esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Accommodation Level and Regulation Marginals
To further validate the effectiveness of the evaluation indices proposed in this paper, the proposed indices are utilized to evaluate and compare the optimal scheduling results of the CGS comprising retrofitted TPUs and RESs with the pre-retrofit TPUs and RESs CGS presented in Reference [22]. In [22], an optimal scheduling of CGS is proposed without considering the RES accommodation potential of the system. The evaluation results are shown in Table 4. The results show that although the CGS with pre-retrofit TPUs already achieves commendable levels of RES utilization and total costs compared with CGS incorporating retrofitted TPUs, the flexibility marginals of the pre-retrofit TPUs are significantly lower, even below the levels before coupling integration. This indicates that TPUs without retrofitting can adequately handle current RES fluctuations after coupling integration. However, they may struggle with further increases in RES variability or capacity expansion within the CGS. The results suggest that the flexibility marginals index proposed in this paper not only directly reflects the level of RES accommodation by TPUs but also effectively indicates the potential of TPUs to accommodate RES. In summary, the results suggest that coupling integration and retrofitting of TPUs not only enhances the accommodation capability of RESs but also significantly improves the power system's flexibility in managing fluctuations. This improvement can allow for a \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Coupling Integration with** & **Coupling Integration** & **Improvement** \\  & **Retrofitted TPUs** & **without Retrofitted TPUs [22]** & **Improvement** \\ \hline \(I_{\text{MT}}^{\text{AL}}\) (\%) & 100 & 99.91 & 0.09 \(\uparrow^{1}\) \\ \(I_{\text{PV}}^{\text{AL}}\) (\%) & 99.97 & 97.84 & 2.13 \(\uparrow\) \\ \(I_{\text{RES}}^{\text{AL}}\) (\%) & 99.99 & 98.92 & 1.07 \(\uparrow\) \\ \(I_{\text{TUT}}^{\text{FM}+}\) & 154.0386 & 150.8950 & 3.14 \(\uparrow\) \\ \(I_{\text{TUT}}^{\text{FM}-}\) & 280.1582 & 207.8130 & 72.34 \(\uparrow\) \\ \(\sum_{t=1}^{T}P_{t}^{\text{WT}}\Delta t\) (MWh) & 6.1327 \(\times\) 10\({}^{5}\) & 6.1269 \(\times\) 10\({}^{5}\) & 0.058 \(\times\) 10\({}^{4}\) \(\uparrow\) \\ \(\sum_{t=1}^{T}P_{t}^{\text{PV}}\Delta t\) (MWh) & 5.5930 \(\times\) 10\({}^{5}\) & 5.4737 \(\times\) 10\({}^{5}\) & 1.19 \(\times\) 10\({}^{4}\) \(\uparrow\) \\ Annual revenues (CNY) & 2.1887 \(\times\) 10\({}^{9}\) & 2.1883 \(\times\) 10\({}^{9}\) & 0.038 \(\times\) 10\({}^{7}\) \(\uparrow\) \\ \hline \hline \end{tabular}  
* \({}^{1}\) \(\uparrow\): Improved value.  
\end{table} 
Table 4: Evaluation results before/after considering retrofitting of TPUs in CGS. higher penetration of RES. The proposed indices in this paper can accurately evaluate the potential of TPUs.
