esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolving Strategies and Materials for Scaffold Development in Regenerative Dentistry
## 4 Discussion
### Mesenchymal Stem Cells and Their Application in Regenerative Dentistry  
Mesenchymal stem cells (MSCs) are one of the elements which are crucial to the process of tissue regeneration [64; 96; 97; 98]. They can be, in their entirety, quite easily extracted from various types of body tissues; however, they strictly require suitable conditions to fully prosper. Different types of MSCs exert various effects on different kinds of human tissues and numerous studies and clinical applications have confirmed utility and advantageous properties of mesenchymal stem cells as a part of scaffold grafts in regenerative dentistry [99; 100; 101].  
Bone marrow stem cells (BMSCs) were seeded onto collagen scaffolds that have shown extracellular matrix (ECM) mimicking ability, which is crucial for biological processes and cell synthesis. Additionally, vascularization and new bone tissue formation were boosted via an increase in BMP-2 and b-FGF markers [43]. Dental stem cells (DSCs) can be retrieved from several parts of the oral cavity, e.g., from deciduous teeth, apical papillae, and periapical cysts. Apart from DSCs, adipose tissue stem cells (ADSCs), induced pluripotent stem cells (iPSCs) and exosomes, which are small membrane vesicles isolated from cells, are of greatest importance in regenerative dentistry [103; 33; 104].  
Many authors have studied applications and effectiveness of MSCs in relation to various scaffold media. Stem cells derived from exfoliated human deciduous teeth (SHEDs) were studied by [PERSON] et al. SHEDs were seeded onto carbonate-apatite scaffolds to support the bone remodeling process in post-extraction cavities. The study in vivo showed higher levels of bone morphogenic proteins (BMP-2 and BMP-7) and a lower degree of matrix metalloproteinase (MMP-8) expression together with better osteogenesis in rats [38].  
In the study by [PERSON] et al., the impact of material stiffness and topographical micropatterned alignment of Gel-MA hydrogels on apical papilla stem cells (APSCs) was explored. Cells expressed higher viability, higher proliferation response, guided self-alignment, as well as an increase in ALP expression, leading to better odontogenic differentiation ability compared to scaffolds with no micropatterned structural alignment [105].  
MSCs derived from human inflamed periapical cysts (hPCy-MSCs) seeded onto PLA-mineral doped (dicalcium phosphate dihydrate (DCPD), and/or hydraulic calcium silicate (CaSi)) scaffolds promoted higher DMP-1 and RUNX-2 activity, resulting in better osteogenesis and cell proliferation rates [33]. [PERSON] et al. studied a short peptide sequence with the potential to activate and inhibit osteogenesis [104].  
As an alternative for regular stem cells, [PERSON] et al. presented how exosomes participate in cellular regulatory processes. Interestingly, their potential rises when combined with suitable ceramic scaffolds [74; 106]. There is a need to further investigate mechanisms of MSCs' action, integration within scaffold systems, their responses to multiple factors.
## 3 Networks and Protocols of Communication
### Radio Frequency Technologies  
Long Range  
LoRa is mainly being used to deploy applications in machine-to-machine (M2M) communication and IoT. This is because such deployments can benefit from the low-power specification for communication between electronic devices. In addition, LoRa has an adaptive data rate algorithm that helps to maximize the battery life and the network's ability to transmit data [25]. LoRa is a Low Power Wide Area Network (LPWAN) communication system that has long-distance transmission capabilities and receives support from companies such as IBM, Semtech, and Actility, which are members of the LoRa Alliance, for the development of the communication network. The objective of LoRa development is to create a low-power network for transmitting data from end devices that transmitted a certain amount of data packets at a given time. The LoRa network, developed by Semtech, is designed to work at a frequency of 433, 868, and 915 MHz (depending on locality) and with a transmission rate between 0.25-50 kbps [26].  
