{c}{**Right Arch Footing**} \\ \cline{2-7}  & \(S_{1}\) & \(S_{3}\) & \(S_{1}\) & \(S_{3}\) & \(S_{1}\) & \(S_{3}\) \\ \hline Outer side & 1 & 28.6 & 43.7 & 19.3 & 20.3 & 24.8 & 27.8 \\ Neutral axis & 2 & 17.4 & 17.0 & 14.5 & 17.1 & 18.6 & 16.6 \\ Inner side & 3 & 13.8 & 12.8 & 13.6 & 10.5 & 13.4 & 8.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The maximum stress of typical section of tunnel lining (unit: MPa). lining into small pieces. Moreover, the seam pre-cut in the lining by the machine greatly weakens the resistance of the tunnel lining. The subway tunnel lining will collapse under the action of the blasting impact stress and the gravity of the surrounding rock, which makes it possible to quickly remove the tunnel lining.
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 6 Conclusions  
In this paper, we proposed a mobility management scheme for 5G mmWave massive MIMO networks based on the unific measurement report for both cell-level handover and beam-level switching. We proposed a novel user's state machine to evaluate the mobility performance for a practical 5G mobile communication system. With the state machine, we evaluated several important performance metrics such as uplink and downlink overhead, RLF rates, SINR, and accuracy of beam-pair selection in the system-level-simulation. Our algorithm obtained higher gain of the performance under the UDN scenario compared to the scenario of the single-gNB in terms of overhead and RLF rates compared to the conventional schemes. Therefore, the proposed scheme showed more effective results under the practical scenario where both handover and beam switching are considered rather than an ideal scenario where only beam-switching is considered as in the previous works. Furthermore, our algorithm achieved considerable improvements in terms of overhead performance, low RLF rates, the accuracy of beam pair selection, and SINR performances in the environment close to the practical system.  
Figure 6: SINR results in relation to 3-schemes in single-gNB environment.  
Figure 7: SINR results in relation to 3-schemes in the UDN environment.  
**Author Contributions:** Conceptualization, Y.J., J.L., and [PERSON]; methodology, [PERSON]; software, [PERSON]; validation, [PERSON] and [PERSON]; investigation, [PERSON]; resources, [PERSON] and [PERSON]; data curation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], [PERSON]; visualization, [PERSON] and [PERSON]; supervision, [PERSON]; project administration, [PERSON]; Funding acquisition, [PERSON] and D.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korean Government (MSIT, No.2017-0-00316, Development of Fundamental Technologies for the Next, Generation Public Safety Communications), and the Basic Science Research Program through the National Research Foundation (NRF) funded by the Korea government (MIST) (No. NRF-2019R1I1A1A01058976).  
**Conflicts of Interest:** The authors declare no conflicts of interest.