## 3 Results and Discussion
### Effective Atomic Number
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Windshield Glass** & **Element, i** & **Weight Fraction,** & **No of Electrons per gram of** & **Fractional Weight Contributions of** & **Effective Atomic** \\
**from Car Brand** & **Element, i** & **W\({}_{i}\)(\%)** & **N\({}_{e}\) = (N\({}_{x}\) \(\times\) Z \(\times\) N\({}_{y}\)/A\({}_{\rm AW}\)** & **Each Element, a\({}_{i}\)** & **Number, Z\({}_{\rm eff}\)** \\ \hline  & Si & 24.8 & \(7.43\times 10^{22}\) & \(2.48\times 10^{-1}\) & \\  & Na & 8.06 & \(2.32\times 10^{22}\) & \(7.75\times 10^{-2}\) & \\  & K & 0.05 & \(1.46\times 10^{20}\) & \(4.88\times 10^{-4}\) & \\ Honda & C & 5.05 & \(1.52\times 10^{22}\) & \(5.07\times 10^{-2}\) & \\  & O & 50.9 & \(1.53\times 10^{23}\) & \(5.11\times 10^{-1}\) & 12.2 \\  & Ca & 8.88 & \(2.67\times 10^{22}\) & \(8.91\times 10^{-2}\) & \\  & Mg & 1.68 & \(4.99\times 10^{21}\) & \(1.67\times 10^{-2}\) & \\  & Al & 0.61 & \(1.77\times 10^{21}\) & \(5.91\times 10^{-3}\) & \\ \hline  & Si & 24.7 & \(7.40\times 10^{22}\) & \(2.47\times 10^{-1}\) & \\  & Na & 8.96 & \(2.58\times 10^{22}\) & \(8.62\times 10^{-2}\) & \\  & K & 0.27 & \(7.90\times 10^{20}\) & \(2.64\times 10^{-3}\) & \\ Toyota & C & 4.8 & \(1.44\times 10^{22}\) & \(4.82\times 10^{-2}\) & 12.0 \\  & O & 50.6 & \(1.52\times 10^{23}\) & \(5.08\times 10^{-1}\) & \\  & Ca & 7.27 & \(2.18\times 10^{22}\) & \(7.30\times 10^{-2}\) & \\  & Mg & 2.35 & \(6.98\times 10^{21}\) & \(2.33\times 10^{-2}\) & \\  & Al & 1.14 & \(3.31\times 10^{21}\) & \(1.10\times 10^{-2}\) & \\ \hline  & Si & 28.8 & \(8.64\times 10^{22}\) & \(2.89\times 10^{-1}\) & \\  & Na & 9.85 & \(2.84\times 10^{22}\) & \(9.48\times 10^{-2}\) & \\  & K & 0.10 & \(2.93\times 10^{20}\) & \(9.78\times 10^{-4}\) & \\  & C & 2.12 & \(6.38\times 10^{21}\) & \(2.13\times 10^{-2}\) & \\ Proton & O & 50.3 & \(1.51\times 10^{23}\) & \(5.06\times 10^{-1}\) & 11.8 \\  & Ca & 5.16 & \(1.55\times 10^{22}\) & \(5.18\times 10^{-2}\) & \\  & Mg & 2.48 & \(7.37\times 10^{21}\) & \(2.46\times 10^{-2}\) & \\  & Al & 0.96 & \(2.78\times 10^{21}\) & \(9.31\times 10^{-3}\) & \\  & S & 0.22 & \(6.61\times 10^{20}\) & \(2.21\times 10^{-3}\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Elemental composition of car windshield glass samples from EDX analysis. We note that, as the oxide glasses are not conductive, the Au coating was used in the preparation of the SEM analysis, and the carbon tape was utilized as the conductive \"bridge\" connected from the top surface of the sample to the sample holder. In this matter, the employment of carbon tape may contribute to the low weight fraction percentage of carbon content in the studied samples. The effective atomic number of soft tissues for humans is typically taken to be 7.22, TL results need to be corrected via careful calibration in the final reconstruction of absorbed dose to the human body [20].
## 4 Factors to Study in VANETs
### Number of Warning Vehicles  
In traffic safety applications, vehicles may send safety messages to other vehicles in order to prevent collisions or to ask for emergency services. We consider that vehicles may operate in warning or normal mode. Warning mode vehicles inform other vehicles about their abnormal status by sending warning messages periodically. Normal mode vehicles participate in the diffusion of these warning packets and, periodically, they also send \(beacons\) with information about themselves, such as their position and speed.  
This factor is important since the more vehicles are in the warning mode, the more network traffic, thus increasing redundant rebroadcasts that provoke heavy contention and long-lasting collisions.