The typical LoRa-based communication network, as defined in the standard LoRaWAN by the LoRa Alliance, is based on a star topology in which gateways relay messages between end devices and the main network [27]. Overall, LoRa has been approached as a network that will bring benefits to the IoT ecosystem. Thus, LoRa is necessary for communications that are carried out over long distances. When compared to short-range protocols, such as Wi-Fi and Bluetooth, it has been shown to be quite advantageous, although there are some disadvantages in transmission speed and limitations of the size of the payload [25].  
LoRaWAN is a new wireless protocol from the IoT family that has recently evolved and is gaining popularity in embedded systems powered by a low-power battery. LoRaWAN operates by transferring small amounts of data at short intervals and long-range [28]. There are many communication technology options for implementing SMs, such as GSM/GPRS, Wi-Fi, Bluetooth, ZigBee, Radio Mesh, and LoRa. Technology GSM/GPRS access is used for three-phase meters because they are used to transfer lots of data such as load profile, final billing, possible breaches, and events that occur in the meters. LoRaWAN is the best choice to be implemented in smart grids, for having long-range and low power, being open-source, and lower cost than GSM/GPRS. The frequency range reserved for the use of communication by LoRaWAN ranges from 902 to 928 MHz in America or 433/868 depending on locality [29].
# Numerical Study of Flow Separation Control over a Hump Airfoil Using a Novel Sweeping Jet Actuator
## 1 Introduction
It has been shown that SWJ actuators can effectively control flow separation over trailing edge flaps at deflection angles of up to 40\({}^{\circ}\), providing the necessary control authority. [PERSON] et al. [33] applied an SWJ actuator to a hump airfoil, comparing its performance to 2004 data. They found that, at the same flow rate, the SWJ actuator produced greater flow acceleration and smaller separation bubbles, proving more effective  
Figure 1: Conceptual design of a representative SWJ actuator [22]. for flow control than steady suction or unsteady zero-net-mass-flow actuators. [PERSON] et al. [25] used a two-dimensional NASA hump model with an integrated SWJ actuator to explore the potential of these actuators in reducing flow separation, demonstrating their clear effectiveness in flow control. [PERSON] et al. [36] experimentally studied the oscillation frequency, oscillation angle, and modal structure of fluid oscillators at different lengths. They found that the oscillation frequency is directly proportional to the flow velocity and inversely proportional to the scale size, while the oscillation angle is inversely proportional to the flow rate and directly proportional to the scale size. [PERSON] et al. [37] made similar findings regarding oscillation frequency. The frequency of the sweeping jet is related to its size and momentum coefficient. As the model is scaled and the momentum coefficient changes during flow control, the frequency of the sweeping jet actuator will also change. This variability raises uncertainty about whether the sweeping jet actuator has an optimal frequency similar to the driving frequency of a synthetic jet. Moreover, if such an optimal frequency exists, it may not be consistently maintained due to changes in size or increases in the momentum coefficient, potentially hindering optimal control efficiency. Therefore, determining if there is an optimal frequency for the sweeping jet actuator in flow control and how to maintain it becomes a critical issue. In response to this issue, in this article, a new Sweeping jet actuator was designed to determine the effects of frequency and momentum coefficient on flow control effectiveness. It can achieve frequency variation while maintaining the momentum coefficient. The Hump airfoil, as a classic airfoil, has well-studied separation characteristics, which facilitates comparison of this study's results with other research. Therefore, in this study, the new sweeping jet actuator is applied to the Hump airfoil to investigate the effects of frequency and velocity ratio on flow control.
## 4 Proposed Methodology
### Solution Based on the Recommendation System
#### 4.2.5 Combining Results  
In this step, we adopt a mixed hybridization strategy that simultaneously leverages collaborative and content-based recommendations. Each method returns a recommended node, and the hybrid recommender system selects the final node by comparing its spatial proximity to the target user.  
To determine the best location, the system calculates the 3D Euclidean distance between the current position of the user and each recommended node.  
Let  
* \(u=(x_{u},y_{u},z_{u})\) be the 3D coordinates of the user.
* \(n_{cf}=(x_{cf},y_{cf},z_{cf})\) be the coordinates of the node recommended by collaborative filtering.
* \(n_{cb}=(x_{cb},y_{cb},z_{cb})\) be the coordinates of the node recommended by content-based filtering. The Euclidean distance is calculated as \[\text{Distance}(u,n)=\sqrt{(x_{u}-x_{n})^{2}+(y_{u}-y_{n})^{2}+(z_{u}-z_{n})^{2}}\] (4) The node with the shortest distance to the user is selected as the final placement decision. The pseudo-code Algorithm 4 explains this step.  
```
1:Input: Node from collaborative filtering \(n_{cf}\), node from content-based filtering \(n_{cb}\), user position \(u\)
2:Output: Final recommended node \(n_{f}\)
3:functionCombineResults(\(n_{cf},n_{cb},u\))
4: Compute \(d_{cf}\leftarrow\text{EuclideanDistance}(u,n_{cf})\)
5: Compute \(d_{cb}\leftarrow\text{EuclideanDistance}(u,n_{cb})\)
6:if\(d_{cf}<d_{cb}\)then
7:return\(n_{cf}\)
8:else
9:return\(n_{cb}\)
10:endif
11:endfunction
```  
**Algorithm 4** Combining Results
###### Abstract  
When large amounts of wind power and solar photovoltaic (PV) power are integrated into an independent power grid, the intermittent renewable energy destabilizes power output. Therefore, this study explored the unit commitment (UC) optimization problem; the ramp rate was applied to solve problems with 30 and 10 min of power shortage. The data of actual unit parameters were provided by the Taiwan Power Company. The advanced priority list method was used together with a combination of a generalized Lagrangian relaxation algorithm and a random feasible directions algorithm to solve a large-scale nonlinear mixed-integer programming UC problem to avoid local and infeasible solutions. The results showed that the proposed algorithm was superior to improved particle swarm optimization (IPSO) and simulated annealing (SA) in terms of the minimization of computation time and power generation cost. The proposed method and UC results can be effective information for unit dispatch by power companies to reduce the investment costs of power grids and the possibility of renewable energy being disconnected from the power system. Thus, the proposed method can increase the flexibility of unit dispatch and the proportion of renewable energy in power generation.  
renewable energy; unit commitment; priority list method; generalized lagrangian relaxation algorithm; random feasible directions algorithm +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Surface Characterization
#### 3.1.4 Second Harmonic Generation Imaging (SHG)  
Figure 5 illustrates the second harmonic signal image of PET fibers coated with Type I collagen. The PET-COL surface demonstrates a consistently dense green fluorescent signal, signifying the even distribution of collagen, while the PET surface remains devoid of any fluorescent signal. This provides additional evidence that collagen has been evenly applied to the PET surface.
# Exploiting Duplications for Efficient Task Offloading in Multi-User Edge Computing
## 3 System Model
### Energy-Aware Duplication Phase  
From the above duplication-based task offloading algorithm, it can be found that all the subtasks are scheduled by order of its priority, and the optimization objective is the finish time of current subtasks. However, minimizing the finish time of each critical subtask cannot always guarantee the optimization of the overall delay of application. Moreover, an inappropriate duplication may achieve the local optimization solution; however, it may occupy the CPU cycle for successor subtasks, which leads to an increase in the overall completion time and unnecessary energy consumption. Thus, in this subsection, we introduce the energy-aware duplication algorithms (as in Algorithm 2).  
In this algorithm, we remove the duplications that did not help in improving the performance (energy consumption and delay reduction must be comprehensive consideration). Following Algorithm 1, we check each duplication one after another by order of its rank (_line_ 2). Note that in this algorithm, we did not distinguish the duplicate task and an original task, which uniformly called duplication. In the \"for loop\" (_line_ 3 to _line_ 12), we attempt to remove each subtask to see whether it can improve the performance or not. In (_line_ 7), the metric _Ratio_ can be adjusted based on the remaining energy of a specific user device.  