# Energy-Efficient Power Scheduling Policy for Cloud-Assisted Distributed PV System: A TD3 Approach
## 3 Problem Formulation  
In this section, we formulate the electricity scheduling policy for cloud-assisted multi-park distributed PV system as an optimization problem, and the objective is to minimize the total cost of all parks in the system. The problem can be formulated as follows:  
\[\min_{\phi}\sum_{i=1}^{n}C_{i,t} \tag{7}\] \[s.t. C1:E_{i,t}^{load}=E_{i,t,s}+E_{i,t,b}+E_{i,t}^{pv}+E_{i,t}^{ess}\] (8) \[C2:-E_{i,t,max}^{ess}\leq E_{i,t}^{ess}\leq E_{i,t,max}^{ess}\] (9) \[C3:SOC_{i,t+1}=SOC_{i,t}+E_{i,t}^{ess}\] (10) \[C4:0\leq SOC_{i,t}\leq SOC_{max} \tag{11}\]  
where \(E_{i,t,s},E_{i,t,b}\) denote the amount of buying or selling power of park \(i\), \(E_{i,t,max}^{ess}\) indicates the maximum power of charging or discharging, \(SOC_{i,t}\) denotes the power status of the energy storage system at the current moment, \(SOC_{max}\) is the upper boundaries of the electric storage charge state, respectively.  
C1 denotes that the internal operation of the park \(i\) meets the balance of power supply and demand. C2 and C4 indicate that the action and the power amount of the storage device are limited. C3 indicates that the power of the energy storage device at the next moment is equal to the current moment plus the charging and discharging strategy at the current moment.  
The original problem (7) can be addressed by working out the optimal solution of the energy storage action vector \(\phi\), which is a continuous variable. Instead of solving the problem by conventional optimization methods, we propose Deep Reinforcement Learning (DRL) algorithm to find the optimal \(\phi\).
# Analysis of Cascading Failures and Recovery in Freeway Network Under the Impact of Incidents
## 4 Case Study
### Cascading Failure Simulation and Analysis  
We conducted simulations on freeway networks to more thoroughly investigate how different attack strategies influence the cascading failure process and to validate the accuracy of the proposed model. While traffic incidents often exhibit spatial clustering, the timing and duration of these events remain highly random. Accordingly, random attacks (RAs), implemented by unbiasedly selecting road segments for removal, serve to effectively replicate the unpredictability associated with large-scale disturbances such as natural disasters. By measuring the decline in network connectivity and traffic capacity under these random disruptions, this method provides a fundamental baseline for assessing network resilience.  
In addition to random attacks, we introduced deliberate attack strategies to examine the heterogeneous vulnerability mechanisms present in real-world networks. High-betweenness attacks (HBAs) target road segments with the highest betweenness centrality, such as major transportation hubs, thereby simulating global topological collapse resulting from the failure of critical links. High-load attacks (HLAs) focus on segments experiencing the greatest traffic flow, which mimics the cascading congestion that arises from local overload and propagates through the network.
## 2 Materials and Methods
### Design of the DC-AC Controller
#### 2.3.1 Voltage Control (PI)  
The voltage-control loop employs an anti-windup PI controller and regulates the input voltage of the DC bus to the reference value \(V_{ref}\). The controller output is given by:  
\[v_{1}(k)=u_{p}(k)+u_{i}(k), \tag{45}\]  
where \(u_{p}\) and \(u_{i}\) are the proportional part and integral part, respectively. Their values are related in the following way:  
\[u_{p}(k)=e(k)=K_{p}[r(k)-y(k)], \tag{46}\]  
\[u_{i}(k)=u_{i}(k-1)+K_{i}e(k)w(k), \tag{47}\]  
where \(r(k)\) is the reference signal, \(y(k)\) is the system output, \(K_{p}\) is the proportional constant, and \(K_{i}\) is the integral constant. The term \(w(k)\) takes values of 1 or 0, providing a means of deactivating the integrator path when output saturation occurs. It prevents the full-term winding up and improves the response time when recovering from saturation [19].  