Note that the \(\delta_{e}\) only refers to energy reduction in the user devices, and if the duplication the executed in the edge server, the energy cost is equal to 0. It is also worth noting that, once any subtask is removed, the scheduling result of all the succeeded subtasks will be changed (otherwise Algorithm 1 is meaningless), and thus we need to call Algorithm 1 for successor subtasks.  
Through the above-mentioned three phases of the algorithm, we finally obtain an efficient duplication-base and energy-aware task offloading algorithm (DETO) in a single-user MEC system with only one server. In the next section, we extend the DETO algorithm to the multi-user and multi-server edge networks.  
```
1. While there are unchecked subtask in \(Rank_{list}\)
2. select the first unchecked subtask \(T_{i,j}\) in \(Rank_{list}\)
3. For each duplication \(T_{i,j}\)
4. Reschedule all the subtask after \(T_{i,j}\) in \(Rank_{list}\) without \(T_{i,j^{\prime}}\) by Algorithm 1.
5. Compute the time and energy reduction \(\delta_{t}\), \(\delta_{e}\)
7. if\(\delta_{t}\leq 0\) or \(\frac{-\delta_{t}}{\delta_{e}}\leq Ratio\)
8. Delete \(T_{i},j^{\prime}\) and save the current rescheduling
9. else
10. Rollback the delete operation of \(T_{i},j^{\prime}\)
11. end if
12. end For
13. Mark \(T_{i,j}\) as checked
14. endwhile
```  
**Algorithm 2** The energy-aware duplication algorithm.
# DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media
## 4 Experimental Results and Evaluation
### AUC-ROC Analysis  
The True Positive Rate (TPR) and False Positive Rate (FPR) values offer additional insights into the model's high-performance characteristics, which have been calculated using Equations (34) and (35). The values of TPR and FPR for all classes are listed in Table 3. The TPR, also known as sensitivity or recall, signifies the proportion of actual positives correctly identified by the model. For all four classes--'Good', 'Average', 'Neutral', and 'Poor'--the TPR values are notably high, ranging from approximately 97.79% for 'Neutral' to as high as 99.04% for 'Poor'. These high TPRs indicate that the model has an excellent ability to identify cases within each class correctly.  
\[\text{True Positive Rate (TPR)}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{34}\]  
\[\text{False Positive Rate (FPR)}=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{35}\]  
Conversely, the FPR, which measures the proportion of negative cases incorrectly classified as positive, remains notably low across all classes. The FPR ranges from around 0.41% for 'Average' to about 0.78% for 'Poor', signifying that the model has a minimal tendency to generate false alarms. The graphical representations of TPR and FPR have been illustrated in Figure 5. These extremely low FPR values and high TPR values substantiate the model's accuracy in distinguishing between the classes while minimizing the risk of misclassification. Overall, the TPR and FPR metrics affirm that the model performs exceedingly well across all categories.  
Figure 4: The analysis of individual classes of the confusion matrix.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Metrics** & **Good** & **Average** & **Neutral** & **Poor** \\ \hline TPR & 0.9784 & 0.983 & 0.9779 & 0.9904 \\ \hline FPR & 0.0067 & 0.0041 & 0.0057 & 0.0078 \\ \hline \hline \end{tabular}
\end{table}
Table 3: True Positive Rate (TPR) and False Positive Rate (FPR).