The controller's output saturates according to the upper (\(U_{max}\)) and lower (\(U_{min}\)) limits that are programmable by the user, i.e.:  
\[u_{v}(k)=\left\{\begin{array}{ccc}U_{max}&\text{if}&v_{1}(k)>U_{max}\\ U_{min}&\text{if}&v_{1}(k)<U_{min}\\ v_{1}(k)&\text{if}&U_{min}<v_{1}(k)<U_{max}\end{array}\right. \tag{48}\]  
The pre-saturated (\(v_{1}(k)\)) and post-saturated (\(u_{v}(k)\)) terms are compared to determine if saturation is produced, and the following expression finds the value \(w(k)\):  
\[w(k)=\left\{\begin{array}{ccc}0&\text{if}&v_{1}(k)\
eq u_{v}(k)\\ 1&\text{if}&v_{1}(k)=u_{v}(k)\end{array}\right. \tag{49}\]  
By using MATLAB to implement the inverter model found in Section 2 and adjusting its values with the real system, the constants \(K_{p}=1\) and \(K_{I}=0.001\) are obtained, and the limits \(U_{min}=0.025\) and \(U_{max}=0.8\) are defined. Additionally, for the voltage signal \(V_{bus}\), an eight-point average filter is implemented, as shown in Equation (44).
# Efficient Cluster Tree Topology Operation and Routing for IEEE 802.15.4-Based Smart Grid Networks
## 4 Proposed Scheme  
Figure 3 shows an example of the cluster tree structure used in the proposed scheme.  
The IEEE 802.15.4 standard is standardized to support 16 channels in the 2.45 GHz band and 10 channels in the 915 MHz band. While configuring a personal area network (PAN), when a coordinator selects a channel and periodically broadcasts a beacon frame through the channel, devices that receive the beacon frame join the PAN.  
These PANs can be extended to a cluster-tree structure, as illustrated in Figure 3. That is, there may be a plurality of PANs in which different channels are selected in overlapping propagation regions. In Figure 3, all the nodes have a single radio interface; GW (FFD) refers to a gateway, CH (FFD) refers to the coordinator of a lower PAN, and CM (FFD or RFD) refers to a member device belonging to a cluster.  
In addition, the top PAN coordinators connected to the gateway use channels different from those of the adjacent top PAN coordinators. This forms a cluster tree for each channel. For a multi-channel operation, the gateway operates by changing channels in the order [CH1 \(\rightarrow\) CH2 \(\rightarrow\) CH3], according to the schedule.
# Comparing the Diagnostic Accuracy of CBCT
###### Abstract  
Cone beam computed tomography (CBCT) allows for the dimensional evaluation of jaw anatomic structures and the measurement of jawbone mineral density (BMD), to evaluate the relationship between the measured CBCT grayscale (GS) values and (dual X-ray absorptiometry) DXA scores among osteoporotic and normal dental implant participants. In addition, it can help to establish the CBCT GS values in different jaw regions that might indicate or associate osteoporosis. Eighty-one (81) participants (59 females and 22 males) were recruited into this clinical inquiry. Participants' ages ranged between 36 and 75 years (mean age \(\pm\) SD: 57.9 \(\pm\) 9.3 years). The inclusion criteria included patients diagnosed with osteoporosis and were indicated for the dental implant-supported prosthesis.GS values were calculated for each participant using the CBCT radiograph and associated computer software. The average T values obtained from L1, L2, L3 and L4 were recorded and evaluated for one time only, and correlated with CBCT GS values at different jaw positions. Associations between age, gender, GS values, and T values were evaluated by Pearson correlation tests. The GS values at posterior maxilla were able to predict the presence of osteoporosis (\(P<0.001\), \(R\) square = 0.849). On the other hand, attempts to predict T value using GS at different jaw sites showed that GS values at posterior maxilla and anterior mandible were able to predict T value (\(P<0.001\), \(R\) square = 0.924). The CBCT GS values of BMD at different jaw bone regions are significantly related to the DXA T values in the presence of osteoporosis.  