## Appendix A  
The derivation of Equation (3) is presented. Referring to Figure 1, the relative distance \(r\) from \(p_{ARS}\) to elemental mass \(\delta m\) is  
\[r=r_{m}+\Delta r,\]  
where \(\Delta r=[\Delta x\ \Delta y\ \Delta z]^{T}\) is an offset vector from \(p_{ARS}\) to the shifted CG point \(p_{CG}\). Using [PERSON]'s law of motion, the external force \(F_{B}\) in the inertial frame \(O_{I}\) is equal to adding all the linear momentum of \(\delta m\) as in  
\[F_{B}=\sum\delta F_{B}=\sum\left(\frac{d}{dt}\bigg{(}v_{ARS}+\left(\frac{dr}{ dt}\right)_{I}\bigg{)}_{I}\right)\delta m,\]  
where \(V_{ARS}=[u\ v\ w]^{T}\) is the body linear velocity in \(O_{ARS}\), and \(\omega=[p\ q\ r]^{T}\) is the body rate also in \(O_{ARS}\). Both vector sums undergo a time derivative in the inertial frame \(O_{I}\). Simplifying, we obtain the following:  
\[F_{B}=m\frac{d}{dt}(v_{ARS}+\omega\times r)_{I}.\]  
The subscript \(I\) denotes that the derivative is taken in the \(O_{I}\) frame and \(m\) is the total mass. Applying the transport theorem and neglecting the term from the time rate of change in CG location,  
\[F_{B}=m\dot{v}_{ARS}+m\dot{\omega}\times\Delta r+m\omega\times(v_{ARS}+ \omega\times\Delta r),\]  
where \(\dot{v}_{ARS}=\left[\dot{u}\ \dot{v}\ \dot{v}\right]^{T}\) and \(\dot{\omega}=\left[\dot{p}\ \dot{q}\ \dot{r}\right]^{T}\). In addition, \(\left(\dot{\bullet}\right)=\frac{d}{dt}(\bullet)_{ARS}\) denotes the time derivative in the frame \(O_{ARS}\).  
The angular momentum equation is derived in a similar manner. The angular momentum \(H_{B}\) at the point \(p_{ARS}\) is given such that  
\[H_{B}=\int\big{(}r\times(v_{ARS}+\omega\times r)\big{)}\delta m,\]  
where \(v_{\delta m}=v_{ARS}+\omega\times r\) is a velocity of differential mass \(\delta m\). Then, the equation becomes  
\[H_{B}=I_{NERT}\omega+m\Delta r\times v_{ARS}\]  
where \(I_{NERT}\) is the moment of the inertia matrix. The time derivative of the angular momentum in \(O_{I}\) frame becomes  
\[\frac{d}{dt}(H_{B})_{I} =\int\bigg{(}\frac{d}{dt}(r)_{I}\times v_{\delta m}\bigg{)} \delta m+\int\bigg{(}r\times\frac{d}{dt}(v_{\delta m})_{I}\bigg{)}\delta m,\] \[=\int\bigg{(}\bigg{(}\frac{d}{dt}A_{\delta m}\bigg{)}_{I}-\frac{ d}{dt}(A_{ARS})_{I}\times v_{\delta m}\bigg{)}\delta m+M_{B},\]  
where the external moment on the inertial frame is \(M_{B}=\int\bigg{(}r\times\frac{d}{dt}(v_{\delta m})_{I}\bigg{)}\delta m\) by definition. Since \(\frac{d}{dt}(A_{\delta m})_{I}=v_{\delta m}\) and \(\frac{d}{dt}(A_{ARS})_{I}=v_{ARS}\), they become  
\[M_{B} =\frac{d}{dt}(H_{B})_{I}+\int\big{(}(V_{ARS}-v_{\delta m})\times v _{\delta m}\big{)}\delta m,\] \[=\frac{d}{dt}(H_{B})_{I}+mV_{ARS}\times(\omega\times\Delta r).\]  
Finally, the moment equation is obtained as  
\[M_{B} =I_{NERT}\dot{\omega}+m\Delta r\times\dot{V}_{ARS}+\omega\times I _{NERT}\omega\] \[+m\omega\times(\Delta r\times V_{ARS})+mV_{ARS}\times(\omega \times\Delta r).\]  
The derived force \(F_{B}\) and moment \(M_{B}\) equations can be organized as in Equations (3) and (4).
## 2 FRA Applications on Power Transformers  
### Measurement Setup  
FRA measurement in this paper is conducted on a 500 kVA, 11/0.415 kV, Dyn11 three-phase distribution transformer with a de-energized tap changer selector. The tap changer configuration is given in Table 5. The unit has been tanked off to conduct the FRA measurement with normal, short circuit (SC), and open circuit (OC) fault conditions. The un-tanked transformer windings are as shown in Figure 1 of which FRA measurement is carried out on phase-A of the high voltage (HV) with the commonly used end-to-end connection as shown in Figure 2. In this Figure, V\({}_{\text{in}}\) is the input voltage with reference voltage V\({}_{\text{ref}}\) at one terminal of one phase and V\({}_{\text{out}}\) is the output voltage measured at the other terminal of the same phase while the low voltage (LV) winding is open. The FRA signature is the magnitude of the winding transfer function in dB (20 log10 V\({}_{\text{out}}\)/V\({}_{\text{in}}\)) measured in a frequency range of 10 Hz to 2 MHz [27]. Various SC turns are emulated using different tap changer positions while the normal winding condition is considered when the tap is at position 1 by connecting the terminals (4 and 5), as shown in Figure 3a. The minimum SC level is achieved at tap 2 when connecting tap terminals (3 and 5) while tap 1 is still in place, as described in Table 5. The SC at tap 3 is undertaken by connecting terminals (3 and 6), the SC at tap 4 is done by connecting terminals (2 and 6) and the SC at tap 5 is undertaken by connecting terminals (2 and 7) as presented in Table 5 and shown in Figure 3e. On the other hand, OC turns are performed by leaving tap 1 open at terminals (4 and 5).  
A short circuit fault in a power transformer can occur when the condition of insulation between the adjacent turns of the conductor is degraded. In a power transformer, high voltage (HV) and low voltage (LV) windings are represented by series resistance Rs, series resistance Rs, and the resistance Rs are represented by series resistance Rs. The resistance of the conductor is shown in Fig. 3.  
Figure 3: A schematic diagram for staging various SC and OC faults on the transformer winding.  
Figure 2: End-to-end winding FRA measurement connection.  
capacitance Cs, and self-inductance Ls, respectively. The Gg and Cg are the ground conductance and capacitance which represent the insulation of the system. In the short circuit condition, the insulation system is degraded and the impedance (Z) of the winding changes. Short circuit fault is represented by reducing the operated amount of turns in the winding.
# A Knowledge-Guided Competitive Co-Evolutionary Algorithm for Feature Selection
## 2 Background  
### Related Work  
#### 2.1.1 Multi-Objective Optimization Problem  
An MOP involves finding the minimum or maximum values of multiple objective functions under certain constraints [27; 28]. It is generally formulated as follows:  
\[\begin{split}\min/\max& f(X)=(f_{1}(X),f_{2}(X), \ldots,f_{r}(X))\\ \text{s.t.}& g_{i}(X)\geq 0(i=1,2,\ldots,k)\\ & h_{i}(X)=0(i=1,2,\ldots,l)\end{split} \tag{1}\]  
In the above equation, the decision vector \(X=(x_{1},x_{2},\ldots,x_{n})\) and the objective vector \(f(X)\) have multiple optimization combinations, which can be summarized into the following three scenarios:  
* Maximize all sub-objective functions;
* Minimize all sub-objective functions;
* Maximize some sub-objective functions while minimizing others.  
It is possible to transform all optimization objectives into a unified form, either all maximizing or all minimizing, to facilitate the algorithm finding the optimal solution \(X^{*}=\left(x_{1}^{*},x_{2}^{*},\ldots,x_{n}^{*}\right)\) such that \(f(X^{*})\) is optimally achieved while satisfying constraints.  
In this work, for the feature selection problem, we need to optimize two objectives: (1) the number of selected features and (2) the performance of the machine learning tasks using the selected features. Assuming a solution selects k features, then \(f1(x)\) is equal to \(k/D\), representing the ratio of selected features, while \(f2(x)\) represents classification performance. To achieve the minimization of all sub-objective functions, we consider the classification performance as \(1-f1\_score\), where, for a multi-class problem, \(f1\_score\) is the weighted average.