DXA; cone beam computed tomography; osteoporosis; dental implants; bone mineral density +
Footnote †: journal: Applied Sciences  
1
Footnote 1: College of Dentistry, Jouf University, KSA, AlJouf 72345, Saudi Arabia; [EMAIL_ADDRESS] (K.K.G.); [EMAIL_ADDRESS] (M.K.A.); [EMAIL_ADDRESS] (K.C.S.); [EMAIL_ADDRESS] (D.S.); [PERSON]S.R.P.); [EMAIL_ADDRESS] (S.A.H)
## 4 Conclusions  
The research conducted involved the design and synthesis of a composite bone scaffold comprising SiO2@Fe3O4/PVA/HA/\(\beta\)-TCP/PCL with notable photothermal characteristics. Conclusions were deduced from experimental investigations and analyses as outlined below:  
1. The photothermal-sensitive nanoparticles SiO2@Fe3O4, synthesized via hydrolysis and condensation using 5 mL TEOS, exhibited approximately 18 nm SiO2 coatings. These coatings were uniformly distributed, effectively preventing the aggregation of the photothermal-sensitive nanoparticles Fe3O4. Additionally, the micro-pore structure of the fabricated photothermal composite bone scaffold showed uniform distribution, devoid of localized aggregation phenomena. This ensured the avoidance of adverse effects on the properties and applications of the scaffold material.
2. The photothermal composite scaffold with a mass fraction of 5% for photothermal-sensitive nanoparticles exhibited a compressive strength of 5.722 MPa and a contact angle of 58.3\({}^{\circ}\), demonstrating favorable mechanical support and hydrophilicity. This scaffold provides a stable environment for the adhesion and growth of bone tissue cells. Upon exposure to near-infrared light for 40-45 s, the 5% photothermal com  
Figure 8: Temperature variation of composite support. (**a**) PVA/HA/\(\beta\)-TCP/PCL composite bone scaffold; (**b**) 3% photothermal composite scaffold; (**c**) 5% photothermal composite scaffold; (**d**) 7% photothermal composite scaffold.  
posite scaffold induced a temperature increase of 3-6 \({}^{\circ}\)C in different tissue depths, achieving controlled internal temperature variations through mild thermal stimulation. This approach aims to promote osteogenic differentiation and facilitate the repair of bone defects.  
Conceptualization, C.S., Y.X. and S.L.; methodology, C.S., Y.X. and S.L.; validation, C.S., Y.X. and S.L.; formal analysis, C.S., Y.X. and S.L.; investigation, C.S., Y.X. and S.L.; resources, Y.X.; data curation, C.S.; writing--original draft preparation, C.S.; writing--review and editing, Y.X. and S.L.; visualization, C.S., Y.X. and S.L.; supervision, Y.X. and S.L.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (52365053), Natural Science Foundation of Xinjiang Uyghur Autonomous Region (2022D01C34).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Experimental Investigation in Drag Coefficient of Cubes
## 4 Results and Discussion
### Behavior of the Spliced Cube Drag Coefficient
When the Reynolds number is smaller than \(55\), the orientation angle of the spliced cube with \(h/a=0.67\) can maintain a stable range of \(18.41\pm 2^{\circ}\). Then, the orientation angle keeps decreasing till the Reynolds number reaches \(1\times 10^{3}\) and the minimum value of the orientation angle is \(2.43^{\circ}\). When the Reynolds number is larger than \(1\times 10^{3}\), the orientation angle keeps increasing and the maximum orientation angle reaches \(39^{\circ}\). Figure 6: Relationship between drag coefficient and Reynolds number of a spliced cube with \(h/a=0.5\) and \(1.0\)[9]. The experimental drag coefficient of the cube with \(h/a=0.67\) is exhibited in Figure 8. The drag coefficient of the cube with \(h/a=0.5\) and \(h/a=1.0\) is also shown in Figure 8 as a comparison. Since the orientation angle of the spliced cube with \(h/a=0.67\) varies in different Reynolds number ranges, the drag coefficient of the cube is expressed as triangle symbols filled with different colors in the range of \(4\times 10^{-1}<Re<10\), \(10<Re<1\times 10^{3}\) and \(1\times 10^{3}<Re<4.5\times 10^{3}\) in Figure 8, respectively. As can be seen, when the Reynolds number is smaller than 10, the drag coefficients of three types of spliced cubes are very close to each other in the same Reynolds number, indicating that the orientation angle has little effect on the drag coefficient in this Reynolds number range. As the Reynolds number increases to the range of \(10<Re<1\times 10^{3}\), the value of the drag coefficient of the spliced cube with \(h/a=0.67\) is slightly higher than the value of the cube with \(h/a=0.5\) while both of the values are smaller than those of the cube with \(h/a=1.0\). Since the orientation angle of the spliced cube with \(h/a=0.67\) decreases and is finally close to \(0^{\circ}\) as the Reynolds number increases, the drag coefficient error between the cubes with \(h/a=0.67\) and with \(h/a=0\) keeps decreasing, and they nearly overlap with each other in this Reynolds number range. Compared with the drag coefficients of the spliced cube in Figure 6, the drag coefficient of the cube in this Reynolds number range is in direct proportion to the orientation angle. As a result, the upper and lower bound of the cube drag coefficient in this Reynolds number range are those obtained by the cube with \(h/a=1.0\) (\(\alpha=45^{\circ}\)) and \(h/a=0.5\) (\(\alpha=0^{\circ}\)), respectively. When the Reynolds number is larger than \(1\times 10^{3}\), the effect of the orientation angle on the drag coefficient decreases again. The drag coefficients obtained by different cubes converge with each other once again as the Reynolds number increases. Figure 7: Relationship between orientation angle and Reynolds number of a spliced cube with \(h/a=0.67\).
## 4 Maximum Efficiency per Torque Control of PMSM  
From Equation (17) and Equation (32), the loss of the PMSM can be obtained as  
\[P_{loss}=P_{loss\_f}+P_{Fe\_h}=f(\omega_{r},i_{d},i_{q})+f(M) \tag{34}\]  
Additionally, substituting Equation (6) and Equation (24) with Equation (34), the loss of the PMSM can be derived as  
\[\begin{array}{ll}P_{loss\_PMSM}&=P_{loss\_f}+P_{Fe\_h}\\ &=f(\omega_{r},i_{d},i_{q})+f(M)\\ &=f(i_{d},\omega_{r},T_{e})\end{array} \tag{35}\]  
From Equation (35), it can be seen that the loss of the PMSM is a function of the \(d\)-axis current \(i_{d}\), the angular speed \(\omega_{r}\) and the electromagnetic torque \(T_{e}\). Therefore, there must be an optimal flux-weakening \(d\)-axis current where the loss of the PMSM can achieve the minimum value in each const operation point (\(\omega_{r}\) and \(T_{e}\) is const). The optimal \(d\)-axis current \({i_{d}}^{\star}\) can be obtained from Equation (36) as  
\[\left.\frac{\partial P_{loss\_PMSM}}{\partial i_{d}}\right|_{i_{d}=i_{d}^{ \star}}^{\omega_{r}=const,T_{e}=const}=0 \tag{36}\]The MEPT control can make the \(d\)-axis flux-weakening current remain the optimal value \({i_{d}}^{*}\) and achieve the maximum efficiency of the PMSM in the whole operation range. As Equation (36) makes it too difficult to acquire the analytical solution of the optimal current, this paper applied the golden section method to obtain the numerical solution of the optimal current. The golden section is a technique for finding the minimum value of a unimodal function by successively narrowing the range of values inside which the minimum is known to exist. The flowchart of the golden section procedure is shown in Figure 9, where \(\varphi\) is the golden ratio: \(\varphi=\frac{1+\sqrt{5}}{2}=1.618\).